Toward a New Generation of Satellite Land Surface Products? Soil Moisture as an Example

1. Toward a New Generation of Satellite Land Surface Products? Soil Moisture as an Example Catherine Prigent, CNRS, LERMA, Observatoire de Paris, France Filipe Aires, CNRS, LMD, University Paris VI, France

2. I - Toward a New Generation of Satellite Land Surface Products ?

3. A wealth of satellite observations …. but land surface characterization from satellite measurements still very challenging • The signal received by the satellite is a complex combination of contributions from the surface (soil, vegetation, snow…) and possibly the atmosphere (gas, clouds, rain). • No radiative transfer model for soil/vegetation/snow satisfactory for global applications for all wavelengths. Even if it existed, would the inputs be available? • Spatial resolution of satellite observations not always compatible with the processes involved, and often large spatial variability within a satellite field-of-view. • Up to recently, no satellite optimized for the observations of key surface products such as soil moisture: surface parameters only ‘by-products’. Grace (2002) and SMOS (2008) first satellites specifically designed for continental hydrology.

4. Three sources of land surface information: the land surface models, the in situ measurements, the satellite products Complex links between them

5. Roles of the satellite products in the Land Surface Model framework: • inputs (initialization, boundary conditions, assimilations) • evaluation (to complement in situ measurements) • More and more demanding: - To account for the full variability (time and space) of the model dynamics • Simultaneously for different variables of the model (Gupta et al., 1999; McCabe et al., 2005) • To validate a model or to diagnose problem in a model? • different perspectives that can induce changes in the way the satellite relationship is considered • role within model ensembles • How to merge the three sources of land surface data, for a better final product and more predictability?

6. Satellite retrieval of land surface parameters: An ill-posed problem • Satellite-only methodologies: • One instrument / Multi-instruments: - One instrument solution simpler to implement and independent, but difficult to separate contributions from different parameters and often saturation effects (different angles, polarization, frequencies, use of the time scales) - Multi-instruments help separate the various contributions to the signal; more robust to noise; less sensitive to missing data; but more difficult to implement (collocation of several satellites?) • One parameter / Multi-parameters • Retrieval of one parameter often use auxiliary information • To insure consistency between these parameters multi-parameter retrieval (same set of assumptions; benefit from correlations between the variables) • In situ measurements or Land Surface Models to help satellite retrievals: • In situ measurements: - To understand the physics - To parameterize the relationships (are local measurements representative? Scaling problem?) • Land surface model outputs - As ancillary data - Database to train a statistical relationship through a radiative transfer model - To initiate an iterative inversion - Database to train a direct statistical relationship => should always be clearly stated anyway

7. Technical consequences of the multi-instrument / multivariate cases • Multi-channel / multi-instrument / multi-platform retrieval • Possible to develop physically-based algorithms? • => need for a RTM that can handle consistently • all surface types (with the corresponding ancillary infos) • all observation conditions (frequency, active/passive, angles…) • Multi-parameter retrieval • Benefit from the correlations between the variables. How are they specified? • => comes from the covariance matrix in a variational system • => comes from the training data base in a statistical method • Use of a statistical inverse model? • No uncertainties from the forward model • Avoids the estimation of the Jacobians • Makes it possible to work directly with the surface state variables (Aires and Prigent, JGR, 2006)

8. II - Soil Moisture as an Example • Comparison between satellite observations and in situ soil moisture measurements • Development of a multi-satellite retrieval methodology

9. So far: • Satellite studies on soil moisture generally use one type of instrument • passive microwave (ex: Riechle et al., 2004; Njoku et al., 2006) • active microwave (ex: Wagner et al., 2003) • infra-red (ex: Goetz et al., 2002) • What we suggest: • A systematic, extensive, and objective analysis of the existing observations at global scale to thoroughly assess what can be done with the available data: • => to create a consistent record of continental products for at least 10 year • ..with planned missions, long time to wait before having a climate record.... • => two requirements: • available on a global basis with spatial resolution compatible with climatological applications • available on long time series (at least 10 years)

10. The selected satellite observations sensitive to soil moisture: • Passive microwaves: • DMSP / SSM/I passive microwave data (between 19 and 85 GHz, i.e. between • 3.53 mm and 1.58 cm ) • Active microwaves: • ERS scatterometer (5.25 GHz, i.e. 5.71 cm) • Thermal IR: • NOAA / AVHRR and geostationary (Météosat, Goes E and W, GMS) • thermal infrared observations (~12 mm) • Not the raw observations but optimum derived products: • from passive microwave: land surface emissivities (Prigent et al., BAMS, 2006) • from the thermal infrared: amplitude of the diurnal cycle estimated from the ISCCP Ts (Rossow and Garder, BAMS, 1999; Aires et al., JGR, 2004) => significant pre-processing involved

