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Abstract: The future of coastal wetlands will depend on the combined effects of climate change and human impacts from urbanization and coastal management. Disentangling the effects of these factors is difficult, but satellite imagery archives provide a way to track biological and physical changes in wetlands over recent decades to reveal how coastal wetlands have been changing in response to climate and human drivers. In this study, we used Landsat to monitor the conditions of 32 coastal wetlands in southern California from 1984 to 2019 and identify environmental and human drivers of these trends. Wetland conditions were characterized by vegetation greenness, using the normalized difference vegetation index (NDVI), and by habitat composition, derived from areal estimates of wetland and subtidal habitats. Overall, wetlands displayed three types of long‐term response: greening and gaining wetland (10), greening and losing wetland (16), and browning and losing wetland (6). Regional environmental drivers with overall positive effects on wetland NDVI were sea level, wave height, and precipitation, whereas stream discharge, vapor pressure deficit, and air temperature had negative or nonlinear effects. Wetland area change was primarily correlated with sea level, but response was highly contextual among sites. Negative trends in wetland NDVI and area were more common in larger sites with low elevations and in sites with open inlets. Restoration had mixed effects, with only half of the restored sites showing positive changes in NDVI and wetland area post‐restoration. The important work of managing and restoring urban coastal wetlands is complicated by variability and context and requires us to account for the influence of humans and climate as we build a regional understanding of historic, present, and future wetland health.

Glaciers in the Pamir region are experiencing rapid melting and receding due to climate change, which has a significant implication for the Amu Darya river basin. Predominantly, surging glaciers, which undergo unpredictable advances, are potentially leading to the obstruction of high-altitude river channels and also glacial lake outburst floods. This study investigates surging glaciers in the Pamir region, specifically focusing on the Medvezhiy (M) and Russian Geographical Society (RGS) glaciers, using long-term reanalysis data records, unmanned aerial vehicles (UAVs), and satellite data during the last decades. Significant changes in regional climate were identified, including increasing precipitation rates (13.3 mm/year) and land surface temperature (0.1°C/decade). Between 1991 to 2023, the RGS glacier experienced a significant advancement around 1448.3 ± 30.0 m, while the Medvezhiy glacier showed a decrease of −703.5 ± 30.0 m. There is a substantial increase in the number (from 19 to 75) and area (from 4889.7 ± 0.6 m2 to 15345.5 ± 0.6 m2) of RGS lakes along with supra-glacier ponds based on a comparison of ArcGIS base map in 2011 and high-resolution UAV data in 2023. For M glacier, number of lakes increased from 4 to 22 but the lake area declined from 10715.2 ± 0.6 to 365.6 ± 0.6 m2. It was noted that the largest lake in 2011 with an area of 10406.4 ± 0.6 m2 at the southeastern portion of the glacier was not observed in 2023 due to outburst. Both the glaciers have substantially impacted the river flow (Abdukahor river) by obstructing a significant proportion of river channel in recent years and might cause outburst floods. These findings enhance the understanding of glacier dynamics and their impacts on the surrounding areas, emphasizing the urgent need for continued monitoring and appropriate management strategies, with a specific focus on surging glaciers and the associated risks. [ABSTRACT FROM AUTHOR]

Accurate crop classification is crucial for agricultural monitoring and decision-making. Remote sensing's ultimate goal is the precise extraction and classification of crops. Based on a cloud platform, the study area of Guntur district, Andhra Pradesh India, presents a multi-crop classification approach using Sentinel-2 satellite imagery and machine learning techniques. The study area encompasses a diverse agricultural region with three major crop types. After pre-processing, spectral and textural features were extracted. It compares the traditional four machine learning algorithms employed, adding the NDVI, NDBI, MNDWI, and BSI vegetation indices for multi-crop classification enhances accuracy, and offers diverse and complementary information. The overall classification accuracy achieved 95%, with individual crop accuracies ranging from 85 to 96%. The scalable and simple classification method proposed in this research gives full play to the advantages of cloud platforms in data and operation, and the traditional machine learning compared with other algorithms can effectively improve the classification accuracy, and individual areas of crop production are calculated. The results underscored the reliability of GEE-based crop mapping in the region, demonstrating a satisfactory level of classification accuracy, surpassing 97% across distinct time intervals in overall accuracy values, Kappa coefficients, and F1-Score. [ABSTRACT FROM AUTHOR]

Surface soil moisture is a key hydrologic state variable that greatly influences the global environment and human society. Its significant decrease in the Mediterranean region, registered since the 1950s, and expected to continue in the next century, threatens soil health and crops. Microwave remote sensing techniques are becoming a key tool for the implementation of climate-smart agriculture, as a means for surface soil moisture retrieval that exploits the correlation between liquid water and the dielectric properties of soil. In this study, a workflow in Google Earth Engine was developed to estimate surface soil moisture in the agricultural fields of the Marche region (Italy) through Synthetic Aperture Radar data. Firstly, agricultural areas were extracted with both Sentinel-2 optical and Sentinel-1 radar satellites, investigating the use of Dual-Polarimetric Entropy-Alpha decomposition's bands to improve the accuracy of radar data classification. The results show that Entropy and Alpha bands improve the kappa index obtained from the radar data only by 4% (K = 0.818), exceeding optical accuracy in urban and water areas. However, they still did not allow to reach the overall optical accuracy (K = 0.927). The best classification results are reached with the total dataset (K = 0.949). Subsequently, Water Cloud and Tu Wien models were implemented on the crop areas using calibration parameters derived from literature, to test if an acceptable accuracy is reached without in situ observation. While the first model's accuracy was inadequate (RMSD = 12.3), the extraction of surface soil moisture using Tu Wien change detection method was found to have acceptable accuracy (RMSD = 9.4). [ABSTRACT FROM AUTHOR]

Drought is considered to be the most complex but least understood of all natural hazards, affecting more people. Its reappearance in drought-prone areas every few years is almost certain. Also, they lack sudden and easily identified onsets and terminations. Under the background of global climate change, the impact from drought exhibits the characteristics of complexity and multi-process. It has a significant impact on the water resources, agriculture, society, and economy hence needs attention. Vegetation Condition Index (VCI) is used for observing the change in vegetation that causes agricultural drought. Since the land surface temperature has minimum influence from cloud contamination and humidity in the air, so the Temperature Condition Index (TCI) is used for studying the temperature change. Dryness or wetness of soil is a major indicator for agriculture and a comprehensive assessment of vegetation and temperature stress is achieved from MODIS satellite data in Google Earth Engine (GEE) platform for pre and post monsoon season from 2008 to 2022 (15- year period). Vegetation Condition Index (VCI) is used for observing the change in vegetation that causes agricultural drought. Since the land surface temperature has minimum influence from cloud contamination and humidity in the air, so the Temperature Condition Index (TCI) is used for studying the temperature change. The research also incorporates precipitation data from WorldClim to investigate its influence on the Vegetation Health Index (VHI). Mann Kendall trend analysis is employed to examine spatio-temporal variations in drought severity, for both pre-monsoon and post-monsoon seasons. The results emphasize the sensitivity of VHI to shifts in rainfall patterns, providing valuable insights for drought monitoring and management. In essence, this study enhances understanding of drought dynamics and emphasizes the significance of Remote Sensing data and climate information for effective drought assessment and mitigation strategies. [ABSTRACT FROM AUTHOR]

Coastal wetlands are important areas with valuable natural resources and diverse biodiversity. Due to the influence of both natural factors and human activities, the landscape of coastal wetlands undergoes significant changes. It is crucial to systematically monitor and analyze the dynamic changes in coastal wetland cover over a long-term time series. In this paper, a long-term time series coastal wetland remote sensing classification process was proposed, which integrated feature selection and sample migration. Utilizing Google Earth Engine (GEE) and Landsat TM/ETM/OLI remote sensing image data, the selected feature set is combined with the sample migration method to generate the training sample set for each target year. The Simple Non-Iterative Clustering-Random Forest (SNIC-RF) model was ultimately employed to accurately map wetland classes in the Liaohe Estuary from 1985 to 2023 and quantitatively evaluate the spatio-temporal pattern change characteristics of wetlands in the study area. The findings indicate that: (1) After feature selection, the accuracy of the model reached 0.88, and the separation of the selected feature set was good. (2) After sample migration, the overall accuracy of sample classification in the target year ranged from 87 to 94%, along with Kappa coefficients of 0.84 to 0.92, thereby ensuring the validity of classification sample migration. (3) SNIC-RF classification results showed better performance of wetland landscape. Compared with RF classification, the overall classification accuracy was increased by 0.69-5.82%, and the Kappa coefficient was increased by 0.0087-0.0751. (4) From 1985 to 2023, there has been a predominant trend of natural wetlands being converted into artificial wetlands. In recent years, this transition has occurred more gently. Finally, this study offers valuable insights into understanding changes and trends in the surface ecological environment of the Liaohe Estuary. The research method can be extended to other types of wetland classification and the comprehensive application of coastal wetland in hydrology, ecology, meteorology, soil, and environment can be further explored on the basis of this research, laying strong groundwork for shaping policies on ecological protection and restoration. [ABSTRACT FROM AUTHOR]

