Potential for the Detection of Irrigation Events on Maize Plots Using Sentinel-1 Soil Moisture Products

Page, Michel Le; Jarlan, Lionel; Hajj, Mohammad El; Zribi, Mehrez; Baghdadi, Nicolas; Boone, Aaron

doi:10.3390/rs12101621

Cited by 44 publications

(48 citation statements)

References 61 publications

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“…However, as optical imagery is affected by cloud cover, the performance of the crop mapping with such data can be reduced in some cases, particularly for temperate or tropical areas [33] . Other recent studies [34][35][36][37][38][39] have shown that the joint use of optical and radar data improves the robustness of the mapping methods [21,35,37]. Nevertheless, all the studies mentioned above deal with the mapping of irrigated areas in arid or semi-arid climatic areas.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…For this reason, an extensive temporal dataset is still required for the detection of all irrigation events occurring at plot scale. In addition, over irrigated maize plots of southwest France, La page et al [31] investigated the potential of the S 2 MP (Sentinel-1/Sentinel-2-derived soil moisture product [24]) to detect irrigation events at plot scale. The S 2 MP is a soil moisture estimation product mainly derived by coupling Sentinel-1 SAR data and Sentinel-2 optical data using the neural network [24,32].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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“…Th radiation correction for the four bands was performed using the ENVI 5.3 software to convert the digital number (DN) of the images to the surface spectral reflectance. The atmospheric correction was conducted using the FLAASH Atmospheric Correction toolbox using the ENVI software [44,47,[52][53][54][55]. After atmospheric correction, the images were geo-referenced based on 25 ground control points.…”

Section: Gf-1 Datamentioning

confidence: 99%

“…Fortunately, the development of remote sensing technology has led to an effective solution to this problem. Published studies confirmed that the VWC has a significant correlation with the vegetation indices obtained from remote sensing datasets [55][56][57][58][59][60]. The vegetation indices often used to estimate the VWC include the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), enhanced vegetation index (EVI), difference vegetation index (DVI), and ratio vegetation index (RVI) [61][62][63][64].…”

Section: Vegetation Water Content Estimation Modelmentioning

confidence: 99%

“…Previous works have indicated that the one-variable quadratic models, along with the linear and exponential models, are suitable for describing the correlation between the vegetation indices and VWC [55,[61][62][63]. However, in most studies, only one of the vegetation indices (such as the NDVI) with the strongest correlation with the VWC was used in the calculation [61].…”

Section: Vegetation Water Content Estimation Modelmentioning

confidence: 99%

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High-Precision Soil Moisture Mapping Based on Multi-Model Coupling and Background Knowledge, Over Vegetated Areas Using Chinese GF-3 and GF-1 Satellite Data

Han

Wang

et al. 2020

Remote Sensing

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This paper proposes a combined approach comprising a set of methods for the high-precision mapping of soil moisture in a study area located in Jiangsu Province of China, based on the Chinese C-band synthetic aperture radar data of GF-3 and high spatial-resolution optical data of GF-1, in situ experimental datasets and background knowledge. The study was conducted in three stages: First, in the process of eliminating the effect of vegetation canopy, an empirical vegetation water content model and a water cloud model with localized parameters were developed to obtain the bare soil backscattering coefficient. Second, four commonly used models (advanced integral equation model (AIEM), look-up table (LUT) method, Oh model, and the Dubois model) were coupled to acquire nine soil moisture retrieval maps and algorithms. Finally, a simple and effective optimal solution method was proposed to select and combine the nine algorithms based on classification strategies devised using three types of background knowledge. A comprehensive evaluation was carried out on each soil moisture map in terms of the root-mean-square-error (RMSE), Pearson correlation coefficient (PCC), mean absolute error (MAE), and mean bias (bias). The results show that for the nine individual algorithms, the estimated model constructed using the AIEM (mv1) was significantly more accurate than those constructed using the other models (RMSE = 0.0321 cm³/cm³, MAE = 0.0260 cm³/cm³, and PCC = 0.9115), followed by the Oh model (m_v5) and LUT inversion method under HH polarization (mv2). Compared with the independent algorithms, the optimal solution methods have significant advantages; the soil moisture map obtained using the classification strategy based on the percentage content of clay was the most satisfactory (RMSE = 0.0271 cm³/cm³, MAE = 0.0225 cm³/cm³, and PCC = 0.9364). This combined method could not only effectively integrate the optical and radar satellite data but also couple a variety of commonly used inversion models, and at the same time, background knowledge was introduced into the optimal solution method. Thus, we provide a new method for the high-precision mapping of soil moisture in areas with a complex underlying surface.

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Potential for the Detection of Irrigation Events on Maize Plots Using Sentinel-1 Soil Moisture Products

Cited by 44 publications

References 61 publications

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

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

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

High-Precision Soil Moisture Mapping Based on Multi-Model Coupling and Background Knowledge, Over Vegetated Areas Using Chinese GF-3 and GF-1 Satellite Data

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