Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/329786
Type: Artigo
Title: Correlation Maps To Assess Soybean Yield From Evi Data In Parana State, Brazil
Author: Dantas Araujo Figueiredo
Gleyce Kelly; Brunsell
Nathaniel Allan; Higa
Breno Hiroyuki; Rocha
Jansle Vieira; Camargo Lamparelli
Rubens Augusto
Abstract: Vegetation indices are widely used to monitor crop development and generally used as input data in models to forecast yield. The first step of this study consisted of using monthly Maximum Value Composites to create correlation maps using Enhanced Vegetation Index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor mounted on Terra satellite and historical yield during the soybean crop cycle in Parana State, Brazil, from 2000/2001 to 2010/2011. We compared the ability of forecasting crop yield based on correlation maps and crop specific masks. We ran a preliminary regression model to test its ability on yield estimation for four municipalities during the soybean growing season. A regression model was developed for both methodologies to forecast soybean crop yield using leave-one-out cross validation. The Root Mean Squared Error (RMSE) values in the implementation of the model ranged from 0.037 t ha(-1) to 0.19 t ha(-1) using correlation maps, while for crop specific masks, it varied from 0.21 t ha(-1) to 0.35 t ha(-1). The model was able to explain 96 % to 98 % of the variance in estimated yield from correlation maps, while it was able to explain only 2 % to 67 % for crop specific mask approach. The results showed that the correlation maps could be used to predict crop yield more effectively than crop specific masks. In addition, this method can provide an indication of soybean yield prior to harvesting.
Subject: Modis
Crop Yield Forecasting
Vegetation Indices
Editor: Universidade de São Paulo
Cerquera Cesar
Rights: aberto
Identifier DOI: 10.1590/0103-9016-2015-0215
Address: http://www.scielo.br/scielo.php?pid=S0103-90162016000500462&script=sci_arttext
Date Issue: 2016
Appears in Collections:Unicamp - Artigos e Outros Documentos

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