Abstract
The detection of irrigated areas by means of remote sensing is essential to improve agricultural water resource management. Currently, data from the Sentinel constellation offer new possibilities for mapping irrigated areas at the plot scale. Until now, few studies have used Sentinel-1 (S1) and Sentinel-2 (S2) data to provide approaches for mapping irrigated plots in temperate areas. This study proposes a method for detecting irrigated and rainfed plots in a temperate area (southwestern France) jointly using optical (Sentinel-2), radar (Sentinel-1) and meteorological (SAFRAN) time series, through a classification algorithm. Monthly cumulative indices calculated from these satellite data were used in a Random Forest classifier. Two data years have been used, with different meteorological characteristics, allowing the performance of the method to be analysed under different climatic conditions. The combined use of the whole cumulative data (radar, optical and weather) improves the irrigated crop classifications (Overall Accuary (OA) ≈ 0.7) compared to the classifications obtained using each data separately (OA < 0.5). The use of monthly cumulative rainfall allows a significant improvement of the Fscore of irrigated and rainfed classes. Our study also reveals that the use of cumulative monthly indices leads to performances similar to those of the use of 10-day images while considerably reducing computational resources.
Highlights
Human activities have an impact on the different components of the hydrosphere, and 80% of the world’s population is facing water shortages that will worsen with global warming [1]
The classifications with cumulative indices lead to performances slightly inferior to the 10-days classifications (OA = 0.78 in 2017), while significantly reducing the Random Access Memory (RAM) usage and central processing unit (CPU) time, as shown in the Table 6
RAM is reduced by a factor of 2 for the learning phase
Summary
Human activities have an impact on the different components of the hydrosphere, and 80% of the world’s population is facing water shortages that will worsen with global warming [1]. Rational and collective management of water resources has become crucial To achieve this objective, explicit information on agricultural practices and on the amount of water needed for crops over large areas is needed [4]. The detection of irrigated plots is difficult because of the the smaller differences in observed phenology between irrigated and rainfed crops compared to what is observed in semi-arid zones [24,25] This smaller differences is related to local agricultural practices and pedoclimatic conditions
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