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Remote Sensing for Natural Resources    2022, Vol. 34 Issue (2) : 1-9     DOI: 10.6046/zrzyyg.2021126
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A review of remote sensing inversion methods for estimating soil water content based on hyperspectral characteristics
YAN Hongbo1,2(), WEI Wanqiu1, LU Xianjian1,2(), YANG Zhigao1, LI Zhenbao1
1. College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China
2. Guangxi Laboratory of Spatial Information and Mapping, Guilin 541004,China
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Abstract  

The rapid and accurate estimation of soil water content at different spatial and temporal scales is key research content in the fields of hydrology, environment, geology, agriculture, and climate change. However, it is still a challenge to obtain accurate soil water content presently. In the past, the traditional point-based soil sampling and analysis methods were time-consuming and laborious. By contrast, retrieving soil water content using remote sensing images has the advantages of a wide range, high timeliness, low cost, and strong dynamic contrast. In hyperspectral remote sensing, soil water content is related to the wavelength range of soil reflectance. So far, many methods have been used to describe the relationships between soil water content and hyperspectral remote sensing. This paper summarized existing methods for estimating soil water content based on hyperspectral reflectance and divided them into four categories: spectral reflectance methods, function methods, model methods, and machine learning methods. Moreover, this paper compared and analyzed the potential and limitations of different methods in terms of accuracy, complexity, auxiliary data requirements, operability under different modes, and the dependence on soil types. Finally, this study put forward corresponding suggestions for future research on the relationships between soil water content and hyperspectral reflectance.
Keywords hyperspectral      diffuse reflectance      reflectance      soil water content      optical remote sensing     
ZTFLH:  TP79  
Corresponding Authors: LU Xianjian     E-mail: 56403075@qq.com;285922956@qq.com
Issue Date: 20 June 2022
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Hongbo YAN
Wanqiu WEI
Xianjian LU
Zhigao YANG
Zhenbao LI
Cite this article:   
Hongbo YAN,Wanqiu WEI,Xianjian LU, et al. A review of remote sensing inversion methods for estimating soil water content based on hyperspectral characteristics[J]. Remote Sensing for Natural Resources, 2022, 34(2): 1-9.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021126     OR     https://www.gtzyyg.com/EN/Y2022/V34/I2/1
Fig.1  Variation of θ - R
Fig.2  Different classification methods of θ-R relationships
类别 方法 波长/nm N① R2② RMSE③ 代表性文献 优点 缺点
波谱反射率法 反射率差分指数法 2 062,2 250 18 0.69 0.08 Haubrock等[1] 土壤类型影响小 计算相对复杂
反射率指数模型法 350~2 500 4 - - Lobell等[10] 短波红外范围应用良好 不适用土壤体积含水量低于20%的情况
反射率比值指数法 1 944 18 0.68 0.08 刘伟东等[13] 计算方便快速 依赖干燥土壤光谱信息
反射率一阶导数法 1 834,1 836 18 0.63 0.08 刘伟东等[13] 无需先验土壤信息 产生冗余的光谱信息
相对反射率法 1 998 10 0.84 0.04 刘伟东[15] 计算简单快速 不适用于野外
反射率物理模型法 350~2 100 1 >0.99 - van Genuchten[27] 考虑到土壤的物理特性 需获取特定初始信息,应用具有局限性
函数法 反高斯函数法 1 200~2 500 257 0.92 0.03 Whiting等[29] 可以结合各类高光谱模式使用 难以确定输入信息,须对光谱进行额外处理
模型法 光学模型法 350~2 100 1 >0.97 - Haubrock等[1] 无需考虑高光谱反射率 应用具有局限性
机器学习法 SMLR 1 623~2 467 1 571 0.88 5.19 申艳等[38] 减少变量间的共线性问题 信息易丢失,导致模型的过适应性
PCR 400~2 498 802 0.84 0.005 申艳等[38] 应用全部的光谱信息 计算繁琐
PLSR 401~1 699 360 0.97 0.02 Svante等[40]
830~2 630 403 0.96 0.60 李晓明[39] 应用全部的光谱信息 无法解释土壤光谱间的非线性效应
370~1 979 1 160 0.66 0.76 Svante等[40]
ANN 420~800 - 0.77 2.00 Leila等[45] 自动学习分析,无需基础辅助参数 容易过度拟合
SVMR 370~1 979 1 160 0.69 0.72 Vapnik等[46] 无需基础辅助参数 仅适用于密集型计算
MLR 450~2 500 40 0.68~0.96 - 贾学勤等[47] 不受土壤类型影响 忽略某些点的光谱信息
MARS 370~1 979 1 160 0.73 0.67 Friedman[48] 自动建立联系,无需基础辅助参数 过程繁琐
Tab.1  Comparison of different methods for estimating soil water content using hyperspectral characteristics
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