1. Scaling Properties of L-band Passive Microwave Soil Moisture: From SMOS to Paddock Scale
My work is focused on the scaling properties of L-band retrieval of soil moisture, ranging from SMOS to paddock scale
Rocco Panciera1, Jeffrey Walker1, Olivier Merlin1, Jetse Kalma2
and Edward J. Kim3
1 Department of Civil and Environmental Engineering, University of Melbourne, Australia
2 School of Engineering, University of Newcastle, Australia
3 NASA Goddard Space Flight Center, Greenbelt, USA

2. Statement of the Problem
Large footprint of passive microwave observations (30-50km)
Soil moisture retrieval algorithms developed with tower based studies (~10m):
- Homogeneous conditions
- Algorithms are non linear
Soil moisture retrieval from space using passive microwave sensors presents crucial limitation: large footprint size
Land surface is heterogeneous by default at this scale, including variety of landscapes
Instead: Soil moisture retrieval algorithms developed amd parameters estimated at small resolution
Algorithms need to be tested with real coarse scale data
Sub-pixel heterogeneity effect on operational soil moisture retrieval schemes needs to be assessed
4 December 2018
Rocco Panciera

3. Statement of the Problem
Radiative transfer
model
High resolution Tb
Low resolution Tb
Tb = 181.2K
0.28 v/v
Retrieved footprint
moisture content
….What happens at satellite footprint scale?
A simple case
Bare soil
Uniform soil temperature = 317.5K
Uniform soil type = Silty Clay Loam
soil
moisture
0.36 v/v
Mean water content
The effect of non linearity when applying same algorithms across scales is clear in a simple example
Only variability is soil moisture
Smooth soil, silty clay loam
The relationship between Tb and soil moisture is NON linear
If we use the SAME model at different resolution, do we obtain the same area-averaged soil moisture content???? ?
4 December 2018
Rocco Panciera

4. Objective
Verify applicability of current retrieval algorithms at coarse scale (40km) using real L-band data
Our study is focused on SMOS type retrieval approach
Algorithm: SMOS L2 algorithm
Data: NAFE’05 L-band data
4 December 2018
Rocco Panciera

5. Approach 1km pixels Ground soil moisture Aggregation ? 40km SMOS L2
40km soil
moisture
product
1km soil
SMOS L2
40km
footprint
Aggregation ?
Described NAFE’05 regional data (Resolution 1km, 4 dates, Coverage 40km)
Tb data bi-polarized, daily calibrated, 38.4 degree angle
Highlight ground points (say they are now at 2km resolution)
Describe process
Comparison between the two products should indicate wether further Algorithm development is needed for SMOS L2 for heterogeneity effect\
Coincidently, this study is the first validation of the SMOS algorithm with real data
Today I am going to focus on…
40km
4 December 2018
Rocco Panciera

6. Aggregation to 40km Footprint
16 dates
Daily calibrated
Nadir-referenced for comparison
Resolution
62.5m
250m
500m
1km
Mean Tb (K)
257.1
258.4
258.9
258.8
STDEV (K)
9.9
7.8
7.3
5.5
Is linear aggregation legitimate? Aggregation has been verified with Multiresolution data
Comment patterns, retained features but smoothed
Table is example for one day
averaging of TB measurements at high resolution to a lower resolution results in a good agreement with the direct lower resolution measurements.
the results suggest that aircraft data can be reliably averaged up to simulate satellite footprint
More than 80% of the SEE values are contained within the +/- 3K band,
Error standard deviation of 3.2K and 3.4K for Krui and Merriwa respectively.
higher SEE are mainly associated with unusually high heterogeneity steep soil moisture gradient provoked by localised showers.
+1.3
+1.8
+1.7
Linear aggregation of Tb is reliable
4 December 2018
Rocco Panciera

7. Simulated L-band emission
Model Description
SMOS Level-2 algorithm
Mixed pixels, 4 surface types
Bare soil Grassland Crop Forest
Tau-Omega emission model L-MEB for each over type
( Wigneron et al. 2007, Remote Sensing of Environment)
Soil
moisture
L-MEB
model (i)
Land cover type
SOIL TYPE
% surface type “i”
In pixel
Surface type
dependent
parameters
Soil/canopy
temperature
L-band observation
Optimization
Simulated L-band emission
Within each pixel, the brigthness temperatures from each cover type are simulated with a forward model and then aggregated
Parameters driving the forward model are selected and tabulated based on the selected vegetation classes and maps of soil properties
The mixed pixel emission is calculated as a linear combination of the Tb of each land cover, weighted by their respective cover fraction
Heterogeneity is taken into account, BUT soil moisture is the same across surface cover types)
4 December 2018
Rocco Panciera

8. Model Description Assumptions
Soil moisture uniform across the cover types within each pixel
Effective temperature to microwave emission
Optical depth
Optical depth of forests is a constant
No modelling of rainfall interception by plants
Teff modelling very recent. Parameterization of w and b undergoing
Tau of forest constant (with angle, polarization and time) is related to the maximum LAI over the year – dominance of branch effect with respect to leaf effetcs
Only dominant cover type 1km resolution
Open woodland is modelled as grassland
4 December 2018
Rocco Panciera

