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Cyanobacterial harmful algal blooms (HABs) pose a significant threat to aquatic ecosystems, water quality, and public health, particularly in large hypereutrophic lakes. Developing accurate short-term prediction models is essential for early warning and effective management of HABs. This study introduces a Bayesian-based model aimed at predicting HABs in three of China's large hypereutrophic lakes: Lake Taihu, Lake Chaohu, and Lake Hulunhu. By integrating MODIS data from the Terra and Aqua satellites with meteorological data spanning from 2010 to 2018, the model forecasts HABs distributions 1, 4, and 7 days in advance. Validation with meteorological data from 2019 to 2020 showed high accuracy, with 0.83 at the pixel level, 0.74 for zonal predictions, and 0.64 for lake-wide HABs area forecasts. Further evaluation using 2023 weather forecast data yielded similar accuracies of 0.78, 0.57, and 0.62, respectively. In addition to predicting the spatial extent of HABs, the model provides binary HABs maps, outbreak areas, and HABs status within lake zones. This method for building prediction models significantly enhances early warning and management capabilities for HABs, providing a scalable framework that can be adapted to other regions facing similar threats from HABs., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

In the present work, it is the first time an interpretable machine learning model has been developed for the estimation of Particulate Matter 10 µm (PM 10 ) concentrations over India using Aerosol Optical Depth (AOD) from two different satellites, i.e. INSAT-3D and Moderate Resolution Imaging Spectroradiometer (MODIS) for the period of 7 years (2014 to 2020). Ground datasets of AOD are taken from the Aerosol Robotic Network (AERONET) for the validation of satellite-retrieved AOD. The observation of particulate matter (PM) data is acquired from the Central Pollution Control Board (CPCB) station across India. Analysis has been performed on a monthly basis for the given time period. The result shows that AOD products of MODIS exhibit good correlation with AERONET AOD whereas INSAT-3D AOD is not well correlated with AERONET AOD. However, after applying an error envelope and threshold-based filtering technique, we have found that INSAT-3D shows significant correlation with ground-level AOD with approximate correlation of 0.66 for Jaipur and 0.57 for Kanpur exhibiting almost similar performance as MODIS-derived AOD. Satellite AOD data together with ground PM concentration data is used to train the machine learning model (random forest) for the estimation of the PM distribution across India for the year 2020. An encouraging correlation of R-squared (R 2 ) value 0.78 has been observed between the estimated and observed PM 10 concentrations. The model demonstrates effective training, mitigating huge overestimation and underestimation. However, despite closely tracking the trends of estimated PM 10 with observed PM 10 , few instances of overestimation persist. This suggests the need for an expanded training dataset to further refine and enhance the model's accuracy. Finally, the machine learning model used for PM 10 estimation is found to be optimal for a calibrated satellite AOD product., Competing Interests: Declarations. Ethics approval: All authors have read, understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors. Consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Acquiring aerosol vertical distribution information is crucial to accurately quantify the aerosol radiation effect on climate and understand the environmental pollution mechanism of the atmosphere. Passive remote sensing has shown its capability to gain large-scale, high spatiotemporal resolution aerosol vertical information such as aerosol layer height (ALH). However, it is still challenging to extract detailed aerosol vertical distribution information, e.g., aerosol extinction profile (AEP), from passive observations. To fill this gap, this study proposed a hybrid model of Transformer and convolutional neural network (CNN) to estimate the AEP from passive multispectral remote sensing (MODIS) measurements with the aid of three-dimensional reanalysis data (MERRA-2). Specifically, the model is learned to estimate the AEP, which is called AproNet, by using the active space-borne lidar (CALIPSO) data as supervised information. Besides, we design a shape invariant loss (SIL) to better capture the shape characteristics of the AEP and incorporate an auxiliary scene awareness loss (SAL) to enhance the model's generalization capacity and physical reliability outside the CALIPSO orbit. The extensive quantitative experiments show that the AEPs estimated by the proposed model agree well with the CALIPSO measurements with an overall performance of IOA=0.821, R=0.800, MAE= 0.014, and RMSE= 0.041, respectively. Qualitative comparisons also demonstrate the model's reliability in estimating the aerosol three-dimensional spatial distribution. Independent year test and comparisons with ground-based lidar measurements further indicate the robustness of the proposed model despite some degradation in performance. However, the incompleteness and uncertainty of the CALIOP products limited the performance of the proposed model to some extent. In the future, the model needs to be further physically constrained and strengthened with more data sources to improve reliability. In general, this study paves the way for acquiring aerosol extinction profiles with high spatiotemporal resolution over a large geographical space., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Crop phenology is very important in regular crop monitoring. Generally, phenology is monitored through field observation surveys or satellite data. The relationships between ground observations and remotely sensed derived phenological data can enable near-real-time monitoring over large areas, which has never been attempted on hazelnuts. In this study, we extracted phenological metrics derived from MODIS Enhanced Vegetation Index (EVI) in hazelnut production regions and compared them with the spring ground phenological data (BBCH scale) from orchards located in the same area of Turkey over the period from 2019 to 2022. We observed a specific temporal dynamic between remote sensing phenometrics and ground observations. The metrics Greenup, Upturning Date, and Threshold 20% metrics corresponded to the early of EVI growth and were synchronous with the female flowering of hazelnut and ending before bud break. The metrics Threshold 50% and Start of season were associated with the steepest portion of the EVI curve, i.e., canopy greening and thickening, and occurred between ovaries enlargement and leaves unfolding. The metrics Peak of Season, Stabilization Date, and Maturity corresponded to the end of spring vegetative growth. The main outcomes are that (i) female flowering occurred before 20% of vegetation development (BBCH 64P occurred about one month before Threshold 20%), (ii) phenometrics from satellite remote sensing (i.e., Upturning Date and Threshold 20%) well-reflected leaf emergence (r s = 0.30 and r s = 0.32, respectively; p < 0.05) and unfolding (r s = 0.35 and r s = 0.39, respectively; p < 0.05), and (iii) cluster appearance temporally aligned with the peak of the EVI curve (Stabilization Date and BBCH 71P differed by around 4 days). Our method is transferable to operational phenology monitoring, and future applications will consider the senescence season and the effect of environmental variability on the comprehension of vegetation dynamics., (© 2024. The Author(s).)

By monitoring evapotranspiration (ET), the exchange of water and energy between the soil, plants, and the atmosphere can be controlled. Routine estimations of ET on a daily, monthly, and seasonal basis can give relevant information on small-scale agricultural practices, such as the Ribb watershed in Ethiopia. However, MODIS sensors have recently given high temporal resolution ET products across large areas, but their low spatial resolution limits its application on a local scale. The primary goal of the study was to downscale the MODIS ET (1 km) product to a finer spatial resolution at the watershed level. The model's 12 predictor variables (NDVI, EVI, LAI, FVC, SAVI, NDMI, NDWI, Albedo, emissivity, LST, and DEM: slope and elevation) were produced using the random forest (RF) algorithm using Sentinel-2 (S-2) 20 m and Landsat-8 (L-8) 30 m. The RF regression model was used to assess the relationship between predicted variables and downscaled MODIS ET. The FAO-PM ET model, developed from meteorological stations, was validated by R 2 and RMSE for three seasons (rainy, post-rainy, and dry) in 2022. The results were in good agreement with MODIS ET, with an RMSE of 0.22 for S-2 and 0.28 for L-8. In the FAO-PM ET model, the downscaled result showed greater spatial details and better agreement with gage station readings ( R 2 ≈ 0.88 and 0.82 ). Thus, considering the effectiveness and simplicity of machine learning techniques, our study demonstrated the potential for ET downscaling. Furthermore, the study suggests integrating spatiotemporal time series data to reach higher resolution., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

