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Due to the influence of geometric factors, the temperature and humidity profile of lidar's near-field signal was warped when sensing the air environment. In order to perform geometric factor correction on near-field signals, this article proposes different correction solutions for the Mie and Raman scattering channels. Here, the Mie scattering channel used the Raman method to invert the aerosol backscatter coefficient and correct the extinction coefficient in the transition zone. The geometric factor was the ratio of the measured signal to the forward-computed vibration Raman scattering signal. The aerosol optical characteristics were reversed using the corrected echo signal, and the US standard atmospheric model was added to the missing signal in the blind zone, reflecting the aerosol evolution process. The stability and dependability of the proposed algorithm were validated by the consistency between the visibility provided by the Environmental Protection Agency and the visibility acquired via lidar retrieval data. The near-field humidity data were supplemented by the interpolation method in the Raman scattering channel to reflect the water vapor transfer process in the temporal dimension. The measured transmittance curve of the filter, the theoretical normalized spectrum, and the sounding data were used to compute the delay geometric factor. The temperature was retrieved and the near-field signal distortion issue was resolved by applying the corrected quotient of the temperature channel. The proposed algorithm exhibited robustness and universality, enhancing the system's detection accuracy compared to the temperature and humidity data constantly recorded by the probes in the meteorological gradient tower, which have a high correlation with the lidar observation data. The comparison between lidar data and instrument monitoring data showed that the proposed algorithm could effectively correct distorted echo signals in the transition zone, which was of great value for promoting the application of lidar in the meteorological monitoring of the urban canopy layer. [ABSTRACT FROM AUTHOR]

Ratoon rice (RR) has emerged as an active adaptation to climate uncertainty, stabilizing total paddy rice yield and effectively reducing agriculture-related ecological environmental issues. However, identifying key remote sensing parameters for RR under cloudy and foggy conditions is challenging, and existing RR monitoring methods in these regions face significant uncertainties. Here, given the sensitivity of synthetic aperture radar (SAR) backscattering signals to the crop phenological period, this paper introduces a threshold model utilizing Sentinel-1A SAR data and phenological information for mapping RR. The Yongchuan District of Chongqing, which is often cloudy and foggy, was selected as a specific study region where VH-polarized backscatter coefficients of Sentinel-1 images were obtained at 10 m spatial resolution in 2020. Based on the proposed threshold model, the RR extraction overall accuracy was up to 90.24%, F1 score was 0.92, and Kappa coefficient was 0.80. Further analysis showed that the extracted RR boundaries exhibited high consistency with true Sentinel-2 remote sensing images and the RR extracted area was in good agreement with the actual planted area situation. This threshold model demonstrated good applicability in the studied cloudy and foggy region, and successfully distinguished RR from other paddy rice types. The methodological framework established in this study provides a basis for extensive application in China and other significant RR-producing regions globally. [ABSTRACT FROM AUTHOR]

Given the importance of constraining cloud droplet number concentrations (Nd) in low-level clouds, we explore two methods for retrieving Nd from surface-based remote sensing that emphasize the information content in lidar measurements. Because Nd is the zeroth moment of the droplet size distribution (DSD), and all remote sensing approaches respond to DSD moments that are at least 2 orders of magnitude greater than the zeroth moment, deriving Nd from remote sensing measurements has significant uncertainty. At minimum, such algorithms require the extrapolation of information from two other measurements that respond to different moments of the DSD. Lidar, for instance, is sensitive to the second moment (cross-sectional area) of the DSD, while other measures from microwave sensors respond to higher-order moments. We develop methods using a simple lidar forward model that demonstrates that the depth to the maximum in lidar-attenuated backscatter (Rmax⁡) is strongly sensitive to Nd when some measure of the liquid water content vertical profile is given or assumed. Knowledge of Rmax⁡ to within 5 m can constrain Nd to within several tens of percent. However, operational lidar networks provide vertical resolutions of > 15 m, making a direct calculation of Nd from Rmax⁡ very uncertain. Therefore, we develop a Bayesian optimal estimation algorithm that brings additional information to the inversion such as lidar-derived extinction and radar reflectivity near the cloud top. This statistical approach provides reasonable characterizations of Nd and effective radius (re) to within approximately a factor of 2 and 30 %, respectively. By comparing surface-derived cloud properties with MODIS satellite and aircraft data collected during the MARCUS and CAPRICORN II campaigns, we demonstrate the utility of the methodology. [ABSTRACT FROM AUTHOR]

• An innovative two-branch-two-step deep learning model was proposed for the retrieval of POC and PIC from spaceborne lidar, which incorporates physical parameters and measured waveform information, climatologies and anomalies information as input layer in a more seamless and effective manner. • The inclusion of physical parameters significantly enhances both the model's generalizability and its interpretability. • The proposed model demonstrates a commendable performance when benchmarked against in-situ data as well as other machine learning-based models. • The first interannually resolved PIC and POC standing stock estimation by lidar in polar regions are presented. • The implementation of CALIOP in polar regions is crucial in addressing the shortfall inherent in passive measurements. The lidar data estimates the total global PIC standing stock to be 8% higher than MODIS, while the POC standing stock is 17.2% higher, accounting for the conditions during polar winter. POC and PIC are indispensable components in the global ocean carbon cycle, their transport and space distribution being driven by the biological carbon pump and the carbonate pump. However, passive ocean color remote sensing, usually employed for POC and PIC research, experiences serious shortcomings in polar winter conditions due to its reliance on sunlight, leading to scarce data coverage in the polar regions. In contrast, CALIOP has shown considerable promise in high-latitude ocean observing. Past approaches to estimate POC from CALIOP data relied on b bp measurements obtained through the application of algorithms that presume an empirical linear correlation between b bp and the backscatter coefficient measured at 180°. This method does not account for any spatiotemporal variability in the conversion coefficient. Furthermore, the potential of CALIOP to provide estimates of PIC has not been explored yet. Here, we developed an innovative Two-Branch-Two-Step (TBTS) model to estimate POC and PIC from CALIOP data, which effectively expands the spatial coverage of the MODIS products. This method exploits the strength of deep learning while encapsulating the generalizability of physical parameters. This method consists of two branches: (1) a deep learning branch based on lidar attenuated backscatter waveform and (2) a branch focusing on physical parameters, including the total column-integrated depolarization ratio and the subsurface cross-polarized component of column-integrated backscatter. The model's generalizability and accuracy are confirmed through the evaluation of a test dataset and validation using in-situ measurements. Our model outperforms several other prevalent machine learning models. We also dissected the importance of different parts of the input data using SHAP tools, thereby providing insights into the black-box nature of deep learning models. Using CALIOP products, we put forth the inaugural estimation of interannually resolved PIC and POC standing stocks in polar regions. The implementation of CALIOP in polar regions bridges the gap inherent in passive ocean color measurements. Lidar-derived total global PIC standing stock is estimated to be 8% higher than that from MODIS, while the POC standing stock is boosted by 17.2%. The carbon standing stock in polar regions exhibits significant inter-annual variability and apparent seasonal periodicity. Hence, results from this research effort clearly reveal that the exploitation of the CALIOP-derived POC and PIC measurements, in combination with the application of new approaches and algorithms to future space lidar data, will undeniably enhance our comprehension of the polar ocean carbon cycle. However, it is important to acknowledge that the CALIOP products may inherit biases from the MODIS data used for training. Hence, where MODIS data is available, it's still the "better" product to use, but where there isn't MODIS data, the CALIOP product is extremely useful, especially in the polar regions. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) is a remote sensing device that extracts the earth's surface's geo and biophysical characteristics. Classification performance is a major phase in SAR processing. Speckle noise occurs in SAR due to the coherent combination of backscatter signals from different sources. One of the approaches for suppressing the noise from SAR is to utilize local statistics. The proposed architecture evaluates the robustness of several improved filters like Improved Lopez, Improved Boxcar, Improved Guided filter and improved Lee-sigma and verifies their effects on classification accuracy. These filters were designed to overcome the suppression of target points and the blurring of edges. The supervised Wishart classifier with an improved Sparrow Search Algorithm (WC-ISSA) is utilized in the classification. SSA is used to optimize WC parameters and improve classification performance. One of the essential parameters in speckle noise filtering is the size of the sliding window. The window size varies, and the improved filters' performance is evaluated. Further, a growing self-organizing map (GSOM) is used to improve blurring performance. The proposed model is used for deblurring and enhancing the performance of smoothing images. The overall evaluation is carried out on the Matlab platform. The performance of the improved filters is compared to the standard filters, and the performances are compared on the virtual SAR dataset. The implemented results proved that the Extended Lee-sigma performed better than other filters. The PSNR and SSIM obtained by the proposed model were found to be 65.72 and 99.92%, respectively, which is considered to be more effective than other models already in use. [ABSTRACT FROM AUTHOR]

