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Long-term, accurate, stable, and continuous aerosol records from space are a major requirement for climate and atmospheric environment research. Due to the limited period span of the single satellite mission, solving the problem requires the combination of multiple satellite missions. In this study, a novel aerosol retrieval algorithm, named enhanced Land General Aerosol Retrieval (e-LaGA), for Visible/Infrared Imager Radiometer Suite (VIIRS) was developed to continue Moderate-resolution Imaging Spectro-radiometer (MODIS) aerosol retrieval. e-LaGA utilizes a Surface Reflectance (SR) relationship model map to more accurately estimate the SR parameter, optimizes the priori aerosol-type map, and improves the multi-band retrieval strategy using the residual-interpolation approach. Additionally, e-LaGA expands the retrieval ability of the traditional Dark Target (DT) algorithm over bright surfaces, and can more accurately retrieve Aerosol Optical Depth (AOD), Fine-mode AOD (AODF), and Fraction (FMF). The validation using global 533 AERONET sites shows that the volume of matchups of AOD (AODF) is approximately 80000, the correlation coefficient (R) is 0.902 (0.879) and the fraction of meeting the expected error envelope (±0.05 ± 0.15τ) is 0.806 (0.804). The inter-comparison indicates that the accuracy of e-LaGA AOD retrievals is comparable to seven commonly used AOD products. The validation performance and spatial distribution pattern of e-LaGA AODF and FMF are in good agreement with the POLDER (Polarization and Directionality of the Earth's Reflectances) products. e-LaGA is also applied to MODIS sensors. The VIIRS e-LaGA AOD, AODF, and FMF retrievals have high consistency with MODIS e-LaGA. Their average bias of AOD is 0.006, which is smaller than 0.024 for the Deep Blue and 0.011 for the DT. These preliminary results demonstrate the robustness of the e-LaGA algorithm and its potential for establishing a long-term climate record of total and fine-mode aerosol by combining multiple satellite missions, which is expected to reduce the uncertainty of climate change research. [ABSTRACT FROM AUTHOR]

Accurate estimation of the absorption of chromophoric dissolved organic matter (CDOM) in ultraviolet (UV) wavelength range is crucial for understanding photochemical and biochemical processes in the global ocean. Here, we propose a novel UV absorption extrapolation (UVAE) algorithm. It extrapolates the absorption coefficients (a g (λ) where λ is the wavelength in nm) of CDOM in the UV wavelength range from ocean color data in the visible wavelength range observed by satellite remote sensing. The extrapolation is based on the assumption that three characteristic Gaussian bands describe the CDOM UV–visible absorption spectra (275–443 nm). The UVAE algorithm retrieves the a g (275), a g (295), a g (330), and a g (380) using the characteristics of the spectral slope (S) of the absorbance spectra of CDOM at specific wavelength intervals, namely 275–295, 295–330, 330–380, and 380–443 nm. With the UVAE algorithm, the spatial variability of CDOM spectral features can be captured on a global scale using data from the Moderate-resolution Imaging Spectroradiometer (MODIS) Aqua mission. The retrieved a g (275), a g (295), a g (330), and a g (380) values match well with in-situ observed values, with mean absolute percent differences (MAPDs) of 19.2 %, 26.1 %, 36.9 %, and 46.6 %, respectively. The performance of the UVAE algorithm demonstrates a significant improvement compared to that of existing algorithms, especially for a g (λ) at lower wavelengths. [ABSTRACT FROM AUTHOR]

Previous studies have predominantly focused on developing high spatiotemporal resolution PM 2.5 models utilizing moderate-resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) products alongside certain meteorological factors. However, MODIS AOD is not continuous, and there is a problem of missing data, which is not conducive to the study of PM 2.5 in areas without AOD. This study proposes a new method for estimating PM 2.5 concentration in North China, which not only simplifies the complex process associated with the use of AOD, but also partially solves the issue of missing AOD data. This study proposes a machine learning (random forest, RF; back propagation neural network, BPNN) based method for estimating PM 2.5 using meteorological parameters obtained from the fifth-generation reanalysis (ERA5) dataset released by the European Centre for Medium-Range Weather Forecasts and considering the effects of land cover type, digital elevation model, vegetation index and population data. The model performed well, with 10-fold cross-validation (coefficient of determination) R2 and (root-mean-square error) RMSE of 0.79/0.95 and 26.10/13.22 µg/m3, respectively, for BPNN and RF. The estimated hourly performance of the RF model in winter (00:00 to 23:00 BST) with an R2 ranging from 0.92 to 0.96, an RMSE of 11.45 to 16.70 µg/m3. RF model with the best performance and ERA5 were selected to build a high-resolution (0.25°×0.25°) hourly PM 2.5 map (PMM), and the PMM was compared with CHAP, with R2 and RMSE of 0.75 and 20.62 µg/m3, respectively. This study further investigates the impacts of land cover types, digital elevation model, and land surface characteristics on the spatial distribution of PM 2.5 in North China. [ABSTRACT FROM AUTHOR]

• The accuracy of MODIS IR PWV is poor. • The GPS station and MODIS pixel points do not coincide completely in point position. • The problems of spatial interpolation and calibration model are considered in MODIS calibration process. • Compared with the original MODIS IR PWV, the accuracy of the new model is greatly improved in all aspects. • MODIS IR PWV of the new model is in good agreement with GPS PWV. The monitoring of precipitable water vapor (PWV) is of significant importance for meteorological research and rainstorm prediction. The moderate resolution imaging spectroradiometer (MODIS) is one of the most widely used remote sensing PWV instruments aboard the Terra and Aqua satellites. The main objective of this study is to develop a fused inverse distance weighting and the Fourier transform model (FIDWFT) to calibrate MODIS Infrared (IR) PWV. In this research, one year of ground observation data from 17 GPS stations were utilized to evaluate the accuracy of MODIS IR PWV in Hong Kong. Initially, the PWV obtained by one radiosonde was used to verify the GPS PWV. Comparison between GPS and radiosonde showed good agreement with an R-squared of 0.97. After the estimation of GPS PWV in the study area was evaluated, MODIS IR products in the study area were extracted for evaluation. The comparison between MODIS and GPS showed that the R-squared is 0.81 and the mean bias (MB) is −1.60 mm. Considering the spatial interpolation and calibration problems of MODIS products, new model used an inverse distance weighting method to make MODIS IR PWV and GPS PWV completely coincide in space, and fifteen MODIS pixel points were selected for interpolation. Then the MODIS IR PWV was calibrated by GPS PWV using Fourier transform model. Results showed that compared with the MODIS IR PWV before calibration, the R-squared increases from 0.81 to 0.94, the root mean square error decreases from 8.72 mm to 4.98 mm, and the MB decreases from −1.60 mm to −0.83 mm. Therefore, the method is feasible to estimate MODIS IR water vapor, and it achieves better results. [ABSTRACT FROM AUTHOR]

• For the first time the observations of OCO-2 & 3 was combined to access Xco 2 anomalies. • The Amazon biome has more positive XCO 2 anomalies. • The Brazilian biomes more affected by fire was Amazon, Cerrado and Pantanal. • Xco 2 positive anomalies is correlated with Fire Foci observations. Climate change is a challenge to the global community and one of the causes is the increase of greenhouse gases (GHG) in the atmosphere, especially carbon dioxide (CO 2). The major emission source of this gas into the atmosphere comes from the burning of fossil fuels and biomass burning, on the other hand, the main sink comes from biochemical processes such as photosynthesis. Thus, the observation of CO 2 is a key point to understanding sources and sinks. In this context, The Orbiting Carbon Observatory 2 (OCO-2) and 3 (OCO-3), are a NASA dedicated mission to monitor the column-averaged dry-air mole fraction of carbon dioxide (XCO 2) on a global scale. We combined the OCO-2 and OCO-3 observations to study the spatial distribution of XCO 2 anomalies and how some of these anomalies are related to fire occurrence in the Brazilian Biomes during 2020 and 2021 considering two different seasons, Dry and Wet. The fire occurrence was obtained from Fire Information for Resource Management System (FIRMS) that provides the data from the Moderate-Resolution Imaging Spectroradiometer (MODIS) product of active fires and thermal anomalies at Near-Real Time (MCD14DL, collection 6). The OCO-2/3 observations are affected by cloud formations in wet seasons, we observe that the dry period has more observations. The XCO 2 anomaly values range from ∼ 7.0 ppm to −7.0 ppm and mostly positive anomalies occur in Amazon Biome, and this ecosystem has higher average values for all periods (∼0.9 ppm), compared to the other biomes. The fire occurrence was higher in dry periods, especially in 2020 when unprecedented fire outbreaks were registered in Brazil. The most affected biomes were Pantanal, Cerrado, and Amazon. XCO 2 positive anomalies spatially agree with fire foci over some areas, and the correlation values between them ranged from 0.2 to 0.5 depending on the biome and season, and when considering observations with clouds the correlation is slightly higher. We point out for the first time the possibility of using OCO-2 and 3 combined, also, how positive XCO 2 anomalies are related to fire occurrence in different ecosystems and periods, and the role of cloud detection in this relationship. [ABSTRACT FROM AUTHOR]

The ability to infer ocean chlorophyll- a concentrations (Chl a) from spaceborne instruments is key to assessments of global ocean productivity and monitoring of water quality. Here, we present a novel parametric algorithm, OCG, trained on a set of global in situ high-performance liquid chromatography (HPLC) data that leverages Level-3 remote sensing reflectance (R rs ) products from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua satellite. The OCG algorithm leverages more bands than existing algorithms and also provides pixel-wise uncertainty assessments that enable the calculation of the probability of exceeding specific Chl a thresholds. This feature has significant implications for water quality management, particularly in monitoring harmful algal blooms. The OCG surpasses existing algorithms in bias and accuracy without overfitting, especially in coastal areas, where it outperforms the current standard product (CI OC3) by 20 % in median symmetric accuracy. Moreover, the OCG reduces the signed symmetric percentage bias (SSPB) in coastal regions from 41 % (CI OC3) to below 5 %. Globally, the OCG algorithm yields lower Chl a in coastal regions, the Southern Ocean and the Mediterranean Sea, and higher values in the open ocean, particularly in ocean gyres and polar regions. For the Chesapeake Bay and the Baltic Sea, for example, daily OCG estimates for 2002 to 2021 are, on average, 2.9 g/L and 3.7 g/L lower than CI OC3 estimates, respectively. The presented approach also shows great potential for other existing and upcoming sensors, enabling widespread application in remote sensing. [ABSTRACT FROM AUTHOR]

Clumping index (CI), describing the extent of foliage grouping within canopy structures that cause nonrandomness of the leaf spatial distribution within the canopy, is a structural parameter of plant canopies needed in modelling carbon and water cycles of terrestrial ecosystems. CI has been previously retrieved based on an angular index named Normalized Difference between Hotspot and Darkspot (NDHD) derived from multi-angle satellite data. However, since the darkspot reflectance used in NDHD contains shadows influenced by topographical variations, CI retrieved from NDHD is contaminated by topographic effects on multi-angle data. Moreover, the existing global CI products are limited by their spatial resolutions, e.g. 6 km from POLDER and 500 m from Moderate Resolution Imaging Spectroradiometer (MODIS). In this study, we utilized the canopy height (H) metric produced from a spaceborne LiDAR system named Global Ecosystem Dynamic Investigation (GEDI) to implement the mapping for global forest CI at 30 m resolution in the following steps: (1) The H map from GEDI and a CI map from MODIS were employed to establish the relationships between H and CI across flat terrains for different plant functional types (PFT) and vegetation foliage densities characterized using the Simple Ratio (SR); (2) Using the relationships, a new global map of forest CI is derived from the GEDI H and SR maps at 30 m resolution; (3) Field-measured CI values by the Tracing Radiation and Architecture of Canopies (TRAC) instrument at 39 sites over three continents were used to validate the new CI map with satisfactory results at all forest sites: ( R 2 = 0.84, RMSE = 0.0199 for all PTFs; R 2 = 0.42, RMSE = 0.0180 for broadleaf forests, and R 2 = 0.48, RMSE = 0.0206 for needleleaf forests). In the new global high-resolution forest CI map, CI is increased by about 10–15 % and is much less variable with topography over mountainous areas, indicating that topographical effect on CI derivation is greatly reduced. This new map would therefore be highly useful for improving terrestrial carbon and water cycle estimation. [ABSTRACT FROM AUTHOR]

