A5101591231- Remote Sensing and Land Use
- Remote Sensing in Agriculture
- Land Use and Ecosystem Services
- Remote-Sensing Image Classification
- Geographic Information Systems Studies
- Remote Sensing and LiDAR Applications
- Soil Geostatistics and Mapping
- Precipitation Measurement and Analysis
- Service-Oriented Architecture and Web Services
- Advanced Computational Techniques and Applications
- Environmental Changes in China
- China's Ethnic Minorities and Relations
- Smart Agriculture and AI
- Soil Moisture and Remote Sensing
- Environmental Monitoring and Data Management
- Regional Economic and Spatial Analysis
- Automated Road and Building Extraction
- Soil and Land Suitability Analysis
- Coastal and Marine Management
- Cryospheric studies and observations
- Water Quality Monitoring and Analysis
- Water Resources and Sustainability
- Flow Measurement and Analysis
- Human Mobility and Location-Based Analysis
- Advanced Image Fusion Techniques
Chinese Academy of Sciences
2016-2025
Aerospace Information Research Institute
2020-2025
Tsinghua–Berkeley Shenzhen Institute
2024
Harvard University
2024
Yale University
2024
University of Electronic Science and Technology of China
2024
Institut des Sciences des Plantes de Paris Saclay
2024
State Key Laboratory of Remote Sensing Science
2008-2023
University of South China
2023
Hebei University of Engineering
2023
Chlorophyll-a (Chl-a) is an important characterized parameter of lakes. Monitoring it accurately through remote sensing thus great significance for early warnings water eutrophication. Sentinel Multispectral Imager (MSI) images from May to September between 2020 and 2021 were used along with in-situ measurements estimate Chl-a in Lake Chagan, which located Jilin Province, Northeast China. In this study, the extreme gradient boosting (XGBoost) Random Forest (RF) models, had similar...
Soil is a complicated historical natural continuum that presents gradual changes in its properties and geographic area. Conventional soil survey cartography methods on macroscopic scale based grids with coarse resolution are inadequate for the rapid development of precision agriculture. The demand mapping content accuracy has increased as more convenient acquiring multi-source geo-spatial data have been developed, such commonly employed to extract basic units environmental variables related...
Urban functional zones are important space carriers for urban economic and social function. The accurate rapid identification of is great significance to planning resource allocation. However, the factors considered in existing zone methods not comprehensive enough, recognition stops at their categories. This paper proposes a framework that combines multisource heterogeneous data identify categories draw portraits zones. comprehensively describes features from four aspects: building-level...
Building change detection (BuCD) can offer fundamental data for applications such as urban planning and identifying illegally-built new buildings. With the development of deep neural network-based approaches, BuCD using high-spatial-resolution remote sensing images (RSIs) has significantly advanced. These methods, nevertheless, typically demand a considerable number computational resources. Additionally, accuracy these algorithms be improved. Hence, LightCDNet, lightweight Siamese network...
Geo-parcel based crop identification plays an important role in precision agriculture. It meets the needs of refined farmland management. This study presents improved procedure for geo-parcel by combining fine-resolution images and multi-source medium-resolution images. GF-2 with fine spatial resolution 0.8 m provided agricultural farming plot boundaries, GF-1 (16 m) Landsat 8 OLI data were used to transform enhanced vegetation index (EVI) time-series. In this study, we propose a piecewise...
Semantic segmentation is a crucial approach for remote sensing interpretation. High-precision semantic results are obtained at the cost of manually collecting massive pixelwise annotations. Remote imagery contains complex and variable ground objects obtaining abundant manual annotations expensive arduous. The semi-supervised learning (SSL) strategy can enhance generalization capability model with small number labeled samples. In this study, novel adversarial network developed information...
Accurate crop classification is the basis of agricultural research, and remote sensing only effective measuring technique to classify crops over large areas. Optical in regions with good illumination; however, it usually fails meet requirements for highly accurate cloud-covered areas rainy regions. Synthetic aperture radar (SAR) can achieve active data acquisition by transmitting signals; thus, has strong resistance cloud rain interference. In this study, we designed an improved planting...
Accurate spatialization of socioeconomic data is conducive to understand the spatial and temporal distribution human social development status and, thus, effectively support future scientific decision-making. This study focuses on population mapping, which a classical macroeconomic economy. Traditional mapping based rough grids or administrative divisions such as townships often has deficiencies in accuracy pattern prediction. In this article, hence, we employ residential geo-objects basic...
Abstract High-quality ground observation networks are an important basis for scientific research. Here, automatic soil network high-resolution satellite applications in China (SONTE-China) was established to measure both pixel- and multilayer-based moisture temperature. SONTE-China is distributed across 17 field stations with a variety of ecosystems, covering dry wet zones. In this paper, the average root mean squared error (RMSE) station-based well-characterized sites 0.027 m 3 /m...
Precision agriculture has been proposed to improve the sustainability of and solve environmental pollution soil. In precision process, management water fertilizer is carried out on agricultural operation units. Therefore, acquisition accurate soil nutrient distribution information a key step for application digital mapping an effective technology. Significant progress made in over past 20 years. However, current framework was implemented based grids, which not consistent with units...
Accurate and reliable farmland crop mapping is an important foundation for relevant departments to carry out agricultural management, planting structure adjustment ecological assessment. The current identification work mainly focuses on conventional crops, there are few studies parcel-level of horticultural crops in complex mountainous areas. Using Miaohou Town, China, as the research area, we developed a method precise areas using very-high-resolution (VHR) optical images Sentinel-2...
Abstract Digital Ocean is a new research domain of Earth. Because the spatio-temporal, three-dimensional (3D) and intrinsically dynamic nature ocean data, it more difficult to make breakthrough in this domain. The construction China Prototype System (CDOPS) pushes step forward from its operation as mere concept achievement realistic system. In paper, technical framework CDOPS discussed, including function, application layers. Then, two key technologies are studied detail that will enable 3D...
Precise vegetation maps of mountainous areas are great significance to grasp the situation an ecological environment and forest resources. In this paper, while multi-source geospatial data can generally be quickly obtained at present, realize effective mapping in when samples difficult collect due their perilous terrain inaccessible deep forest, we propose a novel intelligent method sample collection for machine-learning (ML)-based mapping. First, employ geo-objects (i.e., polygons) from...
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Derris trifoliate</i> , one of the most notorious invaders Mangroves in South China, seriously threatens growth and stability local ecosystem. In order to effectively control spread stabilize Mangrove ecosystem, it is necessary identify distribution simulate occurrence forests. While previous methods for invasive plant mapping based on satellite images were limited by low temporal spatial...
Cultivated land is crucial for food production and security. In complex environments like mountainous regions, the fragmented nature of cultivated complicates rapid accurate information acquisition. Deep learning has become essential extracting but faces challenges such as edge detail loss limited adaptability. This study introduces a novel approach that combines geographical zonal stratification with temporal characteristics medium-resolution remote sensing images identifying land. The...