The term "hotspot" refers to the sharp increase in the reflectance occurring when incident (solar) and reflected (viewing) directions almost coincide in the backscatter direction. The accurate simulation of hotspot directional signatures is important for many remote sensing applications. The RossThick–LiSparse–Reciprocal (RTLSR) bidirectional reflectance distribution function (BRDF) model is widely used in radiative transfer simulations, and the hotspot model mostly used is from Maignan–Bréon, but it typically requires large values of numerical quadrature and Fourier expansion terms in order to represent the hotspot accurately for its use coupled with atmospheric radiative transfer modeling (RTM). In this paper, we have developed a modified version based on the Maignan–Bréon's hotspot BRDF model that converges much faster numerically, making it more practical for use in the RTMs that require Fourier expansion of BRDF to simulate the top-of-atmosphere (TOA) hotspot signatures, such as in the RTM models using the Doubling–Adding or discrete ordinate method. Using the vector linearized discrete ordinate radiative transfer model (VLIDORT), we found that reasonable TOA–hotspot accuracy can be obtained with just 23 Fourier terms for clear atmosphere and 63 Fourier terms for atmosphere with aerosol scattering. In order to study the impact of molecular and aerosol scattering on hotspot signatures, we carried out a number of hotspot signature simulations with VLIDORT. We confirmed that (1) atmospheric molecule scattering and the existence of aerosol tend to smooth out the hotspot signature at the TOA and that (2) the hotspot signature at the TOA in the near-infrared is larger than in the visible, and its impact by surface reflectance is more significant. As the hotspot amplitude at the TOA with aerosol scattering included is smaller than that with molecular scattering only, the amplitude of hotspot signature at the surface is likely underestimated in the previous analysis based on the POLDER measurements, where the atmospheric correction was based on a single-scatter Rayleigh-only calculation. This modified model can calculate the amplitude of the hotspot accurately, and, as it agrees very well with the original RossThick model away from the hotspot region, this model can be simply used in conditions with and without hotspots. However, there are some differences in this modified model compared to the original Maignan–Bréon model for the scattering angles close to the hotspot point; thus, it may not be appropriate for those who need an exact representation of the hotspot angular signature. [ABSTRACT FROM AUTHOR]

This study proposes a framework to develop a high-resolution snow cover area (SCA) product from freely available spaceborne remote sensing data and utilizes the Sentinel-1 multi-temporal products and MODIS surface reflectance data. The proposed methodology focuses on using the sensitivity of the parameters retrievable from the Sentinel-1 datasets to snow. Different parameters such as the dual polarimetric entropy, mean scattering angle, backscatter coefficients, and the interferometric coherence are integrated with a spatially resampled normalized difference snow index (NDSI) from MODIS data to estimate an equivalent NDSI, which is used for the determination of the SCA at 15 m spatial resolution. The equivalent NDSI is derived using a machine learning-based regression based on support vector machines (SVMs) and the multilayer perceptron (MLP). The experiments are performed for the high elevated regions of the Kunduz and Khanabad watershed of the northern Hindu Kush mountains for the peak winter and early melt season of 2019, corresponding to February and March. The reference SCA for evaluating the results is generated by thresholding the NDSI derived from pan-sharpened Landsat-8 imagery. As compared to MLP, the SCA generated based on the SVM regression showed better performance. Further, compared to spatially resampled MODIS NDSI, both the SVM and MLP results showed better accuracy for snow classification, as determined by the mean conditional kappa coefficients of 0.75, 0.83, respectively, over 0.62. [ABSTRACT FROM AUTHOR]

Sea ice topography information can be obtained from altimetry but these data are spatially and temporally limited compared to recent synthetic aperture radar (SAR) missions such as the RADARSAT Constellation Mission (RCM). We analyze the relationship between sea ice roughness and height obtained from three Ice, Cloud, and Land Elevation Satellite (ICESat)‐2 tracks on two dates in March 2022, with RCM backscatter from 17 images in the McClintock Channel, Canadian Arctic. We analyze how this relationship varies with ice type, polarization, and incidence angle. We find particularly notable relationships between sea ice roughness and horizontal‐transmit/vertical‐receive backscatter for first‐year ice, and sea ice height and backscatter for multi‐year ice. We develop a preliminary model for winter sea ice roughness retrieval using RCM. In comparison with independent ICESat‐2 data in our study region, we find the model performs effectively at estimating a roughness distribution and key roughness statistics, and characterizes spatial variations in roughness at a sub‐kilometer scale. Plain Language Summary: Sea ice topography estimates are important for improving ocean‐climate models, understanding sea ice evolution, and planning ice travel. Estimates can be obtained from altimetry missions, but these data are spatially and/or temporally limited compared to recent synthetic aperture radar missions such as the C‐band RADARSAT Constellation Mission (RCM). By comparing RCM backscatter with measurements of height and roughness obtained using Ice, Cloud, and Land Elevation Satellite (ICESat)‐2 in the Canadian Arctic, we demonstrate the potential for using RCM to estimate sea ice topography. We show there is a notable relationship between sea ice roughness and backscatter on first‐year ice in late winter, particularly in the horizontal‐transmit/vertical‐receive channel, and a notable relationship between sea ice height and backscatter on multi‐year ice. We also observe that the relationship between the RCM signal and roughness varies with incidence angle. Based on this analysis we develop a preliminary model for winter sea ice roughness retrieval using RCM. In comparison with ICESat‐2 data, we find the model performs effectively at estimating key roughness statistics, and characterizes spatial variations in roughness at a sub‐kilometer scale. These findings will facilitate the use of RCM to validate geophysical models, map rough areas of ice, assess ice evolution and develop new topographic models. Key Points: Sea ice roughness measured by Ice, Cloud, and Land Elevation Satellite (ICESat)‐2 and horizontal‐transmit/vertical‐receive RADARSAT Constellation Mission (RCM) backscatter exhibit a notable relationship on late‐winter first‐year iceSea ice height, as measured by ICESat‐2, and RCM backscatter exhibit a notable relationship on late‐winter multi‐year iceWe develop/validate a model, based on comparison with ICESat‐2 data, for estimating sea ice roughness using RCM backscatter in the Arctic [ABSTRACT FROM AUTHOR]

In this work, backscattering signatures of snow-covered lake ice and sea ice from X- and Ku-band synthetic aperture radar (SAR) data are investigated. The SAR data were acquired with the ESA airborne SnowSAR sensor in winter 2012 over Lake Orajärvi in northern Finland and over landfast ice in the Bay of Bothnia of the Baltic Sea. Co-incident with the SnowSAR acquisitions, in situ snow and ice data were measured. In addition, time series of TerraSAR-X images and ice mass balance buoy data were acquired for Lake Orajärvi in 2011–2012. The main objective of our study was to investigate relationships between SAR backscattering signatures and snow depth over lake and sea ice, with the ultimate objective of assessing the feasibility of retrieval of snow characteristics using X- and Ku-band dual-polarization (VV and VH) SAR over freshwater or sea ice. This study constitutes the first comprehensive survey of snow backscattering signatures at these two combined frequencies over both lake and sea ice. For lake ice, we show that X-band VH-polarized backscattering coefficient ( σ o ) and the Ku-band VV/VH-ratio exhibited the highest sensitivity to the snow depth. For sea ice, the highest sensitivity to the snow depth was found from the Ku-band VV-polarized σ o and the Ku-band VV/VH-ratio. However, the observed relations were relatively weak, indicating that at least for the prevailing snow conditions, obtaining reliable estimates of snow depth over lake and sea ice would be challenging using only X- and Ku-band backscattering information. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) provides 10-m weather-independent global Earth surface observations for various tasks such as land cover land use mapping, water body delineation, and vegetation change monitoring. However, the application of SAR imagery has been limited to retrospective tasks by a "first event then observation" rule. Recent studies have proven the feasibility of a one-shot nowcast of backscatters of SAR imagery using meteorological driving forces, soil moisture, geomorphic factors, and previous SAR images collected for the target area. Although the approach is promising, spatial connectivity, more specifically, the influence of the status of surrounding areas of the target location has yet to be considered. To fill that gap, this study proposed two nowcasting frameworks that can integrate precipitation and soil moisture data collected from surrounding areas through spatial aggregation (SA) and by processing spatial series (SS), respectively. The catastrophic 2019 Central US Flooding was used as a case study with the goal of predicting backscatters of SAR imagery captured during the event. The results from SA, SS, and a framework that only considers localized input (S0) are compared against each other as well as with the benchmark performance created with persistence assumption. Results show that S0, SA, and SS outperform the benchmark. More importantly, considering data from upstream neighboring areas further improves prediction accuracy compared to S0 where only inputs from the target region are considered. The gradients of results considering/not considering neighboring data indicates that neighboring data can alter the model's attention on each feature of the localized input matrix. The difference in gradients between SA and SS indicates the way the neighboring information is integrated also matters. The methodology proposed by this study can serve as a building block for more active usage of SAR imagery in forward-looking tasks such as early flood warning and response. [ABSTRACT FROM AUTHOR]

