IF 5.9 1区 农林科学 Q1 AGRONOMY
Yixuan Wang, Nana Yan, Weiwei Zhu, Zonghan Ma, Bingfang Wu
{"title":"A method to estimate the water storage of on-farm reservoirs by detecting slope gradients based on multi-spectral drone data","authors":"Yixuan Wang, Nana Yan, Weiwei Zhu, Zonghan Ma, Bingfang Wu","doi":"10.1016/j.agwat.2024.109241","DOIUrl":null,"url":null,"abstract":"Water storage dynamics in on-farm reservoirs (OFRs) are crucial for irrigation water allocation and utilization, ensuring agricultural development sustainability. Previous studies have primarily relied on the area-storage model to estimate reservoir water storage using meter-level remotely sensed data, which often falls short of accurately capturing the water storage dynamics of OFRs, especially in small OFRs with steep slopes. Hence, we proposed a method to estimate the water storage of OFRs in irrigation areas by integrating multispectral drone data, high-resolution remote sensing data, and ground observations. The water surface area was extracted from multispectral drone data using a Gaussian Mixture Model (GMM) and a threshold segmentation method. Slope gradients were then obtained by identifying the maximum potential slope zone (PSZ) and utilizing high-resolution drone-based Digital Surface Models (DSMs). The dam slopes of the constructed boundaries were automatically computed considering the significant decline of slope gradients. By combining the dam slopes of the OFRs with water depth observations, we estimated the construction depth (H). Subsequently, current water depth was obtained using drone-derived Digital Surface Models (DSMs), calculating the elevation difference between drone-derived OFRs and water surface boundary. Once the OFR morphology was fully constructed, the water storage was calculated based on area-storage and depth-storage methods using the 3D volume module in Arcpy. The derived water storage agrees well with in situ observation (R<ce:sup loc=\"post\">2</ce:sup>: 0.99) using slope gradients, reaching an overall accuracy of 95.2 %, with a root mean square error (RMSE) and a mean absolute error (MAE) of 2785 m<ce:sup loc=\"post\">3</ce:sup> and 1820 m<ce:sup loc=\"post\">3</ce:sup>, respectively. Notably, discernible fluctuations in water storage were observed during the main irrigation phases, highlighting the essential role of OFRs in promoting equitable water resource distribution and enhancing irrigation water management. This integrated approach offers a robust solution for monitoring and managing water storage dynamics in agricultural areas.","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"48 1","pages":""},"PeriodicalIF":5.9000,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Agricultural Water Management","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1016/j.agwat.2024.109241","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRONOMY","Score":null,"Total":0}
引用次数: 0

摘要

农田水库(OFR)的蓄水动态对灌溉水的分配和利用至关重要,可确保农业发展的可持续性。以往的研究主要依靠面积蓄水模型,利用米级遥感数据估算水库蓄水量,往往不能准确捕捉农田水库的蓄水动态,尤其是在坡度较陡的小型农田水库中。因此,我们提出了一种通过整合多光谱无人机数据、高分辨率遥感数据和地面观测数据来估算灌区 OFR 储水量的方法。利用高斯混合模型(GMM)和阈值分割方法从多光谱无人机数据中提取水面面积。然后,通过识别最大潜在坡度区(PSZ)并利用基于无人机的高分辨率数字地表模型(DSM),获得坡度梯度。考虑到坡度梯度的显著下降,自动计算了所建边界的坝坡。通过将 OFR 的坝坡与水深观测数据相结合,我们估算出了施工深度(H)。随后,利用无人机获取的数字地表模型(DSM),计算无人机获取的 OFR 与水面边界之间的高差,从而获得当前水深。一旦 OFR 形态完全构建完成,就可以使用 Arcpy 中的三维体积模块,根据面积存储和深度存储方法计算蓄水量。利用坡度梯度得出的蓄水量与现场观测结果吻合良好(R2:0.99),总体精度达到 95.2%,均方根误差(RMSE)和平均绝对误差(MAE)分别为 2785 立方米和 1820 立方米。值得注意的是,在主要灌溉阶段都观察到了明显的蓄水波动,这突出表明了 OFR 在促进水资源公平分配和加强灌溉水管理方面的重要作用。这种综合方法为监测和管理农业地区的蓄水动态提供了一个强有力的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A method to estimate the water storage of on-farm reservoirs by detecting slope gradients based on multi-spectral drone data
Water storage dynamics in on-farm reservoirs (OFRs) are crucial for irrigation water allocation and utilization, ensuring agricultural development sustainability. Previous studies have primarily relied on the area-storage model to estimate reservoir water storage using meter-level remotely sensed data, which often falls short of accurately capturing the water storage dynamics of OFRs, especially in small OFRs with steep slopes. Hence, we proposed a method to estimate the water storage of OFRs in irrigation areas by integrating multispectral drone data, high-resolution remote sensing data, and ground observations. The water surface area was extracted from multispectral drone data using a Gaussian Mixture Model (GMM) and a threshold segmentation method. Slope gradients were then obtained by identifying the maximum potential slope zone (PSZ) and utilizing high-resolution drone-based Digital Surface Models (DSMs). The dam slopes of the constructed boundaries were automatically computed considering the significant decline of slope gradients. By combining the dam slopes of the OFRs with water depth observations, we estimated the construction depth (H). Subsequently, current water depth was obtained using drone-derived Digital Surface Models (DSMs), calculating the elevation difference between drone-derived OFRs and water surface boundary. Once the OFR morphology was fully constructed, the water storage was calculated based on area-storage and depth-storage methods using the 3D volume module in Arcpy. The derived water storage agrees well with in situ observation (R2: 0.99) using slope gradients, reaching an overall accuracy of 95.2 %, with a root mean square error (RMSE) and a mean absolute error (MAE) of 2785 m3 and 1820 m3, respectively. Notably, discernible fluctuations in water storage were observed during the main irrigation phases, highlighting the essential role of OFRs in promoting equitable water resource distribution and enhancing irrigation water management. This integrated approach offers a robust solution for monitoring and managing water storage dynamics in agricultural areas.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Agricultural Water Management
Agricultural Water Management 农林科学-农艺学
CiteScore
12.10
自引率
14.90%
发文量
648
审稿时长
4.9 months
期刊介绍: Agricultural Water Management publishes papers of international significance relating to the science, economics, and policy of agricultural water management. In all cases, manuscripts must address implications and provide insight regarding agricultural water management.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信