Abstract
The use of remote sensing data provides valuable information to ensure sustainable land cover management. In this paper, the potential of phenological metrics data, derived from Sentinel-2A (S2) and Landsat 8 (L8) NDVI time series, was evaluated using Random Forest (RF) classification to identify and map various crop classes over two irrigated perimeters in Morocco. The smoothed NDVI time series obtained by the TIMESAT software was used to extract profiles and phenological metrics, which constitute potential explanatory variables for cropland classification. The method of classification applied involves the use of a supervised Random Forest (RF) classifier. The results demonstrated the capability of moderate-to-high spatial resolution (10–30 m) satellite imagery to capture the phenological stages of different cropping systems over the study area. Furthermore, the classification based on S2 data presents a higher overall accuracy of 93% and a kappa coefficient of 0.91 than those produced by L8 data, which are 90% and 0.88, respectively. In other words, phenological metrics obtained from S2 time series data showed high potential for agricultural crop-types classification in semi-arid regions and thus can constitute a valuable tool for decision makers to use in managing and monitoring a complex landscape such as an irrigated perimeter.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: 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.