Construction and validation of soil moisture retrieval model in farmland based on Sentinel multi-source data

被引：0
作者：
Guo J. [1 ,2 ]
Liu J. [1 ,2 ]
Ning J. [3 ]
Han W. [4 ]
机构：
[1] College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling
[2] Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling
[3] College of Information Engineering, Northwest A&F University, Yangling
[4] Institute of Soil and Water Conservation, Northwest A&F University, Yangling
来源：
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | 2019年 / 35卷 / 14期
关键词：
Models; Multi-source data; Remote sensing; Retrieval; Sentinel; Soil moisture;
D O I：
10.11975/j.issn.1002-6819.2019.14.009
中图分类号：
学科分类号：
摘要：
As an important component of the earth ecosystem, soil moisture is of great significance in the fields of crop growth monitoring, crop yield estimation, variable irrigation and other related agricultural applications. With the rapid development of the technology and theory of microwave remote sensing, soil moisture retrieval with remote sensing data has been widely used at home and abroad. The multi-source remote sensing data used in this study was acquired from Sentinel-1 radar and Sentinel-2 optical satellites which belong to ESA's Sentinel series and there are great advantages in space, time and data registration in monitoring soil moisture. The study area is located in Yangling Demonstration Zone, Shanxi Province and 45 sampling sites were selected and measured to validate the soil moisture retrieval model. Firstly, to deal with the problem that soil moisture retrieval was greatly affected by surface vegetation covers, this study applied Oh model to retrieve soil moisture after removing the influence of vegetation by water cloud model. Secondly, taking the great advantages of machine learning algorithms into account, the study selected support vector regression (SVR) and generalized regression neural network (GRNN) models to retrieve soil moisture, and the models were constructed with different combinations of characteristic parameters including VH polarization radar backward scattering coefficient and VV polarization radar backward scattering coefficient altitude (H0), local incident angle (LIA) which were calculated out with Sentinel-1 radar remote sensing data and vegetation indexes (normalized difference vegetation index, NDVI; modified soil adjusted vegetation index, MSAVI and difference vegetation index, DVI) which were calculated out with Sentinel-2 optical remote sensing data. Finally, this study defined the equivalent number of occurrences to evaluate the quantitative influence of each characteristic parameter because different parameters had different effect on farmland soil moisture retrieval. The results showed that the soil moisture retrieval accuracy of Oh model was increased after removing vegetation influence by water cloud model. The retrieval accuracies of SVR and GRNN models with MSAVI and NDVI were higher than that of Oh model. The optimal input combination of SVR model composed of five characteristic parameters, including VH polarization radar backward scattering coefficient, VV polarization radar backward scattering coefficient, H0, LIA, and MSAVI had the best retrieval accuracy with correlation coefficient of 0.903 and root mean square error of 0.014cm3/cm3 respectively. The optimal SVR model was used to retrieve the soil moisture in study area and the results were consistent with local rainfall events. The equivalent numbers of occurrences of characteristic parameters from high to low were VH polarization radar backward scattering coefficient, H0, VV polarization radar backward scattering coefficient, LIA, MSAVI, NDVI, DVI. For radar backward scattering coefficients from different polarized channel, VH polarization radar backward scattering coefficient is more sensitive to soil moisture than VV polarization radar backward scattering coefficient. Among the three vegetation indexes, the counting results indicated MSAVI had the strongest correlation with soil moisture content, followed by NDVI and DVI was the weakest. The experimental results showed that the fusion of radar and optical data had great potential in soil moisture retrieval in farmlands. The performances of the constructed model in other farmland types would be further investigated in the future. © 2019, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
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页码：71 / 78
页数：7
相关论文
共 28 条
[1]  
Anagnostopoulos V., Petropoulos G.P., Ireland G., Et al., A modernized version of a 1D soil vegetation atmosphere transfer model for improving its future use in land surface interactions studies, Environmental Modelling and Software, 90, pp. 147-156, (2017)
[2]  
Leng P., Song X., Duan S., Et al., A practical algorithm for estimating surface soil moisture using combined optical and thermal infrared data, International Journal of Applied Earth Observation and Geoinformation, 52, pp. 338-348, (2016)
[3]  
Rahimzadeh-Bajgiran P., Berg A.A., Champagne C., Et al., Estimation of soil moisture using optical/thermal infrared remote sensing in the Canadian Prairies, ISPRS Journal of Photogrammetry and Remote Sensing, 83, 3, pp. 94-103, (2013)
[4]  
Wang H., Magagi R., Goita K., Potential of a two-component polarimetric decomposition at C-band for soil moisture retrieval over agricultural fields, Remote Sensing of Environment, 217, pp. 38-51, (2018)
[5]  
Stamenkovic J., Guerriero L., Ferrazzoli P., Et al., Soil moisture estimation by SAR in alpine fields using Gaussian Process Regressor trained by model simulations, IEEE Transactions on Geoscience and Remote Sensing, 55, 9, pp. 4899-4912, (2017)
[6]  
Nelson D.G.A., Shariati M., Glena R., Et al., Application of the Dubois-model using experimental synthetic aperture radar data for the determination of soil moisture and surface roughness, ISPRS Journal of Photogrammetry and Remote Sensing, 54, 4, pp. 273-278, (1999)
[7]  
Hosseini M., Mcnairn H., Using multi-polarization C-and L-band synthetic aperture radar to estimate biomass and soil moisture of wheat fields, International Journal of Applied Earth Observation and Geoinformation, 58, pp. 50-64, (2017)
[8]  
Xu X., Tolson B.A., Li J., Et al., Comparison of X-band and L-Band soil moisture retrievals for land data assimilation, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 9, pp. 3850-3860, (2017)
[9]  
Pasolli L., Notarnicola C., Bruzzone L., Estimating soil moisture with the Support Vector Regression technique, IEEE Geoscience and Remote Sensing Letters, 8, 6, pp. 1080-1084, (2011)
[10]  
He L., Qin Q., Ren H., Et al., Soil moisture retrieval using multi-temporal Sentinel-1 SAR data in agricultural areas, Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 32, 3, pp. 142-148, (2016)
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