An operational automated mapping algorithm for in-season estimation of wheat area for Punjab, Pakistan

Abstract

ABSTRACT Time series of multi-spectral satellite data are a valuable option for crop-area monitoring, in particular by enabling rapid, accurate, and consistent estimation of crop area. In this study, we demonstrate the use of a turn-key wheat area mapping model using growing season Landsat time-series data to improve the precision of area estimates produced from a sample of field locations at which the presence of percent wheat per sample pixel was determined. The strata used in the sampling design to obtain the current season’s in-situ reference data were created from a wheat map of the previous growing season. We then used a map for the current growing season to serve as an auxiliary variable in a regression estimator of the wheat area. This approach allows for rapid production of such maps and area estimates with less demand for technical expertise in an ongoing wheat monitoring system. Our automated, turn-key classification-tree algorithm produced wheat maps with comparable overall accuracy to those achieved by traditional supervised classification using manual training data collection. Pixel counts from turn-key maps for the years 2013 to 2017 over-estimated wheat area by 0.86% in 2014 to 7.26% in 2017 when compared to the official estimate from the Punjab Crop Reporting Service. Of the several approaches to estimating wheat area were compared, stratified random and simple random sampling augmented by a regression estimator were the two that yielded the smallest standard errors. Incorporating a current season, turn-key map in a regression estimator with data from a simple random sampling design represents a methodologically straightforward, operational implementation of timely, accurate, and precise monitoring of wheat area. Given the importance of wheat production in Punjab to Pakistan’s national economy, this method represents an operationally efficient and effective monitoring of inter-annual shortages or surpluses in support of grain management. An automated, turn-key winter wheat mapping algorithm enables timely area estimation in advance of harvest.