Added utility of temperature zone information in remote sensing-based large scale crop mapping

• Published in: Remote sensing applications Vol. 35
• Main Authors: Donmez, E., Yilmaz, M.T., Yucel, I.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2024
• Subjects: Crop cover mapping; Machine learning; Remote sensing; Study area division; Supervised classification; Temperature zones; Temperature zones; Remote sensing; Crop cover mapping; Supervised classification; Study area division; Machine learning
• ISSN: 2352-9385; 2352-9385
• DOI: 10.1016/j.rsase.2024.101264

Accurate crop cover maps are beneficial for various aspects like water resources management, crop yield prediction, regulation insurance policies, and investigation of the effects of climate change. When making large-scale crop classification, regional harvest time and phenological growth differences occur due to varying temperatures along the study area, and using regional temperature differences while performing crop cover classification may increase the map accuracy. Therefore, in this study, we investigated for the first time the contribution of temperature information over large areas to the classification of agricultural products. Agricultural crop mapping is performed over Türkiye using Sentinel-2 Level-2A images with 10-m spatial resolution acquired between March 15, 2019, and October 15, 2019. In addition to spectral bands, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) are used as classification features. Twenty years of ERA5-Land 2-m temperature data is averaged to divide the study area into three temperature zones Low (LTZ), Medium (MTZ), and High-Temperature Zone (HTZ). Before the classification, feature selection using random forest importance is performed to select the most successful features. After that, a random forest classifier is created for each temperature zone. LTZ reached 89% overall accuracy (OA) with a 0.88 Kappa. MTZ reached 91% OA with 0.92 Kappa, and HTZ reached 94% OA with 0.94 Kappa, giving the best accuracy among the classifiers. Finally, test sets of all temperature zones are combined, and an OA of 92% with a Kappa of 0.93 is achieved with this combined test set. To test the advantage of temperature zoning, classification is also performed without the temperature zones, and it is observed that temperature zoning increases the OA and Kappa by 1%. A land cover classification map is then created using temperature zone classifiers with 34 crop classes and six non-agricultural classes. •Phenology differences within crop classes over large areas can reduce map accuracy.•Dividing a large study area into sub-areas according to temperature information increases crop classification map accuracy.•Selection of crop classes and an adequate number of ground truth samples are crucial for accurate large-scale crop maps.•Random forest classifier is effective and efficient for large-scale crop mapping.•Random forest importance is an easy tool to decrease feature space.