Multi-source Satellite Data Research Articles

Full-coverage estimation of CO2 concentrations in China via multisource satellite data and Deep Forest model.

Monitoring China's carbon dioxide (CO2) concentration is essential for formulating effective carbon cycle policies to achieve carbon peaking and neutrality. Despite insufficient satellite observation coverage, this study utilizes high-resolution spatiotemporal data from the Orbiting Carbon Observatory 2 (OCO-2), supplemented with various auxiliary datasets, to estimate full-coverage, monthly, column-averaged carbon dioxide (XCO2) values across China from 2015 to 2022 at a spatial resolution of 0.05° via the deep forest model. The 10-fold cross-validation results indicate a correlation coefficient (R) of 0.95 and a determination coefficient (R²) of 0.90. Validation against ground-based station data yielded R values of 0.93, and R² values reached 0.81. Further validation from the Greenhouse Gases Observing Satellite (GOSAT) and the Copernicus Atmosphere Monitoring Service Reanalysis dataset (CAMS) produced R² values of 0.87 and 0.80, respectively. During the study period, CO2 concentrations in China were higher in spring and winter than in summer and autumn, indicating a clear annual increase. The estimates generated by this study could potentially support CO2 monitoring in China.

Reconstruction of Typhoon-Induced Ocean Thermal Structures Using Deep Learning and Multi-Source Satellite Data with News Impact Analysis

Reconstructing the three-dimensional thermal structure of the ocean under typhoon conditions presents significant challenges due to the scarcity of observational data, particularly in subsurface regions, and the limitations of existing observation methods in terms of spatial and temporal resolution. Accurate reconstruction of these structures is crucial for understanding the energy exchange between the ocean and typhoons, as this exchange directly influences typhoon intensity and trajectory. To address these challenges, this study introduces a fully connected transformer network (FCT), which integrates fully connected layers with a transformer model. The FCT model leverages the attention mechanisms inherent in the transformer architecture to effectively extract and integrate multi-scale ocean dynamical features. Using data from Typhoon Lekima in 2019, this study reconstructs ocean thermal structures at various depths and achieves an RMSE of 1.03 °C and an MAE of 0.83 °C when validated against Argo data. Furthermore, the model’s robustness was demonstrated through five-fold cross-validation, with the validation loss exhibiting minor fluctuations across folds but remaining stable overall, with an average validation loss of 0.986 °C, indicating the model’s generalizability. Sensitivity analysis also revealed the model’s resilience to variations in key input variables, showing minimal impact on output even with perturbations of up to 10% in input data. In addition, the study incorporates content analysis of typhoon-related news reports from 2011 to 2020, revealing a predominance of political topics, which underscores the central role of government in disaster response, with economic and ecological topics following. This integrated approach not only enhances our understanding of the interactions between ocean thermal structures and typhoon dynamics but also provides valuable insights into the societal impacts of typhoons, as reflected in media coverage, contributing to improved disaster management strategies.

Improving the accuracy of honey bee forage class mapping using ensemble learning and multi-source satellite data in Google Earth Engine

In semi-arid agro-pastoral environments of Africa, beekeeping is widely recognized as an important activity to improve and diversify livelihoods. Although the scientific identification of suitable honey bees (Apis mellifera ssps.) forages may guide beekeepers to set up apiaries or to timely move honey bee colonies to exploit bee forage resources available in various landscapes, the characterization and mapping of bee forage classes is challenging. We evaluated how various data sources and classification algorithms in Google Earth Engine (GEE) affect the accuracy of honey bee forage class mapping in a semi-arid region of Ethiopia. Predictors derived from multi-source satellite data, such as high-resolution Planet imagery (P), Sentinel 1 RADAR (S1), Sentinel 2 multispectral (S2), and Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model (DEM) were tested and best predictors were identified using Forward Feature Selection (FFS). Four machine learning algorithms (Gradient Tree Boost (GTB), Random Forest (RF), Classification and Regression Trees (CART), and Support Vector Machine (SVM)), all available in GEE, were compared and ensembled for honey bee forage class mapping. The results show that the highest accuracy is obtained by all four algorithms when combining P, S1, S2, and DEM compared to using predictors from a single data source or any other combinations. GTB had higher overall accuracy (90.9%) than RF (88.2%), CART (85.5%), or SVM (79.9%). Nonetheless, the highest overall accuracy (94.7%) was obtained when integrating the four machine learning algorithms in an Ensemble Learning Approach (ELA). Applying ELA improved the classification accuracy by 3.8%, 6.5%, 9.2%, and 14.8% compared to single learner classification algorithms (i.e., GTB, RF, CART, and SVM, respectively). This study demonstrates an improved classification accuracy for honey bee forage class mapping in tropical rangeland by applying ELA, which can provide a better approach for monitoring and managing bee forage resources.

