The Potential of Landsat NDVI Sequences to Explain Wheat Yield Variation in Fields in Western Australia

Jianxiu Shen,Fiona H Evans

doi:10.3390/rs13112202

Abstract

Long-term maps of within-field crop yield can help farmers understand how yield varies in time and space and optimise crop management. This study investigates the use of Landsat NDVI sequences for estimating wheat yields in fields in Western Australia (WA). By fitting statistical crop growth curves, identifying the timing and intensity of phenological events, the best single integrated NDVI metric in any year was used to estimate yield. The hypotheses were that: (1) yield estimation could be improved by incorporating additional information about sowing date or break of season in statistical curve fitting for phenology detection; (2) the integrated NDVI metrics derived from phenology detection can estimate yield with greater accuracy than the observed NDVI values at one or two time points only. We tested the hypotheses using one field (~235 ha) in the WA grain belt for training and another field (~143 ha) for testing. Integrated NDVI metrics were obtained using: (1) traditional curve fitting (SPD); (2) curve fitting that incorporates sowing date information (+SD); and (3) curve fitting that incorporates rainfall-based break of season information (+BOS). Yield estimation accuracy using integrated NDVI metrics was further compared to the results using a scalable crop yield mapper (SCYM) model. We found that: (1) relationships between integrated NDVI metrics using the three curve fitting models and yield varied from year to year; (2) overall, +SD marginally improved yield estimation (r = 0.81, RMSE = 0.56 tonnes/ha compared to r = 0.80, RMSE = 0.61 tonnes/ha using SPD), but +BOS did not show obvious improvement (r = 0.80, RMSE = 0.60 tonnes/ha); (3) use of integrated NDVI metrics was more accurate than SCYM (r = 0.70, RMSE = 0.62 tonnes/ha) on average and had higher spatial and yearly consistency with actual yield than using SCYM model. We conclude that sequences of Landsat NDVI have the potential for estimation of wheat yield variation in fields in WA but they need to be combined with additional sources of data to distinguish different relationships between integrated NDVI metrics and yield in different years and locations.

Highlights

Agriculture is Western Australia’s (WA) second-largest export industry, worth aroundAUD 8.5 billion per year, with wheat, canola and barley the top three products
This study investigated the use of sequences of Landsat normalised difference vegetation index (NDVI) for estimating wheat yield in fields in Western Australia (WA)
We found that scalable crop yield mapper (SCYM) estimated yield had lower r and higher RMSE compared to actual yield than the statistical phenology detection (SPD)/+sow dates (SD)/+break of season (BOS) estimates in most years

Summary

Introduction

Agriculture is Western Australia’s (WA) second-largest export industry, worth around. AUD 8.5 billion per year, with wheat, canola and barley the top three products. Grain and oilseed crops are grown in the southwest, in a dryland system with crops dependent on winter rainfall [1,2]. The climate is variable, and it is becoming hotter and drier with climate change. Improving agricultural productivity and sustainability is important for the regional economy and for ensuring ongoing food security as the world’s population grows and the amount of arable land is threatened by competing land needs, degradation and climate change. Precision agriculture (PA) aims to increase net profit per unit area of land and per unit of time in a sustainable manner [3].

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