Sustainable Development Goal-14 (SDG-14) directly demands the protection, conservation, restoration, and sustainable management of the global mangrove ecosystem. Assessment of the development Pakistan has made toward the sustainable management of mangrove ecosystems necessitates the remote sensing-based evaluation of national-level mangrove cover. Using Google Earth Engine (GEE) for geoprocessing 12,000+ 10-m high-spatial resolution Sentinel-2 time-series images (2016-23) and applying random forest (RF) classifier, the current research provides the latest spatial distribution of mangroves along Pakistan's coastline and changes observed for a duration of 8 years. Additionally, this research provides the first spatiotemporal health assessment of Pakistan's national mangrove cover as well. Rational analysis of the results indicated splitting the entire timeline based on two seasons (Jan-Jun and Jul-Dec). Results revealed an overall increase of 1,210 km2 (2023) in the mangrove cover on the national-level; a 3.42 km2 average annual increase from 2016 (1,186 km2). Mangrove gain/loss assessment based on the land use land cover (LULC) transition matrix illustrated 223 km2 gain and 199 km2 loss; a 24 km2 net gain. Declines of 20.28% and 7.91% were found in maximum- and mean-NDVI (2016-23), depicting the deteriorating mangrove health conditions. Likewise, significant Sen's slope analysis (p < 0.05) indicated that 88.8% of all the mangrove-NDVI pixels exhibited an overall decrease, whereas 11.2% pixels showed an overall increase (2016-23). It was concluded that despite showing a growth in the extent, mangrove cover in Pakistan has shown a decline in health, primarily due to deforestation for urban operations and sea-level rise, still making them vulnerable and potentially leading to a disrupted ecosystem, including carbon release in the atmosphere. This study will assist in the formulation of mangrove conservation and management strategies, whereas future research can explore the potentials of land surface temperature (LST) and evapotranspiration in combination to the NDVI for an in-depth analysis of the health status of mangroves. [ABSTRACT FROM AUTHOR]

ABSTRACT The COVID‐19 pandemic, which originated in China at the end of 2019, escalated into a global crisis by March 2020. To mitigate its spread, governments worldwide implemented strict lockdown measures. While these lockdowns had adverse social, economic, and health impacts, they also led to significant environmental improvements in many regions. India's urban environment also significantly improved during lockdown. This study investigates the changes in Land Surface Temperature (LST) across eight major Indian cities, each representing diverse climatic and physiographic zones: Delhi, Dehradun, Lucknow, Kolkata, Bhopal, Bhubaneshwar, Mumbai, and Hyderabad. It aims to enhance the understanding of how sudden reductions in anthropogenic activities influence urban temperatures. The LST was computed for the lockdown period of April to May 2020 and was compared with the pre‐lockdown years of 2018 and 2019 and the post‐lockdown year of 2021, utilizing Landsat thermal data processed through the mono‐window algorithm. The results exhibit significant reductions in LST during the lockdown period. Cities like Delhi, Dehradun, and Lucknow experienced a reduction of 6°C, 5°C, and 4°C, respectively, in LST from pre‐lockdown to lockdown periods. In contrast, cities like Bhopal, Bhubaneswar, Mumbai, and Hyderabad experienced a reduction of around 2°C–3°C. However, the city of Kolkata showed an increase of 3°C from 2019 to 2020. These results highlight the substantial influence of human activities on urban thermal environments and underline the potential benefits of reducing anthropogenic impacts to improve urban thermal well‐being. [ABSTRACT FROM AUTHOR]

Water depth affects many aspects of wetland ecology, hydrology, and biogeochemistry. However, acquiring water depth data is often difficult due to inadequate monitoring or insufficient funds. Satellite-derived bathymetry (SBD) data provides cost-effective and rapid estimates of the water depth across large areas. However, the applicability and performance of these techniques for inland wetlands have not been thoroughly evaluated. Here, a time series of bathymetry data for inland wetlands in West Kentucky and Tennessee were derived from Landsat 8 images using two widely used empirical models, Stumpf and a modified Lyzenga model and three machine learning models, Random Forest, Support Vector regression, and k-Nearest Neighbor. We processed satellite images using Google Earth Engine and compared the performance of water depth estimation among the different models. The performance assessment at validation sites resulted in an RMSE in the range of 0.18–0.47 m and R2 in the range of 0.71–0.83 across all models for depths <3.5 m, while in depths >3.5 m, an RMSE = 1.43–1.78 m and R2 = 0.57–0.65 was obtained. Overall, the empirical models marginally outperformed the machine learning models, although statistical tests indicated the results from all the models were not significantly different. Testing of the models beyond the domain of the training and validation data suggested the potential for model transferability to other regions with similar hydrologic and environmental characteristics. [ABSTRACT FROM AUTHOR]

In recent years, the Chilean agricultural sector has undergone significant changes, but there is a lack of data that can be used to accurately identify these transformations. A study was conducted to assess the effectiveness of different spatial resolutions used by global land cover products (MODIS, ESA and Dynamic World (DW)), in addition to the demi-automated methods applied to them, for the identification of agricultural areas, using the publicly available agricultural survey for 2021. It was found that lower-spatial-resolution collections consistently underestimated crop areas, while collections with higher spatial resolutions overestimated them. The low-spatial-resolution collection, MODIS, underestimated cropland by 46% in 2021, while moderate-resolution collections, such as ESA and DW, overestimated cropland by 39.1% and 93.8%, respectively. Overall, edge-pixel-filtering and a machine learning semi-automated reclassification methodology improved the accuracy of the original global collections, with differences of only 11% when using the DW collection. While there are limitations in certain regions, the use of global land cover collections and filtering methods as training samples can be valuable in areas where high-resolution data are lacking. Future research should focus on validating and adapting these approaches to ensure their effectiveness in sustainable agriculture and ecosystem conservation on a global scale. [ABSTRACT FROM AUTHOR]

High-resolution and accurate precipitation data are essential for hydrological, meteorological, and ecological research at the watershed scale. However, in regions with complex terrain and significant rainfall variability, the limited number of rain gauge stations (RGS) is insufficient, and the spatial resolution of existing satellite precipitation data is too low to capture detailed precipitation patterns at the watershed scale. To address this issue, the downscaling of satellite precipitation products has become an effective method to obtain high-resolution precipitation data. This study proposes a monthly downscaling method based on a random forest model, aiming to improve the resolution of precipitation data in cloudy and rainy regions at mid-to-low latitudes. We combined the Google Earth Engine (GEE) platform with a local Python environment, introducing cloud cover characteristics into traditional downscaling variables (latitude, longitude, topography, and vegetation index). The TRMM data were downscaled from 25 km to 1 km, generating high-resolution monthly precipitation data for the Dongting Lake Basin from 2001 to 2019. Furthermore, we analyzed the spatiotemporal variation characteristics of precipitation in the study area. The results show the following: (1) In cloudy and rainy regions, our method improves resolution and detail while maintaining the accuracy of precipitation data; (2) The response of monthly precipitation to environmental variables varies, with cloud cover characteristics contributing more to the downscaling model than vegetation characteristics, helping to overcome the lag effect of vegetation characteristics; and (3) Over the past 20 years, there have been significant seasonal trends in precipitation changes in the study area, with a decreasing trend in winter and spring (January–May) and an increasing trend in summer and autumn (June–December). These results indicate that the proposed method is suitable for downscaling monthly precipitation data in cloudy and rainy regions of the Dongting Lake Basin. [ABSTRACT FROM AUTHOR]

East African lakes support the food and water security of millions of people. Yet, a lack of continuous long-term water quality data for these waterbodies impedes their sustainable management. While satellite-based water quality retrieval methods have been developed for lakes globally, African lakes are typically underrepresented in training data, limiting the applicability of existing methods to the region. Hence, this study aimed to (1) assess the accuracy of existing and newly developed water quality band algorithms for East African lakes and (2) make satellite-derived water quality information easily accessible through a Google Earth Engine application (app), named LAndsat water QUality retrieval tool for east African lakes (LAQUA). We collated a dataset of existing and newly collected in situ surface water quality samples from seven lakes to develop and test Landsat water quality retrieval models. Twenty-one published algorithms were evaluated and compared with newly developed linear and quadratic regression models, to determine the most suitable Landsat band algorithms for chlorophyll-a, total suspended solids (TSS), and Secchi disk depth (SDD) for East African lakes. The three-band algorithm, parameterised using data for East African lakes, proved the most suitable for chlorophyll-a retrieval (R2 = 0.717, p < 0.001, RMSE = 22.917 μg/L), a novel index developed in this study, the Modified Suspended Matter Index (MSMI), was the most accurate for TSS retrieval (R2 = 0.822, p < 0.001, RMSE = 9.006 mg/L), and an existing global model was the most accurate for SDD estimation (R2 = 0.933, p < 0.001, RMSE = 0.073 m). The LAQUA app we developed provides easy access to the best performing retrieval models, facilitating the use of water quality information for management and evidence-informed policy making for East African lakes. [ABSTRACT FROM AUTHOR]

