Dynamic monitoring of surface soil moisture fluctuations using synthetic aperture radar and data-driven algorithms

IF 2.3 Q2 REMOTE SENSING

Applied Geomatics Pub Date : 2024-12-10 DOI:10.1007/s12518-024-00606-2

Hrushikesh Rajeev, Punithraj Gururaj, Abhishek A Pathak

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Abstract

The primary goal of the study is to employ Synthetic Aperture Radar (SAR) data and efficacy data driven approaches in modeling Surface Soil Moisture (SSM) of cultivable marginal bare fields. Three experimental test fields were selected which are basically cultivable but due water deficiency the fields are left bare. Samples for surface soil moisture, soil surface roughness and bulk density are collected from test fields in grid sampling manner in parallel with SAR data pass over study area. Sentinel-1 A data is pre-processed and each field sampling grid backscattering energy values are obtained. Surface roughness, dielectric constant and backscattered energy were used as input features to model SSM using Random Forest Regression (RFR), Support Vector Regression (SVR) and Back Propagation Artificial Neural Network (BPANN).We observed that BPANN outperformed SVR and RF by accurately predicting soil moisture with RMSE = 0.077 m³m⁻³, bias = 0.013m³m⁻³, and R = 0.94.This study sheds light on small scale agricultural lands which are deficient of water to support crop growth.

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利用合成孔径雷达和数据驱动算法动态监测地表土壤水分波动

本研究的主要目的是利用合成孔径雷达（SAR）数据和有效性数据驱动方法对可耕边缘裸地的表层土壤水分（SSM）进行建模。选择了3块基本可耕但因缺水而光秃秃的试验试验田。在试验田以网格采样方式采集表层土壤水分、表面粗糙度和容重样本，并与研究区上空的SAR数据并行采集。对sentinel - 1a数据进行预处理，得到各场采样网格后向散射能量值。采用随机森林回归（RFR）、支持向量回归（SVR）和反向传播人工神经网络（BPANN），以表面粗糙度、介电常数和后向散射能量为输入特征对SSM模型进行建模。结果表明，BPANN预测土壤湿度的准确性优于SVR和RF， RMSE = 0.077 m3m−3，偏差= 0.013m3m−3,R = 0.94。这项研究揭示了小规模农业用地缺乏水来支持作物生长的问题。

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来源期刊

Applied Geomatics REMOTE SENSING-

CiteScore

5.40

自引率

3.70%

发文量

期刊介绍： Applied Geomatics (AGMJ) is the official journal of SIFET the Italian Society of Photogrammetry and Topography and covers all aspects and information on scientific and technical advances in the geomatics sciences. The Journal publishes innovative contributions in geomatics applications ranging from the integration of instruments, methodologies and technologies and their use in the environmental sciences, engineering and other natural sciences. The areas of interest include many research fields such as: remote sensing, close range and videometric photogrammetry, image analysis, digital mapping, land and geographic information systems, geographic information science, integrated geodesy, spatial data analysis, heritage recording; network adjustment and numerical processes. Furthermore, Applied Geomatics is open to articles from all areas of deformation measurements and analysis, structural engineering, mechanical engineering and all trends in earth and planetary survey science and space technology. The Journal also contains notices of conferences and international workshops, industry news, and information on new products. It provides a useful forum for professional and academic scientists involved in geomatics science and technology. Information on Open Research Funding and Support may be found here: https://www.springernature.com/gp/open-research/institutional-agreements