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Remote Sensing for Natural Resources    2022, Vol. 34 Issue (1) : 142-150     DOI: 10.6046/zrzyyg.2021105
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Remote sensing inversion of desert soil moisture based on improved spectral indices
GAO Qi1,2(), WANG Yuzhen1, FENG Chunhui1, MA Ziqiang3, LIU Weiyang1, PENG Jie1, JI Yanzhen2
1. College of Plant Sciences, Tarim University, Alar 843300, China
2. Prefecture Geological and Environmental Monitoring Station, Changji 831100, China
3. Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100000, China
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Abstract  

Soil moisture is an important indicator affecting dynamic climate changes, vegetation ecological recovery, and land desertification control in arid regions. Using Landsat8 OLI/TIRS multispectral remote sensing images, this study determined the optimal spectral indices by introducing thermal infrared (b10) band to improve nine traditional spectral indices and through significance tests and multiple covariance tests. Then, with the improved spectral indices as the modeling factors and based on the terrain data, this study constructed multispectral comprehensive inversion models of desert soil moisture using the multivariate linear regression (MLR) and random forest (RF) algorithms. Finally, the spatial distribution characteristics of soil moisture and their driving factors were analyzed using the optimal model. The results are as follows: ① The correlation coefficients of the improved spectral indices EBSI, ECI, ECal, ENDVI, and EPDI increased by 0.02~0.11; ② For the prediction datasets of linear and non-linear models, their R 2 increased by 0.12 and 0.05, respectively and their RPD values increased by 0.35 and 0.49, respectively after the spectral indices were improved. Moreover, the RPD value of model RF-II was up to 3.12, and thus this model can accurately predict soil moisture. ③ The accuracy of the non-linear models was significantly higher than that of the linear models. The R 2 of the prediction datasets of MLR-based linear models was only 0.59 and 0.71, while that of the RF-based non-linear models reached 0.86 and 0.91. ④ The distribution of soil moisture was influenced by both natural and artificial factors. The soil moisture content is [0, 5)% and [5, 12)% in the northeastern desert and shows cross-distribution in the southern farmland. Soil moisture is difficult to evaporate in the northern and central desert-oasis transition zones due to inhibiting factors such as the vegetation coverage and surface salt crust, with the content of [15, 20)% and [20, 40)% mostly.
Keywords improved spectral index      satellite remote sensing      soil moisture      desert soils      southern Xinjiang arid zone     
ZTFLH:  TP79S157.2  
Issue Date: 14 March 2022
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Qi GAO
Yuzhen WANG
Chunhui FENG
Ziqiang MA
Weiyang LIU
Jie PENG
Yanzhen JI
Cite this article:   
Qi GAO,Yuzhen WANG,Chunhui FENG, et al. Remote sensing inversion of desert soil moisture based on improved spectral indices[J]. Remote Sensing for Natural Resources, 2022, 34(1): 142-150.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021105     OR     https://www.gtzyyg.com/EN/Y2022/V34/I1/142
Fig.1  Location of the study area and distribution of sampling points
传统光谱指数 传统公式① 文献来源 改进光谱指数 扩展公式
BSI ( b 4 + b 6 ) - ( b 5 + b 2 ) ( b 4 + b 6 ) + ( b 5 + b 2 ) + 1 Polykretis等(2020)[12] EBSI ( b 4 + b 6 ) - ( b 5 + b 2 ) + b 10 ( b 4 + b 6 ) + ( b 5 + b 2 ) + b 10 + 1
CI b 6 b 7 Hengl(2009) [13] ECI b 6 + b 10 b 7 + b 10
Cal b 4 b 3 Boettinger等(2008) [14] ECal b 4 + b 10 b 3 + b 10
RVI b 5 b 4 Jordan (1969) [15] ERVI b 5 + b 10 b 4 + b 10
NDVI b 5 - b 4 b 5 + b 4 Rouse等(1973) [16] ENDVI b 5 - b 4 + b 10 b 5 + b 4 + b 10
DVI b 5 - b 4 Tucker (1979) [17] EDVI b 5 - b 4 + b 10
NDWI b 3 - b 5 b 3 + b 5 McFeeters(1996) [18] ENDWI b 3 - b 5 + b 10 b 3 + b 5 + b 10
PDI ( 1 M 2 + 1 ) ( b 4 + M b 5 ) 葛少青等(2018) [10] EPDI ( 1 M 2 + 1 ) ( b 4 + M b 5 + b 10 )
GVMI ( b 5 + 0.1 ) - ( b 6 + 0.02 ) ( b 5 + 0.1 ) + ( b 6 + 0.02 ) 孙灏等(2012) [19] EGVMI ( b 5 + 0.1 ) - ( b 6 + 0.02 ) + b 10 ( b 5 + 0.1 ) + ( b 6 + 0.02 ) + b 10
Tab.1  Commonly used conventional and improved spectral indices and their calculation formulae
土壤属性 平均值 极大值 极小值 标准差 变异系数/%
土壤水分/% 21.58 44.29 4.30 44.10 30.74
电导率/(dS·m-1) 24.01 79.60 1.07 113.64 44.57
pH值 8.16 9.17 7.37 0.18 5.12
Tab.2  Descriptive statistics of the base characteristics of soil samples
传统光谱指数 相关系数 改进光谱指数 相关系数
BSI -0.51**① EBSI -0.55**
CI 0.70** ECI 0.73**
Cal -0.37** ECal -0.48**
RVI 0.40** ERVI 0.38**
NDVI 0.44** ENDVI 0.50**
DVI 0.31** EDVI 0.28**
NDWI -0.23** ENDWI -0.07
PDI -0.41** EPDI -0.43**
GVMI 0.42** EGVMI 0.41**
Tab.3  Analysis of spectral index correlation coefficients
因子 BSI Cal CI NDVI PDI DVI GVMI RVI NDWI
BSI —
Cal 1.71 —
CI 2.40 1.17 —
NDVI 1.67 1.01 3.54 —
PDI 1.00 1.04 1.25 1.12 —
DVI 1.59 1.01 2.28 16.10 1.02 —
GVMI 2.02 1.00 1.93 2.12 1.09 1.82 —
RVI 2.59 1.20 4.86 10.51 1.05 11.48 1.74 —
NDWI 1.07 1.19 1.54 4.09 1.20 3.32 1.94 1.84 —
Tab.4  Expansion factors for variance between conventional-type spectral indices
因子 EBSI ECal ECI ENDVI EPDI DVI GVMI RVI NDWI
EBSI —
ECal 1.87 —
ECI 2.98 1.43 —
ENDVI 1.48 1.03 3.14 —
EPDI 1.01 1.00 1.17 1.64 —
DVI 1.56 1.03 2.53 3.53 1.03 —
GVMI 2.30 1.00 1.82 1.96 1.11 1.82 —
RVI 2.62 1.33 5.77 3.93 1.06 11.48 1.74 —
NDWI 1.10 1.09 1.49 4.04 1.22 3.32 1.94 1.84 —
Tab.5  Extended inter-spectral index variance expansion factors
建模方法 建模集 预测集
R2 RMSE R2 RMSE RPD
MLR Ⅰ① 0.64 3.99 0.59 4.53 1.48
Ⅱ 0.75 3.30 0.71 3.67 1.83
RF Ⅰ 0.88 2.43 0.86 2.48 2.63
Ⅱ 0.92 1.69 0.91 2.61 3.12
Tab.6  Linear and non-linear inversion model accuracy validation
Fig.2  Spatial distribution characteristics of soil moisture
Fig.3  Area of soil water content distribution under different models
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