11. Example of monthly mean products for each wavelength range Active microwave (ERS scatterometer) (backscattering coefficient) Passive microwave (DMSP / SSM/I) (surface emissivities) Thermal IR (ISCCP) (Ts diurnal amplitude ) Visible and Near-IR (NOAA/AVHRR) (NDVI)

12. II - 1 - Comparison of satellite observations with in situ soil moisture measurements The necessary first step to understand the physics

13. Comparison of satellite observations with in situ soil moisture measurements Region Station Surface Freq Period Depth Illinois 19 Grass 1 - 3/m All year 10cm Iowa 6 Corn 2/m Growth 8cm Russia 171 Cereal 3/m All year 20cm India 11 Grass 4/m All year ~10cm Mongolia 42 Pasture Wheat 3/m Growth Season 10cm The in situ measurements: Global Soil Moisture Data Bank (Robock et al., BAMS, 2000)

14. Comparison of satellite observations with in situ soil moisture measurements • Linear correlation between the satellite observations and in situ soil moisture measurements: • rather low for all satellite obs even the unexpected sign for passive microwave and IR • strongly depends on the region (e.g., from 0.43 in Illinois to -0.32 in Mongolia for passive MW at 19V-H) • correlation with the NDVI for comparison • much better correlation with soil moisture locally, when the spatial variability is avoided Variables Soil Moisture Vegetation (NDVI) Passive MW 19V-H -0.15 -0.70 Passive MW 37V-H -0.12 -0.63 Active MW small ang +0.41 +0.30 Active MW large ang +0.41 +0.37 IR Ts amplitude -0.01 -0.58 (Prigent et al., JGR, 2005)

15. Comparison of satellite observations with in situ soil moisture measurements • Direct or indirect relationship between the satellite observations and the soil moisture? for passive microwave, clearly related to correlation between vegetation and soil moisture

16. Comparison of satellite observations with in situ soil moisture • measurements • satellite observations often more sensitive to vegetation than to soil moisture • correlation between satellite obs and soil moisture through correlation between soil moisture and vegetation (depending on the satellite obs, changes with soil moisture and vegetation add up or cancel each other) • satellite observations contain information on the temporal variability • For soil moisture estimate at a global scale, find a method that: • 1) can exploit the soil moisture/vegetation link • merge sources of observation that have different sensitivity to moisture and vegetation to separate the two

17. II - 2 - Development of a global statistical relationship between satellite observations and soil moisture

18. A global statistical relationship between satellite observations and soil moisture The selected method to establish a global relationship between satellite observation and soil moisture: A statistical model Passive MW Active MW IR Ts Ampl. NDVI Statistical model Soil Moisture Neural Network • Advantages of the neural network statistical model: • Data-fusion of multi-spectral satellite observations • Non-linear model Þ situation-dependent (important for global scale) • No need for a RTM model • Define a link between observations and model that is coherent in time and space with the model and that provides additional constraint to the model • No bias with respect to the model (Aires et al., JGR, 2005)

19. A global statistical relationship between satellite observations and soil moisture A global source of soil moisture information: NWP models from NCEP and ECMWF Good index of soil moisture for temporal and spatial large-scale variability at monthly time-scale (ex:NCEP) Same behavior with satellite observations and in situ measurements

20. A global statistical relationship between satellite observations and soil moisture

21. A global statistical relationship between satellite observations and soil moisture A very flexible method that adapts to a large variety of situations Correlation soil moisture NCEP / vegetation (NDVI) (0.65) Correlation soil moisture NCEP / passive microwave (19V-H) (-0.26) Correlation soil moisture NCEP / active microwave (small ang) (0.58) • Exploits the various relationships between the satellite obs, the soil moisture, and the moisture and vegetation correlation. • Uses different NN models depending on satellite observation availability

22. A global statistical relationship between satellite observations and soil moisture Soil Moisture Retrieval NCEP model Satellite derived estimate

23. A global statistical relationship between satellite observations and soil moisture RMS Error Statistics Mean r.m.s. error = 5% (close to the 4% SMOS objective) Consistency checking between model output and satellite observations (ex: too dry in South America in NCEP) NCEP ECMWF

24. III - Conclusion and perspective • A multi-satellite statistical methodology to constrain land surface models • Application to soil moisture • A systematic and objective analysis of the satellite obs sensitivity to soil moisture, at a local (in situ measurements) and global (NWP products) scales • Merging of satellite data is powerful: helps separate the contributions of the various parameters and to untangle them (soil moisture / soil moisture): 5% retrieval accuracy on a global basis • Method more robust to noise or lacking data in one instrument • Use for consistency checking of GSWP-2 models underway • Similar methodology to be tested for turbulent fluxes? Both fluxes and intermediate variables at one time? • To be efficient, this exercise has to be performed in close collaboration with the modelers …