Estimation of district-level spring barley yield in southern Sweden using multi-source satellite data and random forest approach

Remote sensing observations and artificial intelligence algorithms have emerged as key components for crop yield estimation at various scales during the past decades. However, the utilization of multi-source satellite data and machine learning for estimating aggregated crop yield at the regional level in Europe has been only scarcely explored. Our study aims to bridge this research gap by focusing on the district-level spring barley yield estimation in southern Sweden from 2017 to 2022. We developed an estimation method with the random forest (RF) approach using four satellite-derived products along with two climate variables. These variables were used individually and in combinations as inputs for the RF approach. The results showed that vegetation indices (VIs) outperformed solar-induced chlorophyll fluorescence (SIF) in barley yield estimation, while combining VIs and SIF variables achieved the highest model performance (R2 = 0.77, RMSE = 488 kg/ha). The inclusion of climate variables generally had little added contributions to the model performance. Importantly, barley yield prediction could be achieved two months prior to harvest, using monthly VIs and SIF data from April and May. Our study demonstrated the feasibility of using freely accessible satellite data and the machine learning approach for estimating crop yield at the pan-European regional level. We expect that our proposed methodology can be extended to different crop types and regional-scale crop yield estimation in Europe, benefiting national and local authorities in making agricultural productivity decisions.

Improving rapid flood impact assessment: An enhanced multi-sensor approach including a new flood mapping method based on Sentinel-2 data

Rapid flood impact assessment methods need complete and accurate flood maps to provide reliable information for disaster risk management, in particular for emergency response and recovery and reconstruction plans. With the aim of improving the rapid assessment of flood impacts, this work presents a new impact assessment method characterized by an enhanced satellite multi-sensor approach for flood mapping, which improves the characterization of the hazard. This includes a novel flood mapping method based on the new multi-temporal Modified Normalized Difference Water Index (MNDWI) that uses multi-temporal statistics computed on time-series of Sentinel-2 multi-spectral satellite images. The multi-temporal aspect of the MNDWI improves characterization of land cover over time and enhances the temporary flooded areas, which can be extracted through a thresholding technique, allowing the delineation of more precise and complete flood maps. The methodology, if implemented in cloud-based environments such as Google Earth Engine (GEE), is computationally light and robust, allowing the derivation of flood maps in matters of minutes, also for large areas.The flood mapping and impact assessment method has been applied to the seasonal flood occurred in South Sudan in 2020, using Sentinel-1, Sentinel-2 and PlanetScope satellite imagery. Flood impacts were assessed considering damages to buildings, roads, and cropland. The multi-sensor approach estimated an impact of 57.4 million USD (considering a middle-bound scenario), higher than what estimated by using Sentinel-1 data only, and Sentinel-2 data only (respectively 24% and 78% of the estimation resulting from the multi-sensor approach). This work highlights the effectiveness and importance of considering multi-source satellite data for flood mapping in a context of disaster risk management, to better inform disaster response, recovery and reconstruction plans.

Exploring the potential of multi-source satellite remote sensing in monitoring crop nutrient status: A multi-year case study of cranberries in Wisconsin, USA