NASA has released the latest Moderate Resolution Imaging Spectroradiometer (MODIS) Multi-Angle Implementation of Atmospheric Correction (MAIAC) Collection 6 (C6) and Collection 6.1 (C6.1) aerosol optical depth (AOD) products with 1 km spatial resolution. This study validated and compared C6 and C6.1 MAIAC AOD products with AERONET observations in terms of accuracy and stability, and analyzed the spatiotemporal characteristics of AOD at different scales in China. The results show that the overall accuracy of MAIAC products is good, with correlation coefficient (R) > 0.9, mean bias (BIAS) < 0.015, and the fraction within the expected error (EE) > 68 %. However, after the algorithm update, the accuracy of Terra MAIAC aerosol products C6.1 has significantly decreased. The accuracy of the products varies with the region. The accuracy of C6.1 in North China, Central East China, and West China, is comparable to or even exceeds that of C6, but performs poorly in South China. In addition, the stability of the updated C6.1 MAIAC aerosol products has not seen significantly improvement. The metrics of no product can all meet the stability goals of the Global Climate Observing System (GCOS, 0.02 per decade) in China. C6.1 improves the retrieval frequency in many regions and temporarily solves the problem of AOD discontinuity at the boundaries of different aerosol models in C6, but there are some fixed climatological AOD blocks (AOD = 0.014) in the eastern Tibetan Plateau region. Both C6 and C6.1 can capture similar annual variation characteristics of AOD to those observed at the AERONET sites. The study provides possible references for improving the MAIAC algorithm and building long-term stable aerosol records., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Svalbard, located between 76°30'N and 80°50'N, is among the regions in the world with the most rapid temperature increase. We processed a cloud-free time-series of MODIS-NDVI for Svalbard. The dataset is interpolated to daily data during the 2000-2022 period with 232 m pixel resolution. The onset of growth, with a clear phenological definition, has been mapped each year. Then the integrated NDVI from the onset (O) of growth each year to the time of average (2000-2022) peak (P) of growth (OP NDVI) have been calculated. OP NDVI has previously shown high correlation with field-based tundra productivity. Daily mean temperature data from 11 meteorological stations are compared with the NDVI data. The OP NDVI values show very high and significant correlation with growing degree days computed from onset to time of peak of growth for all the meteorological stations used. On average for the entire Svalbard, the year 2016 first had the highest greening (OP NDVI values) recorded since the year 2000, then the greening in 2018 surpassed 2016, then 2020 surpassed 2018, and finally 2022 was the year with the overall highest greening by far for the whole 2000-2022 period. This shows a rapid recent greening of Svalbard very strongly linked to temperature increase, although there are regional differences: the eastern parts of Svalbard show the largest variability between years, most likely due to variability in the timing of sea-ice break-up in adjacent areas. Finally, we find that areas dominated by manured moss-tundra in the polar desert zone require new methodologies, as moss does not share the seasonal NDVI dynamics of tundra communities., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Stein Rune Karlsen reports financial support was provided by Norwegian Space Agency. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Non-linear trend detection in Earth observation time series has become a standard method to characterize changes in terrestrial ecosystems. However, results are largely dependent on the quality and consistency of the input data, and only few studies have addressed the impact of data artifacts on the interpretation of detected abrupt changes. Here we study non-linear dynamics and turning points (TPs) of temperate grasslands in East Eurasia using two independent state-of-the-art satellite NDVI datasets (CGLS v3 and MODIS C6) and explore the impact of water availability on observed vegetation changes during 2001-2019. By applying the Break For Additive Season and Trend (BFAST01) method, we conducted a classification typology based on vegetation dynamics which was spatially consistent between the datasets for 40.86 % (459,669 km 2 ) of the study area. When considering also the timing of the TPs, 27.09 % of the pixels showed consistent results between datasets, suggesting that careful interpretation was needed for most of the areas of detected vegetation dynamics when applying BFAST to a single dataset. Notably, for these areas showing identical typology we found that interrupted decreases in vegetation productivity were dominant in the transition zone between desert and steppes. Here, a strong link with changes in water availability was found for >80 % of the area, indicating that increasing drought stress had regulated vegetation productivity in recent years. This study shows the necessity of a cautious interpretation of the results when conducting advanced characterization of vegetation response to climate variability, but at the same time also the opportunities of going beyond the use of single dataset in advanced time-series approaches to better understanding dryland vegetation dynamics for improved anthropogenic interventions to combat vegetation productivity decrease., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Ecosystem models are valuable tools to make climate-related assessments of change when ground-based measurements of water and carbon fluxes are not adequately detailed to realistically capture geographic variability. The Carnegie-Ames-Stanford Approach (CASA) is one such model based on satellite observations of monthly vegetation cover to estimate net primary production (NPP) of terrestrial ecosystems., Results: CASA model predictions from 2015 to 2022 for Western Europe revealed several notable high and low periods in growing season NPP totals in most countries of the region. For the total land coverage of France, Greece, Italy, Portugal, and Spain, 2018 was the year with the highest terrestrial plant growth, whereas 2017 and 2019 were the years with the highest summed NPP across the UK, Germany, and Croatia. For most of Western Europe, 2022 was the year predicted with the lowest summed plant growth. Annual precipitation in most countries of Western Europe gradually declined from a high average rate in 2018 to a low average precipitation level in 2022., Conclusions: The CASA model predicted decreased growing season NPP of between - 25 and - 60% across all of Spain, southern France, and northern Italy from 2021 to 2022, and much of that plant production loss was detected in the important cropland regions of these nations., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

In this study, we applied a multivariate logistic regression model to identify deforested areas and evaluate the current effects on environmental variables in the Brazilian state of Rondônia, located in the southwestern Amazon region using data from the MODIS/Terra sensor. The variables albedo, temperature, evapotranspiration, vegetation index, and gross primary productivity were analyzed from 2000 to 2022, with surface type data from the PRODES project as the dependent variable. The accuracy of the models was evaluated by the parameters area under the curve (AUC), pseudo R 2 , and Akaike information criterion, in addition to statistical tests. The results indicated that deforested areas had higher albedo (25%) and higher surface temperatures (3.2 °C) compared to forested areas. There was a significant reduction of the EVI (16%), indicating water stress, and a decrease in GPP (18%) and ETr (23%) due to the loss of plant biomass. The most precise model (91.6%) included only surface temperature and albedo, providing important information about the environmental impacts of deforestation in humid tropical regions., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Drought has a significant impact on crop growth and productivity, highlighting the critical need for precise and timely soil moisture estimation to mitigate agricultural losses. This study focuses on soil moisture retrieval in northern Hebei Province during July 2012, utilizing eight widely employed remote sensing drought indices derived from MODIS satellite data. These indices were cross-referenced with measured soil moisture levels for analysis. Based on their correlation coefficients, a composite remote sensing drought index set comprising six indices was identified. Furthermore, a radial basis function neural network (RBFNN) was employed to estimate soil relative humidity. The accuracy evaluation of the soil moisture estimation model, which integrates multiple remote sensing drought indices and the RBFNN, demonstrated clear superiority over models relying on single drought indices. The model achieved an average estimation accuracy of 87.54 % for soil relative humidity at a depth of 10 cm (SM10) and 87.36 % for a 20 cm depth (SM20). The root mean square errors (RMSE) for the test sets were 0.093 and 0.092, respectively. Validation results for July 2013 indicated that the inversion accurately reflected the actual soil moisture conditions, effectively capturing dynamic moisture changes. These results fully verify the reliability and practicability of the model. These findings introduce a novel approach to local agricultural soil moisture estimation, with significant implications for enhancing agricultural water resource management and decision-making processes., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:The author declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier Ltd.)

Lake water surface temperature (LWST) is a critical component in understanding the response of freshwater ecosystems to climate change. Traditional estimation of LWST estimation considers water surface bodies to be static. Our work proposes a novel open-source web application, IMPART, designed for estimating dynamic LWST using Landsat reflectance and MODIS temperature datasets from 2004 to 2022. Results presented globally for over 342 lakes reveal a root mean square deviation of 0.86 °C between static and dynamic LWST. Additionally, our results demonstrate that 57% of the lakes exhibit a statistically significant difference between the static and dynamic LWST values. Improved LWST will ultimately enhance our ability to comprehensively monitor and respond to the impacts of climate change on freshwater ecosystems worldwide. Furthermore, based on the Koppen-Geiger climate classification, our zonal analysis demonstrates the deviation between static and dynamic LWST. It identifies specific zones where considering waterbodies as dynamic entities is essential., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

As Southeast Asia grapples with extreme heat occurrences in recent years, mapping which areas are clustered with elevated temperatures is crucial for monitoring the at-risk population. Identifying the contributing factors to the warming trends in these areas is also vital in formulating adaptation and mitigation strategies. This dataset comprises land surface temperature (LST) in three metropolises in the region - Metropolitan Manila, Bangkok Metropolitan Area, and Greater Jakarta - downloaded and processed from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. We used MODIS' inherent grid system to map LST values at the satellite image's most granular level. We combined them with selected environmental and socioeconomic variables, including building and built-up areas, areas of greeneries, industrial zones, and water bodies, nighttime light (to approximate areas of economic activities), gridded population, distance from water bodies, and indicators on which urban infrastructures, i.e. roads and airports, are present in each grid. Available in shapefile and comma-separate variable file format, this dataset is useful for urban studies in these three cities. The dataset can be easily updated as additional data on LST and other variables becomes available., (© 2024 The Author(s).)

