In the context of monitoring and assessment of water consumption in the agricultural sector, the objective of this study is to build an operational approach capable of detecting irrigation events at plot scale in a near real-time scenario using Sentinel-1 (S1) data. The proposed approach is a decision tree-based method relying on the change detection in the S1 backscattering coefficients at plot scale. First, the behavior of the S1 backscattering coefficients following irrigation events has been analyzed at plot scale over three study sites located in Montpellier (southeast France), Tarbes (southwest France), and Catalonia (northeast Spain). To eliminate the uncertainty between rainfall and irrigation, the S1 synthetic aperture radar (SAR) signal and the soil moisture estimations at grid scale (10 km × 10 km) have been used. Then, a tree-like approach has been constructed to detect irrigation events at each S1 date considering additional filters to reduce ambiguities due to vegetation development linked to the growth cycle of different crops types as well as the soil surface roughness. To enhance the detection of irrigation events, a filter using the normalized differential vegetation index (NDVI) obtained from Sentinel-2 optical images has been proposed. Over the three study sites, the proposed method was applied on all possible S1 acquisitions in ascending and descending modes. The results show that 84.8% of the irrigation events occurring over agricultural plots in Montpellier have been correctly detected using the proposed method. Over the Catalonian site, the use of the ascending and descending SAR acquisition modes shows that 90.2% of the non-irrigated plots encountered no detected irrigation events whereas 72.4% of the irrigated plots had one and more detected irrigation events. Results over Catalonia also show that the proposed method allows the discrimination between irrigated and non-irrigated plots with an overall accuracy of 85.9%. In Tarbes, the analysis shows that irrigation events could still be detected even in the presence of abundant rainfall events during the summer season where two and more irrigation events have been detected for 90% of the irrigated plots. The novelty of the proposed method resides in building an effective unsupervised tool for near real-time detection of irrigation events at plot scale independent of the studied geographical context.
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.
The Global Ecosystem Dynamics Investigation (GEDI) Light Detection And Ranging (LiDAR) altimetry mission was recently launched to the International Space Station with a capability of providing billions of high-quality measurements of vertical structures globally. This study assesses the accuracy of the GEDI LiDAR altimetry estimation of lake water levels. The difference between GEDI’s elevation estimates to in-situ hydrological gauge water levels was determined for eight natural lakes in Switzerland. The elevation accuracy of GEDI was assessed as a function of each lake, acquisition date, and the laser used for acquisition (beam). The GEDI elevation estimates exhibit an overall good agreement with in-situ water levels with a mean elevation bias of 0.61 cm and a standard deviation (std) of 22.3 cm and could be lowered to 8.5 cm when accounting for instrumental and environmental factors. Over the eight studied lakes, the bias between GEDI elevations and in-situ data ranged from -13.8 cm to +9.8 cm with a standard deviation of the mean difference ranging from 14.5 to 31.6 cm. Results also show that the acquisition date affects the precision of the GEDI elevation estimates. GEDI data acquired in the mornings or late at night had lower bias in comparison to acquisitions during daytime or over weekends. Even though GEDI is equipped with three identical laser units, a systematic bias was found based on the laser units used in the acquisitions. Considering the eight studied lakes, the beams with the highest elevation differences compared to in-situ data were beams 1 and 6 (standard deviations of -10.2 and +18.1 cm, respectively). In contrast, the beams with the smallest mean elevation difference to in-situ data were beams 5 and 7 (-1.7 and -2.5 cm, respectively). The remaining beams (2, 3, 4, and 8) showed a mean difference between -7.4 and +4.4 cm. The standard deviation of the mean difference, however, was similar across all beams and ranged from 17.2 and 22.9 cm. This study highlights the importance of GEDI data for estimating water levels in lakes with good accuracy and has potentials in advancing our understanding of the hydrological significance of lakes especially in data scarce regions of the world.