A precise and up-to-date Land Use and Land Cover (LULC) valuation serves as the fundamental basis for efficient land management. Google Earth Engine (GEE), with its numerous machine learning algorithms, is now the most advanced open-source global platform for rapid and accurate LULC classification. Thus, this study explores the dynamics of the LULC changes between 1993 and 2023 using Landsat imagery and the machine learning algorithms in the Google Earth Engine (GEE) platform. Focus group discussion and key informant interviews were also used to get further data regarding LULC dynamics. Support Vector Machine (SVM), Random Forest (RF), and Classification and Regression Trees (CART) were demonstrated for LULC classification. Six LULC types (agricultural land, grazingland, shrubland, built-up area, forest and bareland) were identified and mapped for 1993, 2003, 2013, and 2023. The overall accuracy and kappa coefficient demonstrated that the RF using images comprising auxiliary variables (spectral indices and topographic data) performed better than SVM and CART. Despite being the most common type of LULC, agricultural land shows a trend of shrinking during the study period. The built-up area and bareland exhibits a trend of progressive expansion. The amount of forest and shrubland has decreased over the last 20 years, whereas grazinglands have exhibited expanding trends. Population growth, agricultural land expansion, fuelwood collection, charcoal production, built-up areas expansion, illegal settlement and intervention are among causes of LULC shifts. This study provides reliable information about the patterns of LULC in the Robit watershed, which can be used to develop frameworks for watershed management and sustainability. [ABSTRACT FROM AUTHOR]

Forest Canopy Height (FCH) is a crucial parameter that offers valuable insights into forest structure. Spaceborne LiDAR missions provide accurate FCH measurements, but a significant challenge is their point-based measurements lacking spatial continuity. This study integrated ICESat-2's ATL08-derived FCH values with multi-temporal and multi-source remote sensing (RS) datasets to generate continuous FCH maps for northern forests in Iran. Sentinel-1/2, ALOS-2 PALSAR-2, and FABDEM datasets were prepared in Google Earth Engine (GEE) for FCH mapping, each possessing unique spatial and geometrical characteristics that differ from those of the ATL08 product. Given the importance of accurately representing the geometrical characteristics of the ATL08 segments in modeling FCH, a novel Weighted Kernel (WK) approach was proposed in this paper. The WK approach could better represent the RS datasets within the ATL08 ground segments compared to other commonly used resampling approaches. The correlation between all RS data features improved by approximately 6% compared to previously employed approaches, indicating that the RS data features derived after convolving the WK approach are more predictive of FCH values. Furthermore, the WK approach demonstrated superior performance among machine learning models, with random forests outperforming other models, achieving a coefficient of determination (R2) of 0.71, root mean square error (RMSE) of 4.92 m, and mean absolute percentage error (MAPE) of 29.95%. Furthermore, in contrast to previous studies using only summer datasets, this study included spring and autumn data from Sentinel-1/2, resulting in a 6% increase in R2 and a 0.5-m decrease in RMSE. The proposed methodology filled the research gaps and improved the accuracy of FCH estimations. [ABSTRACT FROM AUTHOR]

This study is motivated in response to persistent seasonal burning of rice crop-residues in various parts of Thailand. The major aims are to assess dry above ground biomass (dry-AGB) and estimate carbon stock in the rice-crop area of Buak Kang and Chae Chang subdistricts, San Kamphaeng district, Chiang Mai using Google Earth Engine (GEE), as well as develop a web platform for visualization and monitoring. The methodology includes satellite imagery and field-data acquisition, factor identification, model development, accuracy assessment, and launching an open-access web platform. The dry weight and carbon content of the sample rice plants collected during fieldwork were analyzed in a science laboratory. The study establishes a robust model for estimating dry-AGB by correlating the results from the laboratory with the backscatter coefficients of various parameters derived from Sentinel-1 imagery captured on 24.09.2023 (Ascending). The best fit model has R² value of 0.852, which estimated total 16019.13 Tons dry-AGB and 3524.21 Tons carbon stock in the total identified rice-crop area of 34.61 km². The GEE apps based web platform facilitates calculation and visualization of dry-AGB and carbon stock supporting effective monitoring and management of rice-crop in the study area. [ABSTRACT FROM AUTHOR]

The development of anthropic activities and climate change effects impact worldwide species' ecosystems and habitats. Habitats' adequate prediction can be an important tool to assess current and future trends. In addition, it allows strategies development for their conservation. The Neltuma pallida of the forest region in northern Peru, although very significant, has experienced a decline in recent years. The objective of this research is to evaluate the current and future distribution and conservation status of N. pallida in the Peruvian dry forest under climate change (Location: Republic of Peru). A total of 132 forest presence records and 10 variables (bioclimatic, topographic, and soil) were processed and selected to obtain the current and future distribution for 2100, using Google Earth Engine (GEE), RStudio, and MaxEnt. The area under the curve values fell within the range of 0.93–0.95, demonstrating a strong predictive capability for both present and future potential habitats. The findings indicated that the likely range of habitats for N. pallida was shaped by factors such as the average temperature of wettest quarter, maximum temperature of warmest month, elevation, rainfall, and precipitation of driest month. The main suitable areas were in the central regions of the geographical departments of Tumbes, Piura, and Lambayeque, as well as in the northern part of La Libertad. It is critical to determine the habitat suitability of plant species for conservation managers since this information stimulates the development of policies that favor sustainable use programs. In addition, these results can contribute significantly to identify new areas for designing strategies for populations conserving and recovering with an ecological restoration approach. [ABSTRACT FROM AUTHOR]

Assessing Leaf Area Index (LAI) is one of the key indicators to study and understand cropland's productivity. To this end, Sentinel-2 (S2) ideally serves to monitor LAI given its high spatial and temporal resolution. Thanks to the cloud computing prowess of the revolutionary Google Earth Engine (GEE), assessing the temporal changes in LAI over a large area can be achieved quickly, making crop health monitoring in near real-time plausible. To achieve this, this work has integrated the Gaussian Process Regression (GPR) algorithm and the PROSAIL model into GEE to map LAI values for the crops being cultivated accurately. The Automated Radiative Transfer Mode Operator (ARTMO) software generated PROSAIL simulation spectra. The GPR model was trained using the PROSAIL simulation spectra and optimized through the Euclidean distance-Based Diversity (EBD) Active Learning (AL) method. The final GPR model was validated against in-situ data collected on 23rd February, 2023 from the research farms of the Indian Agricultural Research Institute (IARI), New Delhi, India. Through ARTMO, the validated GPR model produced an estimated LAI map over croplands managed by IARI, with an accuracy of 0.688 R2 value, and these LAI values and a precision of 0.96 R2 were observed between the LAI values observed in the ARTMO LAI map and the GEE-generated map. This model was then run into GEE to study the temporal changes in LAI values for Mustard, Chickpea, and Wheat, three prominent crops for the Rabi season. These temporal LAI patterns can assist in understanding the cropland's phenological changes. Thanks to GEE, crop phenology monitoring can be efficiently realized using temporal LAI estimates. [ABSTRACT FROM AUTHOR]

The study of the spatio-temporal distribution and evolution trends of water resources in large regions plays an important role in the study of regional water resource planning, regional economic and social development, and water disasters. In this study, a Landsat multi-index relationship and water probability thresholding method is developed based on the Google Earth Engine (GEE) platform, which can support the integration of multiple Landsat satellites. The algorithm jointly combines multiple remote sensing metrics along with the calculation of water probability to produce an interannual water body product for the Jianghan Plain on a 20-year time series. The results indicate that the Landsat multi-index relationship algorithm used in this study has high accuracy in extracting long-term water bodies in extensive, flat terrain areas such as the Jianghan Plain, with an overall accuracy (OA) of 97.23%. By analyzing the water body products and landscape patterns, we have identified the following features: (1) From 2002 to 2021, the changes in river water bodies in the Jianghan Plain were relatively small, and some lakes experienced a shrinkage in area. Overall, there is a strong correlation between water distribution and precipitation. (2) The complexity index of water bodies shows a strong negative correlation with effective irrigation area and population, indicating a strong mutual influence between water bodies and socio-economic activities. (3) Through the study of the distribution characteristics of built-up areas and the water system, it was found that for large rivers, the larger the size of the river, the more built-up areas are nearby. Most extensive built-up areas are located near large rivers. This study contributes to providing methods and data support for urban planning, water resource management, and disaster research in the Jianghan Plain. [ABSTRACT FROM AUTHOR]