• This manuscript improves the accuracy of Hyrcanian forests maps from ALOS/PALSAR product by providing local thresholds. • HV polarimetric band is more efficient in comparison to HH polarization for forest detection purposes. • The final Hyrcanian forests map was validated with various global products and shows high accuracy. • The extent changes of Hyrcanian forests was investigated over ten years (2007–2017). This study aims to monitor the extent changes of Hyrcanian forests using ALOS-/PALSAR-1 and ALOS/PALSAR-2 (PALSAR) mosaic data with HH and HV polarimetric bands from 2007 to 2017. To date, no comprehensive studies have been conducted on the total cover area of the Hyrcanian forests. This study attempts to provide specific and local threshold values for to extract the Hyrcanian forests cover through careful examination of satellite data over ten years. In this study, the acquired 57 PALSAR images were calibrated and converted to γ0 (dB) for the years 2007, 2010, and 2017. Then, the backscattering value ranges of the Hyrcanian forests were determined using the Decision Tree classification method. The backscattering values range from −14.5 to −7.2 dB and −17.5 to −8.5 dB for the years 2007 and 2010 and −18.5 to −7.5 dB for 2017 in the HV-band and from −7.5 to −14.5 dB for 2007 and 2010 and −6.2 to −12.5 dB for 2017 in the HH band correspond to forest. Finally, the results were compared with Japanese Aerospace Exploration Organization (JAXA) forest and non-forest (F/NF) maps from PALSAR data for validation purposes; the overall accuracies and Kappa coeffitients were 92.55%, 0.82% (2007), 91.14%, 0.79% (2010), and 88.19%, 0.74 (2017), respectively. This result shows that values between −7.2 and −13.5 dB are the best range for the Hyrcanian forests' extent extraction from PALSAR image in the HV band. The output were further validated using three independent sources: (1) High-Resolution Google Earth (GE) image; (2) F/NF maps from the JAXA; and (3) MOD13Q1 Products (250 m spatial resolution). The results show good consistency with the mentioned datasets. [ABSTRACT FROM AUTHOR]

Understanding crop types and their annual cycles is key to managing natural resources, especially when the pressures on these resources are attributable to climate change and social, environmental, and economic policies. In recent years, the space sector's development, with programs such as Copernicus, has enabled a greater availability of satellite data. This study uses a multi-sensor approach to retrieve crop information by developing a Proof of Concept for the integration of high-resolution SAR imagery and optical data. The main goal is to develop a pre-automatized processing chain that explores the temporal dimension of different crop. Results are related to the advantage of using a multi-sensor approach to retrieve vegetation biomass and vertical structure for the identification of phenological stages and different crops. The novelty consists of investigating the multi-temporal pattern of radiometric indices and radar backscatter to detect the different phenological stages of each crop, identifying the Day of the Year (DoY) in which the classes showed greater separability. The current study could be considered a benchmark for the exploitation of future multi-sensor missions in downstream services for the agricultural sector, strengthening the evolution of Copernicus services. [ABSTRACT FROM AUTHOR]

Phytoplankton is the most important primary producer in the ocean, owing to its pivotal role at the base of the food chain and in the ocean carbon cycle. To better estimate ocean productivity, the retrieval of accurate observations of phytoplankton carbon (Cphy) biomass from ocean color satellites is of the utmost importance. When Cphy is retrieved using the backscattering signal generated by marine particles, the signal due to non‐algal particles—NAP (bbNAP) must be removed. However, algorithms currently used to retrieve bbNAP do not work globally, especially in the large mid‐ocean subtropical gyres. Here, we propose a new model to derive Cphy globally, including all subtropical gyres, that accounts for the estimation of phytoplankton photoacclimation when retrieving bbNAP. The new model shows more valid retrievable bbNAP coefficients than the previously published methods and values mainly around 0.0011 m−1, consistent with coefficients estimated with in situ observations. Moreover, the retrieved Cphy concentrations show a relative error of 7% with respect to in situ measured picophytoplankton carbon data. Our research indicates that the photoacclimation status of phytoplankton must be addressed to confidently retrieve bbNAP and Cphy at the global scale from ocean color measurements. Plain Language Summary: Chlorophyll‐a is often considered as the best proxy for phytoplankton biomass in the ocean. However, in low‐nutrient surface waters of the open oceans, the relationship between chlorophyll‐a and phytoplankton biomass can be weak or even negative. To get a better measure of phytoplankton biomass, researchers have started to use particulate backscattering coefficient (bbp). But before this can be used to measure phytoplankton biomass, the non‐algal particle fraction (bbNAP) needs to be deducted from bbp. In this study, we propose a model to more accurately determine bbNAP, and use satellite data to estimate sea‐surface phytoplankton biomass in the ocean. Our model can be used in subtropical regions, where other methods may not work. This will help us to better understand the variability of phytoplankton biomass in the global ocean. Key Points: A photoacclimation model is employed to retrieve both phytoplankton carbon and non‐algal particles from ocean color dataThe new model performs well in open oceans, especially in subtropical gyres, validated by an in situ phytoplankton carbon biomass data setPrevious studies using a constant backscattering of non‐algal particles notably overestimated phytoplankton carbon in subtropical gyres [ABSTRACT FROM AUTHOR]

Given the importance of constraining cloud droplet number concentrations (Nd) in low-level clouds, we explore two methods for retrieving Nd from surface-based remote sensing that emphasize the information content in lidar measurements. Because Nd is the zeroth moment of the droplet size distribution (DSD), and all remote sensing approaches respond to DSD moments are at least two orders greater than the zeroth moment, deriving Nd from remote sensing measurements has significant uncertainty. At minimum, such algorithms require extrapolation of information from two other measurements that respond to different moments of the DSD. Lidar, for instance, is sensitive to the second moment (cross-sectional area) of the DSD, while other measures from microwave sensors respond to higher-order moments. We develop methods using a simple lidar forward model that demonstrates that the depth to the maximum in lidar attenuated backscatter (rmax) is strongly sensitive to Nd when some measure of the liquid water content vertical profile is given or assumed. Knowledge of rmax to within 5 m can constrain Nd to within several 10's of percent. However, operational lidar networks provide vertical resolutions or >15 m, making a direct calculation of Nd from rmax prohibitively uncertain. Therefore, we develop a Bayesian optimal estimation algorithm that brings additional information to the inversion, such as lidar-derived extinction and radar reflectivity near cloud top. This statistical approach provides reasonable characterizations of Nd and effective radius (re) to within approximately a factor of 2 and 30%, respectively. By comparing surfacederived cloud properties with MODIS satellite and aircraft data collected during the Marcus and Capricorn 2 campaigns, we demonstrate the utility of the methodology. [ABSTRACT FROM AUTHOR]

Given the importance of constraining cloud droplet number concentrations (Nd) in low-level clouds, we explore two methods for retrieving Nd from surface-based remote sensing that emphasize the information content in lidar measurements. Because Nd is the zeroth moment of the droplet size distribution (DSD), and all remote sensing approaches respond to DSD moments are at least two orders greater than the zeroth moment, deriving Nd from remote sensing measurements has significant uncertainty. At minimum, such algorithms require extrapolation of information from two other measurements that respond to different moments of the DSD. Lidar, for instance, is sensitive to the second moment (cross-sectional area) of the DSD, while other measures from microwave sensors respond to higher-order moments. We develop methods using a simple lidar forward model that demonstrates that the depth to the maximum in lidar attenuated backscatter (rmax) is strongly sensitive to Nd when some measure of the liquid water content vertical profile is given or assumed. Knowledge of rmax to within 5 m can constrain Nd to within several 10's of percent. However, operational lidar networks provide vertical resolutions or >15 m, making a direct calculation of Nd from rmax prohibitively uncertain. Therefore, we develop a Bayesian optimal estimation algorithm that brings additional information to the inversion, such as lidar-derived extinction and radar reflectivity near cloud top. This statistical approach provides reasonable characterizations of Nd and effective radius (re) to within approximately a factor of 2 and 30 %, respectively. By comparing surface-derived cloud properties with MODIS satellite and aircraft data collected during the Marcus and Capricorn 2 campaigns, we demonstrate the utility of the methodology. [ABSTRACT FROM AUTHOR]

In this study, we aimed to understand the uncertainty of measured values of the particle backscattering coefficient (bbp) obtained using the in situ backscattering meter Hydroscat-6P in three highly turbid water bodies with distinct optical properties and assess their impact on the estimation results through inversion. The field observations were conducted in Tokyo Bay, where organic matter dominates, Lake Kasumigaura, where cyanobacteria are abundant and mixed with inorganic suspended matter, and the estuary of Bangpankong River in the Upper Gulf of Thailand, where inorganic suspended matter is highly concentrated. To understand the uncertainty of bbp measurements, radiative transfer simulations were performed using Hydrolight, and the influence of bbp measurements on the reproduction calculation of remote sensing reflectance (Rrs) was assessed. The results revealed that the reproducibility of Rrs varied among the water bodies, with a particularly pronounced overestimation compared with the measured values in the highly scattering environments of Lake Kasumigaura and the Upper Gulf of Thailand. This overestimation originated from the optical path length without light attenuation correction (σ correction) in Hydroscat-6P, and the σ correction amplified the overestimation of Rrs. Although the cause of this overestimation is unclear, it suggests that the calculation conditions of the radiative transfer model and the effect of multiple scattering between the sensors of Hydroscat-6P may be contributing factors. The magnitude of this overestimation ranged from 64 to 75% in Lake Kasumigaura and from 29 to 39% in the Upper Gulf of Thailand, significantly affecting the accuracy verification of bbp estimation using inversion models. These results highlight the uncertainty of bbp measurements in highly turbid water bodies with high light scattering, emphasizing the need for careful attention to the quality of previously acquired in situ data when evaluating the performance of inversion algorithms. [ABSTRACT FROM AUTHOR]

