A5100671875- Remote Sensing in Agriculture
- Fire effects on ecosystems
- Remote Sensing and LiDAR Applications
- Remote Sensing and Land Use
- Synthetic Aperture Radar (SAR) Applications and Techniques
- Landslides and related hazards
- Plant Water Relations and Carbon Dynamics
- Remote-Sensing Image Classification
- Soil Moisture and Remote Sensing
- Geochemistry and Geologic Mapping
- Land Use and Ecosystem Services
- Cryospheric studies and observations
- Rough Sets and Fuzzy Logic
- Atmospheric and Environmental Gas Dynamics
- Data Mining Algorithms and Applications
- Soil Geostatistics and Mapping
- Geographic Information Systems Studies
- Advanced Computational Techniques and Applications
- Data Management and Algorithms
- Spectroscopy and Chemometric Analyses
- Fire Detection and Safety Systems
- Climate change and permafrost
- Climate variability and models
- Environmental Quality and Pollution
- Precipitation Measurement and Analysis
Huadong Hospital
2025
Fudan University
2025
University of Electronic Science and Technology of China
2015-2024
Southern Medical University
2024
China Southern Power Grid (China)
2024
Second Xiangya Hospital of Central South University
2024
Kobe University
2024
Central South University
2024
Guangdong Academy of Medical Sciences
2024
81th Hospital of PLA
2024
Solar-induced chlorophyll fluorescence (SIF) has been increasingly used as a proxy for terrestrial gross primary productivity (GPP). Previous work mainly evaluated the relationship between satellite-observed SIF and gridded GPP products both based on coarse spatial resolutions. Finer resolution (1.3 km × 2.25 km) measured from Orbiting Carbon Observatory-2 (OCO-2) provides first opportunity to examine SIF-GPP at ecosystem scale using flux tower data. However, it remains unclear how strong is...
In this study, the Standardized Precipitation Evaporation Index (SPEI) was applied to characterize drought conditions in Southwest China from 1982–2012. The SPEI calculated by precipitation and temperature data for various accumulation periods. Based on SPEI, multi-scale patterns, trend, spatio-temporal extent of were evaluated, respectively. results explicitly showed a drying trend China. mean values at five time scales all decreased significantly. Some moderate severe droughts captured...
The spatiotemporal distribution of soil moisture over the Tibetan Plateau is important for understanding regional water cycle and climate change. In this paper, surface in northeastern estimated from time-series VV-polarized Sentinel-1A observations by coupling cloud model (WCM) advanced integral equation (AIEM). vegetation indicator WCM represented leaf area index (LAI), which smoothed interpolated Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LAI eight-day products. AIEM...
Chlorophyll is an essential pigment for photosynthesis in crops, and leaf chlorophyll content can be used as indicator crop growth status help guide nitrogen fertilizer applications. Estimating plays important role precision agriculture. In this study, a variable, rate of change reflectance between wavelengths ‘a’ ‘b’ (RCRWa-b), derived from situ hyperspectral remote sensing data combined with four advanced machine learning techniques, Gaussian process regression (GPR), random forest (RFR),...
Fuel moisture content (FMC) of live vegetation is a crucial wildfire risk and spread rate driver. This study presents the first daily FMC product at global scale 500 m pixel resolution from Moderate Resolution Imaging Spectroradiometer (MODIS) radiative transfer models (RTMs) inversion techniques. Firstly, multi-source information parameterized PROSPECT-5 (leaf level), 4SAIL (grass shrub canopy level) GeoSail (tree RTMs to generate three look-up tables (LUTs). Each LUT contained most...
Soil moisture is vital for the crop growth and directly affects yield. The conventional synthetic aperture radar (SAR) based soil monitoring often influenced by vegetation cover surface roughness. machine-learning methods are not constrained physical parameters have high nonlinear fitting capabilities. In this study, were applied to estimate over winter wheat fields during its growing season. RADARSAT-2 data with quad polarizations 240 sample plots in study area acquired collected,...