In the western Andes, climate changes have led to drastic ecological changes during the Pleistocene and Holocene. Given the debate surrounding precipitation pattern changes and the lack of research on lakes in the Chilean Altiplano, this study aims to assess recent climate changes. The paper presents an innovative methodology based on Google Earth Engine (GEE), utilizing fluctuations in water levels in endorheic lakes as natural precipitation indicators. Three lakes (Chungará, Miscanti, and Miniques) in isolated drainage systems were studied, where changes in water levels directly reflect rainfall variations. Data from Landsat-OLI 8, Landsat-ETM+, Landsat-TM 5, and MODIS spanning 31 years were processed using the Google Earth Engine platform. The shapes of the water bodies were extracted using hue saturation value (HSV) composites. The surface areas of the lakes were compared with precipitation data from national meteorological stations and the Tropical Rainfall Measuring Mission (TRMM) using linear regression analyses. Both lake area and rainfall volume showed a decrease over time, with varying trends depending on environmental conditions. However, the analysis consistently indicates a reduction in the area and volume of Chilean lakes corresponding to observed rainfall patterns over the past three decades. [ABSTRACT FROM AUTHOR]

Regular detection of land-use and land-cover (LULC) changes with high accuracy is necessary for natural resources management and sustainable urban planning. The produced LULC maps from Google Earth Engine (GEE) also illustrate the transformation of the LULC for the respective landscape over time. The selected study area, Cottbus City and the Spree-Neisse district in northeastern Germany, has undergone significant development over the past decades due to various factors, including urbanization and industrialization; also, the landscape has been converted in some areas for post-mining activities. Detection of LULC changes that have taken place over the last few decades thus plays a vital role in quantifying the impact of these factors while improving the knowledge of these developments and supporting the city planners or urban management officials before implementing further long-term development initiatives for the future. Therefore, the study aims to (i) detect LULC changes for the time slices 2002 and 2022, testing machine learning (ML) algorithms in supervised and unsupervised classification for Landsat satellite imageries, and (ii) validate the newly produced LULC maps with the available regional database (RDB) from the federal and state statistical offices, Germany, and the Dynamic World (DW) near real-time 10 m global LULC data set powered by artificial intelligence (AI). The results of the Random Forest (RF) and the Smilecart classifiers of supervised classification using Landsat 9 OLI-2/TIRS-2 in 2022 demonstrated a validation accuracy of 88% for both, with Kappa Index (KI) of 83% and 84%, respectively. Moreover, the Training Overall Accuracy (TOA) was 100% for both years. The wekaKMeans cluster of the unsupervised classification also illustrated a similar transformation pattern in the LULC maps. Overall, the produced LULC maps offered an improved representation of the selected region's various land-cover classes (i.e., vegetation, waterbodies, built areas, and bare ground) in the last two decades (20022 to 2022). [ABSTRACT FROM AUTHOR]

[Objective] The objectives of this study are to clarify the evolution characteristics of eutrophication and the status of comprehensive treatment of water in Dianchi Lake as well as the main driving factors, so as to provide scientific support for water pollution control and ecological restoration in Dianchi Lake. [Methods] The Dianchi Lake was taken as the research object. GEE and remote sensing monitoring were used to analyze the spatiotemporal variation of cyanobacteria bloom in Dianchi Lake during 2002-2021 and the relationship between cyanobacteria bloom and environmental factors. [Results] The variation of cyanobacteria bloom area showed significant interannual characteristics during 2002-2021, and the peak values of cyanobacteria bloom area in 2006, 2013 and 2016 were 1.952×107, 2.010×10 and 1.743×107 m², respectively. The valley values of cyanobacteria bloom area in 2005, 2010 and 2014 were 1.243×107 m², 1.156×107 m² and 1.163×107 m², respectively. After 2016, it fluctuated steadily and had no obvious change trend on the whole. In 2021, the area was 1.709 × 107 m². At the monthly scale, the bloom of cyanobacteria mainly occurred from July to December, and reached its peak in August. It continued to decrease from November to February, and reached its trough in February. Through statistical analysis of the characteristics of cyanobacteria bloom and environmental factors in Dianchi Lake, the results showed that the cyanobacteria bloom had a positive correlation with temperature and precipitation, a significant negative correlation with wind direction (p< 0.05), and the closest significant correlation with wind direction (p<0.05). Among the four meteorological factors, the wind direction was the most important, and the easterly wind direction had a significant promoting effect on the coverage of cyanobacteria bloom in Dianchi Lake. [Conclusion] The overall fluctuation of cyanobacteria in Dianchi Lake remained stable without significant evolution trend, and the important driving role of wind direction and wind speed on cyanobacteria outbreak should be further considered in the future in term of water ecological restoration of Dianchi Lake. [ABSTRACT FROM AUTHOR]

The Tonle Sap Lake (TSL) landscape is a region of vast natural resources and biological diversity in the heart of Southeast Asia. In addition to serving as the foundation for a highly productive fisheries system, this landscape is home to numerous globally threatened species. Despite decades of recognition by several government and international agencies and the fact that nine protected areas have been established within this region, natural land cover such as grasslands have experienced considerable decline since the turn of the century. This project used local expert knowledge to train and validate a random forest supervised classification of Landsat satellite imagery using Google Earth Engine. The time series of thematic maps were then used to quantify the conversion of grasslands to croplands between 2004 and 2023. The classification encompassed a 10 km buffer surrounding the landscape, an area of nearly 3 million hectares. The average overall accuracy for these thematic maps was 82.5% (78.5–87.9%), with grasslands averaging 76.1% user's accuracy. The change detection indicated that over 207,281 ha of grasslands were lost over this period (>59.5% of the 2004 area), with approx. 89.3% of this loss being attributed to cropland expansion. The results of this project will inform conservation efforts focused on local-scale planning and the management of commercial agriculture. [ABSTRACT FROM AUTHOR]

Presently, a significant portion of the global population resides in metropolitan areas where air pollution levels are usually high. The primary objective is to utilize satellite data to identify the concentration of pollutants, unlike the traditional method which utilizes a series of ground-based detectors. The study aims to analyze the distribution of different pollutants in the air over the Gujarat state, India. The study comprised the utilization of Sentinel 5-P data sets for mapping Carbon monoxide (CO), Nitrogen dioxide (NO2), Sulfur dioxide (SO2), Methane (CH4), and Formaldehyde (HCHO). Google Earth Engine platform was used for the processing of satellite imagery and maps were prepared using ArcGIS 10.3. The satellite data sets of different pollutants were processed and assessed for three seasons i.e., winter, summer, and monsoon to analyze the effect of climatic conditions as well on the concentration level over the study area. The concentration of CO ranges between 0.0295–0.0401 mol/m2. The average concentration of SO2 is 0.00047 mol/m2 whereas the average concentration of NO2 ranges from 0–0.00021 mol/m2 and formaldehyde concentrations values range from 0.00015 to 0.00026 mol/m2 over the year. The concentration range for methane is 1780–1940 ppb for the study area. The results exhibit that the northern part of Gujarat mainly consisting of Kutch, Banas Kantha, and Patan renders the lowest concentration of all air pollutants while the central and southern regions consisting of cities like Valsad, Surat, Bharuch, Vadodara, and Ahmedabad have recorded the peak values in all the seasons. The findings suggest that the increase in the levels of different pollutants is caused by human activities, industrialization, and urbanization. [ABSTRACT FROM AUTHOR]

[Display omitted] • Retrieval of key mangrove traits based on Sentinel-2 surface reflectance data. • Gaussian Processes Regression (GPR) was trained on PROSAIL simulations and active learning intelligent sampling techniques. • GPR model gives insight in the most relevant bands and delivers per-pixel uncertainty interval. • Optimal GPR models were implemented in Google Earth Engine for end-user mapping over space and time. • Time series gap-filling via Whittaker smoothing enabled temporal decomposition to monitor mangrove trait dynamics. As mangrove forests are facing escalating threats from anthropogenic and natural stressors, Earth observation capabilities are providing an unprecedented view of these vital ecosystems. This study aimed to leverage the extensive archive and continuous stream of Sentinel-2 (S2) data and its integration into cloud-computing platforms to map key mangrove traits, indicative of ecosystem health and resilience. Specifically, a hybrid retrieval model implemented in Google Earth Engine (GEE) was operationalized for quantitative estimation of multiple mangrove traits from S2. For this purpose, the leaf-to-canopy radiative transfer model (RTM) PROSAIL in synergy with non-parametric Gaussian Processes Regression (GPR) were selected for hybrid retrieval models of effective Leaf Area Index (LAI e), Leaf Chlorophyll Content (C a b ), Leaf Water Thickness (C w), and Leaf Dry Matter (C m). GPR models were trained and optimized with a PROSAIL-based simulated dataset in combination with active learning sampling techniques to reduce the training dataset towards the most informative samples. Validation against in situ measurements showed high accuracy models achieved for LAI e ( R 2 = 0.693, NRMSE = 12.03 %) and Cab ( R 2 = 0.689, NRMSE = 13.29 %), while moderate performances were obtained for Cm ( R 2 = 0.597, NRMSE = 16.37 %) and Cw ( R 2 = 0.442, NRMSE = 16.66 %). The GPR models were subsequently implemented into GEE for processing of a time series data stream ranging from 2019 to 2023, together with the cloud-gap filling by applying a Whittaker smoother. Seamless maps of four key mangrove traits were generated simultaneously with per-pixel uncertainty estimation. The entire GEE-based workflow enables time series decomposition to uncover trends and seasonal dynamics. Supported by field validation, this study demonstrates a scalable approach for spatiotemporal monitoring of key mangrove traits using hybrid retrieval models via GEE implementation. These advances may contribute to national-scale monitoring in Vietnam and beyond to support mangrove ecosystem protection efforts and management thereof. [ABSTRACT FROM AUTHOR]

