Abstract
As an indispensable ecological parameter, surface soil moisture (SSM) is of great significance for understanding the growth status of vegetation. The cooperative use of synthetic aperture radar (SAR) and optical data has the advantage of considering both vegetation and underlying soil scattering information, which is suitable for SSM monitoring of vegetation areas. The main purpose of this paper is to establish an inversion approach using Terra-SAR and Landsat-7 data to estimate SSM at three different stages of corn growth in the irrigated area. A combined scattering model that can adequately represent the scattering characteristics of the vegetation coverage area is proposed by modifying the water cloud model (WCM) to reduce the effect of vegetation on the total SAR backscattering. The backscattering from the underlying soil is expressed by an empirical model with good performance in X-band. The modified water cloud model (MWCM) as a function of normalized differential vegetation index (NDVI) considers the contribution of vegetation to the backscattering signal. An inversion technique based on artificial neural network (ANN) is used to invert the combined scattering model for SSM estimation. The inversion method is established and verified using datasets of three different growth stages of corn. Using the proposed method, we estimate the SSM with a correlation coefficient R ≥ 0.72 and root-mean-square error R M S E ≤ 0.043 cm 3 /cm 3 at the emergence stage, with R ≥ 0.87 and R M S E ≤ 0.046 cm 3 /cm 3 at the trefoil stage and with R ≥ 0.70 and R M S E ≤ 0.064 cm 3 /cm 3 at the jointing stage. The results suggest that the method proposed in this paper has operational potential in estimating SSM from Terra-SAR and Landsat-7 data at different stages of early corn growth.
Highlights
Surface soil moisture (SSM) accounts for only a small part of the entire water cycle, it is a crucial basis for various hydrological, biological, and biogeochemical processes
We provide a combined scattering model based on a modified water cloud model (MWCM) (see Section 3.1 and Equations (10) and (11)) and an empirical model proposed by El Hajj et al [27] to determine the relationship between surface soil moisture (SSM) and synthetic aperture radar (SAR) backscattering coefficient
To ensure that the data of each stage can be divided into calibration and verification sample points, the data of each stage is first divided into two parts, and the calibration sample points of all stages are used to parameterize the combined scattering model
Summary
Surface soil moisture (SSM) accounts for only a small part of the entire water cycle, it is a crucial basis for various hydrological, biological, and biogeochemical processes. It controls the distribution between surface infiltration and surface runoff, and is a primary factor for energy exchange between land and atmosphere [1]. The accurate acquisition of SSM with high temporal and spatial resolution is important for precision irrigation, crop growth monitoring and field estimation [2]. As a kind of active microwave remote sensing with high spatial resolution and high penetrating ability, synthetic aperture radar (SAR) has been widely studied in the restoration of SSM [21,22,23,24,25,26,27,28,29]
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