Synergy of Sentinels-3 sensors: On the transfer function between infrared SST and along-track altimeter observations

Abstract

In the context of global change and population growth, agricultural practices highly impact water availability and quality. Water management strategies in term of quantity and quality have to be analyzed at a regional catchment scales. Yet, crop consumption that account for the main water and nutrient fluxes at the catchment scale needs to be monitored at a high spatial (crop extension) and temporal resolution (crop growth period). This study, funded by an ESA Living Planet Fellowship, aims at demonstrating the improvement brought by synergetic observations of Sentinel-1 and 2 in agro-hydrological modeling. Geo-information time-series such as Leaf Area Index (LAI) with S2 and soil moisture or biomass with S1 are used to re-set soil and agricultural practices parameters of a crop model coupled with a hydrological model.

Two contrasted water management issues are used as demonstrator: stream water nitrate pollution in Gascogne region in south-west of France and groundwater irrigation shortages in Deccan Plateau, in south-India.

In south-west of France, Topography Nitrogen Transfer and Transformation model (TNT2) has been previously used on a small experimental catchment (3.5km2, Ferrant et al., 2014a). Leaf Area Index (LAI) derived from S2 type time series (Formosat-2) over 5 years is used to optimize the -1 crop calendars by resetting the seeding dates and 2- within-field crop growth heterogeneity by calibrating soil parameters (depth and porosity) defining the Soil Water Holding Capacity. The synergetic use of S1 time-series (soil moisture and saturated area extent detection) and S2 time series (Leaf Area Index profiles at the pixel size) allows improving soil parameter determination and spatial calibration of hydrological functioning. These spatial calibrations are being evaluated in term of in-stream nitrogen and water fluxes.

In south-India, the Soil Water Assessment Tool model (SWAT) has been previously used on medium scale catchments (around 1000 km2) to simulate the groundwater extraction shortages (Ferrant et al., 2014b). In this area, farmers irrigate very small plots (<0.1ha), so that high resolution satellite images are needed to accurately identify the extent of irrigated areas. Previous work were limited in term of availability of such data over a period of several years and larger areas. Combining S1 biomass and soil moisture estimation with land cover classification using S2 type time series (from spot5take5 experiment) allows deriving irrigated area extent on several agro-climate contexts of South-India. This seasonal geo-information is used to force the Soil Water Assessment Tool model, an agro-hydrological model adapted to large scale basins, to derive spatial groundwater extraction as a function of climatic variability. An extension of this study to the Sentinel ground coverage will make use of SMOS water fraction and GRACE continental water estimates