Remote sensing imagery is one of the most widely used data sources for large-scale Earth observations with consistent spatial and temporal quality. However, the current usage scenarios of Earth's surface remote sensing images, such as those generated from Landsat, Sentinel 2, and SAR, are largely limited to retrospective tasks, as they are often used to reconstruct existing phenomena, such as land use change, flood inundation, and wildfire. This study proposes MA-SARNet, a one-shot nowcasting framework built with a modified MA-Net structure and ResNet50 as the backbone, to predict the backscatter values of Synthetic-Aperture Radar (SAR) images using the previous SAR observations, precipitation, soil moisture, and geomorphic data layers as input. The model was trained, validated, and tested with SAR images collected during the catastrophic 2019 Midwest U.S. Floods that affected several states on the Missouri and Mississippi River tributaries. Compared to the benchmark performance, model predictions show an increase of 31.9% and 17.8% for the mean and median AAI (Assemble Accuracy Index) and an increase of 37.9% and 15.1% for the mean and median NSE (Nash-Sutcliffe Efficiency) on the test set. Results showed that the flood extent derived from backscatter predictions has less misclassifications caused by pixel-level noise compared to the flood map derived using the real backscatters. Results from spatial and temporal robustness tests demonstrate that the model has sufficient generalization potential and does not require further fine-tuning to work with new data, and therefore proves its usefulness in real-time Earth surface prediction tasks that are aimed at fast response to and mitigation for upcoming floods on a tight time schedule. [ABSTRACT FROM AUTHOR]

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We analyze cirrus cloud measurements from two dual-instrument cloud spectrometers, two hygrometers and a backscattersonde with the goal of connecting cirrus optical parameters usually accessible by remote sensing with microphysical size-resolved and bulk properties accessible in situ. Specifically, we compare the particle backscattering coefficient and depolarization ratio to the particle size distribution, effective and mean radius, surface area density, particle aspherical fraction, and ice water content. Data were acquired by instruments on board the M55 Geophysica research aircraft in July and August 2017 during the Asian Monsoon campaign based in Kathmandu, Nepal, in the framework of the StratoClim (Stratospheric and upper tropospheric processes for better climate predictions) project. Cirrus clouds have been observed over the Himalayan region between 10 km and the tropopause, situated at 17–18 km. The observed particle number densities varied between 10 and 10-4 cm -3 in the dimensional range from 1.5 to 468.5 µm in radius. Correspondingly, backscatter ratios from 1.1 up to 50 have been observed. Optical-scattering theory has been used to compare the backscattering coefficients computed from the measured particle size distribution with those directly observed by the backscattersonde. The aspect ratio of the particles, modeled as spheroids for the T-matrix approach, was left as a free parameter to match the calculations to the optical measures. The computed backscattering coefficient can be brought into good agreement with the observed one, but the match between simulated and measured depolarization ratios is insufficient. Relationships between ice particle concentration, mean and effective radius, surface area density, and ice water content with the measured backscattering coefficient are investigated for an estimate of the bulk microphysical parameters of cirrus clouds from remote sensing lidar data. The comparison between particle depolarization and aspherical fraction as measured by one of the cloud spectrometers equipped with a detector for polarization represents a novelty since it was the first time the two instruments were operated simultaneously on an aircraft. The analysis shows the difficulty of establishing an univocal link between depolarization values and the presence and amount of aspherical scatterers. This suggests the need for further investigation that could take into consideration not only the fraction of aspheric particles but also their predominant morphology. [ABSTRACT FROM AUTHOR]

Sustainable Drainage Systems (SuDS) monitoring is very often intrusive and need onsite personnel to be carried out. The application of remote sensing in SuDS still is an area for further development, especially in vegetation-based techniques, representing a gap in the field. This research proposes an exploratory method combining Synthetic Aperture Radar (SAR) images data and onsite measurements to develop models of performance. Linear regression models were obtained for the computing of the soil moisture using the following variables: backscatter coefficient (σ°), temperature, normalized difference vegetation index (NDVI) and topographic wetness index (TWI), reaching medium to high values for its predictive capacity, ranging from 0.53 and 0.66 using σ° and temperature. The most influential variable was found to be the temperature. This investigation opens the path for future research in the use of remote sensing tools in vegetation-based SuDS monitoring with homogeneous plant species, highlighting the need for further research. [ABSTRACT FROM AUTHOR]

Atmospheric aerosols have a pronounced effect on climate dynamics at both regional and global scales, but the magnitude of these effects is subject to considerable uncertainties. A major contributor to these uncertainties is the incomplete understanding of aerosol’s vertical structure, largely due to observational limitations. Spaceborne lidars can directly observe the vertical distribution of aerosols globally, and are increasingly used in atmospheric aerosol remote sensing. As the first spaceborne High Spectral Resolution Lidar (HSRL), the ALADIN instrument onboard the Aeolus satellite was operational from 2018 to 2023. With its sophisticated design, ALADIN can retrieve aerosol backscatter and extinction coefficients separately without an assumption of the lidar ratio. This study is dedicated to assessing the performance of ALADIN’s aerosol retrieval capabilities by comparing them with CALIOP measurements. A statistical analysis of retrievals from both instruments during the June 2020 Saharan dust event indicates good consistency between the observed backscatter and extinction coefficients. A detailed comparison of extinction coefficients for dust layers reveals that ALADIN is more susceptible to signal attenuation than CALIOP. During this extreme dust event, CALIOP-derived aerosol optical depth (AOD) exhibited large discrepancies with MODIS Aqua measurements. Using collocated ALADIN observations to revise the dust lidar ratio to 63.5 sr, AODs retrieved from CALIOP are increased by 46%, improving the comparison with MODIS data. Further, the combination of measurements from ALADIN and CALIOP can enhance the tracking of aerosols’ vertical transport. This study demonstrates the potential for spaceborne HSRL to retrieve aerosol optical properties. It highlights the benefits of spaceborne HSRL in directly obtaining the lidar ratio, significantly reducing uncertainties in extinction retrievals. This work paves the way for forthcoming spaceborne HSRL missions, particularly the ESA ATLID space lidar (set for a 2024 launch) and Aeolus-2. [ABSTRACT FROM AUTHOR]

Atmospheric aerosols have a pronounced effect on climate dynamics at both regional and global scales, but the magnitude of these effects is subject to considerable uncertainties. A major contributor to these uncertainties is the incomplete understanding of aerosol's vertical structure, largely due to observational limitations. Spaceborne lidars can directly observe the vertical distribution of aerosols globally, and are increasingly used in atmospheric aerosol remote sensing. As the first spaceborne High Spectral Resolution Lidar (HSRL), the ALADIN instrument onboard the Aeolus satellite was operational from 2018 to 2023. With its sophisticated design, ALADIN can retrieve aerosol backscatter and extinction coefficients separately without an assumption of the lidar ratio. This study is dedicated to assessing the performance of ALADIN's aerosol retrieval capabilities by comparing them with CALIOP measurements. A statistical analysis of retrievals from both instruments during the June 2020 Saharan dust event indicates good consistency between the observed backscatter and extinction coefficients. A detailed comparison of extinction coefficients for dust layers reveals that ALADIN is more susceptible to signal attenuation than CALIOP. During this extreme dust event, CALIOP-derived aerosol optical depth (AOD) exhibited large discrepancies with MODIS Aqua measurements. Using collocated ALADIN observations to revise the dust lidar ratio to 63.5 sr, AODs retrieved from CALIOP are increased by 46 %, improving the comparison with MODIS data. Further, the combination of measurements from ALADIN and CALIOP can enhance the tracking of aerosols' vertical transport. This study demonstrates the potential for spaceborne HSRL to retrieve aerosol optical properties. It highlights the benefits of spaceborne HSRL in directly obtaining the lidar ratio, significantly reducing uncertainties in extinction retrievals. This work paves the way for forthcoming spaceborne HSRL missions, particularly the ESA ATLID space lidar (set for a 2024 launch) and Aeolus-2. [ABSTRACT FROM AUTHOR]

Using Remote sensing data is essential in monitoring agricultural crop phenology and food security. The availability of optical and SAR imagery can provide the best insights into understanding the behavior of temporal characteristics of phenological stages of multiple agricultural crops. The study was carried out in the region situated in the Karasai district of Almaty region using the temporal Sentinel-1 data and Sentinel-2 optical data during the growth period of agricultural crops. NDVI values from the Sentinel-2 and Multitemporal VH/VV backscatter intensity from Sentinel-1 SAR with the sample data were used to characterize the backscatter and vegetation stage behavior of multiple crops. Crop growth parameters were calculated using Google Earth Engine platform. Google Earth Engine is a cloud-based platform that allows users to visualize and analyze satellite images of the Earth and make geospatial analysis. The results indicate that the phenological stages of the agricultural crop growth cycle may be recognized and distinguished based on the temporal variations of NDVI values and in SAR parameters that were detected. Also according to the result of the study it is visible that the trend charts of backscattering values are quite correlated with the NDVI value. NDVI with backscatter values of VV/VH can be considered as one of the beneficial tools for distinguishing and analyzing the phenological changes of different types of agricultural crops. [ABSTRACT FROM AUTHOR]