• Earthquake anomalies are analyzed for Mw 6.5 Idaho Earthquake America. • Significant anomalies occur in LST, AT, RH, AP and OLR. • Anomalies are studied using statistical, NARX, MLP and CWT methods. • NARX predicted anomalies are more clear than other methods. Remote sensing has became a powerful tool for identifying lithosphere and atmosphere anomalies associated with the impending Earthquakes (EQs) in the vicinity of seismic breeding zone. In this paper, Land Surface Temperature (LST) of both the daytime and nighttime from the Moderate Resolution Imaging Spectroradiometer (MODIS) along with Air Temperature (AT), Relative Humidity (RH), Air Pressure (AP) and Outgoing Longwave Radiations (OLR) are studied for the 2020 Idaho (USA) EQ of Mw 6.5. We found the EQ induced surface and atmospheric parameters anomalies just one day prior to EQ main shock using statistical analysis. Moreover, we observed a sharp increment in LST and AT followed by the drop in both AP and RH, which are responsible for cooling the hot gases emitted from epicenter during preparation period. Also, we observed a large increase in OLR on the same day confirming these anomalous variations to be related with the main shock. Furthermore, these abrupt variations are also confirmed using neural networks (nonlinear autoregressive network with exogenous inputs (NARX), multilayer perceptron (MLP) and continuous wavelet transformation (CWT). These multi-parameter and multi-technique analyses can contribute to assist in the main shock forecasting in future with an enhanced cluster of satellite observations. [ABSTRACT FROM AUTHOR]

Precipitable Water Vapour (PWV) measurements derived from the Moderate-resolution Imaging Spectroradiometer (MODIS) exhibit considerable promise for global high-resolution PWV monitoring but with limited accuracy. This study proposes a correction model for MODIS PWV, leveraging the least squares method (LS) and fast Fourier transform (FFT). The study used Global Navigation Satellite System (GNSS) PWV products (the accuracy is equivalent to Radiosonde-derived PWV) from 230 stations provided by the Crustal Movement Observation Network of China (CMONOC) for 2016 to 2019, serving as the ground truth for correcting MODIS PWV. Subsequently, the model undergoes an external coincidence test utilising the data from 2020. Initially, a linear or quadratic expression (LS model) was established through the application of the least squares method, effectively capturing the trends between the two PWV datasets. Then the periodic characteristics of residuals from the LS model were analysed by FFT. The outcomes reveal that within regions and seasons abundant in PWV, the quadratic expression is more consistent with the trend. The primary variation period of residuals exhibits variability across regions, with the annual period emerging as the primary fluctuation pattern in the majority of regions. Following this, we constructed the correction model (LSF model) that considered the cycle characteristics of residuals with the model type adopted for the LS model as the main part. After correction by the LSF model, the root-mean-square error (RMSE) of MODIS PWV decreased by 33.1%, 55.8%, 33.0%, and 19.4% in four seasons, respectively. Compared to the LS model, the LSF model showcased relative enhancements of the coefficient of determinations (R2) by 119.8%, 99.6%, 165.9%, and 39.6% in the four seasons, respectively, with an overall further decrease of 35.7% in RMSE. The LSF model demonstrates a superior degree of fitting and yields improved correction effects, thus effectively enhancing the reliability of MODIS PWV. [ABSTRACT FROM AUTHOR]

The contextual surface temperature-vegetation index (TVX) models have been widely used for the retrieval of soil moisture (SM) and evapotranspiration (ET). One of the key premises for their application is to determine quantitatively the theoretical boundaries of this contextual TVX space. Usually these theoretical boundaries are determined based on land surface energy balance principle. Although sound in physical mechanism, the complex parameterization involved has hampered its remote sensing (RS) applications. Our recent studies show that ground-based SM observations can be used for a quick and continuous retrieval of these theoretical boundaries. The main disadvantage lies in its reliance on in-situ SM observations. Fortunately the development of microwave RS technology has made it possible to monitor SM at both regional and global scale. Under this background, a practical framework based on the contextual TVX models was proposed in this paper for continuous ET estimation through the combination of microwave-based SM products and MODIS (Moderate Resolution Imaging Spectroradiometer) products. Daily actual ET over the Southern Great Plains of the United States of America was calculated as the product of net radiation and evaporative fraction (EF). The focus of this framework was to investigate how optical and microwave RS can be combined to achieve a quick and continuous retrieval of EF independently of ground observations. Specifically, EF was retrieved from the contextual TVX models, the theoretical boundaries of which were calibrated at the annual scale based on an optimization scheme. Assuming the seasonal variation of the theoretical dry edge follows a cosine function of solar zenith, the essence of this optimization scheme is to search for the optimal amplitude parameter that maximizes the correlation between microwave-based SM observations and soil moisture index retrieved from the contextual TVX models. Two microwave-based SM products including SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) were adopted to demonstrate this optimization scheme. The correlation coefficient, root mean square error and bias of EF estimates calibrated by SMAP were 0.700, 0.146 and 0.047, and those achieved by SMOS were 0.663, 0.174 and 0.168, respectively. The optimization scheme has not only made it possible to conduct a continuous monitoring of ET based entirely on RS observations but also achieved the downscaling of microwave satellite SM products. Thus the combination of optical and microwave RS holds great potential for coupled and continuous estimation of SM and ET over large scale. [ABSTRACT FROM AUTHOR]

Accurate characterization of plant phenology is of great importance for monitoring global carbon, water, and energy cycling. Remotely sensed satellite observations have been widely used to estimate land surface phenology across multiple spatial scales in the last three decades. Recent development on satellite solar-induced chlorophyll fluorescence (SIF) observations have opened an opportunity to monitor the seasonality of plant growth from the perspective of photosynthesis phenology. The SIF observations from the TROPOspheric Monitoring Instrument (TROPOMI) with high spatial resolution (up to 7 km × 3.5 km pixels) and near-daily global coverage provide unprecedented opportunity to observe photosynthetic and land surface phenology from space. However, the performance of TROPOMI SIF-derived phenology has not been systematically evaluated. In this study, we used flux tower gross primary productivity (GPP) and PhenoCam green chromatic coordinate (Gcc) data as the benchmark to verify phenology metrics derived from satellite observations. The phenology metrics including the start (SOS), end (EOS), length of growing season (LOS), and the peak of growing season (POS) were estimated from TROPOMI SIF, normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), near‐infrared reflectance of vegetation (NIRv), and Global Vegetation Phenology product (MCD12Q2), and the latter four were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) across six vegetation types over North America during the period of 2018–2020. We found that the overall agreements of SIF (R2 ranging from 0.30 to 0.63) against GPP-estimated phenology were stronger than NDVI (0.17–0.41), EVI (0.19–0.39), NIRv (0.23–0.62), and MCD12Q2 (0.19–0.48) derived phenological events. In reference to GPP and Gcc-estimated phenology, SIF also generally has the least error and bias compared to other satellite remote sensing-derived phenology metrics. No significant differences were found between SIF and GPP-derived phenology (P>0.05, two-tailed t -test). In addition, we found that the spatial distribution of SIF-derived phenology reflected the expected latitudinal patterns in phenology dates. SOS, EOS, LOS, and POS observed by MCD12Q2 appeared to be earlier, later, longer, and earlier than TROPOMI SIF-derived phenology, respectively. SIF-based phenological transition dates more closely tracked GPP-based phenology dates, indicating TROPOMI SIF could be a great measure to track photosynthesis seasonality and land surface phenology. [ABSTRACT FROM AUTHOR]

[Display omitted] Urban thermal anisotropy (UTA) drastically impacts satellite-derived urban surface temperatures and fluxes, and consequently makes it difficult to gain a more comprehensive understanding of global urban climates. However, UTA patterns and associated biases in observed urban climate variables have not been investigated across an adequate number of global cities with diverse contexts; nor is it known whether there are globally measurable factors that are closely related to the UTA intensity (UTAI, quantified as the maximum difference between the nadir and off-nadir urban thermal radiation). Here we investigate the UTAI over more than 5500 cities worldwide using multi-angle land surface temperature (LST) observations from 2003 to 2021 provided by Moderate Resolution Imaging Spectroradiometer (MODIS). The results show that the global mean UTAI can reach 5.1, 2.7, 2.4, and 1.7 K during summer daytime, winter daytime, summer nighttime, and winter nighttime, respectively. Using nadir LST observations as a reference, our analysis reveals that UTA can lead to an underestimation of satellite-based urban surface sensible heat fluxes (H) by 45.4% and surface urban heat island intensity (I s) by 43.0% when using LST observations obtained from sensor viewing zenith angles (VZAs) of ±60°. Practitioners can limit the biases of H and I s within ±10% by using LSTs from sensor VZAs within ±30°. We also find that UTAI is closely related to urban impervious surface percentage and surface air temperature across global cities. These findings have implications for angular normalization of satellite-retrieved instantaneous LST observations across cities worldwide. [ABSTRACT FROM AUTHOR]

The leaf area index (LAI) retrieval methods based on traditional neural networks require a large number of training samples constructed from remote sensing data or simulation data using radiative transfer models. Furthermore, the training samples for the neural networks are sensor-specific. Therefore, the existing training samples for a sensor cannot be directly applied to estimate LAI values from remote sensing data acquired by another sensor. In addition, a large number of currently available LAI ground measurements (considered as "true values") are not used to construct training datasets of the neural networks to further improve the accuracy of the retrieved LAI values. In this study, a method based on deep transfer learning is proposed to retrieve LAI values from the Visual Infrared Imaging Radiometer Suite (VIIRS) surface reflectance data based on the existing training dataset established from the Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data and the fused LAI values of the MODIS and Carbon cYcle and Change in Land Observational Products from an Ensemble of Satellites (CYCLOPES) LAI products. A transferable model is constructed by using a deep belief network (DBN) composed of a restricted Boltzmann machine (RBM) and a back propagation (BP) network. The DBN is pretrained by the existing training dataset, and the hyperparameters of the DBN are determined by using a Bayesian optimization algorithm. Then, the RBM parameters of the pretrained DBN are frozen, and the BP parameters are fine-tuned by a small sample dataset composed of the LAI ground measurements and the corresponding VIIRS surface reflectance data. Finally, the VIIRS surface reflectance data are inputted into the fine-tuned DBN to estimate LAI values. The retrieved LAI values were cross-compared with the MODIS and VIIRS LAI products and were directly evaluated with LAI ground measurements at the Ground-Based Observations for Validation (GBOV) and Implementing Multi-Scale Agricultural Indicators Exploiting Sentinels (IMAGINES) sites. The results demonstrate that the retrieved LAI values at these sites with different vegetation types show reasonable seasonality and the spatial distributions of the retrieved LAI values are similar to those of the MODIS and VIIRS LAI products. The direct validation results show that the LAI values retrieved by the DBN with fine-tuned BP parameters (RMSE over the USA: 0.48, RMSE over Europe: 0.71) are obviously superior to the LAI values retrieved by the DBN without fine-tuned BP parameters (RMSE over the USA: 0.70, RMSE over Europe: 0.98). This study demonstrates that deep transfer learning can effectively retrieve LAI values from the VIIRS surface reflectance data with limited LAI ground measurement samples and the existing training dataset. [ABSTRACT FROM AUTHOR]

