Daily Estimation of Soil Moisture over Beijing-Tianjin-Hebei Region based on General Regression Neural Network Model

被引：0
|
作者：
Deng Y. [1 ]
Ling Z. [2 ]
Sun N. [1 ]
Lv J. [1 ,3 ]
机构：
[1] Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Faculty of Geographical Science, Beijing Normal University, Beijing
[2] Beibu Gulf Key Laboratory of Environment Change and Resources Use, School of Geography and Planning, Nanning Normal University, Nanning
[3] State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing
来源：
Journal of Geo-Information Science | 2021年 / 23卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Beijing-Tianjin-Hebei Region; Downscaling; GRNN; Machine learning; Remote sensing; Retrieval algorithms; Soil moisture; Soil Moisture Active Passive(SMAP);
D O I：
10.12082/dqxxkx.2021.200149
中图分类号：
学科分类号：
摘要：
Surface Soil Moisture (SM) plays an important role in the land-atmosphere interaction and hydrological cycle. Low spatiotemporal resolution (i.e., 25~40 km and 2~3 days) microwave-based SM products such as the Soil Moisture and Ocean Salinity (SMOS) and the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) limit their application in regional scale studies. The Soil Moisture Active Passive (SMAP) and Copernicus Sentinel 1A/B microwave active-passive surface soil moisture product (L2_SM_SP) has a higher spatial resolution (3 km), but its temporal resolution is coarse from 4 to 20 days due to the narrow overlapped swath width. In this study, we developed a machine learning algorithm using the General Regression Neural Network (GRNN) to improve the spatiotemporal resolution of the L2_SM_SP product based on multi-source remote sensing data. Land Surface Temperature (LST), Multi-band Reflectance, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Elevation, Slope, Longitude (Lon), and Latitude (Lat) were selected as input variables to simulate the L2_SM_SP soil moisture in GRNN model. Results show that: (1) GRNN-estimated soil moisture and the original estimates of L2_SM_SP were strongly correlated (r=0.7392, RMSE=0.0757 cm3/cm3); (2) the correlation between GRNN estimates and original L2_SM_SP product at typical dates of different seasons varied a lot. The correlation in spring was the lowest (rSpr=0.6152, RMSESpr=0.0653 cm3/cm3). While the correlation in winter was the strongest (rWin=0.8214, and RMSEWin=0.0367 cm3/cm3). The correlation in summer and autumn was close to each other (rSum=0.6957, rAut=0.7053, RMSESum=0.0754 cm3/cm3, and RMSEAut=0.0694 cm3/cm3); and (3) in 2016, the soil moisture in summer and autumn of the study area was significantly higher than that that in other seasons. In terms of spatial distribution, the soil moisture in the Bashang plateau area was low, while the soil moisture along coastal areas was obviously higher. In this study, we successfully improved the spatiotemporal resolution of L2_SM_SP product over Beijing-Tianjin-Hebei region from 3 km, and 4~20 days to 1 km, and 1 day. Its spatial coverage was also extended. The improved soil moisture product is of great significance for future eco-hydrological assessment, climate prediction, and drought monitoring in Beijing-Tianjin-Hebei region. © 2021, Science Press. All right reserved.
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页码：749 / 761
页数：12
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