A timing and precise diagnosis of crop nutrient status is essential for optimizing management practices that promote environmentally friendly and enhanced crop yields. Although plant tissue analysis has conventionally been employed to evaluate the nutritional status of crops, this method cannot capture the spatial variability of crop nutrients. In contrast, satellite-based remote sensing can monitor the nutrient status of crops across expansive areas. This study explored the capability of multi-source satellite images (PlanetScope-4: 3 m, 4 bands; PlanetScope-8: 3 m, 8 bands; Sentinel-2: 10–60 m, 13 bands; PRISMA: 30 m, 239 bands) in mapping 12 foliar nutrients in cranberries. Three machine learning approaches, including partial least squared regression (PLSR), support vector regression (SVR), random forest regression (RFR), were used to relate foliar nutrients to different types of satellite-derived features (SR: surface reflectance; VI: vegetation indices; TF: texture features) or their combinations (SR+VI, VI+TF and SR+VI+TF). Model performance was compared across different foliar nutrients, modelling approaches and combinations of model input features using R2 (the coefficient of determination) and RRMSE (relative root mean square error, = root mean square error/nutrient range × 100 %). Input features that were important to foliar nutrient modelling were identified. The model performance difference among nutrients was consistent between Planet-4 and Sentinel-2, as well as between Planet-8 and PRISMA. In the Planet-4 and Sentinel-2 derived models, N was best predicted (average R2 = 0.77, average RRMSE=15 %), followed by macronutrients S (0.60–0.63, 11 %), Mg (0.58–0.65, 10–11 %), Ca (0.49–0.51, 9 %), Na (0.69, 22 %), P (0.49, 9 %) and K (0.20, 8 %), and then by all micronutrients(i.e., Fe, Mn, B, Cu and Zn: R2 = 0.04–0.61; RRMSE=16–28 %). In the Planet-8 and PRISMA derived models, macronutrients (i.e., N, P, K, Mg, Ca, S and Na) had lower R2 and RRMSE (R2 = 0.06–0.59; RRMSE=7–57 %) than micronutrients (i.e., Fe, Mn, B, Cu and Zn: R2 = 0.18–0.60; RRMSE=19–66 %). The successful retrieval of foliar nutrients from satellite imagery was influenced by many factors, including the intercorrelation between nutrients and model input features, the data availability at critical growth stages, and satellite images characteristics (e.g., spatial and spectral resolutions). Except for foliar nitrogen, foliar nutrients typically do not exhibit distinct absorption features associated with C, H, N, or O molecular bonds in the 400–2500 nm range. Our results indicate that their successful retrieval can be primarily attributed to the association between foliar nutrients and other leaf components (e.g., pigments, water, and dry matter) that do display spectral features within this range. Our study demonstrated the potential of integrating multi-source satellite data for precise nutrient monitoring over large scales.

Wet-ConViT: A Hybrid Convolutional–Transformer Model for Efficient Wetland Classification Using Satellite Data

Accurate and efficient classification of wetlands, as one of the most valuable ecological resources, using satellite remote sensing data is essential for effective environmental monitoring and sustainable land management. Deep learning models have recently shown significant promise for identifying wetland land cover; however, they are mostly constrained in practical issues regarding efficiency while gaining high accuracy with limited training ground truth samples. To address these limitations, in this study, a novel deep learning model, namely Wet-ConViT, is designed for the precise mapping of wetlands using multi-source satellite data, combining the strengths of multispectral Sentinel-2 and SAR Sentinel-1 datasets. Both capturing local information of convolution and the long-range feature extraction capabilities of transformers are considered within the proposed architecture. Specifically, the key to Wet-ConViT’s foundation is the multi-head convolutional attention (MHCA) module that integrates convolutional operations into a transformer attention mechanism. By leveraging convolutions, MHCA optimizes the efficiency of the original transformer self-attention mechanism. This resulted in high-precision land cover classification accuracy with a minimal computational complexity compared with other state-of-the-art models, including two convolutional neural networks (CNNs), two transformers, and two hybrid CNN–transformer models. In particular, Wet-ConViT demonstrated superior performance for classifying land cover with approximately 95% overall accuracy metrics, excelling the next best model, hybrid CoAtNet, by about 2%. The results highlighted the proposed architecture’s high precision and efficiency in terms of parameters, memory usage, and processing time. Wet-ConViT could be useful for practical wetland mapping tasks, where precision and computational efficiency are paramount.

Multi-Source Remote Sensing Analysis of Yilong Lake’s Surface Water Dynamics (1965–2022): A Temporal and Spatial Investigation

