Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas

Abstract

Abstract:Soil moisture mapping at a high spatial resolution is very important for several applications in hydrology, agriculture and risk assessment. With the arrival of the free Sentinel data at high spatial and temporal resolutions, the development of soil moisture products that can better meet the needs of users is now possible. In this context, the main objective of the present paper is to develop an operational approach for soil moisture mapping in agricultural areas at a high spatial resolution over bare soils, as well as soils with vegetation cover. The developed approach is based on the synergic use of radar and optical data. A neural network technique was used to develop an operational method for soil moisture estimates. Three inversion SAR (Synthetic Aperture Radar) configurations were tested: (1) VV polarization; (2) VH polarization; and (3) both VV and VH polarization, all in addition to the NDVI information extracted from optical images. Neural networks were developed and validated using synthetic and real databases. The results showed that the use of a priori information on the soil moisture condition increases the precision of the soil moisture estimates. The results showed that VV alone provides better accuracy on the soil moisture estimates than VH alone. In addition, the use of both VV and VH provides similar results, compared to VV alone. In conclusion, the soil moisture could be estimated in agricultural areas with an accuracy of approximately 5 vol % (volumetric unit expressed in percent). Better results were obtained for soil with a moderate surface roughness (for root mean surface height between 1 and 3 cm). The developed approach could be applied for agricultural plots with an NDVI lower than 0.75.

Potential of Sentinel-1 Images for Estimating the Soil Roughness over Bare Agricultural Soils

“…This approach consisted of applying two networks one after the other, the first to estimate soil moisture and the second to estimate soil roughness. El Hajj et al [19] showed that the use of VV alone provides better soil moisture estimation in comparison with the use of VH alone or the use of VV and VH together. Accordingly, only the VV polarization was used in this study for estimating soil parameters.…”

“…In El Hajj et al [19], VV polarization provides better results in comparison to VH alone or to the use of VV and VH together. Only the results corresponding to VV are therefore presented.…”

“…For more details on the networks used for estimating mv, readers can refer to [19]. In El Hajj et al [19], VV polarization provides better results in comparison to VH alone or to the use of VV and VH together.…”

Evaluation of SMOS, SMAP, ASCAT and Sentinel-1 Soil Moisture Products at Sites in Southwestern France

“…Several satellite missions and instruments including SMOS (Soil Moisture and Ocean Salinity) [7,8], SMAP (Soil moisture Active and Passive) [9], and ASCAT (Advanced Scatterometer) [10] provide SSM estimates at coarse spatial resolution and very high revisit time (up to 1 day). Recently, SSM maps at very high spatial resolution (up to plot scale) and high revisit time (6 days) derived from Sentinel-1 (S1) satellite were provided for the Occitanie region in the southern part of France [11].…”

Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale

“…Synthetic aperture radar (SAR) and optic data fusion is one of the most widely used approaches [28][29][30][31]. The integration of data acquired by optical sensors and SAR data may provide useful information for reducing ambiguity due to the presence of vegetation.…”