Soil moisture and erosion degree estimation from multichannel microwave remote sensing data

1995 ◽  
Author(s):  
Gennady P. Kulemin ◽  
Andrei A. Kurekin ◽  
Vladimir V. Lukin ◽  
Alexander A. Zelensky
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
Sensing Data

scholarly journals Effect of Land Cover Heterogeneity on Soil Moisture Retrieval Using Active Microwave Remote Sensing Data

2009 ◽  
Vol 1 (2) ◽  
pp. 80-91 ◽  
Author(s):  
Tarendra Lakhankar ◽  
Hosni Ghedira ◽  
Marouane Temimi ◽  
Amir Azar ◽  
Reza Khanbilvardi
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Cover ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
Sensing Data ◽  
Active Microwave Remote Sensing

scholarly journals Integration of Microwave and Optical/Infrared Derived Datasets from Multi-Satellite Products for Drought Monitoring

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1504
Author(s):  
Zhengdong Wang ◽  
Peng Guo ◽  
Hong Wan ◽  
Fuyou Tian ◽  
Linjiang Wang
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Water ◽  
Land Surface ◽  
North China ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
Sensing Data ◽  
Dryness Index

Drought is among the most common natural disasters in North China. In order to monitor the drought of the typically arid areas in North China, this study proposes an innovative multi-source remote sensing drought index called the improved Temperature–Vegetation–Soil Moisture Dryness Index (iTVMDI), which is based on passive microwave remote sensing data from the FengYun (FY)3B-Microwave Radiation Imager (MWRI) and optical and infrared data from the Moderate Resolution Imaging Spectroradiometer (MODIS), and takes the Shandong Province of China as the research area. The iTVMDI integrated the advantages of microwave and optical remote sensing data to improve the original Temperature–Vegetation–Soil Moisture Dryness Index (TVMDI) model, and was constructed based on the Modified Soil-Adjusted Vegetation Index (MSAVI), land surface temperature (LST), and downscaled soil moisture (SM) as the three-dimensional axes. The global land data assimilation system (GLDAS) SM, meteorological data and surface water were used to evaluate and verify the monitoring results. The results showed that iTVMDI had a higher negative correlation with GLDAS SM (R = −0.73) than TVMDI (R = −0.55). Additionally, the iTVMDI was well correlated with both precipitation and surface water, with mean correlation coefficients (R) of 0.65 and 0.81, respectively. Overall, the accuracy of drought estimation can be significantly improved by using multi-source satellite data to measure the required surface variables, and the iTVMDI is an effective method for monitoring the spatial and temporal variations of drought.

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