基于无人机多光谱影像的农田土壤水分监测

doi:10.13474/j.cnki.11-2246.2024.1131

摘要/Abstract

摘要： 利用无人机进行土壤水分监测具有低成本、便捷、快速和准确的特点,对于农田地区智能化管理具有重要的实践意义。本文选取良丰农场作为研究区域,在该区域利用无人机搭载多光谱相机进行土壤水分监测。通过灰色关联度筛选,选择土壤水分敏感的光谱数据,并与实测的土壤水分数据进行回归分析,构建了基于无人机多光谱遥感的土壤水分反演模型。通过对比NIR-RE-G模型和B-R-G-RE-NIR模型得出的结果,发现决定系数R²均大于0.77。B-R-G-RE-NIR模型在R²与RMSE的精度评估结果上较优于NIR-RE-G模型,因此两模型整体上反演结果均精度较高。故本文验证了NIR-RE-G模型和B-R-G-RE-NIR模型在该区域土壤水分监测中的有效性和可行性,这为快速监测大范围农田土壤水分提供了有效的方法和可靠的参考依据。

关键词: 土壤湿度, 多光谱遥感, 无人机, 回归分析

Abstract: Using drones to monitor soil moisture is low-cost, convenient, fast and accurate, and has important practical significance for intelligent management of farmland areas. This study selected Liangfeng Farm as the research area, where a drone equipped with a multispectral camera is used to monitor soil moisture. Through gray correlation screening, soil moisture sensitive spectral data are selected, and regression analysis was performed with the measured soil moisture data to construct a soil moisture inversion model based on UAV multispectral remote sensing. Through comparative analysis of the results of the NIR-RE-G model and the B-R-G-RE-NIR model, it is found that the determination coefficient R² is both greater than 0.77. The B-R-G-RE-NIR model is better than the NIR-RE-G model in terms of accuracy evaluation results of R² and RMSE, so the overall inversion results of both models have higher accuracy. Therefore, this study verified the effectiveness and feasibility of the NIR-RE-G model and the B-R-G-RE-NIR model in soil moisture monitoring in this region, which provides an effective method and reliable reference for rapid monitoring of soil moisture in large-scale farmland.

Key words: soil moisture, multi-spectral remote sensing, UAV, regression analysis

中图分类号:

P237

赵贵平, 徐发俊. 基于无人机多光谱影像的农田土壤水分监测[J]. 测绘通报, 2024, 0(11): 177-182.

ZHAO Guiping, XU Fajun. Farmland soil moisture monitoring based on UAV multispectral imagery[J]. Bulletin of Surveying and Mapping, 2024, 0(11): 177-182.

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