With the acceleration of global warming and the intensification of anthropogenic activities, numerous lakes worldwide are experiencing reductions in their water surface areas. Yilong Lake, a typical shallow plateau lake located on the Yunnan–Guizhou Plateau in China, serves as a crucial water resource for local human production, daily life, and ecosystem services. Hence, long-term comprehensive monitoring of its dynamic changes is essential for its effective protection. However, previous studies have predominantly utilized remote sensing data with limited temporal resolution, thus failing to reflect the long-term variations in Yilong Lake’s water body. This study employs high temporal resolution monitoring, utilizing multi-source satellite data (e.g., KeyHole, Landsat, HJ-1 A/B) images spanning from 1965 to 2022 to investigate the changes in Yilong Lake’s surface area, analyzing the influencing factors and ecological impacts of these changes. The results indicate that from 1965 to 2022, Yilong Lake’s water surface area decreased by 8.33 km2, with a maximum surface area of 40.49 km2 on January 7, 1986, and a minimum surface area of 10.64 km2 on April 20, 2013. These changes are characterized by three significant phases: (1) a rapid shrinking phase (1965–1979); (2) a fluctuating shrinking period (1986–2016); and (3) an expanding recovery phase (2016–2022). Spatially, the most significant shrinkage was observed along the southern and southwestern shores of the lake. The driving factors varied across different periods: sunshine duration was the dominant influence during the rapid shrinking phase (1965–1979), accounting for 82% of the changes; population and cropland area were the main drive factors during the fluctuating shrinking period (1986–2016), accounting for 56% of the changes; and during the expanding recovery phase (2016–2022), the population accounted for 75% of the changes in the lake’s surface area. Currently, the protection of Yilong Lake depends on water supplementation and strict regulation of outflow, resulting in the lake exhibiting characteristics similar to a reservoir. This long-term investigation provides baseline information for future lake monitoring. Our research findings can also guide decision-makers in urban water resource management and environmental protection, ensuring the scientific and rational use of watershed water resources, effectively curbing the shrinkage of Yilong Lake, and achieving long-term sustainable restoration of the lake’s ecology.

Mitigation of urban heat island in China (2000–2020) through vegetation-induced cooling

City afforestation serves as a robust strategy for thermal mitigation. Employing multisource satellite data, we explored the impact of vegetation cover on temperature dynamics across urban and rural sectors in Chinese cities. Our analysis reveals that a 1 % increase in vegetation coverage corresponds to a reduction in surface temperature by 0.099 °C to 0.102 °C in urban environments. However, the cooling effect diminishes from urban to rural areas, where increased vegetation occasionally results in warming. Further, the temperature reduction tends to stabilize at higher vegetation levels, as demonstrated in grid-based evaluations. Significantly, variations in urban population density substantially modulate these cooling effects. Over the period 2000–2020, afforestation increased vegetation coverage by 4.35 % in urban areas, which led to cooling in 67.43 % of the analyzed cities, with an average surface temperature decline of 0.58 °C. Additionally, our findings document a notable attenuation of the urban heat island (UHI) effect over time, with an average decrease of 0.56 ± 0.35 °C. This reduction primarily results from the thermoregulatory effects of changes in vegetation coverage, which cool urban areas and may warm rural ones. Our study underscores the critical role of afforestation in urban climate management and its efficacy in attenuating urban heat waves and UHI effects.

Forest Structure Mapping of Boreal Coniferous Forests Using Multi-Source Remote Sensing Data

Modeling forest structure using multi-source satellite data is beneficial to understanding the relationship between vertical and horizontal structure and image features to provide more comprehensive and abundant information for the study of forest structural complexity. This study investigates and models forest structure as a multivariate structure based on sample data and active-passive remote sensing data (Landsat8, Sentinel-2A, and ALOS-2 PALSAR) from the Saihanba Forest in Hebei Province, Northern China, to measure forest structural complexity, relying on a relationship-driven model between field and satellite data. In this study, we considered the effects of the role of satellite variables in different vertical structure types and horizontal structure ranges, used two methods to stepwise select significant variables (stepwise forward selection and Pearson correlation coefficient), and employed a multivariate modeling technique (redundancy analysis) to derive a forest composite structure index (FSI), combining both horizontal and vertical structure attributes. The results show that optical texture can better represent forest structure characteristics, polarization interferometric radar information can represent the vertical structure information of forests, and combining the two can represent 77% of the variance of multiple forest structural attributes. The new FSI can explain 93% of the relationship between stand structure and satellite variables, and the linear fit R2 to the measured data reaches 0.91, which largely shows the situation of the measured data. The generated forest structure map more accurately reflects the complexity of the forest structure in the Saihanba Forest, achieving a supplementary explanation of the measured data.

