UCS#4: Soil moisture assessment over Africa

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1 UCS#4: Soil moisture assessment over Africa S. Horion, H. Nieto*, T. Tagesson, R. Fensholt * Institute for Sustainable Agriculture CSIC, Córdoba, Spain

Rationale and objectives Accurate estimates of soil moisture in the root zone are key inputs to short-/medium-term meteorological/hydrological modelling, as well as for the monitoring and forecasting

The Soil Moisture and Ocean Salinity mission (SMOS) launched by ESA provides soil moisture retrievals at global scale (revisit time 1-3 days) but at a rather low spatial resolution (ca. 50 km).

2 Rationale and objectives Accurate estimates of soil moisture in the root zone are key inputs to short-/medium-term meteorological/hydrological modelling, as well as for the monitoring and forecasting of plant growth and of natural hazards such as drought and flood. The variability in soil moisture is mainly governed by different rates of evaporation and precipitation. The Soil Moisture and Ocean Salinity mission (SMOS) launched by ESA provides soil moisture retrievals at global scale (revisit time 1-3 days) but at a rather low spatial resolution (ca. 50 km). Overall objective: downscale SMOS data using a soil moisture proxy (TVDI) derived from higher resolution LST and Vegetation Index. Objective 1: to produce long time series of TVDI for the African continent using GlobTemperature datasets from SEVIRI (~4km) and AATSR (1km) according to the method of [Sandholt et al 2002, Stisen et al. 2008]. Objective 2: to use the TVDI time series to downscale SMOS soil moisture retrievals to the SEVIRI and AATSR spatial resolutions according to the method of [Merlin et al., 2012].

3 Work plan Task 1. Data acquisition (ongoing) SEVIRI LST and AATSR LST from GlobTemperature Ancillary optical data for NDVI, SIWSI and LAI SMOS L2 via EOLI-SA/Category 1 access, or process SMOS Near Real Time L1c In situ soil moisture data for the sites of Africa: Senegal (KU), Namibia (KIT), and AMMA supersites in Mali, Niger and Burkina Faso, the latest ones in collaboration with AGRHYMET. Task 2. Satellite processing (early phase) Daily thermal inertia from hourly SEVIRI LST TVDI with SEVIRI (LAI/NDVI vs. dts) and AATSR (LAI/NDVI vs. nadir LST) Task 3. Downscale soil moisture (planned for 2016) Development of the script based on [Merlin et al., 2012]. 1km and 4km downscaled product Task 4. Soil moisture validation in selected ground sites (planned for 2016) SMOS downscaled soil moisture Task 5. Analysis of Soil moisture and vegetation trends (planned for 2016) Task 6. Deliverable writing (planned for 2016)

Temperature and Vegetation Dryness Index, TVDI Related to the soil moisture evaporative efficiency Calculated based on the triangle method (Sandholt et al.

$2008): When plotting LST against NDVI in an area containing a full range of fractional vegetation covers and soil moisture, and assuming homogeneous$

4 Temperature and Vegetation Dryness Index, TVDI Related to the soil moisture evaporative efficiency Calculated based on the triangle method (Sandholt et al. 2002, Stissen et al. 2008): When plotting LST against NDVI in an area containing a full range of fractional vegetation covers and soil moisture, and assuming homogeneous atmospheric forcing, a triangular scatter plot emerges in which a dry edge and wet edge limit can be defined. (Sandholt et al. 2002) The position of each point in that space is determined by the fractional vegetation cover and its surface temperature and this can be related to soil moisture. (Proud et al. 2012)

5 Facilities at Copenhagen University set up a EUMETCAST receiving system focused on collecting real-time data from MSGSEVIRI and with NWC-SAF software for retrieval of atmospheric products implemented in the system developed our own algorithms for retrieval of atmospherically and bidirectionally corrected reflectances, vegetation indices (NDVI and SIWSI), and LST [Proud et al. 2010, Proud et al. 2012] List of facilities and software available: - EUMETCAST Receiving system: PC (Windows platform) + DVB antenna - Several Linux (Ubuntu and CentOS) workstations for processing satellite data - Atmospheric radiative transfer models: RTTOV, MODTRAN, 6S and SMAC - Commercial software for processing: ENVI/IDL, Matlab, Idrisi, and ArcGIS Dahra test site (Senegal, West Africa): running since 2001, serves as a validation site for various remote sensing data and algorithms.

Site description of the Dahra field site in Senegal (15.40 N, 15.

radius of at least 3 km Typical low tree and shrub savanna environment with ~3% tree cover The land is used as grazed rangeland.

6 Site description of the Dahra field site in Senegal (15.40 N, W) Annual rainfall 416 mm Mean annual air temperature 29 C Rainy season from July to October The site is flat and with homogeneous vegetation cover within a radius of at least 3 km Typical low tree and shrub savanna environment with ~3% tree cover The land is used as grazed rangeland. The most dominant tree species were the Balanites aegyptiaca, Acacia tortilis and Acacia Senegal. The area is dominated by fine-leaved annual grasses, mostly C4 plants (e.g. Schoenefeldia gracilis, Digitalia gayana, Dactyloctenium aegypticum, Aristida mutabilis and Cenchrus bifloures)

Land Surface Temperature tower 13 14 6 Flux 6

Meteorological tower 15 10 Site running since

about 15 different types of measurement Since

7 Land Surface Temperature tower Flux 6 tower Meteorological tower Site running since different towers collecting about 15 different types of measurement Since 2002, soil moisture measured at 5cm, 10cm, 30cm, 50cm, 100cm

8 Dahra site (Tagesson et al. 2015) (Proud et al. 2012) More info on the Darha test site: Tagesson T et al. (2015). Ecosystem properties of semiarid savanna grassland in West Africa and its relationship with environmental variability. Global Change Biology, 21(1): doi: /gcb

9 Concluding remarks and expected values of UCS#4 Early stage of research for case study #4 Time series of TVDI should be ready by the end of 2015 and the downscaled soil moisture by the end of 2016 The case study will provide an operational soil moisture product at an adequate spatial (few kilometres) and temporal (sub-weekly) resolution. Useful input for forecasting regional weather as well as drought and flood hazards, but also for hydrological and crop production models. time series analysis of soil moisture will help getting a better understanding of vegetation response to stress conditions, including phenological and typological changes. Scripts and routines will be available in an online repository.

10 Thank you! Contact:

POTENTIALS FOR DETECTING CANOPY WATER STRESS USING GEOSTATIONARY MSG-SEVIRI SWIR DATA

POTENTIALS FOR DETECTING CANOPY WATER STRESS USING GEOSTATIONARY MSG-SEVIRI SWIR DATA Rasmus Fensholt, *Department of Geography and Geology, University of Copenhagen, Denmark Co-workers; Silvia Huber*,