Abstract
The increasing availability of very-high resolution (VHR; <2 m) imagery has the potential to enable agricultural monitoring at increased resolution and cadence, particularly when used in combination with widely available moderate-resolution imagery. However, scaling limitations exist at the regional level due to big data volumes and processing constraints. Here, we demonstrate the Fusion Approach for Remotely-Sensed Mapping of Agriculture (FARMA), using a suite of open source software capable of efficiently characterizing time-series field-scale statistics across large geographical areas at VHR resolution. We provide distinct implementation examples in Vietnam and Senegal to demonstrate the approach using WorldView VHR optical, Sentinel-1 Synthetic Aperture Radar, and Sentinel-2 and Sentinel-3 optical imagery. This distributed software is open source and entirely scalable, enabling large area mapping even with modest computing power. FARMA provides the ability to extract and monitor sub-hectare fields with multisensor raster signals, which previously could only be achieved at scale with large computational resources. Implementing FARMA could enhance predictive yield models by delineating boundaries and tracking productivity of smallholder fields, enabling more precise food security observations in low and lower-middle income countries.
Highlights
An effective means of monitoring agriculture in sub-hectare fields is critical for maintaining global food security, to ensure that it can meet the challenges of a changing climate and population increases [1,2]
Agriculture is sensitive to changes in climate, but failures to keep local temperatures from increasing by more than 2 ◦C are expected to result in losses in aggregate production of wheat, rice, and maize in both tropical and temperate regions [5]
Cloud cover in the region inhibits the use of optical data to monitor these trends, we provide an approach using very-high resolution (VHR) and radar imagery to fuse a fine-scale segmentation of the region with dense time-series data to monitor rice harvesting
Summary
An effective means of monitoring agriculture in sub-hectare (smallholder) fields is critical for maintaining global food security, to ensure that it can meet the challenges of a changing climate and population increases [1,2]. To successfully monitor changes in yield and implement positive crop-level adaptations requires an effective method of future agriculture monitoring. Mapping and monitoring agriculture is crucial for understanding people’s access to food, in regions where crops provide food calories for as much as 90% of the population (e.g., South Asia) [7] from crop fields
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