nerc.ac.uk

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

Li, Huapeng; Zhang, Ce; Zhang, Shuqing; Atkinson, Peter M.. 2020 Crop classification from full-year fully-polarimetric L-band UAVSAR time-series using the Random Forest algorithm. International Journal of Applied Earth Observation and Geoinformation, 87, 102032. 12, pp. https://doi.org/10.1016/j.jag.2019.102032

Before downloading, please read NORA policies.
[img]
Preview
Text
N526360JA.pdf - Published Version
Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0.

Download (3MB) | Preview

Abstract/Summary

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.

Item Type: Publication - Article
Digital Object Identifier (DOI): https://doi.org/10.1016/j.jag.2019.102032
UKCEH and CEH Sections/Science Areas: Soils and Land Use (Science Area 2017-)
ISSN: 0303-2434
Additional Information. Not used in RCUK Gateway to Research.: Open Access paper - full text available via Official URL link.
Additional Keywords: crop classification, multitemporal SAR imagery, polarimetric SAR, Random Forest algorithm, UAVSAR
NORA Subject Terms: Agriculture and Soil Science
Date made live: 06 Jan 2020 15:49 +0 (UTC)
URI: http://nora.nerc.ac.uk/id/eprint/526360

Actions (login required)

View Item View Item

Document Downloads

Downloads for past 30 days

Downloads per month over past year

More statistics for this item...