Crop classification from full-year fully-polarimetric L-band UAVSAR time-series using the Random Forest algorithm

• Published in: International journal of applied earth observation and geoinformation Vol. 87; p. 102032
• Main Authors: Li, Huapeng, Zhang, Ce, Zhang, Shuqing, Atkinson, Peter M.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2020; Elsevier
• Subjects: agricultural management; alfalfa; algorithms; almonds; autumn; biomass; corn; Crop classification; ecosystems; food security; forage; fruits; grasses; hay; Helianthus annuus; Multitemporal SAR imagery; pepper; Polarimetric SAR; polarimetry; Random Forest algorithm; spatial data; spring; summer; synthetic aperture radar; time series analysis; tomatoes; trees; UAVSAR; walnuts; winter wheat; Crop classification; Random Forest algorithm; UAVSAR; Polarimetric SAR; Multitemporal SAR imagery
• ISSN: 1569-8432; 1872-826X
• DOI: 10.1016/j.jag.2019.102032

•Overall accuracy of crop classification reaches 85 %–90 % by using full year UAVSAR.•Polarimetric parameters contribute more than linear polarizations to crop mapping.•The CP parameters are much more important than the FD parameters for crop mapping.•The combined use of four acquisitions is adequate to achieve a nearly optimal accuracy. Accurate and timely information on the distribution of crop types is vital to agricultural management, ecosystem services valuation and food security assessment. Synthetic Aperture Radar (SAR) systems have become increasingly popular in the field of crop monitoring and classification. However, the potential of time-series polarimetric SAR data has not been explored extensively, with several open scientific questions (e.g. the optimal combination of image dates for crop classification) that need to be answered. In this research, the usefulness of full year (both 2011 and 2014) L-band fully-polarimetric Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data in crop classification was fully investigated over an agricultural region with a heterogeneous distribution of crop categories. In total, 11 crop classes including tree crops (almond and walnut), forage crops (grass, alfalfa, hay, and clover), a spring crop (winter wheat), and summer crops (corn, sunflower, tomato, and pepper), were discriminated using the Random Forest (RF) algorithm. The SAR input variables included raw linear polarization channels as well as polarimetric parameters derived from Cloude-Pottier (CP) and Freeman-Durden (FD) decompositions. Results showed clearly that the polarimetric parameters yielded much higher classification accuracies than linear polarizations. The combined use of all variables (linear polarizations and polarimetric parameters) produced the maximum overall accuracy of 90.50 % and 84.93 % for 2011 and 2014, respectively, with a significant increase of approximately 8 percentage points compared with linear polarizations alone. The variable importance provided by the RF illustrated that the polarimetric parameters had a far greater influence than linear polarizations, with the CP parameters being much more important than the FD parameters. The most important acquisitions were the images dated during the peak biomass stage (July and August) when the differences in structural characteristics between most crops were the largest. At the same time, the images in spring (April and May) and autumn (October) also contributed to the crop classification since they respectively provided unique information for discriminating fruit crops (almond and walnut) as well as summer crops (corn, sunflower, and tomato). As a result, the combined use of only four acquisitions (dated May, July, August, and October for 2011 and April, June, August, and October for 2014) was adequate to achieve a nearly-optimal overall accuracy. In light of the promising classification accuracies demonstrated in this research, it becomes increasingly viable to provide accurate and up-to-date crops inventories over large areas based solely on multitemporal polarimetric SAR.