The U-Chang-Shi (Urumqi-Changji-Shihezi) urban cluster, located at the heart of Xinjiang, boasts abundant natural resources. Over the past two decades, rapid urbanization, industrialization, and climate change have significantly threatened the region's ecological livability. To comprehensively, scientifically, and objectively assess the ecological livability of this area, this study leverages the Google Earth Engine (GEE) platform and multi-source remote sensing data to develop a comprehensive evaluation metric: the Remote Sensing Ecological Livability Index (RSELI). This aims to examine the changes in the ecological livability of the U-Chang-Shi urban cluster from 2000 to 2020. The findings show that despite some annual improvements, the overall trend in ecological livability is declining, indicating that the swift pace of urbanization and industrialization has placed considerable pressure on the region's ecological environment. Land use changes, driven by urban expansion and the growth in agricultural and industrial lands, have progressively encroached upon existing green spaces and water bodies, further deteriorating the ecological environment. Additionally, the region's topographical features have influenced its ecological livability; large terrain fluctuations have made soil erosion and geological disasters common. Despite the central plains' vast rivers providing ample water resources, over exploitation and ill-conceived hydrological constructions have led to escalating water scarcity. The area near the Gurbantunggut Desert in the north, with its extremely fragile ecological environment, has long been unsuitable for habitation. This study provides a crucial scientific basis for the future development of the U-Chang-Shi urban cluster and hopes to offer theoretical support and practical guidance for the sustainable development and ecological improvement of the region. [ABSTRACT FROM AUTHOR]

The Tigris and Euphrates Basin (TEB) encompasses a wide area, and due to its different geographical and political conditions, each environmental factor in different conditions has different effects on the process of surface water changes. Accordingly, in this research, we aim to evaluate the trend of surface water changes in this basin in 2001-2021 by using the time series of 16 different parameters and the products available on the Google Earth Engine (GEE) platform. Based on the findings, the general trend of surface water changes is increasing, and the water area has reached 8605.9 km2 in 2001 to 10021.8 km2 in 2021. Nevertheless, the spatial-temporal changes of water have been different because the extent of lakes and wetlands in the southern areas of the basin has decreased drastically. On the contrary, it has increased upstream of the basin due to the expansion of various dams and channels. In addition, our findings indicated a high correlation between climatic variables, especially evapotranspiration, and temperature, with temporal changes in water in the region. Thus, the impact of global climate changes on the hydrology and environment of the basin highlights the importance and high sensitivity of the major lakes in the region, such as Razazeh, Tharthar, Hamrin, and Habbaniyah, to climate changes. The present research results may be used to assess surface water in other regions and provide valuable information for the planning and management of global surface water resources. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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Snow plays a crucial role in the global water and energy cycles, and its melting process can have a series of impacts on hydrological or climatic systems. Accurately capturing the timing of snowmelt runoff is essential for the utilization of snow resources and the early warning of snow-related disasters. A synthetic aperture radar (SAR) offers an effective means for capturing snowmelt runoff onset dates (RODs) over large areas, but its accuracy under different land cover types remains unclear. This study focuses on the Sierra Nevada Mountains and surrounding areas in the western United States. Using a total of 3117 Sentinel-1 images from 2017 to 2023, we extracted the annual ROD based on the Google Earth Engine (GEE) platform. The satellite extraction results were validated using the ROD derived from the snow water equivalent (SWE) data from 125 stations within the study area. The mean absolute errors (MAEs) for the four land cover types—tree cover, shrubland, grassland, and bare land—are 24, 18, 18, and 16 d, respectively. It indicates that vegetation significantly influences the accuracy of the ROD captured from Sentinel-1 data. Furthermore, we analyze the variation trends in the ROD from 2017 to 2023. The average ROD captured by the stations shows an advancing trend under different land cover types, while that derived from Sentinel-1 data only exhibits an advancing trend in bare land areas. It indicates that vegetation leads to a delayed trend in the ROD captured by using Sentinel-1 data, opposite to the results from the stations. Meanwhile, the variation trends of the average ROD captured by both methods are not significant (p > 0.05) due to the impact of the extreme snowfall in 2023. Finally, we analyze the influence of the SWE on RODs under different land cover types. A significant correlation (p < 0.05) is observed between the SWE and ROD captured from both stations and Sentinel-1 data. An increase in the SWE causes a delay in the ROD, with a greater delay rate in vegetated areas. These findings will provide vital reference for the accurate acquisition of the ROD and water resources management in the study area. [ABSTRACT FROM AUTHOR]

This study addresses the pressing need for flood extent and exposure information in data-scarce and vulnerable regions, with a specific focus on West Africa, particularly Senegal. Leveraging the Google Earth Engine (GEE) platform and integrating data from the Sentinel-1 SAR, Global Surface Water, HydroSHEDS, the Global Human Settlement Layer, and MODIS land cover type, our primary objective is to delineate the extent of flooding and compare this with flooding for a one-in-a-hundred-year flood event, offering a comprehensive assessment of exposure during the period from July to October 2022 across Senegal's 14 regions. The findings underscore a total inundation area of 2951 square kilometers, impacting 782,681 people, 238 square kilometers of urbanized area, and 21 square kilometers of farmland. Notably, August witnessed the largest flood extent, reaching 780 square kilometers, accounting for 0.40% of the country's land area. Other regions, including Saint-Louis, Ziguinchor, Fatick, and Matam, experienced varying extents of flooding, with the data for August showing a 1.34% overlap with flooding for a one-in-a-hundred-year flood event derived from hydrological and hydraulic modeling. This low percentage reveals the distinct purpose and nature of the two approaches (remote sensing and modeling), as well as their complementarity. In terms of flood exposure, October emerges as the most critical month, affecting 281,406 people (1.56% of the population). The Dakar, Diourbel, Thiès, and Saint-Louis regions bore substantial impacts, affecting 437,025; 171,537; 115,552; and 77,501 people, respectively. These findings emphasize the imperative for comprehensive disaster preparation and mitigation efforts. This study provides a crucial national-scale perspective to guide Senegal's authorities in formulating effective flood management, intervention, and adaptation strategies. [ABSTRACT FROM AUTHOR]

The long-term monitoring of wetland ecosystem functioning is critical because wetlands, which provide multiple services, can be affected by human activities and climate change. The aim of this study was to monitor wetland ecosystem functioning in the long term using the Landsat archive. Four contrasting, Ramsar wetlands were selected in boreal, temperate, arid, and tropical areas. First, the annual sum of the normalized difference vegetation index (NDVI-I) was calculated as an indicator of annual net primary productivity for the period 1984–2021 using the continuous change detection and classification (CCDC) algorithm. Next, the influence of the number of Landsat images and class of land use and land cover (LULC) on the accuracy of the CCDC was investigated. Finally, correlations between annual NDVI-I and climate were analyzed. The results revealed that NDVI-I accuracy was influenced mainly by the LULC class and to a lesser extent by the number of cloud-free Landsat observations. Infra- and inter-site variations in NDVI-I were high and showed an overall increasing trend. NDVI-I was positively correlated with the mean temperature. This study shows that this approach applied in contrasting sites is robust for the long-term monitoring of wetland ecosystem functioning and can be used to improve the implementation of international biodiversity conservation policies. [ABSTRACT FROM AUTHOR]

Rice production is pivotal for ensuring global food security. In Pakistan, rice is not only the dominant Kharif crop but also a significant export commodity that significantly impacts the state's economy. However, Pakistan faces challenges such as abrupt climate change and the COVID-19 pandemic, which affect rice production and underscore the need for predictive models for informed decisions aimed at improving productivity and ultimately the state's economy. This article presents an innovative deep learning-based hybrid predictive model, ResNet50-LSTM, designed to forecast rice yields in the Gujranwala district, Pakistan, utilizing multi-modal data. The model incorporates MODIS satellite imagery capturing EVI, LAI, and FPAR indices along with meteorological and soil data. Google Earth Engine is used for the collection and preprocessing of satellite imagery, where the preprocessing steps involve data filtering, applying region geometry, interpolation, and aggregation. These preprocessing steps were applied manually on meteorological and soil data. Following feature extraction from the imagery data using ResNet50, the three LSTM model configurations are presented with distinct layer architectures. The findings of this study exhibit that the model configuration featuring two LSTM layers with interconnected cells outperforms other proposed configurations in terms of prediction performance. Analysis of various feature combinations reveals that the selected feature set (EVI, FPAR, climate, and soil variables) yields highly accurate results with an R2 = 0.9903, RMSE = 0.1854, MAPE = 0.62%, MAE = 0.1384, MRE = 0.0062, and Willmott's index of agreement = 0.9536. Moreover, the combination of EVI and FPAR is identified as particularly effective. Our findings revealed the potential of our framework for globally estimating crop yields through the utilization of publicly available multi-source data. [ABSTRACT FROM AUTHOR]

