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Improving remotely-sensed crop monitoring by NDVI-based crop phenology estimators for corn and soybeans in Iowa and Illinois, USA

Seo, Bumsuk; Lee, Jihye; Lee, Kyung-Do; Hong, Sukyoung; Kang, Sinkyu

Abstract (englisch):
Weather-related risks in crop production are not only crucial for farmers but also for market participants and policymakers since securing food supply is an important issue for society. Although crop growth condition and phenology represent essential information regarding such risks, extensive observations of these variables are virtually non-existent in many parts of the world. In this study, we developed an integrative approach to re- motely monitor crop growth at a large scale. For corn and soybeans in Iowa and Illinois in the United States (2003–2015), we monitored crop growth and crop phenology with earth observation data and compared it against the United States Department of Agriculture National Agricultural Statistics Service (NASS) crop statistics. For crop phenology, we calculated three phenology metrics (i.e., start of season, end of season, and peak of season) at the pixel level from the MODIS 16-day Normalized Difference Vegetation Index (NDVI). For growth condition, we used two distinct approaches to acquire crop growth condition indicators: a process-based crop growth modeling and a satellite-NDVI-based method. Based on their pixel-wise historical distributions, we monitored relative growth strength and scaled-up that to the state-level. ... mehr



Originalveröffentlichung
DOI: 10.1016/j.fcr.2019.03.015
Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung - Atmosphärische Umweltforschung (IMK-IFU)
Publikationstyp Zeitschriftenaufsatz
Jahr 2019
Sprache Englisch
Identifikator ISSN: 0378-4290, 1872-6852
KITopen-ID: 1000094153
HGF-Programm 12.02.02 (Vor POF, LK 01)
Erschienen in Field crops research
Band 238
Seiten 113-128
Vorab online veröffentlicht am 15.05.2019
Schlagworte Crop growth monitoring, Crop phenology, Crop growth timing, Crop production, Crop growth anomaly
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