Le Yu , Zhenrong Du , Xiyu Li , Qiang Zhao , Hui Wu , Duoji weise , Xinqun Yuan , Yuanzheng Yang , Wenhua Cai , Weimin Song , Pei Wang , Zhicong Zhao , Ying Long , Yongguang Zhang , Jinbang Peng , Xiaoping Xin , Fei Xu , Miaogen Shen , Hui Wang , Yuanmei Jiao , Yong Luo
{"title":"利用近地相机对农田进行监测,促进气候智能型农业的发展","authors":"Le Yu , Zhenrong Du , Xiyu Li , Qiang Zhao , Hui Wu , Duoji weise , Xinqun Yuan , Yuanzheng Yang , Wenhua Cai , Weimin Song , Pei Wang , Zhicong Zhao , Ying Long , Yongguang Zhang , Jinbang Peng , Xiaoping Xin , Fei Xu , Miaogen Shen , Hui Wang , Yuanmei Jiao , Yong Luo","doi":"10.1016/j.csag.2024.100008","DOIUrl":null,"url":null,"abstract":"<div><p>Continuous and accurate monitoring of agricultural landscapes is crucial for understanding crop phenology and responding to climatic and anthropogenic changes. However, the widely used optical satellite remote sensing is limited by revisit cycles and weather conditions, leading to gaps in agricultural monitoring. To address these limitations, we designed and deployed a Near Surface Camera (NSCam) Network across China, and explored its application in agricultural land monitoring and achieving climate-smart agriculture (CSA). By analyzing the image data captured by the NSCam Network, we can accurately assess long-term or abrupt agricultural land changes. According to the preliminary monitoring results, integrating NSCam data with remote sensing imagery greatly enhances the temporal details and accuracy of agricultural monitoring, aiding agricultural managers in making informed decisions. The impacts of abnormal weather conditions and human activities on agricultural land, which are not captured by remote sensing imagery, can be complemented by incorporating our NSCam Network. The successful implementation of this method underscores its potential for broader application in CSA, promoting resilient and sustainable agricultural practices.</p></div>","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"1 1","pages":"Article 100008"},"PeriodicalIF":0.0000,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S295040902400008X/pdfft?md5=6469db54577239abf9e1cab9b8ea62db&pid=1-s2.0-S295040902400008X-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Near surface camera informed agricultural land monitoring for climate smart agriculture\",\"authors\":\"Le Yu , Zhenrong Du , Xiyu Li , Qiang Zhao , Hui Wu , Duoji weise , Xinqun Yuan , Yuanzheng Yang , Wenhua Cai , Weimin Song , Pei Wang , Zhicong Zhao , Ying Long , Yongguang Zhang , Jinbang Peng , Xiaoping Xin , Fei Xu , Miaogen Shen , Hui Wang , Yuanmei Jiao , Yong Luo\",\"doi\":\"10.1016/j.csag.2024.100008\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Continuous and accurate monitoring of agricultural landscapes is crucial for understanding crop phenology and responding to climatic and anthropogenic changes. However, the widely used optical satellite remote sensing is limited by revisit cycles and weather conditions, leading to gaps in agricultural monitoring. To address these limitations, we designed and deployed a Near Surface Camera (NSCam) Network across China, and explored its application in agricultural land monitoring and achieving climate-smart agriculture (CSA). By analyzing the image data captured by the NSCam Network, we can accurately assess long-term or abrupt agricultural land changes. According to the preliminary monitoring results, integrating NSCam data with remote sensing imagery greatly enhances the temporal details and accuracy of agricultural monitoring, aiding agricultural managers in making informed decisions. The impacts of abnormal weather conditions and human activities on agricultural land, which are not captured by remote sensing imagery, can be complemented by incorporating our NSCam Network. The successful implementation of this method underscores its potential for broader application in CSA, promoting resilient and sustainable agricultural practices.</p></div>\",\"PeriodicalId\":100262,\"journal\":{\"name\":\"Climate Smart Agriculture\",\"volume\":\"1 1\",\"pages\":\"Article 100008\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S295040902400008X/pdfft?md5=6469db54577239abf9e1cab9b8ea62db&pid=1-s2.0-S295040902400008X-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Climate Smart Agriculture\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S295040902400008X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Climate Smart Agriculture","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S295040902400008X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Near surface camera informed agricultural land monitoring for climate smart agriculture
Continuous and accurate monitoring of agricultural landscapes is crucial for understanding crop phenology and responding to climatic and anthropogenic changes. However, the widely used optical satellite remote sensing is limited by revisit cycles and weather conditions, leading to gaps in agricultural monitoring. To address these limitations, we designed and deployed a Near Surface Camera (NSCam) Network across China, and explored its application in agricultural land monitoring and achieving climate-smart agriculture (CSA). By analyzing the image data captured by the NSCam Network, we can accurately assess long-term or abrupt agricultural land changes. According to the preliminary monitoring results, integrating NSCam data with remote sensing imagery greatly enhances the temporal details and accuracy of agricultural monitoring, aiding agricultural managers in making informed decisions. The impacts of abnormal weather conditions and human activities on agricultural land, which are not captured by remote sensing imagery, can be complemented by incorporating our NSCam Network. The successful implementation of this method underscores its potential for broader application in CSA, promoting resilient and sustainable agricultural practices.