Mapping Irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain

Abstract

Mapping irrigated plots is essential for better water resource management. Today, the free and open access Sentinel-1 (S1) and Sentinel-2 (S2) data with high revisit time offers a powerful tool for irrigation mapping at plot scale. Up to date, few studies have used S1 and S2 data to provide approaches for mapping irrigated plots. This study proposes a method to map irrigated plots using S1 SAR (synthetic aperture radar) time series. First, a dense temporal series of S1 backscattering coefficients were obtained at plot scale in VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarizations over a study site located in Catalonia, Spain. In order to remove the ambiguity between rainfall and irrigation events, the S1 signal obtained at plot scale was used conjointly to S1 signal obtained at a grid scale (10 km × 10 km). Later, two mathematical transformations, including the principal component analysis (PCA) and the wavelet transformation (WT), were applied to the several SAR temporal series obtained in both VV and VH polarization. Irrigated areas were then classified using the principal component (PC) dimensions and the WT coefficients in two different random forest (RF) classifiers. Another classification approach using one dimensional convolutional neural network (CNN) was also performed on the obtained S1 temporal series. The results derived from the RF classifiers with S1 data show high overall accuracy using the PC values (90.7%) and the WT coefficients (89.1%). By applying the CNN approach on SAR data, a significant overall accuracy of 94.1% was obtained. The potential of optical images to map irrigated areas by the mean of a normalized differential vegetation index (NDVI) temporal series was also tested in this study in both the RF and the CNN approaches. The overall accuracy obtained using the NDVI in RF classifier reached 89.5% while that in the CNN reached 91.6%. The combined use of optical and radar data slightly enhanced the classification in the RF classifier but did not significantly change the accuracy obtained in the CNN approach using S1 data.

Rice Crop Detection Using LSTM, Bi-LSTM, and Machine Learning Models from Sentinel-1 Time Series

“…The rice temporal series almost had a Gaussian behavior for the crop cycles (Figure 8). This Gaussian behavior for rice planting is in agreement with the study developed by Bazzi et al [58]. In the study region (Western Frontier and Upper Valley of Uruguay), favorable sowing periods for medium-cycle cultivars are from September 21 to November 20 [138].…”

“…These denser time-series SAR data allow us to capture short phenological stages, increasing the classification capacity. Monitoring and mapping the pattern of rice cultivation from Sentinel-1 time-series images has been tested in different locations around the world: Bangladesh [56], China [57], France [58], India [56,59], Japan [60], Spain [57], USA [57], Vietnam [57,[61][62][63][64]. Some studies also integrate the Sentinel-1 image with optical images from the Landsat 8 Operational Land Imager (OLI) and the Sentinel-2 A/B [65][66][67][68].…”

Exploiting High-Resolution Remote Sensing Soil Moisture to Estimate Irrigation Water Amounts over a Mediterranean Region

“…In order to do this, high-resolution (1 km) DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) downscaled versions of SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) soil moisture data sets have been used to force the SM2RAIN algorithm to estimate irrigation over an intensely irrigated area in the North East of Spain. The pilot area proved to be particularly suitable to investigate the detectability of the irrigation signal by remote sensing products and for irrigation mapping purposes [19][20][21][22][23]. The evapotranspiration term of the SM2RAIN equation has been improved by incorporating the guidelines about crop evapotranspiration furnished in the FAO (Food and Agriculture Organization) paper n. 56 [24].…”

Smart Farming Becomes Even Smarter With Deep Learning—A Bibliographical Analysis

“…Earlier studies compared manual, current methods, and deep learning techniques. The result of these studies showed that by applying deep learning approaches it is possible to obtain high order features or more accurate results [29,30,59,76,[82][83][84][85][86][87]. However, there are some studies showing that the current methods are better than deep learning or give the same result, concluding that there is no value in applying complex structures [23,31,88,90,91].…”