9. Ancillary Data Landcover
Landsat derived
landcover map (25m)
Dominant cover type
@ 1km resolution
Explain limits of nafe focus areas and approx limits of pixels = 21k aircraft mapping
Land cover map verified by ground visual estimation of dominant land cover,
Preferred to ECOCLIMAP 1km
Dominant cover type that will be used for the radiative transfer
Cover Type %
Grassland
50.7%
Forest
24.3%
Open woodland
8.5%
Crops
4.5%
4 December 2018
Rocco Panciera

10. Ancillary Data Vegetation Water Content
Experimental relationship VWC vs NDWI
Crop
Grassland
Relationship were established between ground measured VWC and NDWI index, which
Was demonstrated to be more efficient then NDVI or LAI for vwc mapping
One relationship for crop, one for grass
VWC
4 December 2018
Rocco Panciera

11. Ancillary Data Soil/Canopy Temperature
Effective soil temperature to microwave emission
8 stations
Ts 2.5cm
Ts15cm
Soil temperature
Soil temperature
Oct-31
1.9
2.0
Nov-07
3.1
Nov-14
5.4
3.3
Nov-21
6.1
3.5
Requires an estimation of T soil depth~= T 30cm
Tsoil 2-4cm
We have available 12 stations with T2.5cm and T15cm
The possibility to use mean values instead of interpolation was explored
Given the small spatial variation and uncertainty added by interpolation, average values were taken. Differece in Teff so calculated <1K
Assumptions:
TSOIL_DEPTH = Average of T15cm
TSOIL_SURF = Average of T2.5cm
TCANOPY = TSOIL_SURF
4 December 2018
Rocco Panciera

12. Ancillary Data Surface Type Dependent Parameters
All these parameters have been calibrated at tower scale, but literature is in fair agreement for most
(that’s why they have been adopted for SMOS L2)
Vegetation parameter b for crops – variety of values in literature. The one adopted is in the lower range for L-band
Still high level of uncertainty for HR. a number of studies suggested dependence of soil moisture for
Crops, grassland with litter and recently bare soil
Several runs varying uncertain surface type parameters (b, albedo, HR) to check for sensitivity
HR was found crucial and dependence on sm important to reproduce dynamic. Still research topic. Here this formulation assumed from experimental data
(Wigneron, J. P. ,Personal communication)
HR= * (SM)
(Saleh et al. ,2007
SMOSREX site)
4 December 2018
Rocco Panciera

13. Validation of 1km Product Results to Date
Nov 7
Nov 14
Nov 21
Retrieved soil moisture (v/v)
To date, the validation of LMEB at 1km is this.
Oct 31 removed due to likelyhood of water interception
Comments to patterns:
Effect of FAO soil type
Effect of soil temperature change in time (deltaTb<7,delta Ts<5)
4 December 2018
Rocco Panciera

14. Validation of 1km Product Results to Date
Soil moisture error (v/v)
(retrieved – observed)
Nov 7
Nov 14
Nov 21
Bare (14pts)
12.1
6.9
7.5
Grassland (113pts)
11.8
10.1
6.7
Crop (9pts)
19.9
10.2
6.0
Forest (4pts)
14.8
4.9
5.6
No evident patterns, Errors appear bigger and underestimation on sandy soils on wet
RMSE reveal we are doing bad on crops  variety of crops treated as one, and unique value of parameter b  suggest need for better estimation
Note difference in number of points in these averages
RMSE %v/v
12.4
9.7
6.7
Bias % v/v
+1.7
-4.9
+1.5
4 December 2018
Rocco Panciera

15. Parameter Calibration
Estimation HR= a + b *(SM)
HR greater impact, and crucial to model soil moisture dynamics
Literature suggests is a function of SM for all surface cover types – still research topic
Calibrated resolution
Saleh formulation seems accurate for bare, grass and some crops
It appears that at very dry values HR discreases again
Makes sense if think about “dielectric roughness” effect
BARC: low grass cover, no litter
ELBARA: moderate grass cover, litter
4 December 2018
Rocco Panciera

16. Where To Improve? Improve estimation of HR = f(soil moisture)
Improve estimation of b for crops
Finer scale soil type map
Better TEFF estimation from ground data
Use MODIS thermal to estimate TCANOPY
Improvement in landcover map
4 December 2018
Rocco Panciera

17. Conclusions
Validation of SMOS L2 retrieval scheme with real data 1km resolution
Demonstrated reliability of linear Tb aggregation for satellite footprint simulation
Insights into effect of crucial surface roughness parameter at aircraft scales
NEXT………….
Retrieval from simulated 40km Satellite pixel
Assessment of performance of algorithm at different scales
4 December 2018
Rocco Panciera

18. Thank you
4 December 2018
Rocco Panciera