Abstract
Soil moisture is the crucial carrier of the global hydrologic cycle and the dynamic energy balance regulation process. Therefore, it is of great significance to monitor surface soil moisture content (SMC) accurately for the study of the natural ecological environment. The Hapke model is the most widely used photometric model in soil remote sensing research, but the development of this model is limited by the lack of valid multi–angular data. The main innovations of this paper have two aspects: (1) A novel soil moisture retrieval approach based on the Hapke (SMR–Hapke) model is derived by exploring the relationship between single scattering albedo (SSA) and SMC on the optical bands from 400 to 2400 nm. The performance of the proposed model was verified on a dataset consisting of four different soil samples, and the experimental results indicated that the inverted soil moisture from SMR–Hapke model coincided with the measurement values, with the R2 being generally more than 0.9 in the solar domain. (2) The SMR–Hapke model has been reduced to a linear form on the SWIR field and a physically-based normalized difference soil moisture index N D S M I H a p k e has been proposed. Based on the laboratory-based hyperspectral data, we compared the performance of N D S M I H a p k e with other traditional soil moisture indices using linear regression analysis, and the results demonstrate that the proposed N D S M I H a p k e had a great potential for estimating SMC with R2 values of 0.88. Finally, high–resolution SMC map was produced by combining the Sentinel–2 MSI data with N D S M I H a p k e . This study provides a novel extended Hapke model for the estimation of surface soil moisture content.
Highlights
Soil moisture, one of the main ingredients of terrestrial ecosystems, links the global energy and hydrologic cycles by modulating the partitioning of sensible and latent heat fluxes [1], and profoundly affects the spatio–temporal variation of climatic conditions [2]
Based on the laboratory-based hyperspectral data, we compared the performance of NDSMIHapke with other traditional soil moisture indices using linear regression analysis, and the results demonstrate that the proposed NDSMIHapke had a great potential for estimating soil moisture content (SMC) with R2 values of 0.88
The model contains five physical and empirical parameters to determine the conversion between moisture content and reflectance; (ii) developed a new normalized difference soil moisture index (NDSMIHapke ) from the linearized SMR–Hapke model in the shortwave infrared (SWIR) region, and compared the performance with other moisture index models; (iii) high-resolution SMC map was produced combining the Sentinel–2 multispectral instrument (MSI) multispectral data and NDSMIHapke
Summary
One of the main ingredients of terrestrial ecosystems, links the global energy and hydrologic cycles by modulating the partitioning of sensible and latent heat fluxes [1], and profoundly affects the spatio–temporal variation of climatic conditions [2]. With the development of machine learning, statistical models combining hyperspectral data for soil moisture retrieval have emerged in recent years [18,19], such as support vector machine (SVM) [20,21], artificial neural networks (ANNs) [22,23], etc These statistical models have certain limitations in explaining the variation of reflectance in terms of the spectral mechanism. The model contains five physical and empirical parameters to determine the conversion between moisture content and reflectance; (ii) developed a new normalized difference soil moisture index (NDSMIHapke ) from the linearized SMR–Hapke model in the shortwave infrared (SWIR) region, and compared the performance with other moisture index models; (iii) high-resolution SMC map was produced combining the Sentinel–2 MSI multispectral data and NDSMIHapke. Tw: transmittance αB: the specific absorption coefficient of in situ water L: the thickness of the water layer free parameters: ε, L a1: the ratio of the absorption coefficient of soil water to the scattering coefficient of soil with a water content of θ1 free parameters: a1 r(θs, θo, φ): the soil bidirectional reflectance model α: absorption coefficient ξ: equivalent water thickness free parameters : ω, B0, h, b, c, b , c , ξ
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