A5002818077- Plant Water Relations and Carbon Dynamics
- Remote Sensing in Agriculture
- Engineering Education and Technology
- Land Use and Ecosystem Services
- Climate change impacts on agriculture
- Smart Agriculture and AI
- Advanced Data Processing Techniques
- Hydrology and Watershed Management Studies
- Remote Sensing and LiDAR Applications
- Water resources management and optimization
- Distributed and Parallel Computing Systems
- Remote Sensing and Land Use
- Sustainable Agricultural Systems Analysis
- Rangeland Management and Livestock Ecology
- Robotics and Automated Systems
- Irrigation Practices and Water Management
- Precipitation Measurement and Analysis
- Biocrusts and Microbial Ecology
- Soil erosion and sediment transport
- Climate variability and models
- Graph Theory and Algorithms
- Flood Risk Assessment and Management
- Water-Energy-Food Nexus Studies
- Hydrology and Drought Analysis
- Electrocatalysts for Energy Conversion
Chinese Academy of Sciences
2015-2025
Shuguang Hospital
2023-2025
Shanghai University of Traditional Chinese Medicine
2023-2025
Aerospace Information Research Institute
2019-2025
University of Chinese Academy of Sciences
2019-2025
State Key Laboratory of Remote Sensing Science
2019-2025
Nanjing University of Science and Technology
2025
Shanghai University
2023-2024
Jiangnan University
2024
Shanghai Jiao Tong University
2019-2023
Abstract. As a linkage among water, energy, and carbon cycles, global actual evapotranspiration (ET) plays an essential role in agriculture, water resource management, climate change. Although it is difficult to estimate ET over large scale for long time, there are several datasets available with uncertainty associated various assumptions regarding their algorithms, parameters, inputs. In this study, we propose long-term synthesized product at kilometer spatial resolution monthly temporal...
Yield prediction is essential in food security, trade, and field management. However, due to the associated complex formation mechanisms of yield, accurate timely yield remains challenging remote sensing-based crop monitoring domains. In this study, a framework soybean integrating extreme gradient boosting (XGBoost) multidimensional feature engineering was developed at county level United States using publicly available datasets. Excellent accuracy values were obtained for over 959 counties...
Taking advantage of multiple new remote sensing data sources, especially from Chinese satellites, the CropWatch system has expanded scope its international analyses through development indicators and an upgraded operational methodology. The approach adopts a hierarchical covering four spatial levels detail: global, regional, national (thirty-one key countries including China) “sub-countries” (for nine largest countries). thirty-one encompass more that 80% both production exports maize, rice,...
Abstract. The global distribution of cropping intensity (CI) is essential to our understanding agricultural land use management on Earth. Optical remote sensing has revolutionized ability map CI over large areas in a repeated and cost-efficient manner. Previous studies have mainly focused investigating the spatiotemporal patterns ranging from regions entire globe with coarse-resolution data, which are inadequate for characterizing farming practices within heterogeneous landscapes. To fill...
Many African countries are facing increasing risks of food insecurity due to rising populations. Accurate and timely information on the spatial distribution cropland is critical for effective management crop production yield forecast. Most recent products (2015 2016) derived from multi-source remote sensing data available public use. However, discrepancies exist among these products, level discrepancy particularly high in several Africa regions. The overall goal this study was identify...
The distribution of corn cultivation areas is crucial for ensuring food security, eradicating hunger, adjusting crop structures, and managing water resources. emergence high-resolution images, such as Sentinel-1 Sentinel-2, enables the identification at field scale, these images can be applied on a large scale with support cloud computing technology. Hebei Province major production area in China, faces serious groundwater overexploitation due to irrigation. Corn was mapped using...
Accurate precipitation data at high spatiotemporal resolution are critical for land and water management the basin scale. We proposed a downscaling framework Tropical Rainfall Measuring Mission (TRMM) products through integrating Google Earth Engine (GEE) Colaboratory (Colab). Three machine learning methods, including Gradient Boosting Regressor (GBR), Support Vector (SVR), Artificial Neural Network (ANN) were compared in framework. vegetation indices (Normalized Difference Vegetation Index,...
To achieve the Sustainable Development Goals (SDGs), high-quality data are needed to inform formulation of policies and investment decisions, monitor progress towards SDGs evaluate impacts policies. However, landscape is changing. With emerging big cloud-based services, there new opportunities for collection, influencing both official collection processes operation programmes they monitor. This paper uses cases examples explore potential crowdsourcing public earth observation (EO) products...
Accurate and early crop-type maps are essential for agricultural policy development food production assessment at regional national levels. This study aims to produce a map with acceptable accuracy spatial resolution in northern Mongolia by optimizing the combination of Sentinel-1 (S1) Sentinel-2 (S2) images Google Earth Engine (GEE) environment. A total three satellite data scenarios set, including S1 alone, S2 S2. In order avoid impact gaps caused clouds on crop classification, this...
The application of machine learning in crop yield prediction has gained considerable traction, yet uncertainties persist regarding the impact trends on these predictions and differences between detrending methods. In our study, we utilized extreme gradient boosting (XGBoost) to scrutinize effects no trend processing (NTP), input year as a feature (IYF), average (IAYF), linear (ILYF), global method (GDT) maize soybean Midwestern United States. Based findings, compared with that NTP,...
Timely and efficient monitoring of crop phenology at a high spatial resolution are crucial for the precise effective management agriculture. Recently, satellite-derived vegetation indices (VIs), such as Normalized Difference Vegetation Index (NDVI), have been widely used detection terrestrial ecosystems. In this paper, framework is proposed to detect using spatio-temporal data fused from Systeme Probatoire d'Observation de la Tarre5 (SPOT5) Moderate Resolution Imaging Spectroradiometer...
In Mongolia, the monitoring and estimation of spring wheat yield at regional national levels are key issues for agricultural policy food management as well economy society a whole. The remote sensing data technique have been widely used crop production in world. For current research, nine indices were tested that include normalized difference drought index (NDDI), water (NDWI), vegetation condition (VCI), temperature (TCI), health (VHI), multi-band (NMDI), visible shortwave infrared (VSDI),...
This study designed an approach to derive land-cover in the South Africa with insufficient ground samples, and made a case demonstration Nzhelele Levhuvu catchments, Africa. The method was developed based on integration of Landsat 8, Sentinel-1, Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), Google Earth Engine (GEE) platform. Random forest classifier 300 trees is employed as classification model. In order overcome defect data, stratified sampling used generate...