基于光学遥感和微波遥感的土壤水分反演模型研究

doi:10.3969/j.issn.1673-503X.2023.01.009

Abstract

Abstract:

Based on the Sentinel-1 and Landsat 8 image data on May 17, 2020, and in combination with the field manual moisture measurement data, taking Chaoyang of Liaoning province as an example, the soil moisture was retrieved using the vegetation temperature index method and the optical collaborative microwave remote sensing inversion algorithm, respectively, and a high-precision soil moisture forecasting model was built.The results showed that the Vegetation Temperature Index (TVDI) based on optical remote sensing cannot well retrieve farmland soil moisture.Microwave reflection could be better feedback on the spatial variation of soil moisture, and the fitting accuracy of VV polarization of Sentinel-1 radar data for soil moisture (R²=0.71) is better than VH polarization (R²=0.27).The improved water cloud model (WCM) based on Global Vegetation Moisture Index (GVMI) has the best performance (R²=0.80).Using collaborative inversion of microwave and optical remote sensing can retrieve high-spatial-resolution and high-precision farmland soil moisture data, which is helpful for agricultural drought monitoring.

Key words: Soil moisture, Sentinel data, Optical remote sensing, Forecasting model

CLC Number:

P407.8

Jing-li WANG, Tian-jiao GAO, Rong-ping LI, Peng-cheng YU, Yun-tao MA, Na MI, Ri-hong WEN, Kai ZHANG. Research on soil moisture retrieval model based on optical remote sensing and microwave remote sensing[J]. Journal of Meteorology and Environment, 2023, 39(1): 73-82.

Figures/Tables 11

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传感器	波段	波长范围/μm	空间分辨率/m
OLI	1-COASTAL/AEROSOL	0.43~0.45	30
	2-Blue	0.45~0.51	30
	3-Green	0.53~0.59	30
	4-Red	0.64~0.67	30
	5-NIR	0.85~0.88	30
	6-SWIR1	1.57~1.65	30
	7-SWIR2	2.11~2.29	30
	8-PAN	0.50~0.68	15
	9-Cirrus	1.36~1.38	30
TIRS	10-TIR1	10.60~11.19	100
TIRS	11-TIR2	11.50~12.51	100

植被指数	计算公式
归一化植被指数(NDVI, Normalized Difference Vegetation Index)	${\rm{NDVI}} = \frac{{{\rho _{{\rm{NIR}}}} - {\rho _R}}}{{{\rho _{{\rm{NIR}}}} + {\rho _R}}}$
比值植被指数(RVI, Ratio Vegetation Index)	$\mathrm{RVI}=\frac{\rho_{\mathrm{NIR}}}{\rho_{\mathrm{R}}}$
大气阻抗植被指数(ARVI, Atmospherically Resistant Vegetation Index)	$\mathrm{ARVI}=\frac{\rho_{\mathrm{NIR}}-\left(2 \rho_R-\rho_{\mathrm{BLUE}}\right)}{\rho_{\mathrm{NIR}}+\left(2 \rho_R-\rho_{\mathrm{BLUE}}\right)}$
修正土壤调整植被指数(MSAVI, Modified Soil Adjusted Vegetation Index)	$\mathrm{MSAVI}=\frac{2 \rho_{\mathrm{NIR}}+1-\sqrt{\left(2 \rho_{\mathrm{NIR}}+1\right)^2-8\left(\rho_{\mathrm{NIR}}-\rho_R\right)}}{2}$

参数	综合方式	草地	冬小麦	牧场
A	0.0012	0.0009	0.0018	0.0014
B	0.091	0.032	0.138	0.084

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Research on soil moisture retrieval model based on optical remote sensing and microwave remote sensing

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