The use of satellite data for crop yield gap analysis

By David B. Lobell. 2013

368 citations recorded by the source corpus.

Abstract Field experiments and simulation models are useful tools for understanding crop yield gaps, but scaling up these approaches to understand entire regions over time has remained a considerable challenge. Satellite data have repeatedly been shown to provide information that, by themselves or in combination with other data and models, can accurately measure crop yields in farmers’ fields. The resulting yield maps provide a unique opportunity to overcome both spatial and temporal scaling challenges and thus improve understanding of crop yield gaps. This review discusses the use of remote sensing to measure the magnitude and causes of yield gaps. Examples from previous work demonstrate the utility of remote sensing, but many areas of possible application remain unexplored. Two simple yet useful approaches are presented that measure the persistence of yield differences between fields, which in combination with maps of average yields can be used to direct further study of specific factors. Whereas the use of remote sensing may have historically been restricted by the cost and availability of fine resolution data, this impediment is rapidly receding.

Subfields: Ecology, Ecology, Evolution, Behavior and Systematics, Plant Science

View Paper: https://www.semanticscholar.org/paper/dffb8d68fee32975c08db65c8727ee7acfb33a4d

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In Ecology

In Ecology, Evolution, Behavior and Systematics

In Plant Science

Citations in the corpus

Machine Learning: An Applied Econometric Approach (2017, background): “Donaldson and Storeygard (in this journal, 2016) provide an overview of the growing literature in economics using satellite data, including how luminosity at night correlates with economic output (Henderson, Storeygard, and Weil 2012) or estimating future harvest size (Lobell 2013).”
Smallholder maize area and yield mapping at national scales with Google Earth Engine (2019, methodology): “Quantifying yield heterogeneity across a region can be useful in many aspects, for example, understanding yield progress and drivers of yield gaps (Lobell, 2013), optimizing management practice and designing crop insurance programs (Cooper et al., 2012).”
Wheat Yield Forecasting for the Tisza River Catchment Using Landsat 8 NDVI and SAVI Time Series and Reported Crop Statistics (2021, background): “Although Landsat (or similar sensors such as SPOT) is also the main source of data with sufficient spatial resolution in most agricultural areas, but with a 16-day gap between successive images, it can be difficult to obtain enough clear images within a growing season in very humid years in Hungary ”