乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T18:23:00Z","timestamp":1732040580144},"reference-count":75,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,6,19]],"date-time":"2021-06-19T00:00:00Z","timestamp":1624060800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Spatial information embedded in a crop model can improve yield prediction. Leaf area index (LAI) is a well-known crop variable often estimated from remote-sensing data and used as an input into crop models. In this study, we evaluated the assimilation of LAI derived from high-resolution (both spatial and temporal) satellite imagery into a mechanistic crop model, a simple algorithm for yield estimate (SAFY), to assess the within-field crop yield. We tested this approach on spring wheat grown in Israel. Empirical LAI models were derived from the biophysical processor for Sentinel-2 LAI and spectral vegetation indices from Sentinel-2 and PlanetScope images. The predicted grain yield obtained from the SAFY model was compared against the harvester\u2019s yield map. LAI derived from PlanetScope and Sentinel-2 fused images achieved higher yield prediction (RMSE = 69 g\/m2) accuracy than that of Sentinel-2 LAI (RMSE = 88 g\/m2). Even though the spatial yield estimation was only moderately correlated to the ground truth (R2 = 0.45), this is consistent with current studies in this field, and the potential to capture within-field yield variations using high-resolution imagery has been demonstrated. Accordingly, this is the first application of PlanetScope and Sentinel-2 images conjointly used to obtain a high-density time series of LAI information to model within-field yield variability.<\/jats:p>","DOI":"10.3390\/rs13122395","type":"journal-article","created":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T01:50:15Z","timestamp":1624240215000},"page":"2395","source":"Crossref","is-referenced-by-count":22,"title":["Studying the Feasibility of Assimilating Sentinel-2 and PlanetScope Imagery into the SAFY Crop Model to Predict Within-Field Wheat Yield"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-6186-8941","authenticated-orcid":false,"given":"V.S.","family":"Manivasagam","sequence":"first","affiliation":[{"name":"Agricultural Research Organization, Volcani Institute, Institute of Soil, Water and Environmental Sciences, HaMaccabim Road 68, P.O. Box 15159, Rishon LeZion 7528809, Israel"},{"name":"Amrita School of Agricultural Sciences, Amrita Vishwa Vidyapeetham, J. P. Nagar, Arasampalayam, Myleripalayam, Coimbatore 642 109, Tamil Nadu, India"}]},{"given":"Yuval","family":"Sadeh","sequence":"additional","affiliation":[{"name":"School of Earth, Atmosphere and Environment, Monash University, Clayton, VIC 3800, Australia"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-7873-5619","authenticated-orcid":false,"given":"Gregoriy","family":"Kaplan","sequence":"additional","affiliation":[{"name":"Agricultural Research Organization, Volcani Institute, Institute of Soil, Water and Environmental Sciences, HaMaccabim Road 68, P.O. Box 15159, Rishon LeZion 7528809, Israel"}]},{"given":"David J.","family":"Bonfil","sequence":"additional","affiliation":[{"name":"Field Crops and Natural Resources Department, Agricultural Research Organization, Gilat Research Center, M.P. Negev 8531100, Israel"}]},{"given":"Offer","family":"Rozenstein","sequence":"additional","affiliation":[{"name":"Agricultural Research Organization, Volcani Institute, Institute of Soil, Water and Environmental Sciences, HaMaccabim Road 68, P.O. Box 15159, Rishon LeZion 7528809, Israel"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/bs.agron.2018.11.002","article-title":"Seasonal crop yield forecast: Methods, applications, and accuracies","volume":"Volume 154","author":"Basso","year":"2019","journal-title":"Advances in Agronomy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1016\/j.agrformet.2010.07.008","article-title":"On the use of statistical models to predict crop yield responses to climate change","volume":"150","author":"Lobell","year":"2010","journal-title":"Agric. For. 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