Modelling and prediction of crop losses from NOAA polar-orbiting operational satellites. Issue 3 (3rd May 2016)

• Record Type: Journal Article
• Authors: Kogan, Felix; Guo, Wei; Strashnaia, Anna; Kleshenko, Alexander; Chub, Olga; Virchenko, Oleg
• Is Part Of: Geomatics, natural hazards & risk. Volume 7:Issue 3(2016)
• Page Start: 886
• Page End: 900
• Publication Date: 2016-05-03
• Subjects: Geomatics -- Periodicals; Geomatics; Periodicals; 526.905
• DOI: 10.1080/19475705.2015.1009178
• Languages: English
• ISSNs: 1947-5705

Abstract

Abstract : Weather-related crop losses have always been a concern for farmers, governments, traders, and policy-makers for the purpose of balanced food supply/demands, trade, and distribution of aid to the nations in need. Among weather disasters, drought plays a major role in large-scale crop losses. This paper discusses utility of operational satellite-based vegetation health (VH) indices for modelling cereal yield and for early warning of drought-related crop losses. The indices were tested in Saratov oblast (SO), one of the principal grain growing regions of Russia. Correlation and regression analysis were applied to model cereal yield from VH indices during 1982–2001. A strong correlation between mean SO's cereal yield and VH indices were found during the critical period of cereals, which starts two–three weeks before and ends two–three weeks after the heading stage. Several models were constructed where VH indices served as independent variables (predictors). The models were validated independently based on SO cereal yield during 1982–2012. Drought-related cereal yield losses can be predicted three months in advance of harvest and six–eight months in advance of official grain production statistic is released. The error of production losses prediction is 7%–10%. The error of prediction drops to 3%–5% in the years of intensive droughts.