Monitoring temporal and spatial changes in the mountain seasonal snowpack is a key step to better quantify the impact of recent climate warming on this very sensitive environment. In this context, to support the research on large scale, there is an urgent need of consistent and accurate data sets over global mountain regions. This paper presents a dataset derived from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery collection 6.1 onboard of Terra satellite containing information on snow cover area and snow cover phenology (duration, start and end of the season) at 500 m ground resolution and covering the global mountain regions as delineated by the Global Mountain Biodiversity Assessment (GMBA) shapefile. The dataset contains yearly averaged snow cover area (SCA), snow cover duration (SCD), first snow day (FSD) and last snow day (LSD). The snow cover area SCA is based on snow cover fraction daily maps averaged on a yearly basis. To derive the snow phenology variables, SCD, FSD, LSD, a second-order autoregressive approach was adopted to interpolate the data thus reducing the impact of cloud coverage. The dataset was processed for the entire MODIS time series starting from October 2000 up to September 2023 and it is provided on yearly basis considering the hydrological year timeframe: from 1 st October to 30 th September of the subsequent year for the Northern Hemisphere and from 1 st April to 31 st March of the subsequent year for the Southern Hemisphere. The use of the data can be manyfold from quantifying the snowpack changes in the last 23 years, to addressing the impact of snowpack changes on water resources and vegetation phenology., (© 2024 The Author(s).)

Fine-mode aerosol optical depth (fAOD) is a vital proxy for the concentration of anthropogenic aerosols in the atmosphere. Currently, the limited data length and high uncertainty of the satellite-based data diminish the applicability of fAOD for climate research. Here, we propose a novel pretrained deep learning framework that can extract information underlying each satellite pixel and use it to create new latent features that can be employed for improving retrieval accuracy in regions without in situ data. With the proposed model, we developed a new global fAOD (at 0.5 μm) data from 2001 to 2020, resulting in a 10% improvement in the overall correlation coefficient ( R ) during site-based independent validation and a 15% enhancement in non-AERONET site areas validation. Over the past two decades, there has been a noticeable downward trend in global fAOD (-1.39 × 10 -3 /year). Compared to the general deep-learning model, our method reduces the global trend's previously overestimated magnitude by 7% per year. China has experienced the most significant decline (-5.07 × 10 -3 /year), which is 3 times greater than the global trend. Conversely, India has shown a significant increase (7.86 × 10 -4 /year). This study bridges the gap between sparse in situ observations and abundant satellite measurements, thereby improving predictive models for global patterns of fAOD and other climate factors.

The ability of the land surface temperature (LST) and normalized difference vegetation index (NDVI) to examine land surface change is regarded as an important climate variable. However, no significant systematic examination of urbanization concerning environmental variables has been undertaken in the narrow valley of Thimphu, Bhutan. Therefore, this study investigated the impact of land use/land cover (LULC) dynamics on LST, NDVI, and elevation, using Moderate Resolution Imaging Spectroradiometer (MODIS) data collected in Thimphu, Bhutan, from 2000 to 2020. The results showed that LSTs varied substantially among different land use types, with the highest occurring in built-up areas and the lowest occurring in forests. There was a strong negative linear correlation between the LST and NDVI in built-up areas, indicating the impact of anthropogenic activities. Moreover, elevation had a noticeable effect on the LST and NDVI, which exhibited very strong opposite patterns at lower elevations. In summary, LULC dynamics significantly influence LST and NDVI, highlighting the importance of understanding spatiotemporal patterns and their effects on ecological processes for effective land management and environmental conservation. Moreover, this study also demonstrated the applicability of relatively low-cost, moderate spatial resolution satellite imagery for examining the impact of urban development on the urban environment in Thimphu city., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The high uncertainty regarding global gross primary production (GPP) remains unresolved. This study explored the relationships between phenology, physiology, and annual GPP to provide viable alternatives for accurate estimation. A statistical model of integrated phenology and physiology (SMIPP) was developed using GPP data from 145 FLUXNET sites to estimate the annual GPP for various vegetation types. By employing the SMIPP model driven by satellite-derived datasets of the global carbon uptake period (CUP) and maximal carbon uptake capacity (GPPmax), the global annual GPP was estimated for the period from 2001 to 2018. The results demonstrated that the SMIPP model accurately predicted annual GPP, with relative root mean square error values ranging from 11.20 to 19.29% for forest types and 20.49-35.71% for non-forest types. However, wetlands, shrublands, and evergreen forests exhibited relatively low accuracies. The average, trend, and interannual variation of global GPP during 2001-2018 were 132.6 Pg C yr -1 , 0.25 Pg C yr -2 , and 1.57 Pg C yr -1 , respectively. They were within the ranges estimated in other global GPP products. Sensitivity analysis revealed that GPPmax had comparable effects to CUP in high-latitude regions but significantly greater impacts at the global scale, with sensitivity coefficients of 0.85 ± 0.23 for GPPmax and 0.46 ± 0.28 for CUP. This study provides a simple and practical method for estimating global annual GPP and highlights the influence of GPPmax and CUP on global-scale annual GPP., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Climate change still adversely affects agriculture in the sub-Saharan Africa. There is need to strengthen early action to bolster livelihoods and food security. Most governments use pre- and post-harvest field surveys to capture statistics for National Food Balance Sheets (NFBS) key in food policy and economic planning. These surveys, though accurate, are costly, time consuming, and may not offer rapid yield estimates to support governments, emergency organizations, and related stakeholders to take advanced strategic decisions in the face of climate change. To help governments in Kenya (KEN), Zambia (ZMB), and Malawi (MWI) adopt digitally advanced maize yield forecasts, we developed a hybrid model based on the Regional Hydrologic Extremes Assessment System (RHEAS) and machine learning. The framework is set-up to use weather data (precipitation, temperature, and wind), simulations from RHEAS model (soil total moisture, soil temperature, solar radiation, surface temperature, net transpiration from vegetation, net evapotranspiration, and root zone soil moisture), simulations from DSSAT (leaf area index and water stress), and MODIS vegetation indices. Random Forest (RF) machine learning model emerged as the best hybrid setup for unit maize yield forecasts per administrative boundary scoring the lowest unbiased Root Mean Square Error (RMSE) of 0.16 MT/ha, 0.18 MT/ha, and 0.20 MT/ha in Malawi's Karonga district, Kenya's Homa Bay county, and Zambia's Senanga district respectively. According to relative RMSE, RF outperformed other hybrid models attaining the lowest score in all countries (ZMB: 25.96%, MWI: 28.97%, and KEN: 27.54%) followed by support vector machines (ZMB: 26.92%, MWI: 31.14%, and KEN: 29.50%), and linear regression (ZMB: 29.44%, MWI: 31.76%, and KEN: 47.00%). Lastly, the integration of VI and RHEAS information using hybrid models improved yield prediction. This information is useful for NFBS bulletins forecasts, design and certification of maize insurance contracts, and estimation of loss and damage in the advent of climate justice., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Exploring the temporal and spatial dynamics of vegetation coverage in the Heilongjiang Basin and its response to climate change can provide a theoretical basis and data support for integrated basin management for three countries (Mongolia, China and Russia) in the region. We used MOD13Q1 remote sensing data from Google Earth Engine (GEE) platform between 2000 and 2020 to process the normalized vegetation index (NDVI) through the maximum value composites method, and calculated the vegetation coverage (FVC) using the dimidiate pixel model. The Sen+MK trend analysis method was employed to monitor the dynamics of FVC, while the Pearson correlation coefficient was utilized to quantify the responses of FVC to climate change. The results showed that the overall FVC in the Heilongjiang Basin exhibited a slight decreasing trend during 2000-2020, with an annual rate of 0.1%. The FVC in Mongolia showed a fluctuating increase trend (0.13%), while slight decrease trends were observed for Russia (0.15%) and China (0.08%). The FVC predominantly slightly degraded and severely degraded, accounting for 34% and 17% of the area, respectively, while the significantly improved area only accounted for 9%. The impact of precipitation on FVC in the study area was significantly greater than that of temperature. The proportion of areas where precipitation and temperature had a significant impact on FVC was 8.2% and 2.2%, respectively. The correlation coefficient between precipitation and FVC was the highest in Mongolia ( r =0.446, P <0.05), and the lowest in Russian region ( r =-0.442, P < 0.05).

