Framework for agricultural performance assessment based on MODIS multitemporal data
Abstract
We present a hierarchical classification framework for automated detection and mapping of spatial patterns of agricultural performance using satellite-based Earth observation data exemplified for the Aral Sea Basin (ASB) in Central Asia. The core element of the framework is the derivation of a composite agricultural performance index which is composed of different subindicators taking into account cropping intensity, crop diversity, crop rotations, fallow land frequency, land utilization, water use efficiency, and water availability. We derive these subindicators from net primary productivity and evapotranspiration data obtained from the MODIS sensor on board the Terra satellite during the observation period from 2000 to 2016, as well as from cropland maps created through multiannual classification of normalized difference vegetation index (NDVI). We classified pixel-based NDVI time series covering more than 8 10 6 ha of irrigated cropland based on a hierarchical approach concatenating unsupervised and supervised classification techniques to automatically generate and refine training labels, which are then used to train a decision fusion classifier, achieving an average overall accuracy of 78%. The results give unprecedented insights into spatial patterns of agricultural performance in the ASB. The proposed method is transferable and applicable for global-scale mapping, and the results of this remote sensing-aided assessment can provide important information for regional agricultural planning purposes.