Using global Sentinel-1 radar backscatter data, we systematically map the locations of glaciers with surge-type activity during 2017–22. Patterns of pronounced increases or decreases in the strongest backscatter between two winter seasons often indicate large changes in glacier crevassing, which we treat here as a sign of surge-type activity. Validations against velocity time series, terminus advances and crevassing found in optical satellite images confirm the robustness of this approach. We find 115 surge-type events globally between 2017 and 2022, around 100 of which on glaciers already know as surge-type. Our data reveal a pronounced spatial clustering in three regions, (i) Karakoram, Pamirs and Western Kunlun Shan (~50 surges), (ii) Svalbard (~25) and (iii) Yukon/Alaska (~9), with only a few other scattered surges elsewhere. This spatial clustering is significantly more pronounced than the overall global clustering of known surge-type glaciers. The 2017–22 clustering may point to climatic forcing of surge initiation. [ABSTRACT FROM AUTHOR]

Impacts of small‐scale surface irregularities, or surface roughness, of atmospheric ice crystals on lidar backscattering properties are quantified. Geometric ice crystal models with various degrees of surface roughness and state‐of‐the‐science light‐scattering computational capabilities are utilized to simulate the single‐scattering properties across the entire practical size parameter range. The simulated bulk lidar and depolarization ratios of polydisperse ice crystals at wavelength 532 nm are strongly sensitive to the degree of surface roughness. Comparisons of these quantities between the theoretical simulations and counterparts inferred from spaceborne lidar observations for cold cirrus clouds suggest a typical surface‐roughness‐degree range of 0.03–0.15 in the cases of compact hexagonal ice crystals, which is most consistent with direct measurements of scanning electron microscopic images. To properly interpret lidar backscattering observations of ice clouds, it is necessary to account for the degree of surface roughness in light‐scattering computations involving ice crystals. Plain Language Summary: Lidar (Light Detection and Ranging) instruments on satellites use reflected, or backscattered, laser beams to investigate ice clouds in the atmosphere. However, it has long been a challenge to interpret lidar signals, called backscattering properties, to infer ice cloud characteristics accurately. This study uses theoretical simulations to investigate how small‐scale surface irregularities of ice crystals affect the lidar signals associated with ice clouds. These simulations demonstrate the significant impacts of small‐scale surface irregularities of ice crystals on backscattering. Based on comparisons between the theoretical simulations and satellite lidar observations, it is necessary to assume a moderate degree of small‐scale surface irregularities to explain lidar observations of typical ice clouds. Key Points: The sensitivity of the backscattering properties to the surface roughness of atmospheric ice crystals is theoretically investigatedThe depolarization ratio is substantially sensitive to the degree of surface roughness of ice crystalsCompact hexagonal ice models with degrees of surface roughness ranging 0.03–0.15 reasonably explain the Cloud‐Aerosol Lidar with Orthogonal Polarization backscattering signals [ABSTRACT FROM AUTHOR]

The term "hotspot" refers to the sharp increase of reflectance occurring when incident (solar) and reflected (viewing) directions coincide in the backscatter direction. The accurate simulation of hotspot directional signatures is important for many remote sensing applications. The RossThick-LiSparse-Reciprocal (RTLSR) Bidirectional Reflectance Distribution Function (BRDF) model is widely used in radiative transfer simulations, but it typically requires large values of numerical quadrature and Fourier expansion terms in order to represent the hotspot accurately. In this paper, we have developed an improved hotspot BRDF model that converges much faster, making it more practical for use in atmospheric radiative transfer simulations of top-of-atmosphere (TOA) hotspot signatures. Using the VLIDORT RT model, we found that reasonable TOA hotspot accuracy can be obtained with just 23 Fourier terms for clear atmospheres, and 63 Fourier terms for atmospheres with aerosol scattering. We carried out a number of hotspot signature simulations with VLIDORT to study to the impact of molecular and aerosol scattering on hotspot signatures. We confirmed that (1) atmospheric scattering tends to smooth out the hotspot signature at the TOA, but has no impact on hotspot width; and (2) the hotspot signature at the TOA in the near-infrared is larger than in the visible, and has an obvious increase with the solar zenith angle. As the hotspot amplitude at the TOA with aerosol scattering included is smaller than that with molecular scattering only, the amplitude of hotspot signature at the surface is likely underestimated in the previous analysis based on the POLDER measurements, where the atmospheric correction was based on a single-scatter Rayleigh-only calculation. We also draw attenuation to a scaling factor of 3/4 which has been applied to the Ross-Thick kernel with hotspot correction. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) remote sensing technology has the advantage of all-weather observation and can acquire time-series images with crop growth period, which has great potential for applications such as crop phenology analysis. However, available studies primarily focus on conducting statistical and crop growth analyses based on the polarization or backscatter intensities of SAR images, and the exploration of polarization scattering information in SAR images is not sufficient. To comprehensively reflect the polarization characteristics and scattering mechanisms of crop at different growth stages, we propose a new method for extracting vegetation descriptors from Sentinel-1 dual-polarimetric SAR data. The method combines the backscattering intensity and polarization decomposition information to construct a normalized index q , which is used to generate three vegetation descriptors: the co-pol purity parameter (m cp ), the pseudo-scattering angle (θ cp ), and the pseudo-scattering entropy (H cp ). Further, a novel unsupervised clustering framework, founded on H cp and θ cp , has been proposed. This framework establishes six zones (named as Z1 to Z6) representing distinct physical scattering mechanisms, and by statistically sampling point data, it can determine the growth stage of the crops For validating the performance of the proposed vegetation descriptors and clustering framework, we conducted a three-year experiment using four crops from two publicly available datasets, namely wheat and canola from the Carman in Canada (Test site-1), corn and soybeans from Iowa in the United States (Test site-2). The experimental results indicate that m cp , θ cp , and H cp exhibit regular changes at different growth stages of crops from planting to maturity, with m cp and θ cp gradually decreasing while H cp gradually increasing. Within the entire phenology window, θ cp changes by approximately 42°, while both m cp and θ cp varies by about 0.9, and the sampling points shift from the Z2 to the Z5 zone. The vegetation descriptors are highly sensitive to the growth status of crops, and the clustering framework can also effectively respond to different growth stages of vegetation. Furthermore, the vegetation descriptors and clustering framework proposed in this study have the potential for extended application to different crop types and other polarimetric SAR data sources. [ABSTRACT FROM AUTHOR]

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Satellite-derived soil moisture fields received attention due to their large spatial coverage and spatial resolution that suits many applications. The sensors used vary from passive (e.g., LANDSAT-8) to active (e.g., SENTINEL-1) with varying accuracy problems. Passive sensing can only determine relative indices between pixels within a vegetation class and not the real value of moisture. Active sensing suffers from the sensitivity of its detecting behaviour to the level of moisture (anomalous backscatter). The above problems impose limitations on the application without frequent ground-based calibration. The paper investigates possible models to improve the estimation of soil moisture using the powers of the two sensors. In addition, a Hydrologic Surface Moisture indicator (HSM) is included as a third source of information. The paper tests modeling combinations of the three soil moisture predictors (Landsat-8, Sentinel-1, and HSM). The models are validated using in-situ measurements. The results showed that Landsat-8 data can be rescaled using HSM to provide the actual soil moisture in the soil. On the other side, it is possible to remove the anomaly from the Sentinel-1 backscatter using either Landsat-8 or HSM. The elimination of the above problems explained a significant portion of the differences between the two sensors. [ABSTRACT FROM AUTHOR]

The Pomeranian lakes in Northern Poland and the nearby coastal waters of the Baltic Sea belong to optically complex water bodies characterised by high eutrophication levels. These water types require a local approach when developing bio-optical algorithms that combine the inherent and the apparent properties of seawater. Well-established local algorithms are of great value for understanding and addressing rapid changes in water quality related mostly to human activities in coastal and near-shore zones, as well as in optically similar lakes. Our research analyses the possibility of using the multi-parameter algorithms of absorption a(λ), backscattering bb(λ) and remote sensing reflectance Rrs(λ), originally developed for the coastal waters of the Southern Baltic Sea, for three selected Pomeranian lakes. Our multi-parameter algorithms are based on the input concentrations of the biogeochemical components measured in the lake waters, i.e., chlorophyll a (Chl a), suspended particulate matter (SPM), inorganic suspended particulate matter (SPMinorg), the sum of the surface concentrations of accessory pigments (ΣC) and coloured dissolved organic matter with a wavelength of 400 nm (aCDOM(400)). Rrs(λ) and a(λ) output values were compared with independent measurements of these parameters conducted in the lake waters at 20 sampling stations. Our algorithm output values of bb(λ) were compared to the values obtained based on the algorithm provided by Ficek, previously developed and validated for Pomeranian lakes, at the same stations. The statistical analyses conducted afterwards showed that the multi-parameter algorithms of Rrs(λ) and a(λ) for the Southern Baltic Sea are sufficient to be used for the stations investigated in the aforementioned three lakes. Specifically, the correlations between the bb(λ) values obtained based on the Ficek algorithm and the bb(λ) values obtained using our multi-parameter algorithm reveal a statistical error rate of less than 20%. [ABSTRACT FROM AUTHOR]