Land surface phenology (LSP) is beneficial to understand ecosystem response to climate change, vegetation and crop type discrimination, and ecological modeling. However, the existing efforts based on coarse resolution data (≥500 m) cannot perform well in regions with higher spatial heterogeneity and multi-cropping system, such as China. Given that the majority of 10 m/30 m-based phenological research has focused on North America and Europe, developing spatiotemporally explicit LSP data in China is imperative. More importantly, the existing 30 m LSP products are mainly suitable for vegetation types with a single vegetation cycle, but cannot work well for biomes with complex seasonality (e.g., multiple growth cycles). Here we first harmonized three vegetation indices, i.e., the normalized difference vegetation index (NDVI), two-band enhanced vegetation index (EVI2), and land surface water index (LSWI) from Landsat-7/8 and Sentinel-2 imagery on the Google Earth Engine (GEE) platform. We then developed a new 30 m LSP algorithm that unified different phenological cycle-seeking processes per vegetation type and improved the existing algorithm. Furthermore, we used the algorithm to estimate the LSP product (LSP30CHN) for 2016–2019 across China, suitable for all vegetation types. The validation results showed a reasonably high accuracy (R2 > 0.6, RMSE < 15 days, mostly) of the LSP30CHN data against multi-sources in-situ observational (e.g., PhenoCam) and satellite-retrieved vegetation phenology data. Moreover, LSP30CHN data showed a consistent pattern but finer spatial details with the 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) phenology product (MCD12Q2) at the homogenous area. We also found that phenological differences between LSP30CHN and MCD12Q2 increased with surface fragmentation, suggesting the potential of LSP30CHN to delineate phenological information on more fragmented landscapes. In contrast, the 500 m LSP data cannot provide such details in the regions with mixed cropping structures (e.g., corn, rice, and soybean) and multiple cropping index (e.g., single- and double-cropping systems). This study offers high accuracy of the LSP map for China, valuable for finer phenology-based services such as field-level crop management and agricultural phenology monitoring. It opens up new insights about exploring large-scale refined agricultural management and ecological assessment for other regions with complicated, fragmented landscapes and vegetation seasonality. [ABSTRACT FROM AUTHOR]

The earthquakes prediction based on LST anomalies remain a contradicting debate due to limited and short-term data around the earthquake's epicenter. The results are varying in different case studies, only few research matches with pre seismic LST anomalies while many are not found to be significant for earthquakes predication. This study assessed temperature anomalies in different earthquakes events of Mw ≥ 6.0 in Pakistan from 2000 to 2020. The present study uses Moderate Resolution Imaging Spectroradiometer (MODIS) satellite thermal imagery data over the epicenter region to analyze thermal anomaly associated and earthquake prediction. The surface temperature growth before the earthquake event is estimated using the satellite thermal infrared images carrying valuable earthquake precursory information. The study area Pakistan is prone to active seismicity due to Northern Head-on Collision of Indian-and Eurasian plate; Southern Makran Subduction Zone of Indian and Arabian plate and the regional Chaman transform boundary. The results show pre seismic temperature variation range between 30 °C − 54 °C, which seems to be a good prospect for linking thermal anomalies to earthquake prediction. [ABSTRACT FROM AUTHOR]

PM 2.5 is the main component of most haze, and the presence of high concentrations of PM 2.5 in the air for an extended time can cause serious effects on human health, so there is an urgent need for research work related to PM 2.5. Traditional PM 2.5 monitoring uses ground-based monitoring stations with low spatial resolution. Other studies have retrieved the Moderate Resolution Imaging Spectroradiometer aerosol optical depth product by the dark-target algorithm. However, the estimated PM 2.5 concentration on the ground will produce missing values, which will lead to the reduction of spatial and temporal resolution. Based on this, this study proposes a machine learning algorithm to estimate PM 2.5 by using the fifth generation reanalysis (ERA5) data set published by the European Center for Medium-range Weather Forecasts (ECMWF). In this study, two different methods of back propagation neural network (BPNN) and random forest (RF) were used to develop the models. Firstly, the meteorological parameters (precipitable water vapor, water vapor pressure and relative humidity, etc.) and pollution parameters (O 3 , CO, NO 2 , SO 2 , PM 10 , and PM 2.5) were used to establish PM 2.5 model in 2021. The results showed that the R2 and RMSE for BPNN and RF were 0.94/0.96 and 10.37/8.77 µg/m3, respectively. Then, due to the lack and the low spatial resolution of the pollution parameters, using only the ERA5 meteorological data with the high spatiotemporal resolution to develop the PM 2.5 model in winter, the R2 of the RF model (0.93) was 0.05 higher and the RMSE (12.50 µg/m3) was 4.19 µg/m3 lower than that of the BPNN model, which indicates that it is feasible to develop the PM 2.5 model using only meteorological parameters. Finally, using the RF model of the second stage and ERA5 meteorological data with a spatial resolution of 0.05° (obtained by cubic spline interpolation) to generate the hourly PM 2.5 map of Beijing and compare it with China High Air Pollutants dataset, the R2 and RMSE of Beijing were 0.78 µg/m3 and 14.85 µg/m3, respectively. On this basis, it is found that the areas with high PM 2.5 concentration are close to the areas with serious pollution in Hebei Province by analyzing the PM 2.5 map of Beijing, and area transport and human activities are important sources of air pollution. [ABSTRACT FROM AUTHOR]

The relationships between particulate matter with aerodynamic diameters less than 2.5 µm (PM 2.5), aerosol optical depth (AOD), and cloud parameters were investigated during high PM 2.5 concentrations from 1 January 2016 to 31 December 2021 in Bangkok using satellite remote sensing data from Moderate Resolution Imaging Spectroradiometer (MODIS). Cloud parameters included: Cloud effective radius (CER), Cloud fraction (CF), Cloud optical depth (COD), Cloud liquid water (CLW), Cloud top pressure (CTP), and Cloud top temperature (CTT). The analysis showed a strong negative correlation between PM 2.5 and CF. There was a negative correlation between CF and AOD. In addition, PM 2.5 showed a negative relationship with CER. In contrast, PM 2.5 showed a positive correlation with CTP and CTT. A negative correlation was detected between COD and PM 2.5. During high PM 2.5 concentrations, cloud parameters are significantly influenced by PM 2.5 concentrations in the atmosphere. The three-month of CER, CF, CLW, COD, CTP, and CTT ranges from 16.34 μm to 37.05 μm, 0.22 to 0.86, 15.67 g/m2 to 169.80 g/m2, 1.14 to 19.06, 261.67 hPa to 991.67 hPa, and −66.26C to –22.17C, respectively. Consequently, Altostratus clouds most appear during high PM 2.5 concentrations in Bangkok. The findings may help better understand Bangkok's cloud properties and atmospheric aerosols, providing more information for rainmaking during high PM 2.5 concentrations. [ABSTRACT FROM AUTHOR]

In late 2016, NASA launched the first constellation of the global navigation satellite system reflectometry (GNSS-R) small satellites called the Cyclone Global Navigation Satellite System (CYGNSS). The stable data quality and continuous free availability of CYGNSS scientific data provided a new method for flood monitoring. However, owing to the pseudorandom distribution of CYGNSS data, researchers must always choose between high temporal resolution and high spatial resolution during the performance of flood monitoring based on CYGNSS data. For floods caused by extreme precipitation with sudden and short durations, the current flood mapping based on CYGNSS data cannot be updated in near real time. However, the near real time update of the flood distribution range is meaningful for postdisaster emergency response and rapid rescue. This study aimed to address this problem using a newly proposed spatial interpolation method based on previously observed behaviour (POBI). First, a method for calculating the surface reflectivity of the CYGNSS was introduced, followed by the principle of the POBI spatial interpolation method. The applicability of the POBI method in Henan Province, China, was then analysed, and by using the flood in Henan Province, China, in July 2021 as an example, the feasibility of CYGNSS near real time flood mapping based on the POBI method was evaluated. Based on the results, near real time and 3 km flood distribution monitoring results can be obtained using the proposed new method. The results were evaluated using MODIS (Moderate Resolution Imaging Spectroradiometer) images and compared with the observations of SMAP (Soil Moisture Active Passive) and GPM (Global Precipitation Measurement) in the same period. The results show that the flooded areas obtained by CYGNSS correspond to the inundated areas in MODIS images and are also in high agreement with the SMAP. In addition, CYGNSS allows for finer mapping and quantification of inundation areas and flood duration. Moreover, we also discussed the potential of CYGNSS to detect floods in shorter periods of time (a few hours) and did a preliminary evaluation using precipitation data from meteorological stations. The results are also highly consistent. [ABSTRACT FROM AUTHOR]

Thermal infrared land surface temperature (LST) data from satellites often contain extensive missing values due to high cloudiness degree, which severely hinders their use in applications. Despite the many methods developed, common methods, such as fusion with microwave or reanalysis data and the surface energy budget approach, still remain subject to important limitations and uncertainties, such as dependency on coarse resolution data and difficulty in interpolation for large-scale missing LST data. In this study, we proposed a stepwise framework for estimating missing cloudy-sky LST values of Moderate Resolution Imaging Spectroradiometer (MODIS) from informative samples owing to the solar-cloud-satellite geometry (SCSG) effect, by which satellite imagery records the cloudy-sky LST values of a portion of pixels. We first estimated the clear-sky LST equivalents for all cloud-affected pixels via a similarity-based approach and then determined unknown LSTs for cloudy pixels by training a machine-learning model on cloudy-sky LST values observed owing to the SCSG effect. We demonstrated the utility of this approach by using MODIS/Aqua daytime LST data over Qinghai-Tibet Plateau (QTP) and validated the interpolation results against representative in-situ LST observations and two recently published all-weather LST datasets. When compared to the corresponding in-situ measurements, the interpolated cloudy-sky LST values showed satisfactory accuracy with a mean absolute error (MAE) value of 3.99 °C and a coefficient of determination (R 2) value of 0.74, while the MODIS/Aqua clear-sky LST values led to an MAE value of 2.66 °C and an R 2 value of 0.86. Compared to the two all-weather LST datasets, results of this study showed the highest accuracy over the data-gap-filled regions in terms of all quantitative performance metrics, more natural transition textures, and better representation of seasonal characteristics. The proposed framework has the advantage of relying on the MODIS family data and handling extensive missing data as well as triggers opportunities to leverage the SCSG effect to produce high-quality all-weather LST data. [ABSTRACT FROM AUTHOR]

Coastal zones are essential ecosystems due to their provision of invaluable ecosystem services. However, the geomorphologic characteristics of coastal zones are becoming more complex and changeable due to global warming, sea-level rise (SLR), and the intensification of anthropogenic activities. Therefore, accurate and timely knowledge of coastal land cover types (including tidal flats, coastal vegetation, and year-long water cover) is needed for coastal research and sustainable management. To date, land cover products for coastal areas are mainly derived from moderate resolution imaging spectroradiometer images, but few studies have used Sentinel-1 synthetic aperture radar (S1) and Sentinel-2 Multispectral Instrument (S2) images, which can provide more detailed maps. We developed a Rule-based Time Series Classification (RTSC) approach to map coastal land cover types at a 10 m resolution, combining S1/S2 time-series images (2015–2019) and Google Earth Engine (GEE). These products were developed for the coastal zone of the Yellow Sea (YS), East Asia, which is an essential ecosystem protecting a coastal population of 60 million people from storms and SLR effects. Accuracy assessment showed that the annual maps of coastal land cover had high overall accuracy. The coastal land cover types for the YS in 2019 comprised 3593.42 km2 of tidal flats, 28,506.98 km2 of coastal vegetation, and 5436.92 km2 of coastal year-long water. The interannual dynamics of the coastal land cover area in the YS during 2015–2019 were smaller. This study provides a promising method that combines S1/2 time series, a RTSC approach, and GEE to map coastal land cover areas at large scales. The 10 m resolution maps generated in this study are the most current dataset of coastal land cover types for the YS, and they potentially provide a basis for the sustainable management and conservation of this important coastal zone. [ABSTRACT FROM AUTHOR]