Chlorophyll a (Chla) concentration can be used as an indicator of algal biomass, and the accumulation of algal biomass in water column is essential for the emergence of surface blooms. By using Moderate Resolution Imaging Spectrometer (MODIS) data, a machine learning algorithm was previously developed to assess algal biomass within the euphotic depth (B eu ). Here, a long-term B eu dataset of Lake Taihu from 2003 to 2020 was generated to examine its spatio-temporal dynamics, sensitivity to environmental factors, and variations in comparison to the surface algal bloom area. During this period, the daily B eu (total B eu within the whole lake) exhibited temporal fluctuations between 40 and 90 t Chla, with an annual average of 63.32 ± 5.23 t Chla. Notably, it reached its highest levels in 2007 (72.34 t Chla) and 2017 (73.57 t Chla). Moreover, it demonstrated a clear increasing trend of 0.197 t Chla/y from 2003 to 2007, followed by a slight decrease of 0.247 t Chla/y after 2017. Seasonal variation showed a bimodal annual cycle, characterized by a minor peak in March ∼ April and a major peak in July ∼ September. Spatially, the average pixel-based B eu (total B eu of a unit water column) ranged from 21.17 to 49.85 mg Chla, with high values predominantly distributed in the northwest region and low values in the central region. The sensitivity of B eu to environmental factors varies depending on regions and time scales. Temperature has a significant impact on monthly variation (65.73%), while the level of nutrient concentrations influences annual variation (55.06%). Wind speed, temperature, and hydrodynamic conditions collectively influence the spatial distribution of B eu throughout the entire lake. Algal bloom biomass can capture trend changes in two mutant years as well as bimodal phenological changes compared to surface algal bloom area. This study can provide a basis for scientific evaluation of water environment and a reference for monitoring algal biomass in other similar eutrophic lakes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The Forel Ule water color index (FUI) based on satellite inversion can characterize the comprehensive characteristics of water quality on a large spatiotemporal scale. The high-frequency observations and rich historical data of the MODIS surface reflectance product (MODIS-500 m) provide important data support for monitoring the FUI of inland lakes. However, MODIS-500 m has only three bands in the visible light range, resulting in significant uncertainty in FUI inversion. To address this problem, this study developed an improved FUI inversion model using 500 synthetic spectra covering natural waters. The model, with a performance threshold set at 170° (FUI = 8), used a segmented algorithm across the entire color space. Validated with on-site measurement datasets (3500 samples), the model exhibited excellent performance, with mean relative error (MRE) and root mean square error (RMSE) of 1.71 % and 3.63°, respectively. Compared to existing models, it was more suitable for long-term FUI inversion in various types of lakes, particularly in eutrophic regions. Subsequently, the model was applied to MODIS-500 m observations from 2000 to 2022, revealing the spatiotemporal dynamics of FUI in 180 large lakes and reservoirs (hereinafter referred to as lakes) in China. The results indicated that the long-term mean FUI in the study area was 9, with 7 and 12 in the western and eastern regions, respectively, showing a distinct spatial distribution of "blue in the west and green in the east." The mean change rate of hue angle for all lakes was -0.085°/yr, showing an overall decreasing trend. Environmental factors' relative contributions to long-term water color changes in each lake region were quantified using the multiple general linear model (GLM). Although each lake region exhibited different driving forces, they were primarily influenced by vegetation, lake surface area, and anthropogenic factors. Additionally, the seasonal types of lake water color were analyzed, with the west and east showing opposite patterns, reflecting the significant influence of topographic features and seasonal changes in climate on water color. The research results provide techniques for accurate inversion of FUI using MODIS-500 m data, while deepening the understanding of long-term water color changes in inland lakes in China., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Total phosphorus (TP) is non-optically active, thus TP concentration (CTP) estimation using remote sensing still exists grand challenge. This study developed a deep neural network model (DNN) for CTP estimation with synchronous in-situ measurements and MODIS-derived remote sensing reflectance (R rs ) (N = 3916). Using DNN, the annual and intra-annual CTP spatial distributions of the Great Lakes since 2002 were reconstructed. Then, the reconstructions were correlated to nine potential factors, e.g., Chlorophyll-a, snowmelt, and cropland, to explain seasonal and long-term CTP variations. The results showed that DNN reliably estimated CTP from MODIS R rs , with R 2 , mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and root mean squared logarithmic error (RMSLE) of 0.83, 1.05 μg/L, 2.95 μg/L, 9.92%, and 0.13 on the test set. The near-surface CTP in the Great Lakes decreased significantly (p < 0.05) during 2002 - 2022, primarily attributed to cropland reduction, coupled with improvements in basin natural ecosystems. The sensitivity analysis verified the model robustness when confronted with input feature changes < 35%. This result along with the marginal difference between CTP derived from two sensors (R 2 = 0.76, MAE = 2.12 μg/L, RMSE = 2.51 μg/L, MAPE = 11.52%, RMSLE = 0.24) suggested the model transferability from MODIS to VIIRS. This transformation facilitated optimal usage of MODIS-related archive and enhanced the continuity of CTP estimation at moderate resolution. This study presents a practical method for spatiotemporal reconstruction of CTP using remote sensing, and contributes to better understandings of driving factors behind CTP variations in the Great Lakes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Drought events threaten freshwater reservoirs and agricultural productivity, particularly in semi-arid regions characterized by erratic rainfall. This study evaluates a novel technique for assessing the impact of drought on LULC variations in the context of climate change from 2018 to 2022. Various data sources were harnessed, encompassing Sentinel-2 satellite imagery for LULC classification, climate data from the CHIRPS and AgERA5 databases, geomorphological data from JAXA's ALOS satellite, and a drought indicator (Vegetation Health Index (VHI)) derived from MODIS data. Two classifier models, namely gradient tree boost (GTB) and random forest (RF), were trained and assessed for LULC classification, with performance evaluated by overall accuracy (OA) and kappa coefficient (K). Notably, the GTB model exhibited superior performance, with OA > 90% and a K > 0.9. Over the period from 2018 to 2022, Fez experienced LULC changes of 19.92% expansion in built-up areas, a 34.86% increase in bare land, a 17.86% reduction in water bodies, and a 37.30% decrease in agricultural land. Positive correlations of 0.81 and 0.89 were observed between changes in agricultural LULC, rainfall, and VHI. Furthermore, mild drought conditions were identified in the years 2020 and 2022. This study emphasizes the importance of AI and remote sensing techniques in assessing drought and environmental changes, with potential applications for improving existing drought monitoring systems., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Aerosol optical depth (AOD) serves as a crucial indicator for assessing regional air quality. To address regional and urban pollution issues, there is a requirement for high-resolution AOD products, as the existing data is of very coarse resolution. To address this issue, we retrieved high-resolution AOD over Kanpur (26.4499°N, 80.3319°E), located in the Indo-Gangetic Plain (IGP) region using Landsat 8 imageries and implemented the algorithm SEMARA, which combines SARA (Simplified Aerosol Retrieval Algorithm) and SREM (Simplified and Robust Surface Reflectance Estimation). Our approach leveraged the green band of the Landsat 8, resulting in an impressive spatial resolution of 30 m of AOD and rigorously validated with available AERONET observations. The retrieved AOD is in good agreement with high correlation coefficients (r) of 0.997, a low root mean squared error of 0.035, and root mean bias of - 4.91%. We evaluated the retrieved AOD with downscaled MODIS (MCD19A2) AOD products across various land classes for cropped and harvested period of agriculture cycle over the study region. It is noticed that over the built-up region of Kanpur, the SEMARA algorithm exhibits a stronger correlation with the MODIS AOD product compared to vegetation, barren areas and water bodies. The SEMARA approach proved to be more effective for AOD retrieval over the barren and built-up land categories for harvested period compared with the cropping period. This study offers a first comparative examination of SEMARA-retrieved high-resolution AOD and MODIS AOD product over a station of IGP., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The temperature of surface and epilimnetic waters, closely related to regional air temperatures, responds quickly and directly to climatic changes. As a result, lake surface temperature (LSWT) can be considered an effective indicator of climate change. In this study, we reconstructed and investigated historical and future LSWT across different scenarios for over 80 major lakes in mainland Southeast Asia (SEA), an ecologically diverse region vulnerable to climate impacts. Five different predicting models, incorporating statistical, machine and deep learning approaches, were trained and validated using ERA5 and CHIRPS climatic feature datasets as predictors and 8-day MODIS-derived LSWT from 2000 to 2020 as reference dataset. Best performing model was then applied to predict both historical (1986-2020) and future (2020-2100) LSWT for SEA lakes, utilizing downscaled climatic CORDEX-SEA feature data and multiple Representative Concentration Pathway (RCP). The analysis uncovered historical and future thermal dynamics and long-term trends for both daytime and nighttime LSWT. Among 5 models, XGboost results the most performant (NSE 0.85, RMSE 1.14 °C, MAE 0.69 °C, MBE -0.08 °C) and it has been used for historical reconstruction and future LSWT prediction. The historical analysis revealed a warming trend in SEA lakes, with daytime LSWT increasing at a rate of +0.18 °C/decade and nighttime LSWT at +0.13 °C/decade over the past three decades. These trends appeared of smaller magnitude compared to global estimates of LSWT change rates and less pronounced than concurrent air temperature and LSWT increases in neighbouring regions. Projections under various RCP scenarios indicated continued LSWT warming. Daytime LSWT is projected to increase at varying rates per decade: +0.03 °C under RCP2.6, +0.14 °C under RCP4.5, and +0.29 °C under RCP8.5. Similarly, nighttime LSWT projections under these scenarios are: +0.03 °C, +0.10 °C, and +0.16 °C per decade, respectively. The most optimistic scenario predicted marginal increases of +0.38 °C on average, while the most pessimistic scenario indicated an average LSWT increase of +2.29 °C by end of the century. This study highlights the relevance of LSWT as a climate change indicator in major SEA's freshwater ecosystems. The integration of satellite-derived LSWT, historical and projected climate data into data-driven modelling has enabled new and a more nuanced understanding of LSWT dynamics in relation to climate throughout the entire SEA region., Competing Interests: Declaration of competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Wildfires produce smoke that can affect an area >1000 times the burn extent, with far-reaching human health, ecologic, and economic impacts. Accurately estimating aerosol load within smoke plumes is therefore crucial for understanding and mitigating these impacts. We evaluated the effectiveness of the latest Collection 6.1 MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm in estimating aerosol optical depth (AOD) across the U.S. during the historic 2020 wildfire season. We compared satellite-based MAIAC AOD to ground-based AERONET AOD measurements during no-, light-, medium-, and heavy-smoke conditions identified using the Hazard Mapping System Fire and Smoke Product. This smoke product consists of maximum extent smoke polygons digitized by analysts using visible band imagery and classified according to smoke density. We also examined the strength of the correlations between satellite- and ground-based AOD for major land cover types under various smoke density levels. MAIAC performed well in estimating AOD during smoke-affected conditions. Correlations between MAIAC and AERONET AOD were strong for medium- (r = 0.91) and heavy-smoke (r = 0.90) density, and MAIAC estimates of AOD showed little bias relative to ground-based AERONET measurements (normalized mean bias = 3 % for medium, 5 % for heavy smoke). During two high AOD, heavy smoke episodes, MAIAC underestimated ground-based AERONET AOD under mixed aerosol (i.e., smoke and dust; median bias = -0.08) and overestimated AOD under smoke-dominated (median bias = 0.02) aerosol. MAIAC most overestimated ground-based AERONET AOD over barren land (mean NMB = 48 %). Our findings indicate that MODIS MAIAC can provide robust estimates of AOD as smoke density increases in coming years. Increased frequency of mixed aerosol and expansion of developed land could affect the performance of the MAIAC algorithm in the future, however, with implications for evaluating wildfire-associated health and welfare effects and air quality standards., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Lu Liang reports financial support was provided by National Science Foundation., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Several recent studies have indicated a strong relationship between extensive wildfires in Siberia and synoptic-scale weather processes. In this study, we used the concept of fire spread events to investigate the relationships between synoptic and surface-level weather conditions and extensive wildfires in Siberia during 2001-2022 using the MODIS and ERA5 reanalysis products. We analyzed the spatio-temporal features and seasonality of fire spread events in the region and found that most of them occurred in the central part of Eastern Siberia (ES) during the summer months, following the wildfire season in the region. A significant positive trend in the annual count of fire spread events was found in ES, coinciding with non-significant negative trends in cloud cover and precipitation and non-significant positive trends in air temperature and the fire weather index. Results show that in the ES region, which accounts for 46 % of the total number of considered events, the main driver of fire spread events is the formation of a positive geopotential height anomaly, which, based on the pattern of the meridional wind component, indicates the presence of an anticyclone above the area of fire spread events. The presence of a high-pressure zone causes a decrease in cloud cover over regions with fires, leading to increases in the amount of incoming solar radiation and surface air temperature and a decrease in precipitation. These conditions contribute to the drying of fuel and an increase in the overall fire hazard level, which in turn leads to an intensification of the combustion process, as evidenced by an increase in the radiative power of fires., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Lakes in taiga and tundra regions may be silently undergoing changes due to global warming. One of those changes is browning in lake color. The browning interacts with the carbon cycle, ecosystem dynamics, and water quality in freshwater systems. However, spatiotemporal variabilities of browning in these regions have not been well documented. Using MODIS remote sensing reflectance at near ultraviolet wavelengths from 2002 to 2021 on the Google Earth Engine platform, we quantified long-term browning trends across 7616 lakes (larger than 10 km 2 ) in taiga and tundra biomes. These lakes showed an overall decreased trend in browning (Theil-Sen Slope = 0.00015), with ∼36% of these lakes showing browning trends, and ∼1% of these lakes showing statistically significant (p-value <0.05) browning trends. The browning trends more likely occurred in small lakes in high latitude, low ground ice content regions, where air temperature increased and precipitation decreased. While temperature is projected to increase in response to climate change, our results provide one means to understand how biogeochemical cycles and ecological dynamics respond to climate change., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Pregnant women are highly vulnerable to environmental stressors such as ambient particulate matter (PM). Particularly during their 3rd trimester, their bodies undergo significant oxidative stresses. To further consolidate this dialogue into practice, the current study evaluated healthy pregnant women (n = 150 housewives; 18-40 years old; gestation age >36 weeks) from the highly polluted city of Yazd, Iran, from September to November 2021. The aerosol optical depth (AOD) data retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) were employed as influencing variables and validated using field-collected PM 10 data (r = 0.62, p-value <0.01). The links between blood platelet count, enzymes (SGOT, SGPT, LDH, bilirubin), metabolic products (urea and acid uric) and different combinations of AOD data were assessed using the Generalized Additive Model. The results showed a high temporal variability in AOD (0.94 ± 0.51) but a spatially stable distribution pattern. The mean AOD during the 3rd trimester, followed by that of the three-month peak, were identified as the most significant non-linear predictors, while the mean AOD during the 1st trimester and throughout the entire pregnancy showed no significant associations with any of the biomarkers. Considering the associations found between AOD variables and maternal oxidative stresses, urgent planning is required to improve the urban air quality for sensitive subpopulations., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The frequent occurrence of extreme weather events is one of the future prospects of climate change, and how ecosystems respond to extreme drought is crucial for response to climate change. Taking the extreme drought event in the Tropic of Cancer (Yunnan section) during 2009-2010 as a case study, used the standardized precipitation evapotranspiration index to analyse the impact of extreme drought on enhanced vegetation index (EVI), leaf area index (LAI) and gross primary productivity (GPP), and to analyzed the post extreme drought vegetation recovery status. The results indicate the following: (1) Due to the cumulative effects of drought and vegetation phenology, vegetation growth in the months of March to May in 2010 was more severely affected. (2) Compared to EVI and LAI, GPP is more sensitive to drought and can accurately indicate areas where drought has impacted vegetation. (3) Following an extreme drought event, 70% of the vegetation can recover within 3 months, while 2.87-6.57% of the vegetation will remain unrecovered after 6 months. (4) Cropland and grassland show the strongest response, with longer recovery times, while woodland and shrubland exhibit weaker responses and shorter recovery times. This study provides a reference for the effects of extreme drought on vegetation., (© 2024. The Author(s).)

