Abstract
Methods based on Sentinel-1 data were developed to monitor crops and fields to facilitate the distribution of subsidies. The objectives were to (1) develop a methodology to predict individual crop species or or management regimes; (2) investigate the earliest time point in the growing season when the species predictions are satisfactory; and (3) to present a method to assess the uncertainty of the predictions at an individual field level. Seventeen Sentinel-1 synthetic aperture radar (SAR) scenes (VV and VH polarizations) acquired in interferometric wide swath mode from 14 May through to 30 August 2017 in the same geometry, and selected based on the weather conditions, were used in the study. The improved k nearest neighbour estimation, ik-NN, with a genetic algorithm feature optimization was tailored for classification with optional Sentinel-1 data sets, species groupings, and thresholds for the minimum parcel area. The number of species groups varied from 7 to as large as 41. Multinomial logistic regression was tested as an optional method. The Overall Accuracies (OA) varied depending on the number of species included in the classification, and whether all or not field parcels were included. OA with nine species groups was 72% when all parcels were included, 81% when the parcels area threshold (for incorporating parcels into classification) was 0.5 ha, and around 90% when the threshold was 4 ha. The OA gradually increased when adding extra Sentinel-1 scenes up until the early August, and the initial scenes were acquired in early June or mid-May. After that, only minor improvements in the crop recognition accuracy were noted. The ik-NN method gave greater overall accuracies than the logistic regression analysis with all data combinations tested. The width of the 95% confidence intervals with ik-NN for the estimate of the probability of the species with the largest probability on an individual parcel varied depending on the species, the area threshold of the parcel and the number of the Sentinel-1 scenes used. The results ranged between 0.06–0.08 units (6–8% points) for the most common species when the Sentinel-1 scenes were between 1 June and 12 August. The results were well-received by the authorities and encourage further research to continue the study towards an operational method in which the space-borne SAR data are a part of the information chain.
Highlights
The Introduction section discusses the importance of studying Sentinel-1 time series data in the mapping of crops, and relates our work with the state-of-the-art in the context of the study.The introduction is divided into two sub-sections
The data sets were split into training data (2/3) and validation data (1/3) both with the improved k nearest neighbour method (ik-NN) method and the multinomial logistic regression
The full Sentinel-1 data sets were tested with the multinomial logistic regression, starting either on 1 June or 14 May
Summary
The Introduction section discusses the importance of studying Sentinel-1 time series data in the mapping of crops, and relates our work with the state-of-the-art in the context of the study.The introduction is divided into two sub-sections. The Introduction section discusses the importance of studying Sentinel-1 time series data in the mapping of crops, and relates our work with the state-of-the-art in the context of the study. The European Union is interested in a more cost-efficient way of controlling the management of crop fields, e.g., the utilization of the space-borne remote sensing data. Clouds and haze often prevent the acquisition of applicable optical area images in such a way that, e.g., even a single cloud free Sentinel-2 can not be acquired from the growing season from each region in most of the European countries. Space-borne SAR data are the only remote sensing data source suitable for rapid and near real-time assessment of crop fields during the growing season in European countries. Since the advent of the Copernicus programme Sentinel-1 SAR satellites that are capable to provide repeated acquisitions every six days (with two satellites), the potential for continuous monitoring of crops was established
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