Controversy surrounds the reported dry season greening of the Central Amazon forests based on the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS). As the solar zenith angle decreases during the dry season, it affects the sub-pixel shade content and artificially increases Near-infrared (NIR) reflectance and EVI. MODIS' coarse resolution also creates a challenge for cloud and terrain filtering. To reduce these artifacts and then validate MODIS seasonal spectral patterns we use 16 years of 1 km resolution MODIS-MAIAC (Multi-Angle Implementation of Atmospheric Correction) images, corrected to a nadir view and 45° solar zenith angle, together with an improved cloud filter. Then we show that the 30 m Landsat-8 Operational Land Imager (OLI) surface reflectance over two Landsat scenes provides independent evidence supporting the MODIS-MAIAC seasonality for EVI, NIR, and GCC (an additional important vegetation index, green chromatic coordinate). Our empirical method for controlling for sun-sensor geometry effects in Landsat scenes encompasses the use of seasonally distinct images that have similar solar zenith angles and cloud-free pixels on flat uplands having the same phase angle. We extended this validation to nine Amazon sub-basins comprising ∼546 Landsat-8 images. Our study shows that the dry-season green-up pattern observed by MODIS is corroborated by Landsat-8, and is independent of satellite data artifacts. To investigate the mechanisms driving these seasonal changes we further used Central Amazon tower-mounted RGB cameras providing a 4-year record at the Amazon Tall Tower (ATTO, 2°8′36″S, 59°0′2″W) and a 7-year record at the Manaus k34 tower (2°36′33″ S, 60°12′33″W) to obtain monthly upper canopy green leaf cover (a proxy for Leaf Area Index - LAI) and monthly leaf age class abundances (based on the age since leaf flushing, by crown). These were compared to seasonal patterns of GCC and EVI in small MODIS-MAIAC windows centered on each tower. MODIS-MAIAC GCC was positively correlated with newly flushed leaves (R2 = 0.76 and 0.44 at ATTO and k34, respectively). EVI correlated strongly with the abundance of mature leaves (R2 = 0.82 and 0.80) but was poorly correlated with LAI (R2 = 0.20 and 0.41, respectively). Therefore, seasonal spectral patterns in the Central Amazon are likely controlled by leaf age variation, not quantity of leaf area. [ABSTRACT FROM AUTHOR]

Atmospheric correction of airborne hyperspectral imaging spectroscopy (AHIS) to obtain high-quality surface reflectance is the prerequisite for remote sensing applications. Over the last decades, different atmospheric correction methods have been developed based on radiative transfer models (RTMs), however, the relative performances of different algorithms are unclear. Automated operational atmospheric correction methods to process large-volume AHIS data in a high-accurate and high-throughput manner are still lacking. Therefore, this study proposed an operational atmospheric correction pipeline for deriving surface reflectance from AHIS data. To ensure the accuracy and efficiency of the pipeline, we focused on three specific aspects: (1) selecting a suitable RTM for the development of atmospheric lookup tables (LUTs) by comparing the commercial MODerate resolution atmospheric TRANsmission (MODTRAN) and open-sourced Library for Radiative TRANsfer (LibRadTRAN) models, where the widely-used software, Atmospheric/Topographic Correction for Airborne Imagery (ATCOR), was used as benchmarks; (2) identifying key atmospheric correction parameters and determining suitable sources for parameter retrievals including AHIS, Moderate Resolution Imaging Spectroradiometer (MODIS), and AErosol RObotic NETwork (AERONET); and (3) testing the performance of using machine learning emulators to speed up the RTM-based atmospheric correction. Results indicate that (1) atmospheric correction based on MODTRAN LUTs can produce surface reflectance accurately with mean absolute errors < 0.05 and cosine similarities > 0.98 compared to field measurements, which is comparable to the software ATCOR and slightly outperforms the LibRadTRAN LUTs; (2) sobol global sensitivity analysis demonstrates that in the atmospheric correction, visibility and water vapor are two key parameters that can be accurately derived from AHIS in contrast to MODIS or AERONET data; and (3) Random Forest emulators can produce accurate estimations of surface reflectance with mean absolute errors < 0.03 and cosine similarities > 0.98 for higher processing efficiency and determine a suitable set of wavelengths for retrieving atmospheric visibility and water vapor. The proposed atmospheric correction pipeline also improved the four-stream radiative transfer theory for airborne applications by considering adjacent effects from airborne surrounding pixels and can also be applied for atmospheric correction of hyperspectral data from spaceborne missions. [ABSTRACT FROM AUTHOR]

We present the variation of unusual atmospheric phenomena, aerosols, to understand the preseismic irregularities for two major earthquakes in Japan. We consider aerosol optical depth and Angstrom exponent data retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument onboard the Terra satellite to establish possible connections between earthquakes and the generation of aerosols. Variation of the aerosol parameters shows significant changes before the April 15, 2016, Kumamoto earthquake (M = 7.0 , h = 10 km) and the November 21, 2016, Fukushima earthquake (M = 6.9 and h = 9 km), where M indicates the Richter magnitude and h indicates the focal depth. To identify the source of the aerosol particles, we use the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT-4). This model uses both Lagrangian and Eulerian approaches to compute trajectories and establish a source-receptor relationship. We compute backward trajectories to check whether the aerosol generated near the epicenter is due to the preseismic processes or is transported from other areas. From our results, we conclude the fine-mode aerosols are generated in the vicinity of the epicenter, 3–7 days before the earthquakes. [ABSTRACT FROM AUTHOR]

Clouds are one of the most significant and uncertain components in weather forecasting and climate prediction. The 20-year detailed liquid water cloud effective radius (CER) over China from 2001 to 2020 are systematically studied based on the Moderate Resolution Imaging Spectroradiometer cloud products. The spatial distributions of CER show slight alterations of seasonal patterns of changes over China during 2001–2020. The monthly mean CER values over China vary from 12.7 μm in November to nearly 14.4 μm in July. The summer season has the largest average CER, followed by spring and winter, while autumn exhibits relatively lower CER levels over China. The mean CER values during 2001–2020 are found to be 14.7, 11.8 and 13.0 μm over the Tibetan Plateau, inland and coastal regions of China, respectively. High CERs in spring and winter are seen in the Tibet Plateau, whereas the reverse is true over the inland and coastal regions. The impacts of precipitable water vapor (PWV) and aerosol optical depth on the CER over China are complex, whereas the correlations between CER and PWV can be generally expressed by a two-stage linear fitting, showing distinct turning points in different seasons and regions. The CER over different regions of China shows an increase with enhanced PWV under low or high water vapor conditions as opposed to an increase of CER under moderate water vapor levels. Our study indicates a high CER in the Tibet Plateau and low over the Sichuan Basin, and significantly distinct impact of PWV on the CER in the Tibet Plateau. • A 20-year MODIS-based water cloud effective radius over China is assessed. • A high CER in the Tibet Plateau and low over the Sichuan Basin are seen. • Significantly distinct impacts of PWV on CER in the Tibet Plateau compared to other regions. [ABSTRACT FROM AUTHOR]

The stability of a climate data record (CDR) is essential for evaluating long-term trends in surface temperature using remote sensing products. In the case of a satellite-derived CDR of land surface temperature (LST), this includes the stability of processing steps prior to the estimation of the target climate variable. Instability in the masking of cloud-affected observations can result in non-geophysical trends in a LST CDR. This paper provides an assessment of cloud detection performance stability over a 25-year LST CDR generated using data from the second Along-Track Scanning Radiometer (ATSR-2), the Advanced Along-Track Scanning Radiometer (AATSR), the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Sea and Land Surface Temperature Radiometer (SLSTR). We evaluate three cloud detection methodologies, one fully Bayesian, one naïve probabilistic and the operational threshold-based cloud mask provided with each sensor, at four in-situ ceilometer sites. Of the 12 algorithm-site combinations assessed, only two (17 %) were stable across the full timeseries with respect to both cloud contamination and missed clear-sky observations. Five (42 %) were stable with respect to missed clear-sky observations only. The associated impacts on LST trends in the CDR could be as large as (+/−)0.73 K per decade (0.43 K per decade above the target stability), which means that attention needs to be paid to this aspect of stability in order to understand uncertainty in long-term observed trends. Given that cloud detection stability has not to our knowledge been previously assessed for any target climate variable, this conclusion may apply more broadly to other satellite-derived CDRs. • Cloud detection stability is assessed for land surface temperature climate records. • Missed-clear sky observations are more stable than cloud contamination over time. • Land surface temperature stability is related to cloud masking stability. • Characteristics of missed-clear sky and falsely-flagged cloud evaluated. [ABSTRACT FROM AUTHOR]

Glacial responses to climate change exhibit considerable heterogeneity. Although global glaciers are generally thinning and retreat, glaciers in the Karakoram region are distinct in their surging or advancing, exhibiting nearly zero or positive mass balance—a phenomenon known as the Karakoram Anomaly. This anomaly has sparked significant scientific interest, prompting extensive research into glacier anomalies. However, the dynamics of the Karakoram anomaly, particularly its evolution and persistence, remain insufficiently explored. In this study, we employed Landsat reflectance data and Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A3 albedo products to developed high-resolution albedo retrieval models using two machine learning (ML) regressions––random forest regression (RFR) and back-propagation neural network regression (BPNNR). The optimal BPNNR model (Pearson correlation coefficient [ r ] = 0.77–0.97, unbiased root mean squared error [ ubRMSE ] = 0.056–0.077, RMSE = 0.055–0.168, Bias = −0.149 ∼ −0.001) was implemented on the Google Earth Engine cloud-based platform to estimate summer albedo at a 30-m resolution for the Karakoram region from 1990 to 2021. Validation against in-situ albedo measurements on three glaciers (Batura, Mulungutti and Yala Glacier) demonstrated that the model achieved an average ubRMSE of 0.069 (p < 0.001), with RMSE and ubRMSE improvements of 0.027 compared to MODIS albedo products. The high-resolution data was then used to identify firn/snow extents using a 0.37 threshold, facilitating the extraction of long-term firn-line altitudes (FLA) to indicate the glacier dynamics. Our findings revealed that a consistent decline in summer albedo across the Karakoram over the past three decades, signifying a darkening of glacier surfaces that increased solar radiation absorption and intensified melting. The reduction in albedo showed spatial heterogeneity, with slower reductions in the western and central Karakoram (−0.0005–0.0005 yr−1) compared to the eastern Karakoram (−0.006 ∼ −0.01 yr−1). Notably, surge- or advance-type glaciers, avalanche-fed glaciers and debris-covered glaciers exhibited slower albedo reduction rates, which decreased further with increasing glacier size. Additionally, albedo reduction accelerated with altitude, peaking near the equilibrium-line altitude. Fluctuations in the albedo-derived FLAs suggest a transition in the dynamics of Karakoram glaciers from anomalous behavior to retreat. Most glaciers exhibited anomalous behavior from 1995 to 2010, peaking in 2003, but they have shown signs of retreat since the 2010s, marking the end of the Karakoram anomaly. These insights deepen our understanding of the Karakoram anomaly and provide a theoretical basis for assessing the effect of glacier anomaly to retreat dynamics on the water resources and adaptation strategies for the Indus and Tarim Rivers. • Machine learning and satellite data reveal declining summer albedo. • Reduction in albedo exhibited heterogeneity in the Karakoram glaciers. • Karakoram Anomaly behavior shifted from anomalies to retreat since the 2010s. • Key insights for Indus and Tarim River water resource management [ABSTRACT FROM AUTHOR]

