Analysis of Sentinel-1 radiometric stability and quality for land surface applications

International audienceLand monitoring using temporal series of Synthetic Aperture Radar (SAR) images requires radiometrically well calibrated sensors. In this paper, the radiometric stability of the new SAR Sentinel-1A 'S-1A' sensor was first assessed by analyzing temporal variations of the backscattering coefficient (sigma°) returned from invariant targets. Second, the radiometric level of invariant targets was compared from S-1A and Radarsat-2 "RS-2" data. The results show three stable sub-time series of S-1A data. The first (between 1 October 2014 and 19 March 2015) and third (between 25 November 2015 and 1 February 2016) sub-time series have almost the same mean sigma°-values (a difference lower than 0.3 dB). The mean sigma°-value of the second sub-time series (between 19 March 2015 and 25 November 2015) is higher than that of the first and the third sub-time series by roughly 0.9 dB. Moreover, our results show that the stability of each sub-time series is better than 0.48 dB. In addition, the results show that S-1A images of the first and third sub-time series appear to be well calibrated in comparison to RS-2 data, with a difference between S-1A and RS-2 lower than 0.3 dB. However, the S-1A images of the second sub-time series have sigma°-values that are higher than those from RS-2 by roughly 1 dB

Vegetation characterization through the use of precipitation-affected SAR signals

Current space-based SAR offers unique opportunities to classify vegetation types and to monitor vegetation growth due to its frequent acquisitions and its sensitivity to vegetation geometry. However, SAR signals also experience frequent temporal fluctuations caused by precipitation events, complicating the mapping and monitoring of vegetation. In this paper, we show that the influence of a priori known precipitation events on the signals can be used advantageously for the classification of vegetation conditions. For this, we exploit the change in Sentinel-1 backscatter response between consecutive acquisitions under varying wetness conditions, which we show is dependent on the state of vegetation. The performance further improves when a priori information on the soil type is taken into account.1010FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP2013/50943-

Determination of Soil Moisture and Vegetation Parameters from Spaceborne C-Band SAR on Agricultural Areas

Soil moisture is an important factor influencing hydrological and meteorological exchange processes at the land surface. As ground measurements of soil moisture cannot provide spatial-ly distributed information, remote sensing of soil moisture using Synthetic Aperture Radar (SAR) offers an alternative. To derive soil moisture from vegetated areas with SAR, the influ-ence of vegetation parameters on SAR backscatter must be considered, though. The first part of the study analyses the potential to use a qualitative soil moisture index from ERS-SAR with high spatial resolution that can be used without ground truth soil moisture and vegetation data. The index ranges from low to high soil moisture instead of giving absolute soil moisture values. The method is applied to agricultural areas in the catchment of the river Rur in Germany. The soil moisture index represents wetting and drying tendencies well when compared to precipitation records and behaves like in-situ soil moisture regarding its variabil-ity. The analysis of spatial patterns from the soil moisture index by using semivariograms re-veals that differences in management that result for example in differences in evapotranspira-tion from one to the next agricultural field, are the only influence on spatial patterns of soil moisture in the Rur catchment. This study confirms the applicability of a high-resolution soil moisture index for monitoring soil moisture changes and to analyze spatial soil moisture pat-terns. The soil moisture index could be used as input to hydrological models and could substi-tute antecedent precipitation, which needs precipitation stations, as a proxy to soil moisture. The second part of the study examines the capability of dual-polarimetric C-Band SAR data with high incidence angles from the Sentinel-1 satellites to derive soil moisture and vegetation parameters quantitatively. A processing scheme for Sentinel-1 Level-1 data is presented to produce images of different SAR observables that are compared to extensive ground meas-urements of soil moisture and vegetation parameters. It shows that soil moisture retrieval is feasible from bare soil and maize with an RMSE of 7 Vol%. From other land use types, dif-ferent vegetation parameters could be retrieved with an error of around 25 % of their range, in median. Neither soil moisture nor vegetation parameters could be derived from grassland and triticale due to the influence of the thatch layer and the missing of a clear row structure. Both grassland and triticale are in contrast to the other crops not sown in rows on our research fields. The analysis has shown that the incidence angle is of main importance for the capability of C-band SAR to derive soil moisture and that the availability of at least one co- and cross-polarized channel is important for the quantitative retrieval of land surface parameters. The dual-pol H2α parameters were not meaningful for soil moisture and vegetation parameter re-trieval in this study

Analysis of Sentinel-1 Radiometric Stability and Quality for Land Surface Applications

Land monitoring using temporal series of Synthetic Aperture Radar (SAR) images requires radiometrically well calibrated sensors. In this paper, the radiometric stability of the new SAR Sentinel-1A “S-1A” sensor was first assessed by analyzing temporal variations of the backscattering coefficient (σ°) returned from invariant targets. Second, the radiometric level of invariant targets was compared from S-1A and Radarsat-2 “RS-2” data. The results show three stable sub-time series of S-1A data. The first (between 1 October 2014 and 19 March 2015) and third (between 25 November 2015 and 1 February 2016) sub-time series have almost the same mean σ°-values (a difference lower than 0.3 dB). The mean σ°-value of the second sub-time series (between 19 March 2015 and 25 November 2015) is higher than that of the first and the third sub-time series by roughly 0.9 dB. Moreover, our results show that the stability of each sub-time series is better than 0.48 dB. In addition, the results show that S-1A images of the first and third sub-time series appear to be well calibrated in comparison to RS-2 data, with a difference between S-1A and RS-2 lower than 0.3 dB. However, the S-1A images of the second sub-time series have σ°-values that are higher than those from RS-2 by roughly 1 dB