Impact of Satellite Wind on Improving Simulation of the Upper Ocean Response to Tropical Cyclones

Accurate modeling of the ocean response to tropical cyclones (TCs) requires high-quality wind fields to force ocean models. In this study, blended wind fields are generated using multi-source satellite data and the Climate Forecast System Reanalysis (CFSR) wind data. We utilize the hybrid wind fields to drive the Regional Ocean Modeling System (ROMS) for simulating oceanic dynamic and thermodynamic parameters. The model’s simulated ocean surface and sub-surface temperatures, as well as current speeds, are generally consistent with satellite and in situ observations collected during TC Winston and Freddy. The results are significantly better than those simulated by ROMS using wind forcing from CFSR alone. These results suggest that incorporating satellite wind data into the atmospheric forcing has the potential to enhance vertical mixing and improve simulations of the upper ocean response to TCs.

South-to-South Cooperation in Multi-Source Satellite Data for Improving Food Security

Abstract. This study aims to highlight the importance of south-to-south cooperation in remote sensing and present preliminary results from our joint project between China and South Africa, focusing on multi-sensor remote sensing data for food security. Scientometric analysis was used to demonstrate growing research output and cooperation among countries in the Global South. Furthermore, the first results of our crop classification methods were presented on a test site in the Sanjiang Plain, China. The preliminary crop classification results from this test site showed promising accuracy. South-to-south cooperation in remote sensing has the potential to address shared challenges and promote sustainable development through exchange of knowledge and resources. The ongoing joint project between China and South Africa demonstrates the benefits of such collaboration in developing robust remote sensing techniques for improved food security monitoring and decision making in the Global South.

Spatio-temporal dynamics of vegetation over cloudy areas in Southwest China retrieved from four NDVI products

The Normalized Difference Vegetation Index (NDVI) is the most commonly used index for assessing vegetation. However, significant differences among various satellite datasets, complex terrain, and the impact of clouds on optical sensors limit vegetation change assessment based on NDVI. To address these issues, this study utilizes multi-source satellite data (GIMMS3g NDVI, CDR AVHRR NDVI, SPOT NDVI, and MODIS NDVI) to monitor vegetation dynamics at different time scales from 1990 to 2020 in Sichuan Province, China. The results indicate that over time, NDVI values from the four NDVI products in Sichuan Province have shown an upward trend. There are certain differences in the spatial distribution and spatial heterogeneity of the change rate of NDVI values among the four NDVI products at different time scales, and the differences are mainly concentrated in the Sichuan Basin (SB) and the Western Sichuan alpine plateau region (WS). Compared with the other three NDVI products, GIMMS NDVI has the highest value but the smallest increase during the study period. The SPOT NDVI value is the smallest, but the increase is relatively large. However, within the overlapping period of the four NDVI datasets, only the annual average of CDR AVHRR NDVI showed a downward trend (slope2000–2013 = −0.0001·a−1). The annual fluctuation of CDR AVHRR NDVI is the smallest, and compared to other NDVI datasets, its correlation with climate factors shows significantly weaker spatial variability. Moreover, the ability of CDR AVHRR NDVI to distinguish different vegetation land cover types is significantly poor (STD = 0.045). The findings of this study will provide a reference for further research on vegetation changes in Sichuan Province and NDVI reconstruction in cloudy areas.

Pre-failure operational anomalies of the Kakhovka Dam revealed by satellite data

On June 6, 2023, the Kakhovka Dam in Ukraine experienced a catastrophic breach that led to the loss of life and substantial economic values. Prior to the breach, the supporting structures downstream of the spillway had shown signs of being compromised. Here, we use multi-source satellite data, meteorological reanalysis, and dam design criteria to document the dam’s pre-failure condition. We find that anomalous operation of the Kakhovka Dam began in November 2022, following the destruction of a bridge segment, which led to persistent overtopping from late April 2023 up to the breach, contributing to the erosion of the spillway foundation. Moreover, our findings also highlight safety and risk-reduction measures pivotal in avoiding such scenarios. To help prevent future disasters, we advocate for greater transparency in the design parameters of key water structures to enable risk management, and conclude that remote sensing technology can help ensuring water infrastructure safety.

