Version 1
: Received: 13 September 2022 / Approved: 13 September 2022 / Online: 13 September 2022 (10:06:14 CEST)
How to cite:
Ibrahim, G.R.F.; Rasul, A.; Abdullah, H. Integration of Sentinel 1 and Sentinel 2 Data for Crop Classification Improvement: Barley and Wheat as an Example. Preprints2022, 2022090169. https://doi.org/10.20944/preprints202209.0169.v1
Ibrahim, G.R.F.; Rasul, A.; Abdullah, H. Integration of Sentinel 1 and Sentinel 2 Data for Crop Classification Improvement: Barley and Wheat as an Example. Preprints 2022, 2022090169. https://doi.org/10.20944/preprints202209.0169.v1
Ibrahim, G.R.F.; Rasul, A.; Abdullah, H. Integration of Sentinel 1 and Sentinel 2 Data for Crop Classification Improvement: Barley and Wheat as an Example. Preprints2022, 2022090169. https://doi.org/10.20944/preprints202209.0169.v1
APA Style
Ibrahim, G.R.F., Rasul, A., & Abdullah, H. (2022). Integration of Sentinel 1 and Sentinel 2 Data for Crop Classification Improvement: Barley and Wheat as an Example. Preprints. https://doi.org/10.20944/preprints202209.0169.v1
Chicago/Turabian Style
Ibrahim, G.R.F., Azad Rasul and Haidi Abdullah. 2022 "Integration of Sentinel 1 and Sentinel 2 Data for Crop Classification Improvement: Barley and Wheat as an Example" Preprints. https://doi.org/10.20944/preprints202209.0169.v1
Abstract
Observing cultivated crops and other forms of land use is an important environmental and economic concern for agricultural land management and crop classification. Crop categorization offers significant crop management data, ensuring food security, and developing agricultural policies. Remote sensing data, especially publicly available Sentinel 1 and 2 data, has effectively been used in crop mapping and classification in cloudy places because of their high spatial and temporal resolution. This study aimed to improve crop type classification by combining Sentinel-1 (Synthetic Aperture Rader (SAR)) data and the Sentinel-2 Multispectral Instrument (MSI) data. In the study, Random Forest (RF) and Classification and Regression Trees (CART) classier were used to classify grain crops (Barley and Wheat). The classification results based on the combination of Sentinel-2 and Sentinel-1 data indicated an overall accuracy (OA) of 93 % and a kappa coefficient (K) of 0.896 for RF and (89.15%, 0.84) for the CART classifier. It is suggested to employ a mix of radar and optical data to attain the highest level of classification accuracy since doing so improves the likelihood that the details will be observed in comparison to the single-sensor classification technique and yields more accurate results.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
