Forecasting wheat yield from weather data and MODIS NDVI using Random Forests for Punjab province, Pakistan. Issue 17 (2nd September 2017)

• Record Type: Journal Article
• Authors: Saeed, Umer; Dempewolf, Jan; Becker-Reshef, Inbal; Khan, Ahmad; Ahmad, Ashfaq; Wajid, Syed Aftab
• Is Part Of: International journal of remote sensing. Volume 38:Issue 17(2017)
• Page Start: 4831
• Page End: 4854
• Publication Date: 2017-09-02
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2017.1323282
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Wheat is the staple food of Punjab province of Pakistan, which contributes more than 75% of the total national production. Accurate and timely forecasting of wheat yield is a cornerstone for monitoring food security and planning for agricultural markets, but the efficiency of the current system for near real-time forecasting should be improved. In this research paper, we developed a model to forecast wheat yield before harvest for each of eight individual districts and for Punjab province as a whole. The model uses weather and Moderate Resolution Imaging Spectroradiometer (MODIS)-derived normalized difference vegetation index (NDVI) data for 2001–2014 (14 years) to calculate Random Forest (RF) statistical models using 15 independent variables. Temperature, rainfall, sunshine hours, growing degree days, and MODIS-derived NDVI for each of the eight districts of Punjab province were used to forecast yield for the year 2014. The same independent variables were used to forecast wheat yield of the whole Punjab from 2001 to 2014 by excluding the respective year from training set. Sunshine hour data were not available for all districts and therefore we tested using temperature data and average latitude-based solar radiation as surrogates. The root mean square errors (RMSEs) of the forecast results of the whole of Punjab province were 147.7 kg ha −1 and 148.7 kg ha −1 with a mean error of less than 5% using average and generic RFs, respectively. Forecasts for individual districts showed R 2 of 0.95 with RMSE of 175.6 kg ha −1 and 5.86% mean error.