Spatially Weighted Estimation of Broadacre Crop Growth Improves Gap-Filling of Landsat NDVI

Fiona H Evans,Jianxiu Shen

doi:10.3390/rs13112128

Abstract

Seasonal climate is the main driver of crop growth and yield in broadacre grain cropping systems. With a 40-year record of 30 m resolution images and 16-day revisits, the Landsat satellite series is ideal for producing long-term records of remotely sensed phenology to build understanding of how climate affects crop growth. However, the time-series of Landsat images exhibits gaps caused by cloud cover, which is common in wet periods when crops reach maximum growth. We propose a novel spatial–temporal approach to gap-filling that avoids data fusion. Crop growth curve estimation is used to perform temporal smoothing and incorporation of spatial weights allows spatial smoothing. We tested our approach using Landsat NDVI data acquired for an 8000 ha study area in Western Australia using a train/test approach where 157 available Landsat-7 images between 2013 and 2019 were used to train the model, and 95 at least 80% cloud-free Landsat-8 images from the same period were used to test its performance. We found that compared to nonspatial estimation, use of spatial weights in growth curve estimation improved correlation between observed and predicted NDVI by 75%, MAE by 31% and RMSE by 75%. For cropland, the correlation is improved by 58%, the MAE by 36% and the RMSE by 76%. We conclude that spatially weighted estimation of crop growth curves can be used to fill spatial and temporal gaps in Landsat NDVI for the purpose of within-field monitoring. Our approach is also applicable to other data sources and vegetation indices.

Highlights

Increasing agricultural production is essential to ensure food security for the world’s growing population as the area of arable land diminishes amid risks posed by climate change
Simpler approach to gap-filling Landsat NDVI that avoids the need for data fusion and can be used to produce long-term hindcasts
We hypothesise that the use of spatial weights in the spatially weighted growth curve (SWGC) method will improve accuracy of prediction of continuous long-term Landsat NDVI sequences compared to using nonspatial growth curve estimation

Summary

Introduction

Increasing agricultural production is essential to ensure food security for the world’s growing population as the area of arable land diminishes amid risks posed by climate change. Precision agriculture aims for within-field optimisation of crop nutrition and management of weeds, pests and disease to allow farmers to increase productivity and profitability and/or reduce inputs to improve sustainability [1]. Seasonal climate conditions are the main determinant of crop growth and yield [2], meaning that within-field management decisions rely on our understanding of how climate drives crop growth. Many farmers make decisions using data from only several years [3], which is insufficient for quantifying the effects of seasonal variability and climate change. Satellite remote sensing offers a cost-effective means of monitoring fields to build our understanding of how crop growth varies in space and time according to soil types, local weather conditions, seasonal climate variability and management.

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