Land subsidence, a silent death, occurs due to various factors like significant reduction in groundwater (GW) levels. It is a widespread phenomenon with irreparable consequences on buildings, infrastructures, and, in severe cases, groundwater aquifers. This study aims to assess the impact of river flow on the acceleration and control of land subsidence in an arid and semi-arid region. To achieve this goal, we analyze the interconnection between GW and SW and investigate the role of the Zayandeh-Rud River's drying up on land subsidence in the Isfahan-Borkhar aquifer in Iran's central plateau. To facilitate this assessment, we utilize the Interferometric Synthetic Aperture Radar (InSAR) technique to estimate the vertical deformation velocity of the aquifer (average land subsidence rate). The results show that the Isfahan-Borkhar aquifer has experienced a significant annual decline of more than 25 m, with an alarming rate exceeding 0.8 m/year. Our analysis of 31 piezometric wells (P-Wells) from 2000 to 2022 reveals a downward monotonic (in 16 P-Wells) and nonmonotonic (in 12 P-Wells) trend in groundwater table changes. Moreover, the GW table in the P-Wells near the river depends entirely on river flow. Furthermore, our findings indicate that river regulation exerts a dominant role in the control of land subsidence. Consequently, when water flows in the Zayandeh-Rud River, the rate of land subsidence declines significantly, particularly in urban regions. Therefore, maintaining a constant flow of water in the river can prevent or reduce ongoing land subsidence in Isfahan., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Satellite-observed land surface phenology (LSP) data have helped us better understand terrestrial ecosystem dynamics at large scales. However, uncertainties remain in comprehending LSP variations in Central Asian drylands. In this article, an LSP dataset covering Central Asia (45-100°E, 33-57°N) is introduced. This LSP dataset was produced based on Moderate Resolution Imaging Spectroradiometer (MODIS) 0.05-degree daily reflectance and land cover data. The phenological dynamics of drylands were tracked using the seasonal profiles of near-infrared reflectance of vegetation (NIRv). NIRv time series processing involved the following steps: identifying low-quality observations, smoothing the NIRv time series, and retrieving LSP metrics. In the smoothing step, a median filter was first applied to reduce spikes, after which the stationary wavelet transform (SWT) was used to smooth the NIRv time series. The SWT was performed using the Biorthogonal 1.1 wavelet at a decomposition level of 5. Seven LSP metrics were provided in this dataset, and they were categorized into the following three groups: (1) timing of key phenological events, (2) NIRv values essential for the detection of the phenological events throughout the growing season, and (3) NIRv value linked to vegetation growth state during the growing season. This LSP dataset is useful for investigating dryland ecosystem dynamics in response to climate variations and human activities across Central Asia., (© 2024 The Author(s).)