Launched by China in 2021, the Sustainable Development Goals Science Satellite 1 (SDGSAT-1) is the world's first science satellite dedicated to serving the United Nations 2030 Agenda for Sustainable Development Goals. In keeping with international aims of this 2030 agenda, the SDGSAT-1 data will be made available for open accee without any restrictions. The Thermal Infrared Spectrometer (TIS) onboard SDGSAT-1 has three thermal infrared channels with a high spatial resolution of 30 m. This enables precise monitoring of land surface temperature (LST), which is one of the most important variables measured by satellite remote sensing. This paper presents the development and validation of three split-window (SW) algorithms and the temperature and emissivity separation (TES) algorithm for SDGSAT-1 TIS data. These algorithms were rigorously tested through simulation, application, and validation to assess their retrieval accuracy and sensitivity. Simulation results indicate that the theoretical accuracy of the SW algorithms exceeds 1.0 K in most cases, and the TES algorithm shows higher retrieval accuracy with an average LST Root Mean Square Error (RMSE) of 0.60 K. With consideration of the comprehensive effects of instrument noise, land surface emissivity, and atmospheric parameter error, the LST retrieval accuracy of SW algorithms remains better than 1.7 K, and that of the TES algorithm is better than 1.5 K under most conditions. The ground validation utilized site data from the Heihe Integrated Observatory Network and the Surface Radiation budget network. The SW and TES algorithms achieved an accuracy of approximately 1.75 and 1.9 K, respectively. Additionally, a cross-validation based on Moderate Resolution Imaging Spectroradiometer (MODIS) data indicated average RMSDs of approximately 2.25 K for SW algorithms and 3.84 K for TES algorithm. Among the algorithms, the three-channel SW algorithm SW-2 has the best overall performance and is recommended as the LST retrieval method for SDGSAT-1 data. The TES algorithm is also suitable for SDGSAT-1 images because of its ability to retrieve LST and emissivity during both daytime and nighttime. • Four typical LST retrieval algorithms are investigated for SDGSAT-1 TIS data. • A three-channel SW algorithm is recommended for generating SDGSAT-1 LST products. • High-resolution (30 m) LST products in both daytime and nighttime can be derived. [ABSTRACT FROM AUTHOR]

The Advanced Baseline Imager (ABI) sensors on the Geostationary Operational Environment Satellite-R series (GOES-R) broaden the application of global vegetation monitoring due to their higher temporal (5–15 min) and appropriate spatial (0.5–1 km) resolution compared to previous geostationary and current polar-orbiting sensing systems. Notably, ABI Land Surface Phenology (LSP) quantification may be improved due to the greater availability of cloud-free observations as compared to those from legacy GOES satellite generations and from polar-orbiting sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS). Geostationary satellites sense a location with a fixed view geometry but changing solar geometry and consequently capture pronounced temporal reflectance variations over anisotropic surfaces. These reflectance variations can be reduced by application of a Bidirectional Reflectance Distribution Function (BRDF) model to adjust or predict the reflectance for a new solar geometry and a fixed view geometry. Empirical and semi-empirical BRDF models perform less effectively when used to predict reflectance acquired at angles not found in the observations used to parameterize the model, or acquired under hot-spot sensing conditions when the solar and viewing directions coincide. Consequently, using a fixed solar geometry or even the geometry at local solar noon may introduce errors due to diurnal and seasonal variations in the position of the sun and the incidence of hot-spot sensing conditions. In this paper, a new solar geometry definition based on a Constant Scattering Angle (CSA) criterion is presented that, as we demonstrate, reduces the impacts of solar geometry changes on reflectance and derived vegetation indices used for LSP quantification. The CSA criterion is used with the Ross-Thick-Li-Sparse (RTLS) BRDF model applied to North America ABI surface reflectance data acquired by GOES-16 (1 January 2018 to 31 December 2020) and GOES-17 (1 January 2019 to 31 December 2020) to normalize solar geometry BRDF effects and generate 3-day two-band Enhanced Vegetation Index (EVI2) time series. Compared to the local solar noon geometry, the CSA criterion is shown to reduce solar geometry reflectance and EVI2 time series artifacts. Further, comparison with contemporaneous VIIRS NBAR (Nadir BRDF-Adjusted Reflectance) EVI2 time series is also presented to illustrate the efficacy of the CSA criterion. Finally, the CSA-adjusted EVI2 time series are shown to produce LSP results that agree well with PhenoCam-based observations, with no obvious systematic bias in onsets of vegetation maturity, senescence, and dormancy dates compared to about 10-day bias found with local solar noon adjusted EVI2 time series. • Constant Scattering Angle (CSA) criterion is proposed to normalize ABI reflectance. • CSA criterion minimizes the impact of solar geometry on ABI reflectance and EVI2. • CSA-adjusted ABI EVI2 is comparable with VIIRS NBAR EVI2 time series. • CSA-adjusted ABI EVI2 detects phenometrics similar to PhenoCam observations. [ABSTRACT FROM AUTHOR]

Landsat imagery offers remarkable potential for various applications, including land monitoring and environmental assessment, thanks to its high spatial resolution and over 50 years of data records. However, the presence of atmospheric aerosols greatly hinders the precision of land classification and the quantitative retrieval of surface parameters. There is a pressing need for reliable and accurate global aerosol optical depth (AOD) data derived from Landsat imagery, particularly for atmospheric correction purposes and various other applications. To address this issue, we introduce an innovative framework for retrieving AOD from Landsat imagery over land, which leverages the deep-learning Transformer model (named AeroTrans-Landsat) and operates on the Google Earth Engine (GEE) cloud platform. We gather Landsat 8 and 9 images starting from their launch dates (February 2013 and September 2021, respectively) until the end of 2022, which are used to construct a robust aerosol retrieval model. The global AOD retrievals are then rigorously validated across ∼560 monitoring stations on land using diverse spatiotemporally independent methods. Leveraging information from multiple spectral channels, which contributes to 80 % according to the SHapley Additive exPlanation (SHAP) method, our retrieved AODs from 2013 to 2022 generally agree well with surface observations, with a sample-based cross-validation correlation coefficient of 0.905 and a root-mean-square error of 0.083. Around 86 % and 55 % of our AOD retrievals meet the criteria of Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue expected errors [±(0.05 + 20 %)] and the Global Climate Observation System {[max(0.03, 10 %)]}, respectively. Additionally, our model is not as sensitive to fluctuations in both surface and atmospheric conditions, enabling the generation of spatially continuous AOD distributions with exceptionally fine-scale information over dark to bright surfaces. This capability extends to areas characterized by high pollution levels originating from both anthropogenic and natural sources. • A hybrid Transformer and GEE model is developed for Landsat AOD retrieval. • Multiple spectral channels contribute to 80 % in aerosol retrieval using XAI. • The AeroTrans-Landsat model excels in retrieving global AOD levels (R = 0.905). • We derive the first global monthly 30-m AOD maps from Landsat imagery. [ABSTRACT FROM AUTHOR]

• Snowmelt-Runoff Model (SRM) indicated significant increases in annual and seasonal runoff. • RCP4.5 scenario shows stronger seasonal runoff increases compared to RCP8.5, suggesting potential loss of snow and glaciers due to early warming. • Future hydropower capacity projected to increase by 93 % to 167 % under RCP4.5 and RCP8.5 scenarios, respectively. • Construction of multipurpose dams are crucial to mitigate peak flow risks and ensure resilient EWF resources. Pakistan's hydropower sector depends heavily on glacier and snowmelt water that originates from the Upper Indus Basin (UIB). It is expected that climate change may adversely affect future hydropower generation capacity as a result of fluctuations in the magnitude, seasonality and hydrological extremes of the Indus River flow. This study employed the Degree-Day Snowmelt Runoff Model alongside the Moderate Resolution Imaging Spectroradiometer MODIS and daily ground-based hydro-meteorological data to model the snowmelt runoff response in the UIB. The results indicated a significant increase in the annual and seasonal runoff under both RCP4.5 and RCP8.5 scenarios, suggesting more water availability for hydropower and irrigation. By the end of the century, annual river flow is projected to increase by 28 % to 69 % under the RCP4.5 and RCP8.5 climate scenarios. Consequently, rise in annual river flow is expected to increase the electricity generation capacity of future hydropower projects by 93 % to 167 % under the RCP4.5 and RCP8.5 scenarios, respectively. The construction of robust multipurpose dams may potentially reduce flood risks in downstream areas during peak flows, while also supplying water for hydropower generation and irrigation during low flows. This, in turn, may enhance the resilience of both the hydropower and agriculture sectors in the face of climate change. [ABSTRACT FROM AUTHOR]

This study explores a possibility of improving Advanced Himawari Imager (AHI) surface-sensitive brightness temperature (TB) simulations over land by assimilating land surface temperature (LST) observations from the National Basic Meteorological Observing Stations of China. The Gridpoint Statistical Interpolation 3D-Var regional data assimilation (DA) system is modified to add LST as a new control variable and its background error variances, horizontal correlations and cross-correlations. The background covariances of LST with other control variables are calculated separately for daytime and nighttime samples in summer and winter seasons. A control experiment (ExpCTL) and three LST DA experiments with (ExpLST) and without (ExpLST_NBC) bias correction or with an average of LST within 2° × 2° grid boxes (ExpLST_SO) are conducted. Considering the fact that surface station observations are point measurements while the satellite TBs measure the total radiation effect of earth's surface within fields-of-view, a bias correction is found necessary for LST DA during daytimes (ExpLST). The biases are quantified by the differences from the Moderate-resolution Imaging Spectroradiometer LST retrievals to compensate for the representative differences. The analyzed fields are then used as input to the Community Radiative Transfer Model to simulate TBs of AHI surface-sensitive channels over land. A long-period statistics shows that ExpLST significantly reduces the observations minus simulations (O B) biases and standard deviations of surface-sensitive TBs in terms of reducing the diurnal variations and season dependences of TB biases over different surface types, which also outperforms ExpLST_NBC and ExpLST_SO at daytime. This study suggests a potential benefit of combining the use of LST observations for assimilating surface-sensitive infrared TBs. • An assimilation scheme of land surface temperature station observations is designed for improving AHI radiance simulation. • The GSI system is updated by adding control variable of surface temperature and the background error covariances. • Performance of simulated TBs for surface-sensitive channels are improved in terms of diurnal dependences and surface types. [ABSTRACT FROM AUTHOR]

• Long-term trend analysis of semi-arid soil moisture based on TVDI showed a fluctuating upward trend over the study period. • Soil moisture factors were analyzed objectively and quantitatively using the Optimal Parameter Geographical Detector model. • Vegetation cover dominated the spatial heterogeneity of soil moisture. • Factors interact and enhance each other, elevation can affect vegetation cover and further influence soil moisture dynamics. As an important water conservation and sand–wind barrier, the Zhangjiakou–Chengde district (ZC) is highly important for ecological protection in the Beijing–Tianjin–Hebei region. The research on the variation in soil moisture and its affecting factors is important for early drought warning and the improvement of environmental protection. Based on the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Global Land Data Assimilation System (GLDAS) datasets, the spatiotemporal variation in surface soil moisture in the ZC was simulated from 2001 to 2021 using the temperature vegetation dryness index (TVDI) model. The optimal parameter geographical detector (OPGD) method was used to identify the contributions of 10 factors affecting soil moisture. The results indicate that soil moisture generally fluctuated during 2001–2021. Six phases were identified. Spatially, the soil moisture was higher in the east and lower in the western part of the study area. Approximately 83.09 % of the district experienced a progressive increase in soil moisture. The future soil moisture dynamics trend indicates that 62.98 % of the ZC would shift from dry to wet conditions. The normalized difference vegetation index (NDVI), precipitation, land use types, slope, elevation, temperature, aspect, sand content, silt content, and clay content were analyzed to determine their effects on the soil moisture variation. The interaction analysis revealed that the effect of multiple factors was higher than that of the individual factors. The synergistic interaction between NDVI and elevation had the highest influence on soil moisture. The results of the risk detector showed that the NDVI, precipitation, elevation, slope, and clay content contributed to soil moisture. Meanwhile, the temperature and sand content contributed to soil moisture in the converse manner. The research on soil moisture variations and its impact factors on the ZC has high significance for the efficient utilization of water resources and eco–environmental protection. [ABSTRACT FROM AUTHOR]