Delineating the distribution of tidal flats in the Yangtze River Delta region is of considerable significance for the ecological environment research and sustainable development of the region. However, the tidal flat ecology is greatly threatened by rapid economic development and human disturbance. Information on the tidal flat ecology is necessary for tidal flat extraction studies in Jiangsu Province and Shanghai City. Therefore, using imagery acquired by Sentinel-2 sensors along the coasts of Jiangsu Province and Shanghai City and the Google Earth Engine Cloud Platform, we established a multilayer automatic decision tree classification model based on the maximum spectral index composite algorithm and Otsu algorithm to extract tidal flats. A total of 1011 scene images, acquired from January 1 to December 31, 2021, were collected to generate tidal flat maps of Jiangsu Province and Shanghai City. The overall accuracy obtained by establishing the confusion matrix was 96.1%. The tidal flat areas of Jiangsu Province and Shanghai City in 2021 were 222347 and 41097 hectares, respectively, among which the tidal flat area of Jiangsu Province was 5.4 times that of Shanghai City. Moreover, we compared the results with real images and existing products, which further explained the rationality of the tidal flat results of this study. The 10-m-resolution tidal flat map of Jiangsu Province and Shanghai City obtained in this study can provide support for the sustainable policy management of tidal flats in the Yangtze River Delta region. [ABSTRACT FROM AUTHOR]

This study investigates quantifiable and explicable changes in Land Use and Land Cover (LULC) within the context of a freshwater wetland, Hakaluki Haor, in Bangladesh. The haor is a vital RAMSAR site and Ecologically Critical Area (ECA), which needs to be monitored to investigate LULC change patterns for future management interventions. Leveraging Landsat satellite data, the Google Earth Engine Database, CART algorithm, ArcGIS 10.8 and the R programming language, this study analyses LULC dynamics from 2000 to 2023. It focuses explicitly on seasonal transitions between the rainy and dry seasons, unveiling substantial transformations in cumulative LULC change patterns over the study period. Noteworthy changes include an overall reduction (~51%) in Water Bodies. Concurrently, there is a significant increase (~353%) in Settlement areas. Moreover, vegetation substantially declines (71%), while Crop Land demonstrates varying coverage. These identified changes underscore the dynamic nature of LULC alterations and their potential implications for the environmental, hydrological, and agricultural aspects within the Hakaluki Haor region. The outcomes of this study aim to provide valuable insights to policymakers for formulating appropriate land-use strategies in the area. [ABSTRACT FROM AUTHOR]

Vegetation plays a crucial role in terrestrial ecosystems, and the FVC (Fractional Vegetation Coverage) is a key indicator reflecting the growth status of vegetation. The accurate quantification of FVC dynamics and underlying driving factors has become a hot topic. However, the scale effect on FVC changes and driving factors has received less attention in previous studies. In this study, the changes and driving factors of FVC at multiple scales were analyzed to reveal the spatial and temporal change in vegetation in the Henan section of the Yellow River basin. Firstly, based on the pixel dichotomy model, the FVC at different times and spatial scales was calculated using Landsat-8 data. Then, the characteristics of spatial and temporal FVC changes were analyzed using simple linear regression and CV (Coefficient of Variation). Finally, a GD (Geographic Detector) was used to quantitatively analyze the driving factors of FVC at different scales. The results of this study revealed that (1) FVC showed an upward trend at all spatial scales, increasing by an average of 0.55% yr−1 from 2014 to 2022. The areas with an increasing trend in FVC were 10.83% more than those with a decreasing trend. (2) As the spatial scale decreased, the explanatory power of the topography factors (aspect, elevation, and slope) for changes in FVC was gradually strengthened, while the explanatory power of climate factors (evapotranspiration, temperature, and rainfall) and anthropogenic activities (night light) for changes in FVC decreased. (3) The q value of evapotranspiration was always the highest across different scales, peaking notably at a spatial scale of 1000 m (q = 0.48). [ABSTRACT FROM AUTHOR]

The urban heat island (UHI) effect generated by the development of high-speed urbanization has become one of the major problems affecting the urban ecological environment. As the main body of urbanization in China, China's urban agglomerations are the core areas of urban heat island effect. The purpose of this study is to study the spatial–temporal characteristics and driving factors of surface urban heat island in 19 urban agglomerations in China, with a view to providing theoretical references for the prevention of urban thermal environmental risks. Based on Google Earth Engine (GEE), this paper estimated the surface urban heat island intensity (SUHII) of 19 urban agglomerations in China from 2003 to 2019 using MODIS land surface temperature (LST) data. Correlation analysis and regression analysis were used to explore the correlation between the change of SUHII and driving factors. Finally, the driving factors of SUHII were detected by the geo-detector model. Results showed that (1) the SUHII of 19 urban agglomerations in arid and semi-arid areas of northwestern China is higher than that in humid areas of eastern and southeastern China. (2) The SUHII of 19 urban agglomerations in China generally shows a decreasing trend, and the spatial variation of the change trend is significant. (3) There are positive correlations between SUHII and reference evapotranspiration (ET0), population density (POP), gross domestic product (GDP), nitrogen dioxide (NO2), ozone (O3), and ultraviolet aerosol index (UVAI); negative correlations with normalized difference vegetation index (NDVI), DEM, sulfur dioxide (SO2), carbon monoxide (CO), and formaldehyde (HCHO); the correlations all pass the significance test of P < 0.05 and are statistically significant. (4) The factor detection results showed that NDVI, land cover type (LC), and UVAI were the main driving factors of SUHII. The interaction detection results showed that the interaction between O3 and UVAI had the most significant impact on SUHII. [ABSTRACT FROM AUTHOR]

Algeria, the main fire hotspot on the southern rim of the Mediterranean Basin, lacks a complete fire dataset with official fire perimeters, and the existing one contains inconsistencies. Preprocessed global and regional burned area (BA) products provide valuable insights into fire patterns, characteristics, and dynamics over time and space, and into their impact on climate change. Nevertheless, they exhibit certain limitations linked with their inherent spatio-temporal resolutions as well as temporal and geographical coverage. To address the need for reliable BA information in Algeria, we systematically reconstructed, validated, and analyzed a 40-year (1984–2023) BA product (NEALGEBA; North Eastern ALGeria Burned Area) at 30 m spatial resolution in the typical Mediterranean ecosystems of this region, following international standards. We used Landsat data and the BA Mapping Tools (BAMTs) in the Google Earth Engine (GEE) to map BAs. The spatial validation of NEALGEBA, performed for 2017 and 2021 using independent 10 m spatial resolution Sentinel-2 reference data, showed overall accuracies > 98.10%; commission and omission errors < 8.20%; Dice coefficients > 91.90%; and relative biases < 3.44%. The temporal validation, however, using MODIS and VIIRS active fire hotspots, emphasized the limitation of Landsat-based BA products in temporal fire reporting accuracy terms. The intercomparison with five readily available BA products for 2017, by using the same validation process, demonstrated the overall outperformance of NEALGEBA. Furthermore, our BA product exhibited the highest correspondence with the ground-based BA estimates. NEALGEBA currently represents the most continuous and reliable time series of BA history at fine spatial resolution for NE Algeria, offering a significant contribution to further national and international fire hazard and impact assessments and acts as a reference dataset for contextualizing future weather extremes, such as the 2023 exceptional heat wave, which we show not to have led to the most extreme fire year over the last four decades. [ABSTRACT FROM AUTHOR]

The estimation of water quality properties through satellite remote sensing relies on (1) the optical characteristics of the water body, (2) the resolutions (spatial, spectral, radiometric and temporal) of the sensor and (3) algorithm(s) applied. More than 80% of global water bodies fall under Case I (open ocean) waters, dominated by scattering and absorption associated with phytoplankton in the water column. Globally, previous studies show significant correlations between satellite-based retrieval methods and field measurements of absorbing and scattering constituents, while limited research from Australian coastal water bodies appears. This study presents a methodology to extract chlorophyll a properties from surface waters from near-coastal environments, within 2 km of coastline, in Tasmania, south-eastern Australia. We use general purpose, global, long-time series, multi-spectral satellite data, as opposed to ocean colour-specific sensor data. This approach may offer globally applicable tools for combining global satellite image archives with in situ field sensors for water quality monitoring. To enable applications from local to global scales, a cloud-based geospatial analysis workflow was developed and tested on several sites. This work represents the initial stage in developing a semi-automated near-coastal water-quality workflow using easily accessed, fully corrected global multi-spectral datasets alongside large-scale computation and delivery capabilities. Our results indicated a strong correlation between the in situ chlorophyll concentration data and blue-green band ratios from the multi-spectral sensor. In line with published research, environment-specific empirical models exhibited the highest correlations between in situ and satellite measurements, underscoring the importance of tailoring models to specific coastal waters. Our findings may provide the basis for developing this workflow for other sites in Australia. We acknowledge the use of general purpose multi-spectral data such as the Sentinel-2 and Landsat Series, their corrections and algorithms may not be as accurate and precise as ocean colour satellites. The data we are using are more readily accessible and also have true global coverage with global historic archives and regular, global collection will continue at least 10 years in the future. Regardless of sensor specifications, the retrieval method relies on localised algorithm calibration and validation using in situ measurements, which demonstrates close-to-realistic outputs. We hope this approach enables future applications to also consider these globally accessible and regularly updated datasets that are suited to coastal environments. [ABSTRACT FROM AUTHOR]

