The spatial distribution of crop types from MODIS data: Temporal unmixing using Independent Component Analysis

Abstract Remote sensing plays an important role in delivering accurate and timely information on the location and area of major crop types with important environmental, economic, and policy considerations. The purpose of this paper is to report on an unsupervised signal processing algorithm called Independent Component Analysis (ICA) to temporally decompose MODerate-resolution Imaging Spectroradiometer (MODIS) data to automatically map major crop types in three agricultural regions covering parts of Kansas and Nebraska in the US and a third in northwestern Turkey. The approach proposed here is based on the premise that the temporal profiles of individual crops are observed as mixtures with a moderate-resolution sensor when cultivated fields are smaller than the spatial resolution of the observing sensor. The purpose of ICA is to decompose these mixed observations by a remote sensor into individual crop signals, using only the mixed observations without the aid of information about the crop signatures and the mixing process. Results using both synthetic data and real observations suggest that the ICA approach can successfully separate generalized, landscape-level cropping patterns using only available temporal measurements. There is very little need to use complicated indices or derivative spectral products to map crop types using ICA: availability of high temporal observations, either as raw spectral bands or simple vegetation indices is sufficient to identify crop types at the scale of landscapes. Results also suggest that crop map predictions aggregated to coarser resolutions have better accuracy than at native resolution when compared to maps made from fine-scale observations used as ground truth. These accuracies range from RMSE of 15–30% at 500 m to less than 10% at 2000 m. The success of the initial results presented here to automatically map crop distributions across large areas using MODIS data is particularly encouraging given the existing and planned worldwide observations of agriculturally important regions. However, the use of ICA for operational crop monitoring will require algorithms that will take into account prior information on crop growth curves, constrained estimation of independent components, and scaling of mixing vectors to obtain physically possible ranges.