Fire has become a major disturbing factor in boreal forests, and giant forest disturbances play a vital role in regulating the climate under global warming. Therefore, it is essential to investigate the spatiotemporal patterns and main drivers of post-fire vegetation recovery for forest ecological research and post-fire recovery management. However, previous studies have focused on the post-fire forest change within the entire fire perimeter, lacking separate analysis and comparison of the burned zone (BZ) and unburned zone (UNBZ). Here, we propose the utilization of Moderate Resolution Imaging Spectroradiometer land cover type and vegetation index data to monitor vegetation dynamics and explore its drivers after the most serious forest fire in the history of P.R. China in the Greater Hinggan Mountains (GHM). The temporal and spatial patterns of vegetation recovery in the BZ/UNBZ in the GHM were analyzed using the Sen & Mann-Kendall method, Hurst index and coefficient of variation, and their driving mechanisms were explored using GeoDetector and geographically weighted regression. The results showed that there were significant differences in the spatial distribution and fluctuation of vegetation between the BZ and UNBZ, and that the BZ exhibited higher productivity and vigor. Vegetation recovery was influenced by different dominant factors and changed over time, in which land surface temperature and precipitation dominated all the time, whereas topographic relief and elevation had a more significant contribution to vegetation recovery in the BZ and UNBZ, respectively. This study provides a scientific basis for the protection and management of vegetation in disturbed forested areas, particularly after fires. • Differences in vegetation between burned/unburned areas are fully ascertained. • Vegetation in burned zones has higher greenness and vigor than unburned zones. • Land surface temperature and precipitation dominate vegetation recovery. • Optimal vegetation growth conditions in burned and unburned zones were determined. [ABSTRACT FROM AUTHOR]

Operational Earth observation missions, like the Sentinel-3 (S3) satellites, aim to provide imagery for long-term environmental assessment to monitor and analyze vegetation changes and dynamics. However, the S3 archive is limited in temporal availability to the year 2016. Although S3 provides continuity of previous missions, key vegetation products (VPs) including leaf area index (LAI), fraction of photosynthetically active radiation (FAPAR), fractional vegetation cover (FVC), and leaf chlorophyll content (LCC), can be reliably produced from Ocean and Land Colour Instrument (OLCI) data only since the sensors' launch. To overcome this limitation, our study proposes a reconstruction workflow that extends the data record beyond its data acquisition. By using multi-output Gaussian process regression (MOGPR) fusion, we explored guiding predictor VPs from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor for the reconstruction of multi-decadal (spanning two decades, 2002–2022) temporal profiles of four OLCI-derived VPs (S3-MOGPR), moving past S3's launch. We first evaluated three MODIS-derived inputs as predictor variables: LAI, FAPAR, and the Normalised Difference Vegetation Index (NDVI) over nine sites with distinct land covers from the Ground-Based Observations for Validation (GBOV) service. Each predictor produced a distinct time series for the four reconstructed S3 VPs. To determine which predictor variable most accurately reconstructs data streams of the targeted variable, all S3-MOGPR VPs were compared to satellite-based products from the Copernicus Global Land Service (CGLS). MOGPR models were trained for 2019 and compared to reference data. Since MODIS LAI demonstrated the best reconstruction performance of all predictors, S3-MOGPR VPs were fully reconstructed from 2022 back to 2002 using guiding MODIS LAI and evaluated with in-situ data. The most consistent reconstructed product was FVC (R = 0.96 , NRMSE = 0.17) over mixed forests compared to CGLS estimates. FVC also yielded the highest validation statistics (R = 0.93 , ρ = 0.92 , NRMSE = 0.14) over croplands. The highest correlation coefficients were achieved by the predictor variable LAI reconstructing FVC with mean R , ρ and NRMSE = 0.11 among all sites of 0.91 and 0.88, respectively. In the absence of both satellite and ground-based LCC reference measurements, the reconstructed LCC profiles were compared to the OLCI and MERIS Terrestrial Chlorophyll Index (OTCI, MTCI). The correlation metrics provided strong evidence of the reconstructed LCC product's integrity, with the highest correlation over deciduous broadleaf, mixed forests and croplands (R > 0.9). The lowest correlations for all reconstructed variables appeared over evergreen broadleaf forests, driven by the absence of seasonal patterns. Altogether, by leveraging the flexibility of the MOGPR algorithm with guiding historical data, contemporary EO data can be extrapolated into the past. • Reconstructed Sentinel-3 OLCI-based FAPAR, FVC, LAI and LCC between 2002 and 2022. • MOGPR models exploit irregular multi-sensor time series relationships. • Strong agreement with CGLS and GBOV reference data over sites with high seasonality. • Reconstructed LCC data stream is highly consistent with the OTCI/MTCI profiles. • Effective method to globally reconstruct past data streams over distinct land covers. [ABSTRACT FROM AUTHOR]

Elucidating the relationships between production-living-ecological space (PLES) and ecological environments can help identify new strategies to promote sustainable development. The northern slope of the Tianshan Mountains represents a typical complex ecosystem characterized by mountain-oasis-desert interactions in arid regions with prominent human-land conflicts and intricate ecological environments. Here, we analyzed the spatiotemporal variations in land use in the study area from the perspective of PLES. Based on moderate resolution imaging spectroradiometer (MODIS) data, an improved remote sensing ecological index (IRSEI) suitable for arid areas was established to evaluate the eco-environment quality (EEQ) by introducing salinity and cleanliness indexes. A stepwise regression model was then used to identify and quantify the impact of the PLES transfer modes on EEQ. The results showed that: (1) The oasis area was dominated by agricultural production land, whereas the desert and alpine areas were dominated by other and pasture ecological lands, respectively. The oasis area primarily involved the conversion of pasture ecological land to agricultural production land, whereas the desert and alpine areas showed mutual transformation of ecological space. (2) The size relationship of the EEQ in each sub-region was alpine region (0.555) > oasis region (0.509) > desert region (0.424). The EEQ in the study area showed an overall upward trend but decreased in the alpine region. (3) Changes in the PLES and IRSEI primarily occurred in the oasis region. Among the 21 PLES transfer modes that affect EEQ, an increase in agricultural production land was the primary mode that enhanced IRSEI, whereas an increase in impervious surfaces significantly decreased IRSEI. The degree of influence was as follows: industrial and mining production (−0.594) > urban living (−0.462) > rural living land (−0.316). In addition, EEQ is also affected by the combined effects of climate, topography, and other factors. By proposing IRSEI, we highlight the impact of land-use transfer mode on ecological environment quality from the perspective of PLES, which can provide a reference for ecological environment evaluation in arid areas and is of great significance for land-use planning and ecological environment protection. • Land use analysis from the perspective of production-living-ecological space (PLES). • An improved remote sensing ecological index (IRSEI) suitable for arid areas was established. • The main PLES transfer modes affecting the ecological environment were identified. • The results will guide land use planning and ecological sustainable management in arid areas. [ABSTRACT FROM AUTHOR]

The interaction between aerosol and cloud causes one of the largest uncertainties in external forcing to our climate system, highlighting the importance of additional extensive research on aerosol – cloud interaction (ACI). Thus, in this study nearly two-decades (2000−2021) of Moderate Resolution Imaging Spectroradiometer (MODIS) derived aerosol and cloud dataset have been analysed to examine the warm cloud sensitivity to aerosols over the Northern Indian Ocean (NIO). We found a statistically significant anti-Twomey effect in warm clouds for lower liquid water path (< 70 gm−2) throughout all seasons, except for a pronounced Twomey effect during the monsoon season in the northern region. Nonetheless, limitations in satellite retrievals of cloud droplets prevent definitive conclusions on cloud droplet sensitivity. On other hand, albedo susceptibility indicates existence of cloud brightening in cleaner equatorial oceanic regions. Our results emphasize the importance of considering the distinctness of ACI at regional scale in global climate models which is essential for effectively constraining associated forcing mechanisms and enhancing the reliability of future predictions. • Sensitivity of warm clouds to aerosol variations shows significant spatial and temporal diversity. • A significant prevalence of Twomey and anti-Twomey effect is observed over the NIO. • Warm clouds evolving in relatively cleaner environments exhibit overall brightening potential. • MODIS Terra vs Aqua observation show a notable difference in spatial-temporal distribution of ACI. [ABSTRACT FROM AUTHOR]

Precipitable water vapor (PWV) plays an important role in the global water and energy cycle. Compared with GNSS and radiosonde which are distributed in the form of scatters, near-infrared -derived PWV has a higher spatial resolution, meeting more comprehensive investigation of regional climate change. However, PWV derived from the near-infrared water vapor channels of the Moderate Resolution Imaging Spectroradiometer (MODIS), onboard Aqua and Terra satellites, exhibits limitations of missing data and poor accuracy especially when data are collected under cloudy conditions. Many researches have been made to evaluate and calibrate MODIS-PWV with a cloud-free probability >95%, there is still very little research on improving accuracy of PWV under all-weather conditions. Therefore, an optimal calibration method was proposed, in which a filling algorithm was applied to enhance availability of the PWV data, and a linear and periodic calibration scheme based on the analysis of residuals was utilized to improve its accuracy. The experiment was conducted in Hong Kong with 11 uniformly distributed GNSS stations and the station cross-validation was employed using the GNSS-PWV as the references. The results show that the filling algorithms can first effectively fill the data vacancy and generate complete MODIS-PWV with data coverage reaching up to 100%, further make MODIS-PWV more conducive to construct following calibration model. The R-Square(R2), root mean square error (RMSE), mean absolute error (MAE) and relative error of the MODIS-PWV obtained by the proposed method are 0.67, 9 mm,7 mm and 22.5% compared with GNSS-PWV. In comparison to original MODIS-PWV, the RMSE and MAE are reduced by 62% and 59%. For the four seasons, the average value of RMSE is improved from 25 to 9 mm, 32 to 10 mm, 22 to 9 mm, and 12 to 8 mm, respectively. Moreover, the RMSEs and MAEs are about 6–8 mm and 5–6 mm for station cross-validation in every month. These results confirm that the proposed method can provide a complete and continuous PWV data and improve the accuracy of original MODIS-PWV, which is benefit for the hydrological and ecological research. • An optimal method was proposed to calibrate MODSI-PWV using GNSS observations. • The filling strategy of the proposed method can effectively fill the data missing caused by cloud cover of MODIS-PWV. • The accuracy of MODIS-PWV is improved by the optimal method that considers the periodic factors in PWV sequences. [ABSTRACT FROM AUTHOR]

This study analyzes the productivity of grasslands in a Mediterranean oak-savanna ecosystem, focusing on its linkages with water availability. In these water-controlled ecosystems, grassland environmental preservation and sustainable management depend on quantitatively understanding these links. Productivity and water stress were modeled in southern Spain (2001–2018), integrating meteorological information and MODIS sensor data into a light-use efficiency model and a surface energy balance. The results provided valuable insights into how grasslands behaved during droughts at different spatiotemporal scales. During the most significant droughts, 2004/2005 and 2011/2012, aerial biomass production was reduced by 42 % and 67 %, respectively. The spatial analysis identified the central east side of the region, with low slopes and moderate tree cover, as the most productive area. The biomass production time series classification identified four distinct trends, all showing shifted relationships with similar slopes between production and anomalies of relative evapotranspiration. The seasonal analysis highlighted the importance of autumn, accounting for nearly 30 % of the annual biomass production, which was essential in years with spring water deficits. The proposed methodology provides on-farm grassland production curves depending on water availability (max-mean-min range with a mean error of 15.5 %). Together with weather forecast data, this could help farmers decide on the optimal level of management intensification and stocking rate. Although the regional specificity may limit the study's direct applicability, this scheme offers valuable metrics that could be adapted to other areas under water scarcity conditions. • Modeling pasture productivity together with water stress can aid on-farm management. • Biomass time series classification revealed different production trends. • Seasonal and monthly analyses helped to explain year variability. • Spring is vital for grassland production, as autumn is in a water-deficit year. • Farmers can use this methodology to optimize livestock management during droughts. [ABSTRACT FROM AUTHOR]

