Soil Moisture in the Biebrza Wetlands Retrieved from Sentinel-1 Imagery

Abstract

Soil moisture (SM) plays an essential role in environmental studies related to wetlands, an ecosystem sensitive to climate change. Hence, there is the need for its constant monitoring. SAR (Synthetic Aperture Radar) satellite imagery is the only mean to fulfill this objective regardless of the weather. The objective of the study was to develop the methodology for SM retrieval under wetland vegetation using Sentinel-1 (S-1) satellite data. The study was carried out during the years 2015–2017 in the Biebrza Wetlands, situated in northeastern Poland. At the Biebrza Wetlands, two Sentinel-1 validation sites were established, covering grassland and marshland biomes, where a network of 18 stations for soil moisture measurement was deployed. The sites were funded by the European Space Agency (ESA), and the collected measurements are available through the International Soil Moisture Network (ISMN). The NDVI (Normalized Difference Vegetation Index) was derived from the optical imagery of a MODIS (Moderate Resolution Imaging Spectroradiometer) sensor onboard the Terra satellite. The SAR data of the Sentinel-1 satellite with VH (vertical transmit and horizontal receive) and VV (vertical transmit and vertical receive) polarization were applied to soil moisture retrieval for a broad range of NDVI values and soil moisture conditions. The new methodology is based on research into the effect of vegetation on backscatter () changes under different soil moisture and vegetation (NDVI) conditions. It was found that the state of the vegetation may be described by the difference between  VH and  VV, or the ratio of  VV/VH, as calculated from the Sentinel-1 images. The most significant correlation coefficient for soil moisture was found for data that was acquired from the ascending tracks of the Sentinel-1 satellite, characterized by the lowest incidence angle, and SM at a depth of 5 cm. The study demonstrated that the use of the inversion approach, which was applied to the new developed models and includes the derived indices based on S-1, allowed the estimation of SM for peatlands with reasonable accuracy (RMSE ~ 10 vol. %). Due to the temporal frequency of the two S-1 satellites’ (S-1A and S-1B) acquisitions, it is possible to monitor SM changes every six days. The conclusion drawn from the study emphasizes a demand for the derivation of specific soil moisture retrieval algorithms that are suited for wetland ecosystems, where soil moisture is several times higher than in agricultural areas.

Very High Spatial Resolution Soil Moisture Observation of Heterogeneous Subarctic Catchment Using Nonlocal Averaging and Multitemporal SAR Data

“…Results agreed with previous SAR studies done in other than agricultural areas, such as the Mediterranean [24], Polish wetland [8], and subarctic conditions [10], in showing that C-band SAR data can be used for soil moisture retrieval in diverse landscapes. Our approach, benefiting from the completely new PIMSAR nonlocal despeckling method [41], is unique and also produced good estimates in forest areas, which have been mostly lacking in previous S1 data using studies [24].…”

“…S OIL moisture retrieval has been one of the main topics of synthetic aperture radar (SAR) research for decades [1]- [3]. The emphasis has long been on bare soil [4]- [6] and agricultural land in general [7], but, recently, uncultivated land types also have been studied [8]- [10]. However, SAR data are less applied and tested in forested areas and in mosaic landscapes of subarctic regions with forest, wetlands, and open tundra.…”

“…The thermal infrared temperature has also been shown to be useful for forested area soil moisture estimation using artificial neural networks (ANN) [24]. Optical image-based normalized difference vegetation index, which is related to leaf area index (LAI) for low vegetation, has been shown to improve the soil moisture estimation accuracy [8], [25], either applying the water cloud model or support vector regression (SVR). In the presence of high vegetation, optical imagery and laser scanner data have been used for analyzing the contribution of vegetation to the backscattered signal [9].…”

“…Within a single catchment, there can be surfaces or patches with high CH 4 emission and those with an order of magnitude lower CH 4 sink, controlled mainly by soil moisture [33]. Hence, normal spatial averaging or Lee filtering [8] to reduce speckle is not applicable. Previously, we have studied midsummer soil moisture patterns and related methane fluxes with a combination of SAR, optical, and topographic data for this region [34].…”

Classification of Mediterranean hedgerows: A methodological approximation

“…N =Normal: 40% ≤ 60%) and in which recorded precipitation was around the median. Sentinel-2 enabled a combination of bands (EOS DATA ANALYTICS, 2017) at different resolutions (10 .m - Near-Infrared bands- and 20 m, Red Edge and shortwave infrared bands), which facilitated discrimination of the different levels of surface soil moisture (near-infrared -12-; Red Edge 8 -8A- and Red -4-) applied to agriculture [26] , [27] , [28] , [29] , [30] , to irrigated fields [31] , [32] , [33] and, more precisely, to pasture [34 , 35] . The scenes (a total of four) were mosaicked and contrasted with the use of the nearest neighbour method to obtain a final image in GRID format.…”