{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T12:58:36Z","timestamp":1765976316817,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2019,10,21]],"date-time":"2019-10-21T00:00:00Z","timestamp":1571616000000},"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":"<jats:p>Remotely sensed data have been used in crop condition monitoring for decades. Traditionally, crop growth conditions were assessed by comparing Normalized Difference Vegetation Index (NDVI) of the current year and past years at a pixel scale on the same calendar day. The assumption of this comparison is that the different years\u2019 crops were at the same growing stage on the same day. However, this assumption is often violated in reality. This paper proposes to combine remotely sensed data and meteorological data to assess corn growth conditions at the same growth stages at county level. The proposed approach uses the active accumulated temperature (AAT) computed from Daymet, a daily weather data product, to align different years of NDVI time series at the same growth stages estimated from AATs. The study area covers Carroll County, Iowa. The best index slope extraction (BISE) method and Savitzky\u2013Golay filter are used to filter noise and to reconstruct 11 years of corn growing season NDVI time series from 250 m MODIS daily surface reflectance data product (MOD09GQ). The corn growth stages are identified every year with precise Julian dates from AAT time series. The corn growth conditions are assessed based on the aligned growth stages. The validation of the assessed crop conditions is performed based on National Agricultural Statistics Service (NASS) reports. The study indicates that the crop condition assessment results based on aligned growth stages are consistent with the NASS reported results and they are more reliable than the results based on the same calendar days. The proposed method provides not only crop growth condition information but also crop phenology information. Potentially, it can help improve crop yield prediction since it can effectively measure crop growth changes with NDVI and AAT data.<\/jats:p>","DOI":"10.3390\/rs11202439","type":"journal-article","created":{"date-parts":[[2019,10,21]],"date-time":"2019-10-21T03:40:29Z","timestamp":1571629229000},"page":"2439","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Crop Growth Condition Assessment at County Scale Based on Heat-Aligned Growth Stages"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1645-064X","authenticated-orcid":false,"given":"Yonglan","family":"Qian","sequence":"first","affiliation":[{"name":"National Meteorological Center, China Meteorological Administration, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6532-2663","authenticated-orcid":false,"given":"Zhengwei","family":"Yang","sequence":"additional","affiliation":[{"name":"National Agricultural Statistics Service, U.S. Department of Agriculture, Washington, DC 20250, USA"}]},{"given":"Liping","family":"Di","sequence":"additional","affiliation":[{"name":"Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 20030, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5540-3307","authenticated-orcid":false,"given":"Md. Shahinoor","family":"Rahman","sequence":"additional","affiliation":[{"name":"Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 20030, USA"}]},{"given":"Zhenyu","family":"Tan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China"}]},{"given":"Lei","family":"Xue","sequence":"additional","affiliation":[{"name":"National Meteorological Information Center, China Meteorological Administration, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1865-2846","authenticated-orcid":false,"given":"Feng","family":"Gao","sequence":"additional","affiliation":[{"name":"Agricultural Research Service, U.S. Department of Agriculture, Beltsville, MD 20705, USA"}]},{"given":"Eugene Genong","family":"Yu","sequence":"additional","affiliation":[{"name":"Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 20030, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8456-0547","authenticated-orcid":false,"given":"Xiaoyang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Geospatial Sciences Center of Excellence and Department of Geography, South Dakota State University, Brookings, SD 57007, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/0034-4257(79)90004-X","article-title":"Monitoring corn and soybean crop development with hand-held radiometer spectral data","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. 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