Assessment of multi-date Sentinel-2 data for field-level monitoring of isabgol (Plantago ovata Forsk) cropping practices in India

IF 2.8 3区地球科学 Q2 ASTRONOMY & ASTROPHYSICS

Advances in Space Research Pub Date : 2024-08-06 DOI:10.1016/j.asr.2024.08.001

Paras Hirapara, Sandip Patel, R. Nagaraja Reddy, Sujay Dutta , P. Manivel, B.B. Basak, B.K. Bhattacharya , Manish Das

{"title":"Assessment of multi-date Sentinel-2 data for field-level monitoring of isabgol (Plantago ovata Forsk) cropping practices in India","authors":"Paras Hirapara, Sandip Patel, R. Nagaraja Reddy, Sujay Dutta , P. Manivel, B.B. Basak, B.K. Bhattacharya , Manish Das","doi":"10.1016/j.asr.2024.08.001","DOIUrl":null,"url":null,"abstract":"<div><div>Accurate identification and mapping of isabgol fields help in macro-level planning in the arid and semi-arid regions, where variability is very high due to erratic weather conditions, besides providing the production estimates of the crop. Isabgol is an important medicinal crop cultivated in western India. This study aims to accurately identify isabgol growing area at field level with help of progressive remotely sensed satellite data. Sentinel-2 data was used for the first crop season (2020) and the second crop season (2021) for the isabgol crop classification. Cluster to cluster comparison between satellite driven data and ground control point has been done for accuracy assessment. The producer accuracy ranged from 63.80 to 88.00% for the first crop (2020) and 70.84 to 88.89% for the second crop (2021). Our results were in sync with revenue records data (0.95 and 0.99 correlation for the first and second crop seasons, respectively). We found improved producer accuracy for the first crop over the second crop. The results shown that the time series Sentinel-2 data could be used for isabgol identification in various regions of India. The remote sensing-based methods could be used for precise estimation of isabgol crop acreage will help predict demand and supply. This information is valuable to the researchers, policy makers, pharmaceutical industries, and agronomists to accurately address issues related to import/export of isabgol and price fixation.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"74 11","pages":"Pages 5305-5318"},"PeriodicalIF":2.8000,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Space Research","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0273117724008044","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ASTRONOMY & ASTROPHYSICS","Score":null,"Total":0}

引用次数: 0

Abstract

Accurate identification and mapping of isabgol fields help in macro-level planning in the arid and semi-arid regions, where variability is very high due to erratic weather conditions, besides providing the production estimates of the crop. Isabgol is an important medicinal crop cultivated in western India. This study aims to accurately identify isabgol growing area at field level with help of progressive remotely sensed satellite data. Sentinel-2 data was used for the first crop season (2020) and the second crop season (2021) for the isabgol crop classification. Cluster to cluster comparison between satellite driven data and ground control point has been done for accuracy assessment. The producer accuracy ranged from 63.80 to 88.00% for the first crop (2020) and 70.84 to 88.89% for the second crop (2021). Our results were in sync with revenue records data (0.95 and 0.99 correlation for the first and second crop seasons, respectively). We found improved producer accuracy for the first crop over the second crop. The results shown that the time series Sentinel-2 data could be used for isabgol identification in various regions of India. The remote sensing-based methods could be used for precise estimation of isabgol crop acreage will help predict demand and supply. This information is valuable to the researchers, policy makers, pharmaceutical industries, and agronomists to accurately address issues related to import/export of isabgol and price fixation.

查看原文本刊更多论文

评估多日期哨兵-2 数据对印度伊沙格尔（Plantago ovata Forsk）种植方法的田间监测效果

在干旱和半干旱地区，由于天气条件不稳定，变化非常大，因此准确识别和绘制伊沙格尔田地图除了提供作物产量估算外，还有助于这些地区的宏观规划。伊沙格尔是印度西部种植的一种重要药用作物。本研究旨在借助渐进式遥感卫星数据，准确确定伊沙格尔的田间种植面积。哨兵-2 数据用于第一作物季（2020 年）和第二作物季（2021 年）的伊沙格尔作物分类。卫星数据与地面控制点之间进行了聚类比较，以评估准确性。第一季作物（2020 年）和第二季作物（2021 年）的生产者准确率分别为 63.80% 至 88.00%，70.84% 至 88.89%。我们的结果与收入记录数据一致（第一季和第二季的相关性分别为 0.95 和 0.99）。我们发现第一季作物的生产者准确性比第二季作物高。结果表明，时间序列 Sentinel-2 数据可用于印度不同地区的等幅射识别。以遥感为基础的方法可用于精确估算伊沙格尔作物的种植面积，这将有助于预测供需情况。这些信息对于研究人员、政策制定者、制药业和农学家准确解决与伊沙酚进出口和价格确定有关的问题非常有价值。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Advances in Space Research 地学天文-地球科学综合

CiteScore

5.20

自引率

11.50%

发文量

800

审稿时长

5.8 months

期刊介绍： The COSPAR publication Advances in Space Research (ASR) is an open journal covering all areas of space research including: space studies of the Earth''s surface, meteorology, climate, the Earth-Moon system, planets and small bodies of the solar system, upper atmospheres, ionospheres and magnetospheres of the Earth and planets including reference atmospheres, space plasmas in the solar system, astrophysics from space, materials sciences in space, fundamental physics in space, space debris, space weather, Earth observations of space phenomena, etc. NB: Please note that manuscripts related to life sciences as related to space are no more accepted for submission to Advances in Space Research. Such manuscripts should now be submitted to the new COSPAR Journal Life Sciences in Space Research (LSSR). All submissions are reviewed by two scientists in the field. COSPAR is an interdisciplinary scientific organization concerned with the progress of space research on an international scale. Operating under the rules of ICSU, COSPAR ignores political considerations and considers all questions solely from the scientific viewpoint.