Correlation maps to assess soybean yield from EVI data in Paraná State, Brazil

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 . 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.

Relationship between MODIS Derived NDVI and Yield of Cereals for Selected European Countries

“…Not only NDVI is used for yield forecasting. Other vegetation indices such as SR (Simple Ratio Index), RVI (Ratio Vegetation Index), EVI (Enhanced Vegetation Index), NDRE (Normalized Difference Red Edge Index), FAPAR (Fraction of Absorbed Photosynthetically Active Radiation), SAVI (Soiladjusted vegetation index, GNDVI (Green Normalized Difference Vegetation Index) are also used [23][24][25][26][27][28]. Yield forecasting at regional level, using satellite remote sensing, can be performed by using mean values of the vegetation indices for the total area or only for the total cropland or for the crop masks for individual crops [13,18,29,30].…”

Soybean Yield Estimation and Its Components: A Linear Regression Approach

“…Other approaches have also been developed to estimate grain yield, but they have a lower spatial resolution, limiting its application on a farm level. Among these approaches, the most common is the application of remote sensing techniques based on agro-meteorological [4] and satellite imagery data [5,6]. Regarding this kind of data, efforts have also been made applying advanced algorithms to estimate yield [7].…”