The Vegetation Health Index (VHI) is a metric used to assess the health and condition of vegetation, based on satellite-derived data. It offers a comprehensive indicator of stress or vigor, commonly used in agriculture, ecology, and environmental monitoring for forecasting changes in vegetation health. Despite its advantages, there are few studies on forecasting VHI as a future projection, particularly using up-to-date and effective machine learning methods. Hence, the primary objective of this study is to forecast VHI values by utilizing remotely sensed images. To achieve this objective, the study proposes employing a combined Convolutional Neural Network (CNN) and a specific type of Recurrent Neural Network (RNN) called Long Short-Term Memory (LSTM), known as ConvLSTM. The VHI time series images are calculated based on the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellites. In addition to the traditional image-based calculation, the study suggests using global minimum and global maximum values (global scale) of NDVI and LST time series for calculating the VHI. The results of the study showed that the ConvLSTM with a 1-layer structure generally provided better forecasts than 2-layer and 3-layer structures. The average Root Mean Square Error (RMSE) values for the 1-step, 2-step, and 3-step ahead VHI forecasts were 0.025, 0.026, and 0.026, respectively, with each step representing an 8-day forecast horizon. Moreover, the proposed global scale model using the applied ConvLSTM structures outperformed the traditional VHI calculation method., (© 2024. The Author(s).)

In the present work, the aerosol optical depth (AOD) at 550 nm of the moderate resolution imaging spectroradiometer (MODIS) onboard the Terra satellite was utilised to evaluate the AOD simulations of newly emerged general circulation models (GCMs) of coupled model intercomparison project-phase 6 (CMIP6) over the Indian landmass. Further, the AOD from the CMIP6 models has been compared with its previous generation models from CMIP5 to examine the extent of uncertainties in AOD with reference to the MODIS AOD datasets. The evolution of aerosols over India using the different shared socioeconomic pathways (SSPs) has also been studied till the year 2050. The results show that the CMIP5 and CMIP6 models underestimated the mean annual AOD of the Indian region as a whole. A multi-model mean (MMM) of thirteen GCMs from CMIP6 showed an underestimation of AOD by 40 to 60% over the Indo-Gangetic plains, while an overestimation of 60 to 80% in AOD was observed over the Peninsular and Central Indian regions in comparison with MODIS for the study period of 2001 to 2014. In future simulations, the pathway SSP370 has shown a significant increasing trend of AOD whereas SSP126 and SSP585 have shown significant decreasing trends of AOD by the year 2050. In the future, the changes in the AOD will mainly be contributed by the anthropogenic aerosols (AOA, BC, and Sulphates) emissions in all SSPs. The large bias of MMM with the MODIS requires further research in terms of analysing the accuracy of emission datasets that have been used to simulate the AODs by the CMIP6 models over the Indian region., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

An accurate prediction of the spatial distribution of phytoplankton biomass, as represented by Chlorophyll-a (CHL-a) concentrations, is important for assessing ecological conditions in the marine environment. This study developed a hyperparameter-optimized decision tree-based machine learning (ML) models to predict the geographical distribution of marine phytoplankton CHL-a in the Bay of Bengal. To predict CHL-a over a large spatial extent, satellite-derived remotely sensed data of ocean color features (CHL-a, colored dissolved organic matter, photosynthetically active radiation, particulate organic carbon) and climatic factors (nighttime sea surface temperature, surface absorbed longwave radiation, sea level pressure) from 2003 to 2022 are used to train and test the models. Results obtained from this study have shown the highest concentrations of CHL-a occurred near the Bay's coastal belts and river estuaries. Analysis revealed that aside from photosynthetically active radiation, organic components exhibited a stronger positive relationship with CHL-a than climatic features, which are correlated negatively. Results showed the chosen decision tree methods to all possess higher R 2 and lower root mean square error (RMSE) errors. Furthermore, XGBoost outperforms all other models in predicting the geographic distribution of CHL-a. To assess the model efficacy on seasonal basis, a best performing XGBoost model was validated in the Bay of Bengal region which has shown a good performance in predicting the spatial distribution of Chl-a as well as the pixel values during the summer, winter and monsoon seasons. This study provides the best ML model to researchers for predicting CHL-a in the Bay of Bengal. Further it helps to improve our knowledge of CHL-a spatial dynamics and assist in monitoring marine resources in the Bay of Bengal. It worth noting that the water quality in the Indian Ocean is very dynamic in nature, therefore, additional efforts are needed to test the efficacy of this study model over different seasons and spatial gradients., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Accurate estimation of grassland leaf area index (LAI), fractional vegetation cover (FVC), and aboveground biomass (AGB) is fundamental in grassland studies. The newly launched Ocean and Land Color Imager (OLCI) sensor onboard Sentinel-3 (S3) provides images with comparable spatial and spectral resolution with MODIS data. However, the use of S3 OLCI imageries for vegetation variable estimation is rarely evaluated. This study evaluated the potential of S3 OLCI and MODIS data for estimating grassland LAI, FVC, and AGB in the eastern Eurasian steppe. A Bayesian spatial model (Integrated Nested Laplace Approximation with Stochastic Partial Differential Equation, INLA-SPDE) was used to address spatial autocorrelation of in-situ observation data and to enhance our predictions. Our results showed that the models based on S3 OLCI data presented higher accuracy than models with MODIS data. The RMSEs decreased by 3.7-10.8 %, 3.7-7.5 %, and 1.6-14.2 % for LAI, FVC, and AGB predictions, respectively. Through combinations of multiple predictors, we confirmed the robustness of red edge bands for grassland variable estimation, the models employing red edge variables yielded 3.5 %, 3.2 %, and 0.4 % lower RMSEs than models with conventional visible and NIR bands for LAI, FVC, and AGB prediction, respectively. INLA-SPDE spatial model produced lower bias and higher prediction accuracy than random forest and random forests kriging method in most of the models; the INLA-SPDE predicted LAI and FVC maps also showed a better agreement with ground observations than MODIS and PROBA-V land products., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

Land cover changes have far-reaching effects on climate change and the evolution of ecological environments, making them important components of global change research. Based on MODIS land cover data (MCD12Q1), statistical parameters such as land cover dynamic degree, transition probability, and transition matrix were employed to analyse the spatiotemporal characteristics of global land cover changes from 2001 to 2020. The results indicated that land cover exhibits a latitudinal distribution in mid-latitude regions and a longitudinal distribution in high and low-latitude regions. During the study period, the areas of evergreen needleleaf forests, deciduous needleleaf forests, deciduous broadleaf forests, closed shrublands, and permanent wetlands exhibited fluctuating changes. Evergreen broadleaf forests and barren lands have decreased annually, whereas mixed forests, woody savannas, grasslands, urban and built-up areas, and cropland/natural vegetation mosaics have increased annually. In terms of the dynamic degree, the most significant changes occurred in deciduous broadleaf forests, closed shrublands, permanent wetlands, and cropland/natural vegetation mosaics. Additionally, there were significant variations in the types and regions of land cover changes and transitions. Evergreen needleleaf forests transitioned into grassland types, and the conversion between grassland types was the main transition from mid- to high- latitude regions. In tropical areas, evergreen broadleaf forests, open shrublands, and croplands have shifted towards grassland types as primary transitions. These findings provide significant implications for the in-depth exploration of land-atmosphere interactions, climate change, and numerical models., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

In order to enhance the retrieval accuracy of cloud top height (CTH) from MODIS data, neural network models were employed based on Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data. Three types of methods were established using MODIS inputs: cloud parameters, calibrated radiance, and a combination of both. From a statistical standpoint, models with combination inputs demonstrated the best performance, followed by models with calibrated radiance inputs, while models relying solely on calibrated radiance had poorer applicability. This work found that cloud top pressure (CTP) and cloud top temperature played a crucial role in CTH retrieval from MODIS data. However, within the same type of models, there were slight differences in the retrieved results, and these differences were not dependent on the quantity of input parameters. Therefore, the model with fewer inputs using cloud parameters and calibrated radiance was recommended and employed for individual case studies. This model produced results closest to the actual cloud top structure of the typhoon and exhibited similar cloud distribution patterns when compared with the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) CTHs from a climatic statistical perspective. This suggests that the recommended model has good applicability and credibility in CTH retrieval from MODIS images. This work provides a method to improve accurate CTHs from MODIS data for better utilization., Competing Interests: The authors declare no conflicts of interest.