Climate warming has caused a widespread increase in extreme fire weather, making forest fires longer-lived and larger
Abstract Globe-LFMC 2.0, an updated version of Globe-LFMC, is a comprehensive dataset over 280,000 Live Fuel Moisture Content (LFMC) measurements. These measurements were gathered through field campaigns conducted in 15 countries spanning 47 years. In contrast to its prior version, 2.0 incorporates 120,000 additional data entries, introduces more than 800 new sampling sites, and comprises LFMC values obtained from samples collected until the calendar year 2023. Each entry within provides...
This paper presents a microwave/optical synergistic methodology to retrieve soil moisture in an alpine prairie. The adequately represents the scattering behavior of vegetation-covered area by defining vegetation and below. Integral Equation Method (IEM) was employed determine backscattering underlying soil. modified Water Cloud Model (WCM) used reduce effect vegetation. Vegetation coverage, which can be easily derived from optical data, incorporated this method account for gap information....
Retrieval of vegetation parameters from remotely sensed data using a radiative transfer model is generally hampered by the ill-posed inverse problem, which dramatically decreases precision level retrieved parameters. The purpose this study was to use Bayesian network-based method allow alleviation problem. This achieved introducing correlations between free into their prior joint probability distribution (PJPD), allowing reduction probabilities unrealistic combinations. Three sampling...
Abstract Globe-LFMC is an extensive global database of live fuel moisture content (LFMC) measured from 1,383 sampling sites in 11 countries: Argentina, Australia, China, France, Italy, Senegal, Spain, South Africa, Tunisia, United Kingdom and the States America. The contains 161,717 individual records based on situ destructive samples used to measure LFMC, representing amount water plant leaves per unit dry matter. primary goal calibrate validate remote sensing algorithms predict LFMC....
Detailed spatial information on the presence and properties of woody vegetation serves many purposes, including carbon accounting, environmental reporting land management. Here, we investigated whether machine learning can be used to combine multiple observations training data estimate canopy cover fraction ('cover'), height ('height') above-ground biomass dry matter ('biomass') at 25-m resolution across Australian continent, where possible an annual basis. We trained a Random Forest...
Amazon forests play an important role in the global carbon cycle and Earth's climate. The vulnerability of to drought remains highly controversial. Here we examine impacts 2015 on photosynthesis understand how solar radiation precipitation jointly control forest during severe drought. We use a variety gridded vegetation climate datasets, including solar-induced chlorophyll fluorescence (SIF), photosynthetic active (PAR), fraction absorbed PAR (APAR), leaf area index (LAI), precipitation,...
Previous studies have shown that Live Fuel Moisture Content (LFMC) is a crucial driver affecting wildfire occurrence worldwide, but the effect of LFMC in driving still remains unexplored over southwest China ecosystem, an area historically vulnerable to wildfires. To this end, we took 10-years dynamics retrieved from Moderate Resolution Imaging Spectrometer (MODIS) reflectance product using physical Radiative Transfer Model (RTM) and events extracted MODIS Burned Area (BA) explore relations...
The distribution of soil moisture is important for modeling hydrological and climatological processes to understand the Earth energy cycle balance. major difficulty retrieval in vegetated areas how separate individual scattering contribution moisture, vegetation, surface roughness from backscattered radar signal. In this paper, a semi-empirical method was proposed retrieve Ruoergai prairie using single temporal Radarsat-2 data. It formulated by integrating advanced integral equation model...
Fuel moisture content (FMC) is a crucial variable affecting fuel ignition and rate of fire spread. Much work so far has focused on the usage remote sensing data from multiple sensors to derive FMC; however, little attention been devoted C-band Sentinel-1A data. In this study, we aimed test performance for multi-temporal retrieval forest FMC by coupling bare soil backscatter linear model with vegetation water cloud (WCM). This coupled that linked observed directly FMC, was firstly calibrated...