Forest aboveground biomass (AGB, Mg/ha) measurement is one of the key indicators for carbon storage evaluation. Remote sensing techniques have been widely employed to predict forest AGB. However, little attention has been paid to the implications involved in the preprocessing of satellite data. In this work, considering the areas of low forest AGB in our survey plots, we explored the implications of employing atmospheric correction and speckle filtering with Sentinel-1 (S1) synthetic aperture radar (SAR) and Sentinel-2 (S2) to predict forest AGB using multiple linear regression (MLR) and extreme gradient boosting (XGBoost). In the present study, the types of plots examined included oaks (Quercus spp.), Chinese firs (Cunninghamia lanceolata), and Masson pines (Pinus massoniana), and all of the plots were investigated. Specifically, the feature variables related to S1 (dual polarization and texture measures) and S2 (spectral bands) were modeled individually, and 16 feature sets, including different combinations of S1 and S2 based on different preprocessing measures, were established using MLR and XGBoost. The results show that speckle filtering and atmospheric correction marginally influenced the capacity of the S2 spectral bands, the SAR dual-polarization backscatter, and the SAR-based textural measures in predicting the AGB in our survey plots. The associations between the speckle-filtered and unfiltered SAR images and the S2 Top-of-Atmosphere and Bottom-of-Atmosphere products were considerably strong. Additionally, the texture models generally showed better performances than the raw SAR data. Ultimately, the groups that only encompassed the S2 spectral bands were the best-performing groups among the 16 feature sets, while the groups that included only S1-based data generally performed the worst. [ABSTRACT FROM AUTHOR]

Semantic segmentation of Polarimetric SAR (PolSAR) images is an important research topic in remote sensing. Many deep neural network-based semantic segmentation methods have been applied to PolSAR image segmentation tasks. However, a lack of effective means to deal with the similarity of object features and speckle noise in PolSAR images exists. Thisstudy aims to improve the discriminative capability of neural networks for various intensities of backscattering coefficients while reducing the effects of noise in PolSAR semantic segmentation tasks. Firstly, we propose pre-processing methods for PolSAR image data, which consist of the fusion of multi-source data and false color mapping. Then, we propose a Multi-axis Sequence Attention Segmentation Network (MASA-SegNet) for semantic segmentation of PolSAR data, which is an encoder–decoder framework. Specifically, within the encoder, a feature extractor is designed and implemented by stacking Multi-axis Sequence Attention blocks to efficiently extract PolSAR features at multiple scales while mitigating inter-class similarities and intra-class differences from speckle noise. Moreover, the process of serialized residual connection design enables the propagation of spatial information throughout the network, thereby improving the overall spatial awareness of MASA-SegNet. Within the decoder, it is used to accomplish the semantic segmentation task. The superiority of this algorithm for semantic segmentation will be explored through feature visualization. The experiments show that our proposed spatial sequence attention mechanism can effectively extract features and reduce noise interference and is thus able to obtain the best results on two large-scale public datasets (the AIR-POlSAR-Seg and FUSAR-Map datasets). [ABSTRACT FROM AUTHOR]

Paired measurements of phytoplankton absorption and backscatter, the inherent optical properties central to the interpretation of ocean colour remote sensing data, are notoriously rare. We present a dataset of Chlorophyll a (Chl a) -specific phytoplankton absorption, scatter and backscatter for 17 different phytoplankton groups, derived from first principles using measured in vivo pigment absorption and a well-validated semi-analytical coated sphere model which simulates the full suite of biophysically consistent phytoplankton optical properties. The optical properties of each simulated phytoplankton cell are integrated over an entire size distribution and are provided at high spectral resolution. The model code is additionally included to enable user access to the complete set of wavelength-dependent, angularly resolved volume scattering functions. This optically coherent dataset of hyperspectral optical properties for a set of globally significant phytoplankton groups has potential for use in algorithm development towards the optimal exploitation of the new age of hyperspectral satellite radiometry. [ABSTRACT FROM AUTHOR]

Drought has been known to be a natural hazard reflecting geographic and climatic characteristics. Satellite technology advancements have benefited drought assessment and monitoring to formulate plans for dealing with this slow-onset disaster. However, combining satellite remote sensing (RS) and meteorological data for drought monitoring is lacking in the literature. This study uses satellite RS and meteorological-based drought indicators to assess drought risk in the Ilocos Norte, Philippines. Data analysis included the retrieval of vegetation conditions using Sentinel-1 and Sentinel-2 data. The standardized precipitation index (SPI) and Keetch–Byram drought index (KBDI) were calculated to account for climatic variabilities. Results revealed that the Sentinel-1 backscatter coefficient decreased by −2 dB in the cropland area, indicating crop growth irregularities compared to grassland areas. These irregularities were supported by Sentinel-2 normalized difference vegetation index (NDVI) strong fluctuations during the two-year observation period. A significant coefficient of determination (R2 > 0.60) between the Sentinel-1 backscatter coefficient and Sentinel-2 NDVI was observed for the study area. On the one hand, only KBDI significantly correlated (R2 > 0.60) with the cropland area's RS data-derived drought indicators. These results revealed RS data variability for drought risk management but are still valuable for developing an early warning system. [ABSTRACT FROM AUTHOR]

This paper is devoted to an acoustical method of measuring mesoscale sea and ocean currents. Due to the fact that such currents exhibit variability, long-term studies are of great interest. The aim of this study is to prepare a physical foundation to organize current measurements in an automated way using stationary mounted underwater echosounding systems. An acoustic system operating at a frequency of 1–3 kHz (lower than commercial frequencies) that is capable of sensing echo signals from natural inhomogeneities located at distances of 1 to 10 km was tested. The test was conducted during a two-week marine experiment on the northern shelf of the Black Sea. The acoustic system was mounted on a platform together with a weather station and other tools that provided reference values for further comparison. Scattering from moving particles, as well as from wind waves, provides a general opportunity for sensing of currents at remote points. Since most scatterers exist at a depth of at least 2 m or on the surface, the proposed sensing method is going specialized for currents in upper layers. However, analysis of Doppler spectra of the actual returning (reverberation) signal showed that this kind of scattering was mixed with bottom reverberation (which contains no additional frequency shift), and other signal distortions were present. Thus, we proposed a new method of signal processing that is aware of the regional environment. The described method is based on machine learning, namely on gradient boosting to build decision trees, which compute water current properties. Such a computational routine is preceded by an original acoustic signal feature extraction process. Finally, a precision of an order of magnitude was achieved, and a sensing distance of at least 2 km was proven as a result of this study carried out with available instruments. [ABSTRACT FROM AUTHOR]

The accuracy of most SAR-based flood classification and segmentation derived from semi-automated algorithms is often limited due to complicated radar backscatter. However, deep learning techniques, now widely applied in image classifications, have demonstrated excellent potential for mapping complex scenes and improving flood mapping accuracy. Therefore, this study aims to compare the image classification accuracy of three convolutional neural network (CNN)-based encoder–decoders (i.e., U-Net, PSPNet and DeepLapV3) by leveraging the end-to-end ArcGIS Pro workflow. A specific objective of this method consists of labelling and training each CNN model separately on publicly available dual-polarised pre-flood data (i.e., Sentinel-1 and NovaSAR-1) based on the ResNet convolutional backbone via a transfer learning approach. The neural network results were evaluated using multiple model training trials, validation loss, training loss and confusion matrix from test datasets. During testing on the post-flood data, the results revealed that U-Net marginally outperformed the other models. In this study, the overall accuracy and F1-score reached 99% and 98% on the test data, respectively. Interestingly, the segmentation results showed less use of manual cleaning, thus encouraging the use of open-source image data for the rapid, accurate and continuous monitoring of floods using the CNN-based approach. [ABSTRACT FROM AUTHOR]

The ECO BB9, an instrument designed for characterizing the particulate backscattering coefficient (bbp) of waters in situ, saturates frequently in water with high concentrations of particles. These water types are of increasing interest in water optics research due to the complexity of their contents and gaps in the knowledge of their interactions with the light field. Through serial dilutions, the concentrations of particles can be brought into the instrument range and linear extrapolation can be used to yield a prediction of bbp for the original undiluted solution. The efficacy of this simple approach is explored here using natural and synthetic waters in a controlled laboratory setting. bbp is a key inherent optical property used in validating and calibrating water quality parameters commonly estimated by remote sensing inversion algorithms. For this reason, the synthetic waters were constructed to test the method across water types characteristic of colored dissolved organic matter, non‐algal particulates, and phytoplankton. The dilution method was evaluated by assessment of dilution series accuracy, precision, and constancy of the backscattering spectral slope. It was found to be effective with all water types in this paper. [ABSTRACT FROM AUTHOR]

Nowadays, sea surface analysis and monitoring increasingly use remote sensing, with particular interest in Synthetic Aperture Radar (SAR). Several SAR techniques exist in literature to understand the marine phenomena affecting the sea surface. In this work, we focus on the Doppler Centroid Anomaly (DCA), which accounts for the Doppler shift induced by sea surface movements. Starting from SAR raw data, we develop a processing chain to elaborate them and output the surface velocity map using DCA. The DCA technique has often been presented in the marine literature for estimating sea surface velocity, but more recently it has also been used to detect near-surface wind fields. This paper deals with estimating the sea surface wind field using Doppler information and SAR backscatter, combined with wind information provided by ECMWF and geophysical wind and Doppler model functions. We investigate the application of the approach in the coastal area of the northern Adriatic Sea (Northeast Italy). The test site is interesting, both for its particular orography, as it is a semi-enclosed basin largely surrounded by mountains, and for its complex meteorological phenomena, such as the Bora wind. Results obtained combining SAR backscatter and DCA information show an improvement in wind field estimation. [ABSTRACT FROM AUTHOR]

