Nguyen Thu Thuy, Ngo Huu Hao, Guo Wenshan, Chang Soon Woong, Nguyen Dinh Duc, Nguyen Chi Trung, Zhang Jian, Liang Shuang, Bui Xuan Thanh, Hoang Ngoc Bich
Centre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia.
Centre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia.
Sci Total Environ. 2022 Aug 10;833:155066. doi: 10.1016/j.scitotenv.2022.155066. Epub 2022 Apr 7.
A high-resolution soil moisture prediction method has recently gained its importance in various fields such as forestry, agricultural and land management. However, accurate, robust and non- cost prohibitive spatially monitoring of soil moisture is challenging. In this research, a new approach involving the use of advance machine learning (ML) models, and multi-sensor data fusion including Sentinel-1(S1) C-band dual polarimetric synthetic aperture radar (SAR), Sentinel-2 (S2) multispectral data, and ALOS Global Digital Surface Model (ALOS DSM) to predict precisely soil moisture at 10 m spatial resolution across research areas in Australia. The total of 52 predictor variables generated from S1, S2 and ALOS DSM data fusion, including vegetation indices, soil indices, water index, SAR transformation indices, ALOS DSM derived indices like digital model elevation (DEM), slope, and topographic wetness index (TWI). The field soil data from Western Australia was employed. The performance capability of extreme gradient boosting regression (XGBR) together with the genetic algorithm (GA) optimizer for features selection and optimization for soil moisture prediction in bare lands was examined and compared with various scenarios and ML models. The proposed model (the XGBR-GA model) with 21 optimal features obtained from GA was yielded the highest performance (R = 0. 891; RMSE = 0.875%) compared to random forest regression (RFR), support vector machine (SVM), and CatBoost gradient boosting regression (CBR). Conclusively, the new approach using the XGBR-GA with features from combination of reliable free-of-charge remotely sensed data from Sentinel and ALOS imagery can effectively estimate the spatial variability of soil moisture. The described framework can further support precision agriculture and drought resilience programs via water use efficiency and smart irrigation management for crop production.
一种高分辨率土壤湿度预测方法最近在林业、农业和土地管理等各个领域变得至关重要。然而，对土壤湿度进行准确、稳健且成本不高的空间监测具有挑战性。在本研究中，一种新方法涉及使用先进的机器学习（ML）模型以及多传感器数据融合，包括哨兵 - 1（S1）C波段双极化合成孔径雷达（SAR）、哨兵 - 2（S2）多光谱数据和ALOS全球数字表面模型（ALOS DSM），以在澳大利亚研究区域内以10米空间分辨率精确预测土壤湿度。从S1、S2和ALOS DSM数据融合中总共生成了52个预测变量，包括植被指数、土壤指数、水体指数、SAR变换指数、ALOS DSM衍生指数，如数字模型高程（DEM）、坡度和地形湿度指数（TWI）。采用了来自西澳大利亚的田间土壤数据。研究并比较了极端梯度提升回归（XGBR）与遗传算法（GA）优化器在裸地土壤湿度预测中进行特征选择和优化的性能能力，并与各种场景和ML模型进行了比较。与随机森林回归（RFR）、支持向量机（SVM）和CatBoost梯度提升回归（CBR）相比，具有从GA获得的21个最优特征的所提出模型（XGBR - GA模型）表现出最高性能（R = 0.891；RMSE = 0.875%）。总之，使用结合了来自哨兵和ALOS影像的可靠免费遥感数据特征的XGBR - GA新方法能够有效估计土壤湿度的空间变异性。所描述的框架可通过提高作物生产的用水效率和智能灌溉管理，进一步支持精准农业和抗旱计划。