Soil erosion represents a complex ecological issue that is present on a global level, with negative consequences for environmental quality, the conservation and availability of natural resources, population safety, and material security, both in rural and urban areas. To mitigate the harmful effects of soil erosion, a soil erosion map can be created. Broadly applied in the Balkan Peninsula region (Serbia, Bosnia and Herzegovina, Croatia, Slovenia, Montenegro, North Macedonia, Romania, Bulgaria, and Greece), the Erosion Potential Method (EPM) is an empirical erosion model that is widely applied in the process of creating soil erosion maps. In this study, an innovation in the process of the identification and mapping of erosion processes was made, creating a coefficient of the types and extent of erosion and slumps (φ), representing one of the most sensitive parameters in the EPM. The process of creating the coefficient (φ) consisted of applying remote sensing methods and satellite images from a Landsat mission. The research area for which the satellite images were obtained and thematic maps of erosion processes (coefficient φ) were created is the area of the Federation of Bosnia and Herzegovina and the Brčko District (situated in Bosnia and Herzegovina). The Google Earth Engine (GEE) platform was employed to process and retrieve Landsat 7 Enhanced Thematic Mapper plus (ETM+) and Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) satellite imagery over a period of ten years (from 1 January 2010 to 31 December 2020). The mapping and identification of erosion processes were performed based on the Bare Soil Index (BSI) and by applying the equation for fractional bare soil cover. The spatial–temporal distribution of fractional bare soil cover enabled the definition of coefficient (φ) values in the field. An accuracy assessment was conducted based on 190 reference samples from the field using a confusion matrix, overall accuracy (OA), user accuracy (UA), producer accuracy (PA), and the Kappa statistic. Using the confusion matrix, an OA of 85.79% was obtained, while UA ranged from 33% to 100%, and PA ranged from 50% to 100%. Applying the Kappa statistic, an accuracy of 0.82 was obtained, indicating a high level of accuracy. The availability of a time series of multispectral satellite images for each month is a crucial element in monitoring the occurrence of erosion processes of various types (surface, mixed, and deep) in the field. Additionally, it contributes significantly to decision-making, strategies, and plans in the domain of erosion control work, the development of plans for identifying erosion-prone areas, plans for defense against torrential floods, and the creation of soil erosion maps at local, regional, and national levels. [ABSTRACT FROM AUTHOR]

Ground hazards are a significant problem in the global economy, costing millions of dollars in damage each year. Railroad tracks are vulnerable to ground hazards like flooding since they traverse multiple terrains with complex environmental factors and diverse human developments. Traditionally, flood-hazard assessments are generated using models like the Hydrological Engineering Center–River Analysis System (HEC-RAS). However, these maps are typically created for design flood events (10, 50, 100, 500 years) and are not available for any specific storm event, as they are not designed for individual flood predictions. Remotely sensed methods, on the other hand, offer precise flood extents only during the flooding, which means the actual flood extents cannot be determined beforehand. Railroad agencies need daily flood extent maps before rainfall events to manage and plan for the parts of the railroad network that will be impacted during each rainfall event. A new approach would involve using traditional flood-modeling layers and remotely sensed flood model outputs such as flood maps created using the Google Earth Engine. These new approaches will use machine-learning tools in flood prediction and extent mapping. This new approach will allow for determining the extent of flood for each rainfall event on a daily basis using rainfall forecast; therefore, flooding extents will be modeled before the actual flood, allowing railroad managers to plan for flood events pre-emptively. Two approaches were used: support vector machines and deep neural networks. Both methods were fine-tuned using grid-search cross-validation; the deep neural network model was chosen as the best model since it was computationally less expensive in training the model and had fewer type II errors or false negatives, which were the priorities for the flood modeling and would be suitable for developing the automated system for the entire railway corridor. The best deep neural network was then deployed and used to assess the extent of flooding for two floods in 2020 and 2022. The results indicate that the model accurately approximates the actual flooding extent and can predict flooding on a daily temporal basis using rainfall forecasts. [ABSTRACT FROM AUTHOR]

With the requirement of a macroscopic approach to understanding the relationship between water resources and hydrological phenomena, such as severe droughts under climate change, this study investigates South Korea's long-term terrestrial water storage (TWS) using GRACE satellite data. A detailed analysis of water balance in TWS, based on the GLDAS model outputs in Google Earth Engine, reveals the following results: (1) TWS anomaly shows an average decrease of −33.5 mm year−1 from 2003 to 2016; (2) spatial shifts in TWS anomaly (−1.176 to −0.424 cm) unveil regional water storage dynamics, indicating negative temporal slope changes per grid cell (−0.393 to −0.225); (3) contributions of precipitation to TWS are not always straightforward, due to runoff inefficiencies affecting water storage and groundwater; (4) accessible water, integrating surface water and groundwater linked only to the shallow layer's soil moisture, constrains deep groundwater accounting, emphasizing the need for local groundwater surveys in further research. [ABSTRACT FROM AUTHOR]

Changes in land use and land cover (LULC) have a significant impact on urban planning and environmental dynamics, especially in regions experiencing rapid urbanization. In this context, by leveraging the Google Earth Engine (GEE), this study evaluates the effects of land use and land cover modifications on surface temperature in a semi-arid zone of northwestern Algeria between 1989 and 2019. Through the analysis of Landsat images on GEE, indices such as normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and normalized difference latent heat index (NDLI) were extracted, and the random forest and split window algorithms were used for supervised classification and surface temperature estimation. The multi-index approach combining the Normalized Difference Tillage Index (NDTI), NDBI, and NDVI resulted in kappa coefficients ranging from 0.96 to 0.98. The spatial and temporal analysis of surface temperature revealed an increase of 4 to 6 degrees across the four classes (urban, barren land, vegetation, and forest). The Google Earth Engine approach facilitated detailed spatial and temporal analysis, aiding in understanding surface temperature evolution at various scales. This ability to conduct large-scale and long-term analysis is essential for understanding trends and impacts of land use changes at regional and global levels. [ABSTRACT FROM AUTHOR]

Quickly and accurately obtaining land use information can provide a reference for urban development and ecological environment protection. This study classifies land use types in Yunnan Province based on multi-temporal Landsat image dense time stacking and random forest algorithm on the Google Earth Engine （GEE） platform, analyzes the spatiotemporal change trends of LULC in Yunnan Province, and uses geodetectors to quantitatively evaluate key drivers factor. The results show that, 1） the average overall accuracy and Kappa coefficient of LULC classification in this study are 88. 64% and 86. 01%, respectively, which is highly accurate and meets the data usage requirements. 2） The land types in Yunnan Province are mainly forest land, cultivated land, grassland, and sparse shrub grass mixed land, accounting for 97. 91%-98. 38%. Land use transfer mainly involves the conversion of forest land and cultivated land, and the conversion of grassland and sparse shrub grass mixed land into cultivated land. 3） The land use intensity in central and eastern Yunnan in Yunnan Province is generally higher than that in other regions, while the land use intensity in northwest and southwestern Yunnan is lower. 4） There are significant differences in the influence of different driving factors on LULC. Vegeta‐ tion type, average annual temperature and soil type have a relatively small impact on LULC changes. Elevation, slope, aspect, aver‐ age annual precipitation, population density, GDP and population urbanization rate, etc., generally have a high impact on LULC changes, Among them, GDP, population density, and population urbanization rate have a higher impact on LULC changes. The re‐ search results can provide data basis and support for subsequent ecological and environmental protection policy formulation and re‐ gional sustainable development in Yunnan Province. [ABSTRACT FROM AUTHOR]

The Google Earth Engine (GEE) was used to investigate the performance of the Global Land Data Assimilation System (GLDAS) soil temperature (ST) data against observed ST from 13 synoptic stations over a semiarid region in Iran. Three‐hourly ST data were collected and analyzed in two depths (0–10 cm; 40–100 cm) and 5 years. In each depth, GLDAS‐Noah ST data were evaluated for daily minimum, maximum, and average ST (i.e., Tmin, Tmax, and Tavg). Based on the correlation coefficient, Kling–Gupta Efficiency, and Nash–Sutcliffe Efficiency the overall performance of the GLDAS‐Noah is 0.96, 0.66, and 0.79 for Tmin; 0.97, 0.84, and 0.89 for Tavg; and 0.95, 0.89, and 0.89 for Tmax, respectively in the first layer. Likewise, 0.97, 0.85, and 0.86 for Tmin; 0.97, 0.77, and 0.80 for Tavg; and 0.97, 0.69, and 0.69 for Tmax are obtained in the second layer. However, there is a significant negative bias which tends to underestimate ST in the two investigated layers, given by an average bias over all the stations analyzed of −24%, −12%, and −5% for Tmin, Tavg, and Tmax in the first layer, and average bias of −8%, −13%, and −17% for Tmin, Tavg, and Tmax in the second layer. This study reveals that GLDAS‐Noah‐derived ST can be used in arid regions where little or no observation data is available. Moreover, GEE performed as an advanced geospatial processing tool in regional scale analysis of ST in different layers. [ABSTRACT FROM AUTHOR]

