Irrigation Mapping Using Sentinel-1 Time Series at Field Scale

Abstract

The recently launched Sentinel-1 satellite with a Synthetic Aperture Radar (SAR) sensor onboard offers a powerful tool for irrigation monitoring under various weather conditions, with high spatial and temporal resolution. This research discusses the potential of different metrics calculated from the Sentinel-1 time series for mapping irrigated fields. A methodology for irrigation mapping using SAR data is proposed. The study is performed using VV (vertical–vertical) and VH (vertical–horizontal) polarizations over an agricultural site in Urgell, Catalunya (Spain). With field segmentation information from SIGPAC (the Geographic Information System for Agricultural Parcels), the backscatter intensities are averaged within each field. From the Sentinel-1 time series for each field, the statistics and metrics, including the mean value, the variance of the signal, the correlation length, and the fractal dimension, are analyzed. With the Support Vector Machine (SVM), the classification of irrigated crops, irrigated trees, and non-irrigated fields is performed with the metrics vector. The results derived from the SVM are validated with ground truthing from SIGPAC over the whole study area, with a good overall accuracy of 81.08%. Random Forest (RF) machine classification is also tested in this study, which gives an accuracy of around 82.2% when setting the tree depth at three. The methodology is based only on SAR data, which makes it applicable to all areas, even with frequent cloud cover, but this method may be less robust when irrigation is less dominated to soil moisture change.

Understanding Fields by Remote Sensing: Soil Zoning and Property Mapping

“…Higher values stand for large and dense vegetation and lower values for sparse and short vegetation. However, in the absence of, or for very sparse vegetation, the SAR data might also reflect soil moisture [43,44].…”

Combining ASNARO-2 XSAR HH and Sentinel-1 C-SAR VH/VV Polarization Data for Improved Crop Mapping

“…Therefore, a limitation related to the area of fields could improve the reliability of the classification maps. One approach to improve classification accuracy is the use of more SAR data scenes, since these data offer a significant amount of information related to vegetation parameters and crop height and cover rate [12,26,71,72], which affect the timing of seeding, transplanting, and harvesting. This could be useful for clarifying the differences in phenology among the five crops.…”

Editorial for the Special Issue “Soil Moisture Retrieval using Radar Remote Sensing Sensors”

“…Several authors [2][3][4][5][6] are developing techniques that allow SAR data to be applied to the estimation of soil moisture. They have also proposed various applications that can be used to interpret soil moisture patterns (for example irrigation mapping).…”

“…Bousbih et al [3] and Gao et al [6] investigated the use of radar remote sensing data for irrigation mapping applications. In [3], this involves the statistical analysis of the soil moisture product and normalized difference vegetation index (NDVI) time series.…”

“…In [3], this involves the statistical analysis of the soil moisture product and normalized difference vegetation index (NDVI) time series. In Gao et al [6], the authors directly analyze the statistics of the Sentinel-1 radar time series. In both of these studies, different statistical parameters are used, in order to distinguish between irrigated and non-irrigated agricultural fields.…”