Farmland soil moisture monitoring based on UAV multispectral imagery

doi:10.13474/j.cnki.11-2246.2024.1131

Abstract

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

CLC Number:

P237

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

References

[1] 晏红波, 周国清.地表土壤湿度光学遥感反演方法研究进展[J].亚热带资源与环境学报, 2017, 12(2):82-89.
[2] HUANG Yanbo, CHEN Zhongxin, YU Tao, et al.Agricultural remote sensing big data:management and applications[J].Journal of Integrative Agriculture, 2018, 17(9):1915-1931.
[3] 姚远, 陈曦, 钱静.遥感数据在农业旱情监测中的应用研究进展[J].光谱学与光谱分析, 2019, 39(4):1005-1012.
[4] 孙伟伟, 杨刚, 陈超, 等.中国地球观测遥感卫星发展现状及文献分析[J].遥感学报, 2020, 24(5):479-510.
[5] 杜培军, 夏俊士, 薛朝辉, 等.高光谱遥感影像分类研究进展[J].遥感学报, 2016, 20(2):236-256.
[6] SALAMÍ E, BARRADO C, PASTOR E.UAV flight experiments applied to the remote sensing of vegetated areas[J].Remote Sensing, 2014, 6(11):11051-11081.
[7] 纪景纯, 赵原, 邹晓娟, 等.无人机遥感在农田信息监测中的应用进展[J].土壤学报, 2019, 56(4):773-784.
[8] FAN D, SU X, WENG B, et al.Research progress on remote sensing classification methods for farmland vegetation[J].AgriEngineering, 2021, 3(4):971-989.
[9] MARTÍNEZ J, EGEA G, AGVERA J, et al.A cost-effective canopy temperature measurement system for precision agriculture:a case study on sugar beet[J].Precision Agriculture, 2017, 18(1):95-110.
[10] HOFFMANN H, JENSEN R, THOMSEN A, et al.Crop water stress maps for an entire growing season from visible and thermal UAV imagery[J].Biogeosciences, 2016, 13(24):6545-6563.
[11] HOFFMANN H, NIETO H, JENSEN R, et al.Estimating evaporation with thermal UAV data and two-source energy balance models[J].Hydrology and Earth System Sciences, 2016, 20(2):697-713.
[12] ACEVO-HERRERA R, AGUASCA A, BOSCH-LLUIS X, et al.Design and first results of an UAV-borne L-band radiometer for multiple monitoring purposes[J].Remote Sensing, 2010, 2(7):1662-1679.
[13] MOLINA A J, LLORENS P, GARCIA-ESTRINGANA P, et al.Contributions of throughfall, forest and soil characteristics to near-surface soil water-content variability at the plot scale in a mountainous Mediterranean area[J].Science of the Total Environment, 2019, 647:1421-1432.
[14] 陈硕博, 陈俊英, 张智韬, 等.无人机多光谱遥感反演抽穗期冬小麦土壤含水率研究[J].节水灌溉, 2018(5):39-43.
[15] 王海峰, 张智韬, 付秋萍, 等.低空无人机多光谱遥感数据的土壤含水率反演[J].节水灌溉, 2018(1):90-94.
[16] 韩文霆, 张立元, 牛亚晓, 等.无人机遥感技术在精量灌溉中应用的研究进展[J].农业机械学报, 2020, 51(2):1-14.
[17] 樊湘鹏, 周建平, 许燕.无人机低空遥感监测农情信息研究进展[J].新疆大学学报(自然科学版)(中英文), 2021, 38(5):623-631.
[18] 史舟, 梁宗正, 杨媛媛, 等.农业遥感研究现状与展望[J].农业机械学报, 2015, 46(2):247-260.
[19] 冯珊珊, 梁雪映, 樊风雷, 等.基于无人机多光谱数据的农田土壤水分遥感监测[J].华南师范大学学报(自然科学版), 2020, 52(6):74-81.
[20] 郭燕, 井宇航, 王来刚, 等.基于无人机影像特征的冬小麦植株氮含量预测及模型迁移能力分析[J].中国农业科学, 2023, 56(5):850-865.
[21] ZHAO W, ZHOU C, ZHOU C, et al.Soil salinity inversion model of oasis in arid area based on UAV multispectral remote sensing[J].Remote Sensing, 2022, 14(8):1804.
[22] TAO Huilin, FENG Haikuan, XU Liangji, et al.Estimation of crop growth parameters using UAV-based hyperspectral remote sensing data[J].Sensors, 2020, 20(5):1296.
[23] AINIWAER M, DING Jianli, KASIM N, et al.Regional scale soil moisture content estimation based on multi-source remote sensing parameters[J].International Journal of Remote Sensing, 2020, 41(9):3346-3367.
[24] LALIBERTE A S, GOFORTH M A, STEELE C M, et al.Multispectral remote sensing from unmanned aircraft:image processing workflows and applications for rangeland environments[J].Remote Sensing, 2011, 3(11):2529-2551.