Clarifying vegetation changes and the driving factors can provide references for ecological restoration and sustainable social development. We analyzed vegetation distribution and trend changes in Henan Province and its basin zoning (Haihe River basin zoning, Yellow River basin zoning, Huaihe River basin zoning, Yangtze River basin zoning), with fractional vegetation cover data from 2000 to 2020 based on the Google Earth Engine platform, and by combining Theil-Sen Median trend analysis, Mann-Kendall test, and Hurst index. We also utilized factor detection and factor interaction to explore the individual and mutual influences of natural and anthropogenic factors on vegetation at different scales. The results showed that the fractional vegetation cover (FVC) in Henan Province exhibited a distribution pattern of higher coverage in the south and lower in the north during the study period, predominantly characterized by moderate to high vegetation coverage. The Yangtze River basin zoning had the highest coverage. FVC in Henan Province and its zoning exhibited a consistent pattern of fluctuating upward trends, with all areas showing significant improvement. Particularly, the Yangtze River basin zoning had the largest area of improvement. According to the Hurst index, apart from the possibility of continued improvement in the Huaihe River basin zoning, other zoning would be likely to shift from improvement to degradation in the future. Vegetation changes in Henan Province and its zoning were the result of combined effects of anthropogenic and natural factors, with the influence of these factors changing over time and the dominant factors varying by region. Anthropogenic factors such as land use/cover type and nighttime lighting had a stronger impact on vegetation than natural factors like elevation, slope, and annual mean low temperature. The interaction between factors, particularly between anthropogenic and natural factors, exhibited a nonlinear enhancing pattern. [ABSTRACT FROM AUTHOR]

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Forests provide valuable resources for households in the Philippines, particularly in poor and upland communities. This makes forests an integral part of building resilient communities. This relationship became complex during extreme events such as typhoon occurrence as forests can be a contributor to the intensity and impact of disasters. However, little attention has been paid to forest cover losses due to typhoons during disaster assessments. In this study, forest damage caused by typhoons was measured using harmonic analysis of time series (HANTS) with Landsat-8 Operation Land Imager (OLI) images. The ΔHarmonic Vegetation Index was computed by calculating the difference between HANTS and the actual observed vegetation index value. This was used to identify damaged areas in the forest regions and create a damage map. To validate the reliability of the results, the resulting maps produced using ΔHarmonic VI were compared with the damage mapped from PlanetScope's high-resolution pre- and post-typhoon images. The method achieved an overall accuracy of 69.20%. The accuracy of the results was comparable to the traditional remote sensing techniques used in forest damage assessment, such as ΔVI and land cover change detection. To further the understanding of the relationship between forest and typhoon occurrence, the presence of time lag in the observations was investigated. Additionally, different contributing factors in forest damage were identified. Most of the forest damage observed was in forest areas with slopes facing the typhoon direction and in vulnerable areas such as near the coast and hill tops. This study will help the government and forest management sectors preserve forests, which will ultimately result in the development of a more resilient community, by making it easier to identify forest areas that are vulnerable to typhoon damage. [ABSTRACT FROM AUTHOR]

Understanding the response of ecological quality (EQ) to forest landscape connectivity is essential to global biodiversity conservation and national ecological security. However, quantitatively measuring the properties and intensities within these relationships from a spatial heterogeneity perspective remains challenging. This study takes the Fujian Delta region as its case study. The Google Earth Engine platform was employed to compute the remote sensing ecological index (RSEI), the landscape metrics were applied to represent the structural connectivity of the forest landscape, and the minimum cumulative resistance model was adopted to measure the cost distance index representing the functional connectivity of the forest landscape. Then, the spatial correlation and heterogeneity between the EQ and forest landscape connectivity were analyzed based on spatial autocorrelation and geographical weighted regression at three scales (3, 4, and 5 km). The results showed the following: (1) from 2000 to 2020, the overall EQ increased, improving in 37.5% of the region and deteriorating in 13.8% of the region; (2) the forest landscape structural and functional connectivity showed a small decreasing trend from 2000 to 2020, decreasing by 1.3% and 0.9%, respectively; (3) eight forest landscape structural and functional connectivity change modes were detected under the conditions of an improving or degrading EQ based on the change in RSEI and forest landscape structural and functional connectivity; (4) the geographical weighted regression results showed that compared with the forest landscape structural connectivity index, the cost distance index had the highest explanatory power to RSEI in different scales. The effect of forest landscape functional connectivity on EQ is greater than that of structural connectivity. It provides a scientific reference for ecological environmental monitoring and the ecological conservation decision-making of managers. [ABSTRACT FROM AUTHOR]

The timely and accurate mapping of crop types is crucial for agricultural insurance, futures, and assessments of food security risks. However, crop mapping is currently focused on the post-harvest period, and less attention has been paid to early crop mapping. In this study, the feasibility of using Sentinel-1 (S1) and Sentinel-2 (S2) data for the earliest identifiable time (EIT) for major crops (sunflower, maize, spring wheat, and melon) was explored in the Hetao Irrigation District (HID) of China, based on the Google Earth Engine (GEE) platform. An early crop identification strategy based on the Random Forest (RF) model for HID was proposed, and the performance of the model transfer was evaluated. First, the median synthesis, linear shift interpolation, and the Savitzky–Golay (SG) filter methods were used to reconstruct the time series of S1 and S2. Subsequently, the sensitivity of different input features, time intervals, and data integration to different early crop identifications was evaluated based on the RF model. Finally, the model with optimal parameters was evaluated in terms of its transfer capacity and used for the early mapping of crops in the HID area. The results showed that the features extracted from S2 images synthesized at 10-day intervals performed well in obtaining crop EITs. Sunflower, maize, spring wheat, and melon could be identified 90, 90, 70, and 40 days earlier than the harvest date. The identification accuracy, measured by the F1-score, could reach 0.97, 0.95, 0.98, and 0.90, respectively. The performance of the model transfer is good, with the F1-score decreasing from 0 to 0.04 and no change in EIT for different crops. It was also found that the EIT of crops obtained using S1 data alone was 50–90 days later than that obtained using S2 data alone. Additionally, when S1 and S2 were used jointly, S1 data contributed little to early crop identification. This study highlights the potential of early crop mapping using satellite data, which provides a feasible solution for the early identification of crops in the HID area and valuable information for food security assurance in the region. [ABSTRACT FROM AUTHOR]

Giraffe populations have declined by around 40% in the last three decades. Climate change, poaching, habitat loss, and increasing human pressures are confining giraffes to smaller and more isolated patches of habitats. Masai giraffes (Giraffa tippelskirchi) have been subjected to habitat loss and fragmentation, diseases, poaching, and unpredictable calamities such as wildfires and climate change. In this study, we aimed to identify (1) suitable Masai giraffe habitats within the transboundary landscape of Tsavo‐Mkomazi in Southern Kenya and Northern Tanzania; and (2) key connecting corridors in a multiple‐use landscape for conservation prioritization. We combined Masai giraffe presence data collected through a total aerial survey with moderate resolution satellite data to model habitat suitability at 250 m resolution using species distribution models (SDMs) implemented in Google Earth Engine (GEE). Model accuracy was assessed using area under precision recall curve (AUC‐PR). We then used the habitat suitability index as a resistance surface to model functional connectivity using Circuitscape theory and cost‐weighted distance pairwise methods. Human habitat modification, rainfall, and elevation were the main model predictors of Masai giraffe habitat and corridors. On average, our 10‐fold model fitting attained a good predictive performance with an average AUC‐PR = 0.80 (SD = 0.01, range = 0.79–0.83). The model predicted an area of 15,002 km2 as potential suitable Masai giraffe habitat with over 17% outside protected areas within the landscape. Although Tsavo West National Park formed a key habitat and a key connecting corridor, nonprotected community ranches connecting Tsavo West and Tsavo East National Parks are equally important in maintaining landscape connectivity joining more than two Masai giraffe core areas with low resistance and high permeability. To maintain critical Masai giraffe's habitats and landscape functional connectivity, especially in multiple‐use landscapes, conservation‐compatible land use practices, capacity building, and land use planning should be considered at the outset of any new infrastructure development and land use changes. This modeling shows the potential of utilizing remotely sensed information and ground surveys to guide the management of habitats and their connecting corridors across important African landscapes, complementing existing efforts to identify, conserve, and protect wildlife habitats and their linkage zones. [ABSTRACT FROM AUTHOR]