Satellite-derived vegetation indices (VIs) have been extensively used in monitoring vegetation dynamics at local, regional, and global scales. While numerous studies have explored various factors influencing VIs, a remarkable knowledge gap persists concerning their applicability in mountain areas with complex topographic variations. Here we bridge this gap by conducting a comprehensive evaluation of the topographic effects on ten widely used VIs. We used three evaluation strategies, including: (i) an analytic radiative transfer model; (ii) a 3D ray-tracing radiative transfer model; and (iii) Moderate Resolution Imaging Spectroradiometer (MODIS) products. The two radiative transfer models provided theoretical evaluation results under specific terrain conditions, aiding in the first exploration of the interactions of both shadow and spatial scale effects on VIs. The MODIS-based evaluation quantified the discrepancies in VIs between MODIS-Terra and MODIS-Aqua over flat and rugged terrains, providing new insights into real satellite data across different temporal scales (i.e., from daily to multiple years). Our evaluation results were consistent across these three strategies, revealing three key findings. (i) The normalized difference vegetation index (NDVI) generally outperformed the other VIs, yet all VIs did not perform well in shadow areas (e.g., with a mean relative error (MRE) of 14.7% for NDVI in non-shadow areas and 26.1% in shadow areas). (ii) The topographic impacts exist at multiple spatiotemporal scales. For example, the MREs of NDVI reached 28.5% and 11.1% at 30 m and 3 km resolutions, respectively. The quarterly and annual VIs deviations between MODIS-Terra and MODIS-Aqua also increased with slope. (iii) We found the topography-induced interannual variations in multiple VIs both in simulated data and MODIS data. VIs trend deviations between MODIS-Terra and MODIS-Aqua over the Tibetan Plateau from 2003 to 2020 increased as the slope steepened (i.e., NDVI and enhanced vegetation index (EVI) trend deviations generally doubled). Overall, the sun-target-sensor geometry changes induced by topography, causing shadows in mountains along with obstructions in sensor observations, compromised the reliability of VIs in these terrains. Our study underscores the considerable impacts of topography, particularly shadow effects, on multiple VIs at various spatiotemporal scales, highlighting the imperative of cautious application of VIs-based trend calculation in mountains. • Quantification of topographic effects on VIs with radiative transfer models and MODIS data. • Topographic effects on VIs cannot be ignored in satellite products from 30 m to 3 km resolutions. • Topographic impacts varied at different temporal scales: from daily to multiple years. • NDVI outperformed the other VIs, yet all VIs did not perform well in shadow areas. • Long-term trends from VIs are impacted by topography and may bias mountainous vegetation studies. [ABSTRACT FROM AUTHOR]

Satellite observations from instruments such as the TROPOspheric Monitoring Instrument (TROPOMI) show significant potential for monitoring the spatiotemporal variability of NO 2 , however they typically provide vertically integrated measurements over the tropospheric column. In this study, we introduce a machine learning approach entitled 'S-MESH' (Satellite and ML-based Estimation of Surface air quality at High resolution) that allows for estimating daily surface NO 2 concentrations over Europe at 1 km spatial resolution based on eXtreme gradient boost (XGBoost) model using primarily observation-based datasets over the period 2019–2021. Spatiotemporal datasets used by the model include TROPOMI NO 2 tropospheric vertical column density, night light radiance from the Visible Infrared Imaging Radiometer Suite (VIIRS), Normalized Difference Vegetation Index from the Moderate Resolution Imaging Spectroradiometer (MODIS), observations of air quality monitoring stations from the European Environment Agency database and modeled meteorological parameters such as planetary boundary layer height, wind velocity, temperature. The overall model evaluation shows a mean absolute error of 7.77 μg/m3, a median bias of 0.6 μg/m3 and a Spearman rank correlation of 0.66. The model performance is found to be influenced by NO 2 concentration levels, with the most reliable predictions at concentration levels of 10–40 μg/m3 with a bias of <40%. The spatial and temporal error analyses indicate the spatial robustness of the model across the study area, with better prediction accuracy during the winter months and the associated higher NO 2 concentrations. Despite the complexity and the continental scale of the study area, the XGBoost-based model shows fast execution potential in providing daily estimates of surface NO 2 concentrations over Europe. The Shapley Additive exPlanations (SHAP) value analysis highlights TROPOMI NO 2 tropospheric column density as the main source of information in deriving surface NO 2 concentrations, indicating its significant potential for such studies. The SHAP values also indicate the importance of anthropogenic emission proxy inputs such as VIIRS night lights, in complementing TROPOMI NO 2 values for deriving higher resolution and detailed spatial patterns of NO 2 variations. • Surface NO2 concentrations at 1 km resolution is estimated over Europe using XGBoost. • Satellite-based input features' potential contribution is observed. • SHAP values indicate highest importance of Sentinel-5P TROPOMI observation. • Finer spatial patterns can be derived using VIIRS nightlight. • XGBoost is a good candidate for continental-scale study areas. [ABSTRACT FROM AUTHOR]

There are large temporal variations in the optical properties and types of aerosols. These are due to the effects of environmental and meteorological conditions, industrial and agricultural influences, and other human influences and natural factors in each ecological functional area. Aerosol optical depth AOD and Ångström exponent AE parameters were extracted from the Moderate Resolution Imaging Spectroradiometer MODIS satellite with a 3 km resolution of MOD04_3K and MYD04_3K. The study covers sixteen selected stations in Horn Africa–Ethiopia with neighboring Eritrea, Djibouti, and South Sudan countries clustered into four regions for the periods of 2001–2022 to obtain detailed information on the temporal behaviors and classification of aerosols in each season. The results show that the temporal variability of AOD and AE values is large, with values ranging from 0.00 to 2.10 and 0.67 to 1.23, with minimum values most likely between December 16 and January 07, and maximum values between June 22 and July 24 for both Terra and Aqua in the southeast and northeast clusters, respectively. The lowest AOD and AE values were most common in the Belg (during September–November) and sometimes in Bega (during December–February), while the highest were in the Kiremt season (during June–August). The minimum values were observed at the Kebri Dahar site, and the maximum were at the Erta Ale site, which belongs to Ethiopia. Appropriate AOD and AE thresholds were used to classify the aerosol types into marine (MR), dust (DS), biomass burning (BB), desert dust (DD), urban (UR), and the residual cases were considered mixture (MX) types. The study concludes that marine aerosols were the most abundant at 47.71%, followed by urban aerosols at 28.29%, while desert dusts, enriched at 14.65%, accounted for the third place with free of dust particles. The Mann–Kendall (MK) statistical trend test was applied to the annual time series in all clustered regions and shows an almost positive increasing variability with a slight decrease in the forward series and vice–versa. • The minimum daily AOD and AE were observed in the southeast cluster at the Kebri Dahar site, and the maximum AOD and AE were in the northeast cluster at the Erta Ale site. • The southeast cluster had the lowest AOD and AE values in both instruments, Terra and Aqua, while the northeast cluster had the highest. • The aerosols were at their highest density in the northwest cluster at the Humera site during Bega and at their lowest levels mostly in the northeast cluster at the Juba site in the Kiremt season. • The yearly total forward trend of aerosols was almost increasing, and the reverse was vice versa. • The variations shown in the trend were lowest in the northwest cluster and highest in the northeast cluster. [ABSTRACT FROM AUTHOR]

Microwave remote sensing serves as a complementary tool for soil moisture mapping and monitoring in comparison to the optical and the infrared remote sensing. The key advantage of microwave remote sensing lies in its ability to estimate soil moisture irrespective of weather or atmospheric conditions. However, existing microwave passive soil moisture products are currently available only at a coarse spatial resolution, limiting their utility for various regional hydrological applications. Various studies have introduced different approaches to downscale these satellite soil moisture data products but the modulation of vegetation present on the soil surface is still a challenging task in the field of downscaling satellite soil moisture data products. Therefore, this study presents a novel vegetation modulated soil moisture index developed by incorporating the MODIS (MODerate resolution Imaging Spectroradiometer) NDVI (Normalized Difference Vegetation Index), and LST (Land Surface Temperature). This newly derived index is then used to downscale the coarse resolution SMAP (Soil Moisture Active Passive) soil moisture data product. The proposed downscaling method has been validated using ground - measured soil moisture and compared with conventional downscaling approaches. It is found that the newly developed method, which has a bias error of −0.004 m3/m3 an Unbiased Root Mean Square Error (ubRMSE) of 0.068 m3/m3 of the downscaled soil moisture and demonstrates more intricate spatial variations. • Development of the Novel Vegetation Modulated Soil Moisture Index (VMSMI). • This vegetation index is used for the vegetation modulation. • VMSMI is also used to obtain the high-resolution SMAP soil moisture up to 1 km. • The downscaled SMAP soil moisture performed well compared to the existing research. [ABSTRACT FROM AUTHOR]

• LST of Kuala Lumpur city is 3.8 °C to 4.1 °C higher compared to its surroundings. • The city experiences extreme rainfall, 10 to 43 % more than its surroundings. • There is a strong positive correlation between trends in LST and extreme rainfall events. • The city's hourly rainfall between 2pm and 9pm increased significantly. • Increased rainfall extremes are due to an intensification of convection by higher LST. This study uses high-resolution satellite data to investigate the impact of urbanization on the local climate of Kuala Lumpur, Malaysia. Nonparametric statistical methods, including trend test, spatial correlation, and quantile regression, were employed to quantify interrelationships between satellite nighttime light (NTL), Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST), and Global Satellite Mapping of Precipitation (GSMaP) extreme rainfall events. The results revealed that urbanization increased Kuala Lumpur's LST by 3.8 to 4.1 °C and the 99th percentile rainfall by 10 to 43 % compared to surrounding areas. The correlation analysis showed an association (r = 0.62) between the trends in LST and rainfall extremes, with a significance level of p < 0.05. The NTL trend analysis showed a rapid expansion of the city, particularly in the west, and thus, an expansion of high LST areas and extreme rainfall amounts. Quantile regression results confirmed the influence of increased LST on rainfall extremes. The trends for hourly rainfall revealed increases mainly between 2pm and 9pm in the city, which is attributed to the intensification of convective rainfall caused by increased LST. The study highlights the importance of mitigating urban LST to reduce climatic extremes and promote sustainable urban development. [ABSTRACT FROM AUTHOR]