On 15 January 2022, Hunga volcano erupted, creating an extensive and high-reaching umbrella cloud over the open ocean, hindering traditional isopach mapping and fallout volume estimation. In MODIS satellite imagery, ocean surface water was discolored around Hunga following the eruption, which we attribute to ash fallout from the umbrella cloud. By relating intensity of ocean discoloration to fall deposit thicknesses in the Kingdom of Tonga, we develop a methodology for estimating airfall volume over the open ocean. Ash thickness measurements from 41 locations are used to fit a linear relationship between ash thickness and ocean reflectance. This produces a minimum airfall volume estimate of 1.8 - 0.4 + 0.3 km 3 . The whole eruption produced > 6.3 km 3 of uncompacted pyroclastic material on the seafloor and a caldera volume change of 6 km 3 DRE. Our fall estimates are consistent with the interpretation that most of the seafloor deposits were emplaced by gravity currents rather than fall deposits. Our proposed method does not account for the largest grain sizes, so is thus a minimum estimate. However, this new ocean-discoloration method provides an airfall volume estimate consistent with other independent measures of the plume and is thus effective for rapidly estimating fallout volumes in future volcanic eruptions over oceans., Supplementary Information: The online version contains supplementary material available at 10.1007/s00445-024-01744-6., Competing Interests: Competing interestsThe authors declare no competing interests., (© The Author(s) 2024.)

Crop phenology provides crucial information for determining the appropriate timing of farm management practices and predicting crop yields. Satellite remote sensing has become a burgeoning tool for rapid phenological monitoring over wide spatial regions. However, there are significant timing gaps between the satellite-based phenological feature points and ground-observed physiological growing stages of the target. In this study, a dynamic offset-adjustment strategy that aims to improve the matching degree of the above two is proposed and tested with soybean across 16 states in the United States. A series of remotely sensed phenological transition dates that are characteristics of key growing stages of soybean were retrieved using MODIS time series data over the period 2000-2020 and the offset adjustments to the dates were identified by dynamically adjusting offset values till the minimum RMSE between the remote sensing-based and the ground-observed dates of physiological growing stages were obtained. The results indicated that the offset-adjustment strategy can significantly improve the alignments between remotely sensed phenological dates and field-based physiological growing stages of soybean in contrast to these without taking adjustment, with the average RMSE dropping by 58.58 %, 51.59 %, 31.15 %, 25.33 %, 24.67 % in the downturn, peak of season (POS), upturn, stabilization and recession dates, respectively. Among tested remotely sensed characteristics, the end of season (EOS) dates show the greatest alignment with its corresponding physiological growing stage, i.e., the dropping leaves stage. Comparison of the performance of the upturn date and start of season (SOS) in monitoring the date of the emerged stage indicates that the later one exhibits a better consistency with the ground-observed emerged stage after taking the adjustment, with the average RMSE dropping by 56.52 %. The proposed offset-adjustment strategy offers an approach for adjusting remotely sensed characteristics so to make them more consistent with the ground-observed crop physiological growing stages., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Snow cover is an essential element of the Himalayan region (Third Pole), and it represents the impacts induced by climate change. Recently, studies have reported significant variations in the Himalayan snow cover area, which may impact the livelihood of a large portion of the global population. Therefore, in this study, efforts were made to estimate the association between key climate stressors (CSs), i.e., temperature and precipitation, topography, and temporal variability of snow cover area (SCA) in the Parvati River basin (PRB) of the Indian western Himalayas. In this regard, the PRB has been classified into different elevation zones, i.e., zone I to zone V, ranging from 1100 to 6200 amsl. The databases such as MODIS, MEERA-2, and ASTER DEM V2 have been used to estimate the changes in the SCA and the CSs with changes in elevation and seasons. The linear regression analysis of the dataset from 2001 to 2017 revealed a significant association and increasing trend in the SCA of zone III. However, a significant association could not be established between the elevation and the SCA for the rest of the zones. A zonal seasonal trend investigation of the SCA observed an increasing trend in zones IV and V during the summer season due to a momentous rise in snowfall and a decline in temperature. The SCA has shown a significant declining trend only during the monsoon season in zones IV and V, which is due to a strong negative relationship between the SCA and the temperature of the region. These results demonstrate the amount of SCA in zones of high elevation of the PRB has been declining at an alarming rate, which could negatively influence glaciers' retreat in the near future. Hence, it can be estimated that the outcomes of the study will act as a base for future studies, regional policy formulation, and climate modelling that can further prevent future drastic or extreme events., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Floating marine debris (FMD) is one of the world's most concerning issues due to its potential impact on biodiversity, communities, and ecosystem services. FMD transport and concentrations are driven by fronts, generated by oceanographic processes, and the accumulation of FMD has been reported in gyres, eddies, tidal fronts, salinity fronts, and coastal fronts. This study explores the relationship between fronts and FMD accumulation in the Gulf of Maine (GoM) and the surrounding coastal areas (USA). Frontal edge detection algorithms were applied to sea surface temperature (SST) imagery from the Moderate-resolution Imaging Spectroradiometer (MODIS) between 2002 and 2012. Frontal location is spatially correlated with FMD concentrations collected by the Sea Education Association. Higher concentrations of FMD are associated with frontal frequencies (FF) of 5-10 %. FMD is trapped between fronts and the coastline in accumulation zones. These results highlight the need to consider coastal FMD hotspots, given these are areas of high biodiversity value., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Quantifying crop residue burning across India is imperative, owing to its adverse impacts on public health, the environment, and agricultural productivity. Specific information about the extent and characteristics of agricultural crop burning can verify the emission potential of agricultural systems and thereby facilitate targeted dissemination of agricultural innovations and support policymakers in mitigating the harmful effects. With a focus on district-level burning estimates, our study provides a comprehensive seasonal analysis of agricultural burning in India, including burned area, dry matter burned, and gaseous emissions for seven major crops from 2011 to 2020. To quantify the actual residues burned, we developed a remote sensing-based approach that incorporates the monitoring of agricultural burned area to quantify the actual residues burned. Including this satellite measure of the burned area greatly improves emissions estimates and minimizes error compared to typical approaches, which instead use an assumed fraction of total residues that are burned for each crop type. We estimated that emissions have increased by approximately 75 % for CO and Greenhouse gasses - CO 2 , CH 4 and N 2 O - from 2011 to 2020. Total CO 2 e emissions increased from ~19,340 Gg.yr -1 in 2011 to ~33,834 Gg.yr -1 in 2020. Most emissions occurred during end of the Kharif season, followed by Rabi, caused by the burning of rice and wheat residues. Among the Indian states, Punjab has the highest burning activity, with 27 % (2.0 million hectares) of its total cultivated area burned in 2020. Interestingly, Madhya Pradesh has emerged as the second-largest contributor, accounting for 30 % of the total burned area across India in 2020. Our study demonstrates how satellite data can be used to map agricultural residue burning at scale, and this information can provide crucial insights for policy framing, targeting, and interventions to manage agricultural residues without compromising air quality and climate., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dhanyalekshmi Pillai reports financial support was provided by International Maize and Wheat Improvement Center. Dhanyalekshmi Pillai reports financial support was provided by Max Planck Society., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Given its modern geographical and geomorphological characteristics, along with rapid socio-economic changes, the Nile Delta stands out as one of the world's most dynamic landscapes. The key drivers of the land use change in this region have been the reclamation of delta margins, changes in agricultural practices, and urban expansion. The present study aims to explore the variations in the seasonal daytime and nighttime trends of the land surface temperatures (LST) at this active agronomic system in response to the seasonal variations of vegetation cover as revealed by the normalized difference vegetation index (NDVI) during the past two decades. The data were exclusively acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument for the period from January 2001 to December 2021, where geospatial and statistical analyses were accomplished to construct a LST/NDVI spatio-temporal pattern throughout the Nile Delta. Results revealed a robust negative and a significant relationship between the NDVI and the diurnal LST with high regression coefficients (R 2 ) ranging from 0.78 to 0.97 (p value < 0.05). Maximal seasonal warming trends occurred during harvesting seasons (springs and falls), while the least warming was recorded during winters (the growing seasons). It was also observed that the nocturnal warming (0.72°C/decade) was almost as double as the corresponding value of the daytime trend (0.33°C/decade). The study recognized a seasonal climatic warming throughout the Nile Delta influenced by the human-induced land use change and agricultural practices., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Coarse-mode aerosol optical depths (cAODs) are critical for understanding the impact of coarse particle sizes, especially dust aerosols, on climate. Currently, the limited data length and high uncertainty of satellite products diminish the applicability of cAOD for climate research. Here, we propose a spatiotemporal coaction deep-learning model (SCAM) for the retrieval of global land cAOD (500 nm) from 2001-2021. In contrast to conventional deep-learning models, the SCAM considers the impacts of spatiotemporal feature interactions and can simultaneously describe linear and nonlinear relationships for retrievals. Based on these unique characteristics, the SCAM considerably improved global daily cAOD accuracies and coverages (R = 0.82, root-mean-square error [RMSE] = 0.04). Compared to official products from the multiangle imaging spectroradiometer (MISR), the moderate resolution imaging spectroradiometer (MODIS), and the polarization and directionality of Earth's reflectances (POLDER) instrument, as well as the physical-deep learning (Phy-DL) derived cAOD, the SCAM cAOD improved the monthly R from 0.44 to 0.88 and more accurately captured over the desert regions. Based on the SCAM cAOD, daily dust cases decreased over the Sahara, Thar Desert, Gobi Desert, and Middle East during 2001-2021 (>3 × 10 -3 /year). The SCAM-retrieved cAOD can contribute considerably to resolving the climate change uncertainty related to coarse-mode aerosols. Our proposed method is highly valuable for reducing uncertainties regarding coarse aerosols and climate interactions.

