耦合多尺度遥感与近端传感器估算作物需水量

E. Psomiadis, S. Alexandris, N. Proutsos, Ioannis Charalampopoulos
{"title":"耦合多尺度遥感与近端传感器估算作物需水量","authors":"E. Psomiadis, S. Alexandris, N. Proutsos, Ioannis Charalampopoulos","doi":"10.1117/12.2680125","DOIUrl":null,"url":null,"abstract":"Precision agriculture has been at the cutting edge of research during the recent decade, aiming to reduce water consumption and ensure sustainability in agriculture. The present study aims to estimate the actual water requirements of crop fields based on the Crop Water Stress Index, combining multiple and multiscale data, such as infrared canopy temperature, air temperature, air relative humidity, near-infrared and thermal infrared image data, taken above the crop field using an innovative aerial micrometeorological station (AMMS), and two more compatible and advanced cameras, a multispectral and a thermal mounted in an Unmanned Aerial Vehicle (UAV), along with satellite-derived thermal data. Moreover, ground micrometeorological stations (GMMS) were installed in each crop. The study area was situated in Trifilia (Peloponnese, Greece) and the experimentation was conducted on two different crops, potato, and watermelon, which are representative cultivations of the area. The analysis of the results showed, in the case of the potato field, that the amount of irrigation water supplied in the rhizosphere far exceeds the maximum crop needs reaching values of about 394% more water than the maximum required amount needed by the crop. Finally, the correlation of the different remote and proximal sensors proved to be sufficiently high while the correlation with the satellite data was moderate. The overall conclusion of this research is that proper irrigation water management is extremely necessary and the only solution for agricultural sustainability in the future. The increasing demand for freshwater, mainly for irrigation purposes, will inevitably lead to groundwater overexploitation and deterioration of the area's already affected and semi-brackish coastal aquifers.","PeriodicalId":222517,"journal":{"name":"Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Coupling multiscale remote and proximal sensors for the estimation of crop water requirements\",\"authors\":\"E. Psomiadis, S. Alexandris, N. Proutsos, Ioannis Charalampopoulos\",\"doi\":\"10.1117/12.2680125\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Precision agriculture has been at the cutting edge of research during the recent decade, aiming to reduce water consumption and ensure sustainability in agriculture. The present study aims to estimate the actual water requirements of crop fields based on the Crop Water Stress Index, combining multiple and multiscale data, such as infrared canopy temperature, air temperature, air relative humidity, near-infrared and thermal infrared image data, taken above the crop field using an innovative aerial micrometeorological station (AMMS), and two more compatible and advanced cameras, a multispectral and a thermal mounted in an Unmanned Aerial Vehicle (UAV), along with satellite-derived thermal data. Moreover, ground micrometeorological stations (GMMS) were installed in each crop. The study area was situated in Trifilia (Peloponnese, Greece) and the experimentation was conducted on two different crops, potato, and watermelon, which are representative cultivations of the area. The analysis of the results showed, in the case of the potato field, that the amount of irrigation water supplied in the rhizosphere far exceeds the maximum crop needs reaching values of about 394% more water than the maximum required amount needed by the crop. Finally, the correlation of the different remote and proximal sensors proved to be sufficiently high while the correlation with the satellite data was moderate. The overall conclusion of this research is that proper irrigation water management is extremely necessary and the only solution for agricultural sustainability in the future. The increasing demand for freshwater, mainly for irrigation purposes, will inevitably lead to groundwater overexploitation and deterioration of the area's already affected and semi-brackish coastal aquifers.\",\"PeriodicalId\":222517,\"journal\":{\"name\":\"Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-10-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/12.2680125\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2680125","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Coupling multiscale remote and proximal sensors for the estimation of crop water requirements
Precision agriculture has been at the cutting edge of research during the recent decade, aiming to reduce water consumption and ensure sustainability in agriculture. The present study aims to estimate the actual water requirements of crop fields based on the Crop Water Stress Index, combining multiple and multiscale data, such as infrared canopy temperature, air temperature, air relative humidity, near-infrared and thermal infrared image data, taken above the crop field using an innovative aerial micrometeorological station (AMMS), and two more compatible and advanced cameras, a multispectral and a thermal mounted in an Unmanned Aerial Vehicle (UAV), along with satellite-derived thermal data. Moreover, ground micrometeorological stations (GMMS) were installed in each crop. The study area was situated in Trifilia (Peloponnese, Greece) and the experimentation was conducted on two different crops, potato, and watermelon, which are representative cultivations of the area. The analysis of the results showed, in the case of the potato field, that the amount of irrigation water supplied in the rhizosphere far exceeds the maximum crop needs reaching values of about 394% more water than the maximum required amount needed by the crop. Finally, the correlation of the different remote and proximal sensors proved to be sufficiently high while the correlation with the satellite data was moderate. The overall conclusion of this research is that proper irrigation water management is extremely necessary and the only solution for agricultural sustainability in the future. The increasing demand for freshwater, mainly for irrigation purposes, will inevitably lead to groundwater overexploitation and deterioration of the area's already affected and semi-brackish coastal aquifers.
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
自引率
0.00%
发文量
0
期刊最新文献
Coupling multiscale remote and proximal sensors for the estimation of crop water requirements Satellite and drone multispectral and thermal images data fusion for intelligent agriculture monitoring and decision making support Infrared imaging for proximal and remote detection of soil-borne diseases on wild rocket Design and development of an innovative online modular device for both water and wastewater monitoring Application of optical data from Sentinel-2-MSI for snow cover monitoring on the territory of the mountainous region of Bulgaria
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1