Satellite radar backscatter contains unique information on land surface moisture, vegetation features, and surface roughness and has thus been used in a range of Earth science disciplines. However, there is no single global radar data set that has a relatively long wavelength and a decades-long time span. We here provide the first long-term (since 1992), high-resolution (∼8.9 km instead of the commonly used ∼25 km resolution) monthly satellite radar backscatter data set over global land areas, called the long-term, high-resolution scatterometer (LHScat) data set, by fusing signals from the European Remote Sensing satellite (ERS; 1992–2001; C-band; 5.3 GHz), Quick Scatterometer (QSCAT, 1999–2009; Ku-band; 13.4 GHz), and the Advanced SCATterometer (ASCAT; since 2007; C-band; 5.255 GHz). The 6-year data gap between C-band ERS and ASCAT was filled by modelling a substitute C-band signal during 1999–2009 from Ku-band QSCAT signals and climatic information. To this end, we first rescaled the signals from different sensors, pixel by pixel. We then corrected the monthly signal differences between the C-band and the scaled Ku-band signals by modelling the signal differences from climatic variables (i.e. monthly precipitation, skin temperature, and snow depth) using decision tree regression. The quality of the merged radar signal was assessed by computing the Pearson r , root mean square error (RMSE), and relative RMSE (rRMSE) between the C-band and the corrected Ku-band signals in the overlapping years (1999–2001 and 2007–2009). We obtained high Pearson r values and low RMSE values at both the regional (r≥0.92 , RMSE ≤ 0.11 dB, and rRMSE ≤ 0.38) and pixel levels (median r across pixels ≥ 0.64, median RMSE ≤ 0.34 dB, and median rRMSE ≤ 0.88), suggesting high accuracy for the data-merging procedure. The merged radar signals were then validated against the European Space Agency (ESA) ERS-2 data, which provide observations for a subset of global pixels until 2011, even after the failure of on-board gyroscopes in 2001. We found highly concordant monthly dynamics between the merged radar signals and the ESA ERS-2 signals, with regional Pearson r values ranging from 0.79 to 0.98. These results showed that our merged radar data have a consistent C-band signal dynamic. The LHScat data set (10.6084/m9.figshare.20407857; Tao et al., 2023) is expected to advance our understanding of the long-term changes in, e.g., global vegetation and soil moisture with a high spatial resolution. The data set will be updated on a regular basis to include the latest images acquired by ASCAT and to include even higher spatial and temporal resolutions. [ABSTRACT FROM AUTHOR]

Introduction: Space-borne lidar measurements from sensors such as CALIOP were recently used to retrieve the particulate back-scattering coefficient, bbp, in the upper ocean layers at a global scale and those observations have a strong potential for the future of ocean color with depth-resolved observations thereby complementing the conventional ocean color remote sensed observations as well as overcoming for some of its limitations. It is critical to evaluate and validate the space-borne lidar measurements for ocean applications as CALIOP was not originally designed for ocean applications. Few validation exercises of CALIOP were published and each exercise designed its own validation protocol. We propose here an objective validation protocol that could be applied to any current and future space-borne lidars for ocean applications.Methods: We, first, evaluated published validation protocols for CALIOP bbp product. Two published validation schemes were evaluated in our study, by using in-situ measurements from the BGC-Argo floats. These studies were either limited to day- or nighttime, or by the years used or by the geographical extent. We extended the match-up exercise to day-and nighttime observations and for the period 2010–2017 globally. We studied the impact of the time and distance differences between the in-situ measurements and the CALIOP footprint through a sensitivities study. Twenty combinations of distance (from 9-km to 50-km) and time (from 9 h to 16 days) differences were tested.Results & Discussion: A statistical score was used to objectively selecting the best optimal timedistance windows, leading to the best compromise in term of number of matchups and low errors in the CALIOP product. We propose to use either a 24 h/9 km or 24 h/15 km window for the evaluation of space-borne lidar oceanic products.

The sustainability of Mediterranean ecosystems, even if previously shaped by fire, is threatened by the diverse changes observed in the wildfire regime, in addition to the threat to human security and infrastructure losses. During the two previous years, destructive, extreme wildfire events have taken place in southern Europe, raising once again the demand for effective fire management based on updated and reliable information. Fuel-type mapping is a critical input needed for fire behavior modeling and fire management. This work aims to employ and evaluate multi-source earth observation data for accurate fuel type mapping in a regional context in north-eastern Greece. Three random forest classification models were developed based on Sentinel-2 spectral indices, topographic variables, and Sentinel-1 backscattering information. The explicit contribution of each dataset for fuel type mapping was explored using variable importance measures. The synergistic use of passive and active Sentinel data, along with topographic variables, slightly increased the fuel type classification accuracy (OA = 92.76%) compared to the Sentinel-2 spectral (OA = 81.39%) and spectral-topographic (OA = 91.92%) models. The proposed data fusion approach is, therefore, an alternative that should be considered for fuel type classification in a regional context, especially over diverse and heterogeneous landscapes. [ABSTRACT FROM AUTHOR]

Corn (Zea mays L.) nitrogen (N) management requires monitoring plant N concentration (Nc) with remote sensing tools to improve N use, increasing both profitability and sustainability. This work aims to predict the corn Nc during the growing cycle from Sentinel-2 and Sentinel-1 (C-SAR) sensor data fusion. Eleven experiments using five fertilizer N rates (0, 60, 120, 180, and 240 kg N ha−1) were conducted in the Pampas region of Argentina. Plant samples were collected at four stages of vegetative and reproductive periods. Vegetation indices were calculated with new combinations of spectral bands, C-SAR backscatters, and sensor data fusion derived from Sentinel-1 and Sentinel-2. Predictive models of Nc with the best fit (R2 = 0.91) were calibrated with spectral band combinations and sensor data fusion in six experiments. During validation of the models in five experiments, sensor data fusion predicted corn Nc with lower error (MAPE: 14%, RMSE: 0.31 %Nc) than spectral band combination (MAPE: 20%, RMSE: 0.44 %Nc). The red-edge (704, 740, 740 nm), short-wave infrared (1375 nm) bands, and VV backscatter were all necessary to monitor corn Nc. Thus, satellite remote sensing via sensor data fusion is a critical data source for predicting changes in plant N status. [ABSTRACT FROM AUTHOR]

In polar regions, blowing snow (BLSN) can play a substantial role in regional thermodynamics and radiation properties. The extent of the impact depends upon the depth of the BLSN layer, a property that is difficult to measure and not well understood. The West Antarctic Radiation Experiment (AWARE) Field Campaign saw the deployment of a large suite of observational instruments to McMurdo Station, Antarctica, allowing for in‐depth investigation of BLSN. During the year of the campaign, BLSN occurred ∼7.4% of the time. In this study, additional remote sensing observations are used to supplement an existing ceilometer‐based BLSN characterization algorithm creating a multi‐instrument (MI) depth estimation algorithm to yield layer depths with more certainty. Backscatter coefficient and linear depolarization ratios from the micropulse and high spectral resolution lidar (HSRL) are incorporated along with color ratio derived from HSRL backscatter and Ka‐band radar reflectivity observations. This provided estimates of BLSN depth that were less impacted by noise/artifacts in any one set of observations. When applied to the data from AWARE, the MI algorithm yielded an average depth of 168.2 m with ∼60% of plumes less than 200 m in depth. Events occurring with precipitation saw the most value in the additional instruments, with the suite of observations allowing for more separation of BLSN particles from fallstreaks. Plain Language Summary: This study used multiple lidar and radar instruments to identify the height of blowing snow (BLSN) layers at McMurdo Station in Antarctica. This information can be used to evaluate and improve BLSN models which have had minimal verification above ground. Results of this study compare well to prior studies, however, the suite of instruments demonstrate that agreement is partially due to compensating errors within single instrument (ceilometer) studies. Key Points: Inclusion of additional lidars and radar improve estimates of blowing snow layer heightsCeilometer only algorithms are prone to offsetting height errors due to extinction and fallstreaksFuture in situ observations are paramount to improving and validating this experimental algorithm [ABSTRACT FROM AUTHOR]

Most fog detection from space cannot differentiate fog and low stratus clouds, and cannot estimate fog deposition. This study assessed the feasibility of using spaceborne lidar observations from the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) in fog detection and estimation. We tested the method in the central Namib Desert, Namibia, where frequent fog events occur and fog observations are available. Results showed the CALIPSO backscatter signal at 532 nm can differentiate low clouds and fog due to its high‐resolution vertical profiles. Backscatter signals during fog events were significantly higher than those during non‐fog periods. The R2 between backscatter signals and fog observations was 0.85. Moreover, the backscatter signal was also sensitive to relative humidity variation (R2 = 0.66). These results indicate that the CALIPSO data are feasible to estimate fog occurrence and deposition, providing a new perspective for space‐based fog studies. Plain Language Summary: Fog is an important water resource for ecosystem productivity and functions in many dryland regions. Most fog detection from space cannot differentiate fog and low stratus clouds, and cannot estimate fog deposition. The spaceborne lidar measurements from Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation with high‐resolution vertical profiles provide a possibility to differentiate fog and low stratus clouds from space. A significant correlation was shown between spaceborne lidar signals and ground fog observations. Our findings can help investigate fog formation mechanisms as well as improve the capacity and accuracy of satellite‐based fog detection. Key Points: The first use of spaceborne lidar measurements to retrieve fog depositionThe Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation lidar observations with high‐resolution vertical profiles can differentiate fog and low cloudsThe spaceborne lidar measurements reflect the spatial features of fog and clouds, and can assist to investigate fog formation and identify fog types [ABSTRACT FROM AUTHOR]