Dryland forests worldwide are increasingly threatened by drought stress due to climate change. Understanding the relationships between forest structure and function is essential for managing dryland forests to adapt to these changes. We investigated the structure-function relationships in four dryland conifer forests distributed along a semiarid to subhumid climatic aridity gradient. Forest structure was represented by leaf area index (LAI) and function by gross primary productivity (GPP), evapotranspiration (ET), and the derived efficiencies of water use (WUE = GPP/ET) and leaf area (LAE = GPP/LAI). Estimates of GPP and ET were based on the observed relationships between high-resolution vegetation indices from VENμS and Sentinel-2A satellites and flux data from three eddy covariance towers in the study regions between November 2015 to October 2018. The red-edge-based MERIS Terrestrial Chlorophyll Index (MTCI) from VENμS and Sentinel-2A showed strong correlations to flux tower GPP and ET measurements for the three sites (R 2 cal  > 0.91, R 2 val  > 0.84). Using our approach, we showed that as LAI decreased with decreasing aridity index (AI) (i.e., dryer conditions), estimated GPP and ET decreased (R 2  > 0.8 to LAI), while WUE (R 2  = 0.68 to LAI) and LAE increased. The observed global-scale patterns are associated with a variety of forest vegetation characteristics, at the local scale, such as tree species composition and density. However, our results point towards a canopy-level mechanism, where the ecosystem-LAI and resultant proportion of sun-exposed vs. shaded leaves are primary determinants of WUE and LAE along the studied climatic aridity gradient. This work demonstrates the importance of high-resolution (spatially and spectrally) remote sensing data conjugated with flux tower data for monitoring dryland forests and understanding the intricate structure-function interactions in their response to drying conditions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

The vegetation of a river basin is affected by various climate factors, such as precipitation and land surface temperature (LST). This study explores the best machine learning model for the prediction of normalized difference vegetation index (NDVI) with LST and precipitation as input parameters. The study also determines the correlation between NDVI, LST, and precipitation of the Mahanadi basin from 2003 to 2021. Monthly precipitation data was extracted from the Center for Hydrometeorology and Remote Sensing (CHRS) portal. The Moderate Resolution Imaging Spectroradiometer (MODIS) products were used to derive the LST and NDVI using Google Earth Engine (GEE). Four different machine learning models were used to predict the NDVI of the Mahanadi basin: linear regression (LR), random forest (RF), support vector regression (SVR), and k-nearest neighbors (KNN). The coefficient of determination (R 2 ), root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), and explained variance score (EVS) were calculated to evaluate the performance of the models. The results show that the RF model has the highest R 2 value in both the training and testing sets among these models, indicating that it is the most optimal among these models for predicting NDVI. The SVR model has the lowest RMSE value in the training set, but the KNN model has the lowest RMSE value in the testing set. The results also show that there is a positive correlation between precipitation and NDVI, a negative correlation between precipitation and LST, and between NDVI and LST. This study provides insights into the relationship between NDVI, LST, and precipitation, and the best machine-learning model for predicting NDVI. The findings of this study can be used to improve the management of river basins and to predict the effects of climate change on vegetation., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Ecological drought monitoring is important for regional status assessment and protection of water resources. In this study, we constructed a new ecological drought index, the kernel temperature vegetation drought index (kTVDI), by using the kernel normalized vegetation index (kNDVI) to improve the temperature vegetation drought index (TVDI) in Inner Mongolia. We further analyzed the spatial and temporal distribution of ecological drought in Inner Mongolia during 2000-2022 and the future trend of ecological drought by using segmented linear regression model, Theil-Sen median, Mann-Kendall test, and Hurst index. The results showed that kTVDI performed better in monitoring ecological drought than TVDI. From 2000 to 2022, kTVDI showed a decreasing trend in the growing season in Inner Mongolia, but the change was not significant, and a sudden change occurred in 2016, and the wetting trend after the sudden change was more obvious. During the study period, ecological drought in 23.6% of the areas of Inner Mongolia showed an aggravating trend, and ecological drought was alleviated in 46.5% of the area. In the future, ecological drought would be exacerbated in the eastern part but alleviated in the central and western parts of Inner Mongolia.

The Hindu-Kush-Himalaya is abode to numerous severely flood-prone mountainous stretches that distress vulnerable communities and cause massive destruction to physical entities such as hydropower projects. Adopting commercial flood models for replicating the dynamics of flood wave propagation over such regions is a major constraint due to the financial economics threaded to flood management. For the first instance, the present study attempts to investigate whether advanced open-source models are skillful in quantifying flood hazards and population exposure over mountainous terrains. While doing so, the performance of 1D-2D coupled HEC-RAS v6.3 (the most recent version developed by the U.S. Army Corps of Engineers) is reconnoitred for the first time in flood management literature. The most flood-prone region in Bhutan, the Chamkhar Chhu River Basin, housing large groups of communities and airports near its floodplains, is considered. HEC-RAS v6.3 setups are corroborated by comparing them with 2010 flood imagery derived from MODIS through performance metrics. The results indicate a sizable portion of the central part of the basin experiences very-high flood hazards with depth and velocities exceeding 3 m, and 1.6 m/s, respectively, during 50, 100, and 200-year return periods of floods. To affirm HEC-RAS, the flood hazards are compared with TUFLOW at 1D and 1D-2D coupled levels. The hydrological similarity within the channel is reflected at river cross-sections (NSE and KGE > 0.98), while overland inundation and hazard statistics differ, however, very less significant (<10 %). Later, flood hazards extracted from HEC-RAS are fused with the World-Pop population to estimate the degree of population exposure. The study ascertains that HEC-RAS v6.3 is an efficacious option for flood risk mapping over geographically arduous regions and can be preferred in resource-constrained environments ensuring a minimal degree of anomaly., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Floating macroalgae of Sargassum horneri (S. horneri) in the East China Sea (ECS) has increased in recent years, with ocean warming being one of the driving factors. Yet their possible origins, based on a literature review, are unclear. Here, using multi-sensor high-resolution remote sensing data and numerical experiments for the period of 2015-2023, we show two possible origins of the ECS floating S. horneri, one being local near the Zhejiang coast with initiation in January-February and the other being remote (> 800 km from the first) in the Bohai Sea with initiation in June-November. While their drifting pathways are revealed in the sequential remote sensing imagery, numerical experiments suggest that S. horneri from the remote origin (Bohai Sea) can hardly meander through the strong Yangtze River frontal zone, which may serve as a "wall" to prevent trespassing of surface floating seaweed to the south of the frontal zone, where S. horneri has a local origin. PLAIN LANGUAGE SUMMARY: Sargassum horneri (S. horneri) is a brown macroalgae (seaweed) abundant in surface waters of the East China Sea (ECS), which can serve as a moving habitat, but can also cause major beaching events and environmental problems. Knowledge of its origins is important to help implement mitigation strategies and understand possible ecological impacts along its drifting pathways. Using high-resolution remote sensing images and numerical experiments, we track floating S. horneri in space and time between 2015 and 2023. Two possible origins are identified, one being far away from the ECS and the other being local, both of which are known to have benthic S. horneri. The study also reveals how S. horneri are transported from their source regions resulting in large-scale distributions previously observed in medium-resolution satellite imagery., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)