Analysis of long-term changes in algal bloom pattern and their association with Ocean, atmosphere, and land-based processes across the northern Indian Ocean

Algal blooms are global phenomena in marine and fresh waters. An understanding of the impacts of climate change and anthropogenic activities on marine algal blooms is essential for protecting marine ecosystems. We have studied the long-term changes in the spatial patterns of algal blooms over the northern Indian Ocean and assessed the influence of the ocean, atmosphere, and land-based processes on them. We have analyzed multi-source satellite data (2003 – 2020) of various parameters such as algal concentration, temperature, salinity, sea level, particulate organic carbon, precipitation, wind speed, and river discharge on an open-source cloud computing platform. The seasonality of variables is assessed using a harmonic model, and their spatial trend is estimated using the non-parametric Mann-Kendall test. The spatial relationship between various geophysical parameters and algal bloom concentration is assessed by the cross-correlation test, and the prediction is done using a multivariate autoregression model. Results suggest that each variable exhibits distinguishable seasonal patterns. Algal bloom duration showed a significant increase over time. However, algal bloom coverage showed a significant decline in the coastal waters in all seasons except post-monsoon. The cross-correlation test unveils that lower temperatures, salinity, and sea level promote algal bloom occurrences in coastal waters rather than open waters. Furthermore, the statistical model with a time lag of 2 months shows better performance for prediction of bloom intensity (r2 = 0.86).

Coupling Downscaling and Calibrating Methods for Generating High-Quality Precipitation Data with Multisource Satellite Data in the Yellow River Basin

Remote sensing precipitation data have the characteristics of wide coverage and revealing spatiotemporal information, but their spatial resolution is low. The accuracy of the data is obviously different in different study areas and hydrometeorological conditions. This study evaluated four precipitation products in the Yellow River basin from 2001 to 2019, constructed the optimal combined product, conducted downscaling with various machine algorithms, and performed corrections using meteorological station precipitation data to analyze the spatiotemporal trends of precipitation. The results showed that (1) GPM and MSWEP had the best four evaluation indicators, with R2 values of 0.93 and 0.90, respectively, and the smallest FSE and RMSE, with a BIAS close to 0. A high-precision mixed precipitation dataset, GPM-MSWEP, was constructed. (2) Among the three methods, the downscaling results of DFNN showed higher accuracy. (3) The results, after correction with GWR, could more effectively enhance the accuracy of the data. (4) Precipitation in the Yellow River Basin showed a decreasing trend in January, September, and December, while it exhibited an increasing trend in other months and seasons, with 2002 and 2016 being points of abrupt change. This study provides a reference for the production of high-precision satellite precipitation products in the Yellow River basin.

Advancing Rice Yield Forecasting and Crop Assessment in Kazakhstan’s Kyzylorda Region: A Multisource Satellite Data Approach

The monitoring and prediction of rice yields are essential across various domains, including agriculture, environmental conservation, ecology, agricultural insurance, and land resource management. The central districts of Kazakhstan’s Kyzylorda region, encompassing 70% of the total rice cultivation area, are of particular importance for ensuring national food security. This study focuses on the central Kyzylorda region, specifically the Syrdarya, Zhalagash, and Karmakshi districts, which serve as the primary hub for rice cultivation in Kazakhstan. The primary objective is to advance remote sensing techniques tailored to evaluate crop conditions and forecast rice yields, accounting for the region’s unique soil and climatic attributes. The research methodology involves comprehensive analysis of Sentinel-2 and Landsat-8 satellite data, harmonized with ground-truth information. This analysis encompasses the latest rice crop data for 2020 and precise sowing dates. Extensive field surveys were conducted to gather crucial data on growth, development, crop health, and productivity under specific agrometeorological conditions. The study provides a detailed flowchart outlining the sequential processing of remote sensing data and ground-based information, with a primary focus on forecasting rice yields in the central Kyzylorda districts for 2020. Rigorous satellite data analysis established robust correlations between the Normalized Difference Vegetation Index (NDVI) and various crop conditions, directly linked to rice yield, with high precision (R2 values ranging from 0.82 to 0.91). Validation exercises, cross-referencing satellite data with real-time field data, further enhanced accuracy. The research outcomes have extensive applications, benefiting agriculture, environmental preservation, ecology, insurance, statistics, and land management. This study underscores the significance of addressing critical challenges across various sectors through multifaceted findings.

