Abstract
Fallows are widespread in dryland cropping systems. However, timely information about their spatial extent and location remains scarce. To overcome this lack of information, we propose to classify fractional cover data from Sentinel-2 with biased support vector machines. Fractional cover images describe the land surface in intuitive, biophysical terms, which reduces the spectral variability within the fallow class. Biased support vector machines are a type of one-class classifiers that require labelled data for the class of interest and unlabelled data for the other classes. They allow us to extrapolate in-situ observations collected during flowering to the rest of the growing season to generate large training data sets, thereby reducing the data collection requirements. We tested this approach to monitor fallows in the northern grains region of Australia and showed that the seasonal fallow extent can be mapped with >92% accuracy both during the summer and winter seasons. The summer fallow extent can be accurately mapped as early as mid-December (1–4 months before harvest). The winter fallow extent can be accurately mapped from mid-August (2–4 months before harvest). Our method also detected emergence dates successfully, indicating the near real-time accuracy of our method. We estimated that the extent of fallow fields across the northern grains region of Australia ranged between 50% in winter 2017 and 85% in winter 2019. Our method is scalable, sensor independent and economical to run. As such, it lays the foundations for reconstructing and monitoring the cropping dynamics in Australia.
Highlights
Fallowing is the dryland farming practice of leaving fields idle for part or all of a growing season to increase stored soil water and accumulate mineralised nitrate to safeguard subsequent crop yields [1,2]
We developed a new method to monitor the fallow dynamics in near real-time
Our method combines one-class classifiers with fractional cover images derived from Sentinel-2
Summary
Fallowing is the dryland farming practice of leaving fields idle for part or all of a growing season to increase stored soil water and accumulate mineralised nitrate to safeguard subsequent crop yields [1,2]. Most works have focused on mapping fallows (i) as part of end-of-season crop type maps [10,11,12]; (ii) by elimination from the active cropland [4]; (iii) by analysing normalised time series of vegetation indices [7,13] and spectral matching techniques [14,15]; or (iv) by evaluating a pixel’s greenness status in both space and time [16] These approaches vastly rely on vegetation indices that are sensitive to soil colour and require vast amount of training data, including for the classes that are not of direct interest. Near real-time monitoring of fallows with remote sensing is still lacking in many regions of the world
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