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Wenlong Jing
Publications - 34
Citations - 630
Wenlong Jing is an academic researcher. The author has contributed to research in topics: Environmental science & Biology. The author has an hindex of 9, co-authored 23 publications receiving 311 citations.
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Journal ArticleDOI
Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time Series—A Case Study in Zhanjiang, China
TL;DR: The effectiveness of the solution combining the deep learning models with the incremental classification approach for early crop classification is indicated and is expected to provide new perspectives for early mapping of croplands in cloudy areas.
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A Spatial Downscaling Algorithm for Satellite-Based Precipitation over the Tibetan Plateau Based on NDVI, DEM, and Land Surface Temperature
TL;DR: Land surface temperature features were introduced as new variables in addition to the Normalized Difference Vegetation Index and Digital Elevation Model to improve the spatial downscaling algorithm, and models including land surface temperature variables (LSTs) performed better than those without LSTs, indicating the significance of considering precipitation–land surface temperature when downscaled TRMM 3B43 V7 precipitation data.
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A Comparison of Different Regression Algorithms for Downscaling Monthly Satellite-Based Precipitation over North China
TL;DR: The variable importances of the land surface temperature (LST) feature variables were higher than those of NDVI, which indicates the significance of considering the precipitation–land surface temperature relationship when downscaling TRMM 3B43 V7 precipitation data.
Journal ArticleDOI
Comparison of Different Machine Learning Approaches for Monthly Satellite-Based Soil Moisture Downscaling over Northeast China
TL;DR: The results showed that the RF-downscaled results had superior matching performance to both ESA CCI SM and in-situ measurements, and can positively respond to precipitation variation.
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Mapping Urban Areas with Integration of DMSP/OLS Nighttime Light and MODIS Data Using Machine Learning Techniques
TL;DR: This paper proposes an approach for extracting urban areas with the integration of DMSP-OLS stable nighttime lights and MODIS data utilizing training sample datasets selected from DMSP -OLS andMODIS NDVI based on several simple strategies.