Land surface albedo plays an important role in controlling the surface energy budget and regulating the biophysical processes of natural dynamics and anthropogenic activities. Satellite remote sensing is the only practical approach to estimate surface albedo at regional and global scales. It nevertheless remains challenging for current satellites to capture fine-scale albedo variations due to their coarse spatial resolutions from tens to hundreds of meters. The emerging Sentinel-2 satellites, with a high spatial resolution of 10 m and an approximate 5-day revisiting cycle, provide a promising solution to address these observational limitations, yet their potentials remain underexplored. In this study, we integrated the Sentinel-2 observations with an updated direct estimation approach to improve the estimation and monitoring of fine-scale surface albedo. To enable the capability of the direct estimation approach at a 10-m scale, we combined the 10-m resolution European Space Agency (ESA) WorldCover land cover data and the 500-m resolution Moderate-Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF)/albedo product to build a high-quality and representative BRDF training database. To evaluate our approach, we proposed an integrated evaluation framework leveraging 3-D physical model simulations, ground measurements, and satellite observations. Specifically, we first simulated a comprehensive dataset of Sentinel-2-like surface reflectance and broadband albedo across a variety of geometric configurations using the MODIS BRDF training samples. With this dataset, we built the Look-Up-Tables (LUTs) that connect surface broadband albedo and Sentinel-2 reflectance through a direct angular bin-based linear regression approach, and further coupled these LUTs with the Google Earth Engine (GEE) cloud-computing platform. We next evaluated the proposed algorithm at two spatial levels: (1) 10-m scale for absolute accuracy assessment using the references from the Discrete Anisotropic Radiative Transfer (DART) simulations and flux-site observations, and (2) 500-m scale for large-scale mapping assessment by comparing the estimated albedo with the MODIS albedo product. Lastly, we presented four examples to show the capability of Sentinel-2 albedo in detecting fine-scale characteristics of vegetation and urban covers. Results show that: (1) the proposed algorithm accurately estimates surface albedo from Sentinel-2-like reflectance across different landscape configurations (overall root-mean-square-error (RMSE) = 0.018, bias = 0.005, and coefficient of determination (R2) = 0.88); (2) the Sentinel-2-derived surface albedo agrees well with ground measurements (overall RMSE = 0.030, bias = -0.004, and R2 = 0.94) and MODIS products (overall RMSE = 0.030, bias = 0.021, and R2 = 0.97); and (3) Sentinel-2-derived albedo accurately captures seasonal leaf phenology and rapid snow events, and detects the interspecific (or interclass) variations of tree species and colored urban rooftops. These results demonstrate the capability of the proposed approach to map high-resolution surface albedo from Sentinel-2 satellites over large spatial and temporal contexts, suggesting the potential of using such fine-scale datasets to improve our understanding of albedo-related biophysical processes in the coupled human-environment system. [ABSTRACT FROM AUTHOR]

Satellite temporal resolutions are vital for the timely detection of phytoplankton blooms in inland waters using satellite observations due to rapid phytoplankton migration and reproduction. However, the effects of satellite temporal resolutions on the remote derivation of trends in phytoplankton have not yet been assessed. To address this issue, we first used the daily Moderate–resolution Imaging Spectroradiometer (MODIS)–derived phytoplankton blooms in a typical bloom–frequent lake (Lake Taihu, China) between 2001 and 2020 to simulate a series of phytoplankton bloom data sets with temporal resolutions of 2 days to 30 days. We then revealed the differences in the trends of the phytoplankton blooms and their responses to the climatic factors derived from these data sets; we subsequently quantified the relationships between the differences and the temporal resolutions at daily, monthly, and yearly scales. We found that there were significant inconsistencies in the trends in the phytoplankton bloom areas derived from the simulated series data sets with different temporal resolutions; the relations of the MODIS–derived phytoplankton bloom areas to those derived from the simulated series data sets became weaker as the temporal resolutions of the simulated series data sets decreased; the responses of the phytoplankton blooms to the climatic factors quantified by the simulated series data sets were also varied with the temporal resolutions at daily, monthly, and yearly scales. These results from simulated data were also validated by satellite data of Lake Taihu, Lake Chaohu, and Lake Dianchi, three eutrophic lakes in China. In brief, our results cautioned against the use of satellite data with low temporal resolutions to reveal long–term phytoplankton–bloom dynamics. [ABSTRACT FROM AUTHOR]

Himawari-8, a new-generation geostationary satellite, can retrieve sub-hourly land surface temperatures (LSTs) with moderate spatial resolution, providing a new scale for monitoring the thermal environment in Asia and Oceania. This study evaluated uncertainties of LSTs retrieved by three operational algorithms from Advanced Himawari Imager (AHI) data. We compared two nonlinear split-window algorithms (SOB and WAN algorithms) and one nonlinear three-band algorithm (YAM algorithm). First, the error characteristics of the retrieved LSTs caused by the input parameter errors were simulated under various land-atmospheric conditions using an atmospheric radiative transfer model. Thereafter, retrieved LSTs from actual AHI data were evaluated using in-situ observations from AsiaFlux and OzFlux networks and the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) LSTs. The simulated results showed that the YAM algorithm maintained the highest accuracy, whereas the WAN algorithm had the highest robustness to input errors. The YAM algorithm had the smallest total error including input errors over a wide range of retrieval conditions. Validation of the three algorithms via in-situ LSTs from 12 sites revealed nighttime mean RMSEs for all sites of ∼1.7 °C, and daytime mean RMSEs for semi-arid and humid sites of approximately 3.0 °C and 2.0 °C, respectively. These are comparable to the accuracies reported for LST products with higher spatial resolutions, such as the Moderate Resolution Imaging Spectroradiometer and Landsat. Within the Himawari-8 disk, the estimation error of the YAM algorithm was ∼1.0 °C lower than those of the SOB and WAN algorithms in regions with extremely high viewing angle, temperature, and humidity (e.g., northern China, Australia, and Southeast Asia). Furthermore, AHI LSTs showed closer agreement with ECOSTRESS compared to in-situ LSTs, suggesting the usefulness of ECOSTRESS for assessing the diurnal LSTs derived from geostationary satellites. The resulting LST products and the knowledge of their error characteristics have the potential to improve the collective understanding of terrestrial energy and water cycles based on improved accuracy and robustness. [ABSTRACT FROM AUTHOR]

Meteorological records over the past five decades have shown that climate warming has been faster during the nighttime than during the daytime. However, the responses of vegetation development to asymmetric warming are not well understood due to the misinterpretation of phenology that have resulted from the snowmelt effect. We applied normalized difference vegetation index (NDVI) sources from Global Inventory Modeling and Mapping Studies (GIMMS:1982–2015) and Moderate Resolution Imaging Spectroradiometer (MODIS: 2000–2020) to reveal maximum greenness in high-mountain Asia (HMA). The snow-free normalized difference phenology index (NDPI), which is a 3-band vegetation index that designed to best contrast vegetation from soil and snow, was applied for estimating the start of the growing season (SOS). We found that alpine pastures in HMA generally became greener and the SOS was delayed. Preseason daytime temperatures (T day) and nighttime temperatures (T night) had opposite effects on maximum greenness, with T day being negative and T night being positive. The responses of SOS to T day and T night were highly related with the frequency of meteorological drought events during 2000–2020. In regions with lower frequency of drought events, both daytime and nighttime warming could advance SOS. In regions with higher frequency of drought events, daytime warming could delay SOS, but nighttime warming could advance SOS. The results of our study are of great significance to understand the responses of alpine ecosystems to asymmetric climate warming. Such an understanding is quite valuable for pasture management and future vegetation climate projections. [ABSTRACT FROM AUTHOR]

Spectral diffuse attenuation coefficient of down-welling irradiance (K d (λ)) is a fundamental optical parameter for water color and of significance for heat budget calculation, photosynthesis modeling and carbon sequestration. Uncertainties exist in both in situ measured and satellite derived K d (λ), therefore, evaluation of its data quality is essential. In addition, optical water classification of K d (λ) is still limited. To address these issues, we proposed an optical water classification and quality control model (OC_QC model) based on a globally collected high-quality in situ K d (λ) data. The model consists of the reference K d (λ) spectra for 7 optical water types (OWT, ranging from clear open ocean water to highly turbid water) and a scoring system. An individual K d (λ) spectrum can be assigned an OWT and quality control score (QC score) with this model. The performance of the OC_QC model is tested with independent in situ K d (λ) spectra from the turbid coastal water (Bohai Sea) and clear oceanic water (Indian Ocean). Questionable or erroneous K d (λ) spectra can be identified, suggesting the model's robustness. The model was also evaluated with the Medium Resolution Imaging Spectrometer (MERIS) Level-2 and the Moderate Resolution Imaging Spectroradiometer (MODIS) Level-3 K d (λ) data in the Bohai Sea, Yellow Sea, East China Sea and South China Sea. Satellite derived maps clearly showed the spatial and temporal variations of OWTs and QC scores, and the regions of low-quality K d (λ) were reasonably extracted. Global maps of K d (λ)-based optical water types and corresponding QC scores were also obtained with OC_QC model. With the quality controlled K d (λ) data, the retrieval accuracy of Secchi disk depth was improved. The retrieval accuracies of Chlorophyll-a concentration and total seawater absorption coefficient were also improved with the K d (λ) optical water classification based algorithms. The OC_QC model would be valuable for evaluating K d (λ) data (especially the operational global K d (λ) products), investigation of water optical properties, and development and merging of bio-optical algorithms. [ABSTRACT FROM AUTHOR]

The surface temperature (T s) and vegetation index (VI) triangle method is one of the most famous and widely applied methods for regional evapotranspiration (ET) estimation. However, no robust validation has been performed yet to evaluate its accuracy on a global scale, making its accuracy unclear when compared with other global ET models. The main reason behind it is that most traditional triangle models depend on the T s -VI feature space within a certain spatial domain to parameterize their boundary conditions. The domain dependency makes it impossible to derive universal boundaries for continental or global application. Under this background, this study presented a universal triangle method for ET estimation with Moderate Resolution Imaging Spectroradiometer (MODIS) products and routine meteorological observations. The solution for boundary conditions was performed pixel by pixel without the consideration of spatial domain based configuration. Specifically, the dry boundary was solved theoretically based on the surface energy balance equation, and the wet boundary was substituted by wet-bulb temperature estimated from dew point temperature and air temperature. The validation against ground observations collected from 33 flux towers worldwide shows that this universal triangle method achieved evaporative fraction (EF) estimation with the correlation coefficient of 0.65, the mean absolute error of 0.17, the root-mean-square-error of 0.20, and the bias of 0.13. These four metrics of daily ET estimation were 0.83, 0.71 mm/d, 0.90 mm/d, and 0.41 mm/d, respectively. This analysis is the first comprehensive validation of the triangle method on a global scale. The comparison with early work suggests that our universal triangle method on a global scale has reached a comparable accuracy to traditional triangle models at regional scale. However, the universal triangle method has the capacity to monitor ET continuously pixel by pixel without domain dependency. This is just what most traditional triangle methods do not possess. • A universal triangle model was proposed to estimate evaporative faction and evapotranspiration at global scale. • This global model is comparable to regional triangle models in accuracy. • The model has the capacity to monitor evapotranspiration continuously without domain dependency. • This analysis is the first global validation of the triangle model. [ABSTRACT FROM AUTHOR]

Historically, forestlands have been widely recognized to lose more water through evapotranspiration (ET) than croplands. Using remote sensing data from MODIS (Moderate Resolution Imaging Spectroradiometer) with an 8-day temporal and 500 m spatial resolution, we compared the annual ET between croplands and forestlands in the Yazoo River Basin (YRB), a humid subtropical region in Mississippi, USA, over a 21-year period from 2001 to 2021. Based on the Mann-Kendall test, there were significant increasing trends in annual ET for the croplands (τ > 0.44, p < 0.01) but not for forestland over the 21-year period. According to Pettitt's test, there was an abrupt change (or turning point) in annual ET starting in 2011 for the croplands. Using the time at this turning point (i.e., 2011) along with the Kolmogorov-Smirnov test, we found that there was a very significant difference (α = 0.05) in annual ET between croplands and forestlands with 19 % higher ET in the croplands over the 11-year period from 2011 to 2021. This occurred because of increasing irrigated cropland areas in the YRB during this period, providing more water for ET. Our finding on croplands lost more water than forestlands through ET challenge the traditional concept on how forestlands and croplands influence ET. • Estimate evapotranspiration (ET) in croplands and forestlands using remote sensing data. • Cropland has larger ET than forestlands in Mississippi watersheds in recent 10 years. • Significant increasing trend of ET in croplands but not in forestlands in recent 20 years. • Findings change traditional visions on how forests and crops affect ET. [ABSTRACT FROM AUTHOR]