Mapping saffron fields and their ages with Sentinel-2 time series in north-east Iran

Abstract

Saffron (Crocus sativus L.) is the most expensive spice worldwide and is predominantly produced in the Khorasan Province situated in north-east Iran. Climatic shifts and lowering groundwater tables negatively affect saffron yields in this region, which are determined by environmental factors, agronomical practices, and crop age. Nonetheless, spatially explicit information on changes in saffron cultivation is scarce, underlining a need for better monitoring tools. This study aims to evaluate the utility of Sentinel-2 (S2) time series in accurately mapping saffron fields and their ages (i.e., how many years saffron was cultivated in a field), based on its unique phenology. To separate saffron from other land covers, we first derived 252 spectral-temporal features by calculating 21 spectral features (10 individual bands plus 11 vegetation indices) for each of the 12 months. A Random Forest (RF) algorithm was then used in combination with field data to retain only features of high importance for saffron classification. These features comprised vegetation indices that incorporated spectral information from red, and near- and shortwave infrared bands during the phenological phases of the rapid green-up (February to March) and the dormant period (August to October). The RF classifier resulted in a saffron map for the year 2019 with a high classification accuracy based on these features. Compared against an independent in-situ saffron field dataset, 87.6% of the existing fields were correctly classified as saffron. To assess saffron field ages, we analysed the spectral separability of different age groups using the NDVI time series. We found that NDVI levels between December and May allowed for effectively separating 1st, 2nd, 3rd, 4th-6th, and 7th-8th year saffron fields. An RF-based classification of field ages resulted in an overall accuracy of 86.8%. This study demonstrated that S2 time series data allow for accurately mapping saffron fields and their age groups. Our findings provide a solid basis for mapping saffron across larger areas and for monitoring changes in saffron distribution. Such information is crucial for understanding how anthropogenic and climate change impacts will affect the future of saffron cultivation.

Keywords

• Saffron
• Sentinel-2 time series
• Random Forest
• Spectral-temporal features
• Age-based classification
• Separability analysis