NDLI: Detection of Irrigated and Rainfed Crops in Temperate Areas Using Sentinel-1 and Sentinel-2 Time Series

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Detection of Irrigated and Rainfed Crops in Temperate Areas Using Sentinel-1 and Sentinel-2 Time Series

Content Provider	MDPI
Author	Pageot, Yann Baup, Frédéric Inglada, Jordi Baghdadi, Nicolas Demarez, Valérie
Copyright Year	2020
Description	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.
Starting Page	3044
e-ISSN	20724292
DOI	10.3390/rs12183044
Journal	Remote Sensing
Issue Number	18
Volume Number	12
Language	English
Publisher	MDPI
Publisher Date	2020-09-17
Access Restriction	Open
Subject Keyword	Remote Sensing Irrigation Summer Crops Random Forest High Spatial and Temporal Resolution Temperate Areas
Content Type	Text
Resource Type	Article

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