A better understanding of aerosol–cloud interaction processes is important to quantify the role of clouds and aerosols on the climate system. There have been significant efforts to explain the ways aerosols modulate cloud properties. However, from the observational point of view, it is indeed challenging to observe and/or verify some of these processes because no single instrument or platform has been proven to be sufficient. Discrimination between aerosol and cloud is vital for the quantification of aerosol–cloud interaction. With this motivation, a set of observational field campaigns named balloon-borne aerosol–cloud interaction studies (BACIS) is proposed and conducted using balloon-borne in situ measurements in addition to the ground-based (lidar; mesosphere, stratosphere and troposphere (MST) radar; lower atmospheric wind profiler; microwave radiometer; ceilometer) and space-borne (CALIPSO) remote sensing instruments from Gadanki (13.45 ∘ N, 79.2 ∘ E), India. So far, 15 campaigns have been conducted as a part of BACIS campaigns from 2017 to 2020. This paper presents the concept of the observational approach, lists the major objectives of the campaigns, describes the instruments deployed, and discusses results from selected campaigns. Balloon-borne measurements of aerosol and cloud backscatter ratio and cloud particle count are qualitatively assessed using the range-corrected data from simultaneous observations of ground-based and space-borne lidars. Aerosol and cloud vertical profiles obtained in multi-instrumental observations are found to reasonably agree. Apart from this, balloon-borne profiling is found to provide information on clouds missed by ground-based and/or space-borne lidar. A combination of the Compact Optical Backscatter AerosoL Detector (COBALD) and Cloud Particle Sensor (CPS) sonde is employed for the first time in this study to discriminate cloud and aerosol in an in situ profile. A threshold value of the COBALD colour index (CI) for ice clouds is found to be between 18 and 20, and CI values for coarse-mode aerosol particles range between 11 and 15. Using the data from balloon measurements, the relationship between cloud and aerosol is quantified for the liquid clouds. A statistically significant slope (aerosol–cloud interaction index) of 0.77 found between aerosol backscatter and cloud particle count reveals the role of aerosol in the cloud activation process. In a nutshell, the results presented here demonstrate the observational approach to quantifying aerosol–cloud interactions. [ABSTRACT FROM AUTHOR]

Forests' ecosystems are an essential part of the global carbon cycle with vast carbon storage potential. These systems are currently under external pressures showing increasing change due to climate change. A better understanding of the biophysical properties of forests is, therefore, of paramount importance for research and monitoring purposes. While there are many biophysical properties, the focus of this study is on the in-depth analysis of the connection between the C-band Copernicus Sentinel-1 SAR backscatter and evapotranspiration (ET) estimates based on in situ meteorological data and the FAO-based Penman–Monteith equation as well as the well-established global terrestrial ET product from the Terra and Aqua MODIS sensors. The analysis was performed in the Free State of Thuringia, central Germany, over coniferous forests within an area of 2452 km2, considering a 5-year time series (June 2016–July 2021) of 6- to 12-day Sentinel-1 backscatter acquisitions/observations, daily in situ meteorological measurements of four weather stations as well as an 8-day composite of ET products of the MODIS sensors. Correlation analyses of the three datasets were implemented independently for each of the microwave sensor's acquisition parameters, ascending and descending overpass direction and co- or cross-polarization, investigating different time series seasonality filters. The Sentinel-1 backscatter and both ET time series datasets show a similar multiannual seasonally fluctuating behavior with increasing values in the spring, peaks in the summer, decreases in the autumn and troughs in the winter months. The backscatter difference between summer and winter reaches over 1.5 dB, while the evapotranspiration difference reaches 8 mm/day for the in situ measurements and 300 kg/m2/8-day for the MODIS product. The best correlation between the Sentinel-1 backscatter and both ET products is achieved in the ascending overpass direction, with datasets acquired in the late afternoon, and reaches an R2-value of over 0.8. The correlation for the descending overpass direction reaches values of up to 0.6. These results suggest that the SAR backscatter signal of coniferous forests is sensitive to the biophysical property evapotranspiration under some scenarios. [ABSTRACT FROM AUTHOR]

The variability of surface roughness may lead to relatively large dynamic of backscatter coefficient observed by the synthetic aperture radar (SAR), which complicates the soil moisture (SM) retrieval process based on active remote sensing. The effective roughness parameters are commonly used for parameterizing the soil scattering models, the values of which are often assumed to be constant during different study periods for the same site. This paper investigates the reasonableness of this hypothesis from the perspective of backscatter coefficient simulation and SM retrieval using high resolution SAR data. Three years of Sentinel-1A data from 2016 to 2018 were collected over a sparsely vegetated field within the REMEDHUS SM monitoring network. The advanced integral equation model (AIEM) and Dobson dielectric mixing model were combined for optimizing the effective roughness parameters, as well as simulating the backscatter coefficient and retrieving the SM. The effective roughness parameters were optimized at different temporal periods, such as 2016, 2017, 2018, 2016 + 2017, 2017 + 2018, and 2016 + 2017 + 2018, to analyze their temporal dynamics. It was found that: (1) the effective roughness parameters optimized at different temporal periods are very close to each other; (2) the simulated backscatter from AIEM is consistent with Sentinel-1A observation with root mean square errors (RMSEs) between 1.133 and 1.163 dB and correlation coefficient ® value equals to 0.616; (3) the seasonal dynamics ofin situ SM is well-captured by the retrieved SM with R values floating at 0.685 and RMSEs ranging from 0.049 to 0.052 m3/m3; and (4) inverse of the AIEM with the implementation of effective roughness parameters achieves better performance for SM retrieval than the change detection method. These findings demonstrate that the assumption on the constant effective roughness parameters during the study period of at least three years is reasonable. [ABSTRACT FROM AUTHOR]

Forest and field fires have become a frequent phenomenon in recent years caused by human activities in Indonesia, affecting all forms of forest and field cover. Forest fire-degraded land is more prone to burn again, due to the nature of peatland in Kalimantan. Rapid mapping technology for burnt areas affected by forest fires is needed to obtain this information. The use of remote sensing technology, in the form of synthetic aperture radar (SAR) images, combined with cloud computing processing speeds up data processing and is not affected by the existing cloud cover. The Quick-Mapping employed in this research provides faster mapping time, compared to the currently employed method, based on field report data, to enable a better and more efficient firefighting effort. The data processing is carried out using cloud computing, enabling the processing of large amounts of data. The processing is carried out starting with importing the data, preprocessing to classification running, simultaneously, using the JavaScript programming language. The research classifies the burnt area from backscatter patterns before and after the event in two measurements, namely the radar burn ratio (RBR) and the radar burn difference (RBD). The RBR is defined as the average backscatter ratio at a certain polarization, while RBD is the difference between the average scattering conditions. The composite image for the classification utilizes images from the RBR and RBD with co-polarized (VV) and cross-polarized (VH) backscatter. The burnt area difference is −1.9 for VH and −1.7 for VV, which indicates a lower backscatter, due to forest fire. The classification of the burnt area yields the best overall accuracy of 88.26% with a support vector machine and processing time of 1 h, compared to the currently 12 h needed to provide burnt area maps from field observation data. [ABSTRACT FROM AUTHOR]

Satellite remote sensing plays a crucial role in estimating global primary production. One well-known model is the carbon-based production model (CbPM), which focuses on the estimation of carbon biomass (C ph) via particulate backscattering coefficient at 443 nm (b bp (443)) and emphasizes the physiological characteristics of phytoplankton. However, as there are various remote sensing algorithms for the estimation of b bp (443) and chlorophyll-a concentration (Chl), the impacts introduced by different b bp (443) and Chl products on the estimated water-column integrated primary production (PP eu) by CbPM are unknown, especially with the new CbPM version (CbPM08) (Westberry et al., 2008). In this study, we conducted analyses to examine the impact of Chl, b bp (443), and K d (490) (the diffuse attenuation coefficient at 490 nm) that were derived from ocean color remote sensing on PP eu estimated by CbPM08. Our results revealed that Chl constitutes the primary source of model uncertainty, followed by K d (490), while variations of b bp (443) exhibit negligible impact on the estimation of PP eu. Especially, between Chl and C ph , sensitivity studies indicate that the PP eu values estimated by CbPM08 are fundamentally driven by Chl, rather than by C ph (or b bp (443)). These results provide important insights on understanding the uncertainties associated with CbPM08-estimated PP eu and future directions for further improvement for the estimation of PP eu via this model, as well as its applications on estimating water-column primary production. • CbPM is insensitive to C ph but sensitive to Chl, showing its Chl-driven not C ph. • K d (490) is an important source of uncertainty in CbPM-derived PP eu. • Varied satellite products lead to significant differences in CbPM-derived PP eu. [ABSTRACT FROM AUTHOR]