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Remote Sensing Technology and Application  2020, Vol. 35 Issue (1): 13-22    DOI: 10.11873/j.issn.1004-0323.2020.1.0013
    
Estimation of Soil Moisture of Agriculture Field in the Middle Reaches of the Heihe River Basin based on Sentinel-1 and Landsat 8 Imagery
Shuguo Wang1(),Chunfeng Ma2(),Zebin Zhao2,Long Wei2
1. School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China
2. Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
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Abstract  

Soil moisture is a key variable in land surface system. Using active microwave remote sensing observations, especially Synthetic Aperture Radar (SAR), has been proven a promising way on the estimation of spatial-temporal distribution of surface soil moisture by a lot of studies. However, there is still challenging in this field, because of the impacts caused by surface roughness and vegetation cover. In this context, this paper proposes an optimal estimation approach combined using SAR and optical remote sensing imagery, in order to retrieve vegetation water content, roughness and soil moisture simultaneously. First, water-cloud model is used to correct vegetation effect on microwave scattering process. In this step, vegetation transmittance factor (closed related to vegetation water content) is estimated by using three optical remote sensing indexes, namely, Modified Soil Adjusted Vegetation Index (MSAVI), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI). Second, a cost function is constructed based on SAR observations and Oh model simulations, then soil moisture and surface roughness can be estimated through global optimization by shuffled complex evolution algorithm. The proposed method is performed by using Sentinel-1and Landsat 8 data in the middle researches of the Heihe River Basin, retrieved results are validated against ground measurements. Results show a good agreement between remote sensing estimates and ground measurements, which indicates the proposed method can retrieve soil moisture accurately. For soil moisture, the determination coefficient (R 2) is higher than 0.7, the root mean square error (RMSE) is 0.073 m3/m3. With respect to vegetation water content,R 2 is higher than 0.9 and RMSE is 0.885 kg/m2. In the meantime, it is found that the result of estimated vegetation water content and the parameterization scheme of vegetation parameters have pronounced influence on the accuracy of soil moisture estimates, which need to be further addressed in future research.
Key words:  Soil moisture      SAR      Surface roughness      Vegetation water content      Sentinel-1      Landsat 8     
Received:  06 June 2019      Published:  01 April 2020
ZTFLH:  TP79  
Corresponding Authors:  Chunfeng Ma     E-mail:  swang@jsnu.edu.cn;machf@lzb.ac.cn
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Shuguo Wang
Chunfeng Ma
Zebin Zhao
Long Wei

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Shuguo Wang, Chunfeng Ma, Zebin Zhao, Long Wei. Estimation of Soil Moisture of Agriculture Field in the Middle Reaches of the Heihe River Basin based on Sentinel-1 and Landsat 8 Imagery. Remote Sensing Technology and Application, 2020, 35(1): 13-22.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2020.1.0013     OR     http://www.rsta.ac.cn/EN/Y2020/V35/I1/13

Fig.1  Location of study area and soil moisture observation nodes

Sentinel-1获取日期

（D1, YYYYMMDD）

 Landsat 8日期（D2, DOY） D1-D2

地面土壤水分

（观测样本数）

植被含水量观测日期

（观测样本数）
20141024 2014301 -4 超级站（1）
20141031 2014301 3 超级站（1）
20141112 2014317 -1 超级站（1）
20141117 2014317 4 超级站（1）
20141124 2014333 -5 超级站（1）
20141201 2014333 2 超级站（1）
20141211 2014349 -4 超级站（1）
20141218 2014349 3 超级站（1）
20150111 2015016 -5 超级站（1）
20150118 2015016 2 超级站（1）
20150216 2015048 -1 超级站（1）
20150221 2015048 4 超级站（1）
20150228 2015064 -5 超级站（1）
20150307 2015064 2 超级站（1）
20160505 2016131 -5 超级站（1）
20160512 2016131 2 超级站（1）+WSN（9） 20160515（10）
20160529 2016147 3 超级站（1）+WSN（9） 20160530（10）
20160902 2016243 3 超级站（1）+WSN（9） 20160830（10）
Table 1  Acquisition dates of Sentinel-1 and Landsat 8 images, and acquisition dates and effective sample size of ground measurements
Fig.2  Estimated results of vegetation water content from three methods
Fig.3  Estimated results of soil moisture based on three vegetation water content parameterization methods
Fig.4  Spatial distribution of estimated soil moisture on May 29, 2016
Fig.5  Sensitivity analysis of parameters in Water Cloud Model
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