Soil moisture retrieval on both active and passive microwave data scales

doi:10.13745/j.esf.sf.2023.12.32

Abstract

Abstract:

Soil moisture is a critical parameter in the field of hydrology, agriculture and meteorology, and microwave remote sensing is one of the most effective methods for soil moisture detection. This study uses active and passive microwave data and other multi-source remote sensing data and applies Random forest algorithm to perform soil moisture retrieval on both active and passive microwave data scales. First, on passive microwave data scale, the parameters for land cover and normalized difference vegetation index (NDVI) are spatially optimized. Second, ReliefF method is used for evaluating the importance of input parameters and parameter selection. Last, results by using active and passive microwave data jointly or separately are compared to assess the retrial accuracy and effectiveness of the former method. It was found that on the scale of active microwave data, the joint retrieval method yielded results with higher accuracy (r=0.691, RMSE=0.0796) compared to using only active microwave data (r=0.744, RMSE=0.0848); however, on the scale of passive microwave data, the opposite was true (r=0.939, RMSE=0.0451 using joint data; r=0.944, RMSE=0.0435 using only passive microwave data). The joint retrieval method proved to be applicable on the scale of active microwave data.

Key words: soil moisture, microwave remote sensing, joint retrieval, random forest, spatial optimization, importance evaluation

CLC Number:

LIU Qixin, GU Xingfa, WANG Chunmei, YANG Jian, ZHAN Yulin. Soil moisture retrieval on both active and passive microwave data scales[J]. Earth Science Frontiers, 2024, 31(2): 42-53.

Figures/Tables 13

Fig.1 Schematic diagram of the study area and ISMN monitoring sites

Table 1 The numbers of monitoring sites and values of daily averaged soil moisture observation in each soil moisture network

站网名称	站点数量	日平均土壤含水量观测值总数
ARM	14	25
COSMOS	15	28
FLUXNET-AMERIFLUX	5	29
iRON	7	19
PBO_H2O	104	270
RISMA	17	54
SCAN	143	454
SNOTEL	357	1 135
SOILSCAPE	15	26
USCRN	83	266

Fig.2 Schematic diagram of the structure of random forest. Adapted from [38].

Table 2 Comparison between MODIS land cover classification scheme and the reclassification scheme

MODIS LC图层分类方案	重分类方案
Evergreen Needleleaf Forests(常绿针叶林)	森林
Evergreen Broadleaf Forests(常绿阔叶林)
Deciduous Needleleaf Forests(落叶针叶林)
Deciduous Broadleaf Forests(落叶阔叶林)
Mixed Forests(混交林)
Closed Shrublands(封闭灌丛)	灌木
Open Shrublands(开放灌丛)	灌木
Woody Savannas(热带稀树草原)	草地
Savannas(热带草原)
Grasslands(草地)
Croplands(农田)	农田
Cropland/Natural Vegetation Mosaics (农田/自然植被)	农田
Barren(裸土)	裸土
Permanent Wetlands(永久湿地,去除)	其他(去除)
Urban and Built-up Lands(城市与建筑物,去除)
Water Bodies(水体,去除)
Permanent Snow and Ice(永久冰雪,去除)

Fig.3 Illustration of calculating “percentages of typical land cover classes” and “average NDVIs corresponding to typical land cover classes”

Table 3 Input and output parameters initially selected in two different microwave data scale

数据尺度	输入变量	输出变量
主动微波尺度	σ_VH/VV,θ,NDVI,LST,elevation,slope,DOY	ISMN实测数据
被动微波尺度	T_bH/V,Perc_X,NDVI_X,LST,elevation,slope,DOY	ERA5-Land模拟数据

Table 4 Ranking of the importance of input parameters in active microwave data scale

排序	输入变量
1	slope
2	elevation
3	NDVI
4	LST
5	$σ V H$
6	$σ V V$
7	DOY
8	θ

Table 4 Ranking of the importance of input parameters in active microwave data scale

排序	输入变量
1	slope
2	elevation
3	NDVI
4	LST
5	$σ V H$
6	$σ V V$
7	DOY
8	θ

Table 5 Ranking of the importance of input parameters in passive microwave data scale

排序	输入变量
1	elevation
2	DOY
3	LST
4	NDVI_G
5	T_bH
6	T_bV
7	Perc_F
8	slope
9	Perc_G
10	NDVI_F
11	Perc_C
12	NDVI_S
13	Perc_S
14	NDVI_B
15	NDVI_C
16	Perc_B

Fig.4 Statistical metrics of soil moisture retrieval by adding parameters into the random forest model according to the importance ranking of active microwave data scale

Fig.5 Statistical metrics of soil moisture retrieval by adding parameters into the random forest model according to the importance ranking of passive microwave data scale

Table 6 Optimized input parameters for joint retrieval of soil moisture on two microwave data scale

数据尺度	优选输入变量
主动微波尺度	slope,elevation,NDVI,LST,σ_VH,σ_VV,DOY
被动微波尺度	elevation,DOY,LST,NDVI_G,T_bH,T_bV,Perc_F,slope,Perc_G,NDVI_F

Table 7 Comparison of statistical metrics of joint retrieval of soil moisture using both active and passive microwave data and statistical metrics of soil moisture retrieval respectively using active/passive microwave data

数据尺度	输入变量组合	r	RMSE/(cm³·cm^-3)
主动微波尺度	slope,elevation,NDVI,LST,σ_VH,σ_VV,DOY	0.691	0.084 8
主动微波尺度	slope,elevation,NDVI,LST,σ_VH,σ_VV,DOY,T_bH/V	0.744	0.079 6
被动微波尺度	elevation,DOY,LST,NDVI_G,T_bH,T_bV,Perc_F,slope,Perc_G,NDVI_F	0.944	0.043 5
被动微波尺度	elevation,DOY,LST,NDVI_G,T_bH,T_bV,Perc_F,slope,Perc_G,NDVI_F,σ_VH/VV	0.939	0.045 1

Table 8 Statistical metrics of joint retrieval of soil moisture using both active and passive microwave data and statistical metrics of soil moisture retrieval using passive microwave data after removing the new parameters from the inputs

数据尺度	输入变量组合	r	RMSE/(cm³·cm^-3)
被动微波尺度	elevation,DOY,LST,T_bH,T_bV,slope	0.929	0.048 3
被动微波尺度	elevation,DOY,LST,T_bH,T_bV,slope,σ_VH/VV	0.926	0.049 3

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