Irrigation Events Detection over Intensively Irrigated Grassland Plots Using Sentinel-1 Data

Hassan Bazzi,François Charron,Hatem Belhouchette,Ibrahim Fayad,Nicolas Baghdadi,Mehrez Zribi

doi:10.3390/rs12244058

Abstract

Better management of water consumption and irrigation schedule in irrigated agriculture is essential in order to save water resources, especially at regional scales and under changing climatic conditions. In the context of water management, the aim of this study is to monitor irrigation activities by detecting the irrigation episodes at plot scale using the Sentinel-1 (S1) C-band SAR (synthetic-aperture radar) time series over intensively irrigated grassland plots located in the Crau plain of southeast France. The method consisted of assessing the newly developed irrigation detection model (IDM) at plot scale over the irrigated grassland plots. First, four S1-SAR time series acquired from four different S1-SAR acquisitions (different S1 orbits), each at six-day revisit time, were obtained over the study site. Next, the IDM was applied at each available SAR image from each S1-SAR series to obtain an irrigation indicator at each SAR image (no, low, medium, or high irrigation possibility). Then, the irrigation indicators obtained at each image from each S1-SAR time series (four series) were added and combined by threshold value criteria to determine the existence or absence of an irrigation event. Finally, the performance of the IDM for irrigation detection was assessed by comparing the in situ recorded irrigation events at each plot and the detected irrigation events. The results show that using only the VV polarization, 82.4% of the in situ registered irrigation events are correctly detected with an F_score value reaching 73.8%. Less accuracy is obtained using only the VH polarization, where 79.9% of the in situ irrigation events are correctly detected with an F_score of 72.2%. The combined use of the VV and VH polarization showed that 74.1% of the irrigation events are detected with a higher F_score value of 76.4%. The analysis of the undetected irrigation events revealed that, in the presence of very well-developed vegetation cover (normalized difference of vegetation index (NDVI) ≥ 0.8); higher uncertainty in irrigation detection is observed, where 80% of the undetected events correspond to an NDVI value greater than 0.8. The results also showed that small-sized plots encounter more false irrigation detections than large-sized plots certainly because the pixel spacing of S1 data (10 m × 10 m) is not adapted to small size plots. The obtained results prove the efficiency of the S1 C-band data and the IDM for detecting irrigation events at the plot scale, which would help in improving the irrigation water management at large scales especially with availability and global coverage of the S1 product.

Highlights

In order to meet global food demand, which is affected by the global increase in the world population and climate change, current policies are pushing toward the expansion of the irrigated areas especially in arid and semi-arid regions [1,2,3]
The P value is obtained as a sum of the four p values from the four synthetic aperture radar (SAR) images corresponding to each irrigation event
The irrigation indicators obtained at each image of the four SAR time series were combined and compared to a threshold value to obtain detected irrigation events

Summary

Introduction

In order to meet global food demand, which is affected by the global increase in the world population and climate change, current policies are pushing toward the expansion of the irrigated areas especially in arid and semi-arid regions [1,2,3]. Obtaining information about irrigation timing is of great importance for several studies dealing with food security [6] and water consumption [7]. The difference in the temporal signal between irrigated and non-irrigated crop is based on the fact that irrigation mixed with fertilizers makes the crop grow healthier and faster causing a difference in the captured spectral signature between irrigated and non-irrigated plot. This difference in the spectral signature has been investigated using several vegetation indices such as the NDVI (normalized differential vegetation index) [17] or the GI (greenness index) [18]. Since irrigation increases the soil water content, the detection of irrigated areas could be possible using SAR data that are highly affected by the soil moisture values

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Conclusion