Using POI and multisource satellite datasets for mainland China's population spatialization and spatiotemporal changes based on regional heterogeneity

Geospatial big data and remote sensing data are widely used in population spatialization studies. However, the relationship between them and population distribution has regional heterogeneity in different geographic contexts. It is necessary to improve data processing methods and spatialization models in areas with large geographical differences. We used land cover data to extract human activity, nighttime light and point-of-interest (POI) data to represent human activity intensity, and considered differences in geographical context to divide mainland China into northern, southern and western regions. We constructed random forest models to generate gridded population distribution datasets with a resolution of 500 m, and quantitatively evaluated the importance of auxiliary data in different geographical contexts. The street-level accuracy assessment showed that our population dataset is more accurate than WorldPop, with a higher R2 and smaller deviation. The improved datasets provided broad potential for exploring the spatial-temporal changes in grid-level population distribution in China from 2010 to 2020. The results indicated that the population density and settlement area have increased, and the overall pattern of population distribution has remained highly stable, but there are significant differences in population change patterns among cities with different urbanization processes. The importance of the ancillary data to the models varied significantly, with POI contributing the most to the southern region and the least to the western region. Moreover, POI had relatively less influence on model improvement in undeveloped areas. Our study could provide a reference for predicting social and economic spatialized data in different geographical context areas using POI and multisource satellite data.

A review of satellite-based monitoring of groundwater storage changes and depletion consequences

Groundwater demand is increasing due to global population growth, climate change and rapid urbanization, however, poor planning and over-exploitation are leading to rapid depletion of groundwater, which in turn causes adverse impacts such as land subsidence, soil salinization and water quality deterioration. Groundwater storage (GWS) monitoring is essential to the sustainable management of regional water resources and the prevention of environmental and social issues associated with depleted groundwater resources. Conventional groundwater observation is primarily conducted through groundwater well-level measurements, which requires a lot of time and effort, and is insufficient to accurately reflect GWS changes regionally and monitor large-scale groundwater level changes. The availability of various satellite data makes it easier to study groundwater information effectively. The aim of this paper is to first review the seriousness of groundwater depletion, every year, 15% to 25% of the total global groundwater extraction is overexploited. Then, based on satellite geodetic technologies such as Gravity Restoration and Climate Experiment (GRACE), GRACE Follow-On, Sentinel-1, and Global Navigation Satellite System (GNSS), the basic principles of GWS monitoring are expounded. The reliability of the monitoring results was analyzed through the literature summary, showing that the results were basically consistent with the trends reflected by the measured groundwater samples, and the statistical significance of quantitative comparisons was higher than 0.65. The impact of the consequences of groundwater depletion also deserves our attention. This paper combined with multi-source satellite and tidal data, etc., the feasible research methods are discussed for a series of adverse consequences caused by groundwater depletion.

Constructing a high-precision precipitation dataset on the data-limited Qinghai-Tibet Plateau

The primary constraint hindering a comprehensive understanding of the atmospheric, hydrological, and ecological dynamics on the Qinghai-Tibet Plateau (QTP) is the absence of highly accurate precipitation datasets. Given the limited availability of climate observations and the pronounced spatial disparities in this region, traditional interpolation techniques are ill-suited, necessitating the development of novel methods. In this study, we formulated a random forest model leveraging precipitation data from 209 climate stations and multisource satellite datasets spanning the period from 1960 to 2020. These datasets encompassed low spatial resolution TRMM precipitation products and high spatial resolution NDVI data. This model facilitated the creation of a monthly precipitation dataset at a 1 km resolution for the QTP. Our findings showed that (1) the prevailing precipitation datasets for the QTP exhibited notable deficiencies, characterized by low spatial precision (R² = 0.592, p = 0.015) and incongruent temporal trends when compared to observational data (NSE = 0.398); (2) by incorporating multisource satellite data, we achieved a substantial enhancement in precipitation accuracy. For instance, the introduction of NDVI led to a reduction in precipitation estimation errors by 7.9 mm and 15.4 mm in desert and nondesert regions, respectively. The inclusion of TRMM satellite precipitation data not only improved accuracy in areas with limited climate stations by more than 16 % but also reduced cumulative monthly errors by 46.8 mm, and (3) the precipitation dataset generated through this method displayed a 24.8 % increase in accuracy (R² = 0.84) compared to existing datasets for the QTP, aligning well with actual precipitation trends (NSE = 0.806). This study offers an effective approach for acquiring high-precision precipitation datasets in regions with limited meteorological stations and substantial spatial heterogeneity in precipitation patterns. Furthermore, our method significantly enhances the spatial heterogeneity of precipitation products, potentially serving as a model for the development of datasets in data-scarce areas.