Assessing the Performance of MODIS NDVI and EVI for Seasonal Crop Yield Forecasting at the Ecodistrict Scale

Abstract

Crop yield forecasting plays a vital role in coping with the challenges of the impacts of climate change on agriculture. Improvements in the timeliness and accuracy of yield forecasting by incorporating near real-time remote sensing data and the use of sophisticated statistical methods can improve our capacity to respond effectively to these challenges. The objectives of this study were (i) to investigate the use of derived vegetation indices for the yield forecasting of spring wheat (Triticum aestivum L.) from the Moderate resolution Imaging Spectroradiometer (MODIS) at the ecodistrict scale across Western Canada with the Integrated Canadian Crop Yield Forecaster (ICCYF); and (ii) to compare the ICCYF-model based forecasts and their accuracy across two spatial scales-the ecodistrict and Census Agricultural Region (CAR), namely in CAR with previously reported Remote Sens. 2014, 6 10194 ICCYF weak performance. Ecodistricts are areas with distinct climate, soil, landscape and ecological aspects, whereas CARs are census-based/statistically-delineated areas. Agroclimate variables combined respectively with MODIS-NDVI and MODIS-EVI indices were used as inputs for the in-season yield forecasting of spring wheat during the 2000-2010 period. Regression models were built based on a procedure of a leave-one-year-out. The results showed that both agroclimate + MODIS-NDVI and agroclimate + MODIS-EVI performed equally well predicting spring wheat yield at the ECD scale. The mean absolute error percentages (MAPE) of the models selected from both the two data sets ranged from 2% to 33% over the study period. The model efficiency index (MEI) varied between −1.1 and 0.99 and −1.8 and 0.99, respectively for the agroclimate + MODIS-NDVI and agroclimate + MODIS-EVI data sets. Moreover, significant improvement in forecasting skill (with decreasing MAPE of 40% and 5 times increasing MEI, on average) was obtained at the finer, ecodistrict spatial scale, compared to the coarser CAR scale. Forecast models need to consider the distribution of extreme values of predictor variables to improve the selection of remote sensing indices. Our findings indicate that statistical-based forecasting error could be significantly reduced by making use of MODIS-EVI and NDVI indices at different times in the crop growing season and within different sub-regions.

Yield estimation and forecasting for winter wheat in Hungary using time series of MODIS data

“…Hayes and Decker 1996;Bolton and Friedl 2013;Shao et al 2015), for spring wheat (e.g. Hochheim and Barber 1998;Labus et al 2002;Kouadio et al 2014), for barley (e.g. Kastens et al 2005), for rice (e.g.…”

Estimating forest aboveground biomass by low density lidar data in mixed broad-leaved forests in the Italian Pre-Alps

“…To date many methods have been used to study the vegetation with remote sensing and different spectral indices have been proposed (e.g. NDVI and EVI) (Glenn et al 2008;Kouadio et al 2014;White et al 2014). Despite recent steady advancements in remote sensing techniques, there still remains an inability to reliably quantify plant diversity and totally eliminate environmental interferences (Wang et al 2010;Pettorelli et al 2014).…”

Sweet Corn Yield Simulation Using Normalized Difference Vegetation Index and Leaf Area Index

“…It is necessary to develop and apply in production new approaches for the prediction of crop yields through the mathematical estimation of the productivity related indices, especially, an important modern index obtained by the remote sensing -the normalized difference vegetation index (NDVI). The use of remotely sensed index provides the possibility of making on time predictions of the crops productivity and does not require complicated and prolonged in-field measurements to obtain the input data for the forecasting (Kouadio et al, 2014). Moreover, the NDVI screens are mostly provided in a timely manner and often are free of charge that makes the use of the index more attractive even for the little-scale farmers and scientific institutions with a limited budget (Maas, 1988;Atzberger, 2013).…”