The document summarizes the validation of soil moisture products from the SMOS satellite using in situ soil moisture networks in the US. It finds that SMOS soil moisture retrievals have root mean square errors similar to or better than those of AMSR-E soil moisture products, approaching the target accuracy of 0.04 m3/m3. However, some overestimations in SMOS soil moisture are correlated with precipitation events and may be due to differences in measurement depth between SMOS and in situ sensors. Additionally, the values and variability of vegetation optical depth (tau) retrieved by SMOS do not correlate well with estimates based on NDVI, indicating issues with the tau parameter.

1. SMOS Soil Moisture Validation With U.S. In Situ Networks T. J. Jackson, R. Bindlish, M. Cosh, and T. Zhao USDA ARS Hydrology and Remote Sensing Lab Beltsville. MD 20705 USA [email_address] July 25, 2011

2. Overview One of several contributions to the validation of the SMOS soil moisture product. Robust validation of a global product requires diverse conditions and the overall mission validation must consider and integrate all resources (Yann’s problem). In situ based observations are an important component of soil moisture product validation. However, we have to deal with the disparity of spatial scales of the in situ measurements made at a point (~ 0.00005 km ) and satellite products with much coarser spatial resolution (~ 40 km ). (6 orders of magnitude difference) These issues are common to SMOS and most other passive microwave sensors (AMSR-E, WindSat, SMAP). Approach: Use a combination of a limited set of reliable dense in situ soil moisture observing networks and soil moisture products from AMSR-E. All match the SMOS spatial scale.

3. USDA ARS Watershed Networks Four soil moisture networks (AZ-WG, GA-LR, ID-RC, OK-LW) located in different climatic regions of the U.S. Developed and used as part of the AMSR-E validation program, ~ size of AMSR-E, SMOS…grid cell. Continuous since 2002 Scaling is addressed by integrating multiple points in a watershed supplemented by additional intensive sampling campaigns. AZ-WG OK-LW GA-LR ID-RC

4. Role of AMSR-E in Validating SMOS Soil Moisture Products A variety of soil moisture products are available based on AMSR-E, WindSat, and ASCAT data. We will focus on AMSR-E and the SCA . Before comparing SMOS and AMSR-E soil moisture, consider that differences may be the result of the: Ancillary data (including dielectric mixing model) Improved contributing depth and reduced canopy attenuation of SMOS Diurnal variations in soil moisture associated with overpass times: SMOS (6 am/6 pm), AMSR-E (1:30 am/1:30 pm). Evaporation and/or precipitation events can occur. Not a direct comparison of SMOS vs. AMSR-E retrievals It is a performance comparison of each product versus the in situ data. The AMSR-E SCA product has been validated (in particular for the dense watersheds) and provides a benchmark on retrieval performance.

5. SMOS Validation Results Observed (in situ) vs. estimated (SMOS) soil moisture for each watershed AMSR-E comparisons Closer examination of outliers Vegetation optical depth Period of record Jan 1, 2010-May 31, 2011

6. SMOS Validation (Jan. 2010-May 2011)

7. SMOS Algorithm Performance (Jan. 2010-May 2011) The target is 0.04 m 3 /m 3 Not too bad Difference between asc. and dsc. not that much (except LR) Watershed Product SMOS Asc. 0600 SMOS Dsc. 1800 RMSE Bias R N RMSE Bias R N Walnut Gulch, AZ SMOS 0.033 -0.003 0.826 184 0.030 0.002 0.758 222 Little Washita, OK SMOS 0.041 -0.001 0.780 188 0.043 -0.007 0.792 194 Little River, GA SMOS 0.053 0.030 0.716 205 0.082 0.061 0.577 212 Reynolds Creek, ID SMOS 0.039 -0.023 0.152 51 0.045 -0.016 0.136 42 RMSE (Root mean square error), and Bias are in m 3 /m 3 . R=Linear correlation coefficient, N=Number of samples

8. SMOS Validation: AMSR-E Benchmark SMOS exhibited overestimation that we did not encounter with AMSR-E….why?

9. SMOS and AMSR-E SCA Algorithm Performance (Jan. 2010-May 2011). ~The same level of performance, a few exceptions. Watershed Product SMOS Asc. 0600 AMSR-E Dsc 0130 SMOS Dsc. 1800 AMSR-E Asc. 1330 RMSE Bias R N RMSE Bias R N Walnut Gulch, AZ SMOS 0.033 -0.003 0.826 184 0.030 0.002 0.758 222 AMSR-E 0.026 -0.016 0.505 292 0.032 -0.022 0.479 350 Little Washita, OK SMOS 0.041 -0.001 0.780 188 0.043 -0.007 0.792 194 AMSR-E 0.057 -0.033 0.530 321 0.058 -0.036 0.650 366 Little River, GA SMOS 0.053 0.030 0.716 205 0.082 0.061 0.577 212 AMSR-E 0.051 0.036 0.501 317 0.037 0.020 0.717 346 Reynolds Creek, ID SMOS 0.039 -0.023 0.152 51 0.045 -0.016 0.136 42 AMSR-E 0.041 -0.035 0.172 74 0.044 -0.039 0.045 76 RMSE (Root mean square error), and Bias are in m 3 /m 3 . R=Linear correlation coefficient, N=Number of samples

10. General Comments on SMOS Soil Moisture Retrievals The SMOS RMSE is equal to or better than the AMSR-E SCA values, with the exception of LR (Dsc). The overall SMOS RMSE values are 0.043 m 3 /m 3 (ascending) and 0.056 m 3 /m 3 (descending), which for asc. is very close to meeting the mission target of 0.04 m 3 /m 3 . These compare to the AMSR-E SCA values of 0.047 m 3 /m 3 (descending) and 0.044 m 3 /m 3 (ascending) respectively. The RMSE values do not appear to be impacted by the time of day (morning or afternoon) values (with the exception of LR dsc). Next: Focus on points that do not match up well with either the in situ or the AMSR-E distribution. Examine each watershed separately.

11. A Closer Look at WG SMOS, unlike the AMSR-E SCA, produces some large overestimates (both A&D). Since these points appear to be somewhat anomalous, we investigated possible causes. RFI: eliminated because this would produce underestimates of soil moisture Temperature: would expect to see a diurnal difference Precipitation Vegetation parameterization Errors in TB Examined temporal plots of observed and estimated soil moisture and precipitation.

12. Some of the overestimation of soil moisture appears to be correlated with antecedent or possibly ongoing precipitation. WG Temporal Plots of SMOS and In Situ Soil Moisture and Antecedent Precipitation (Rain Gage) (Asc.)

13. Possible Impacts of Precipitation Overestimation of soil moisture appears to be correlated with antecedent or possibly ongoing precipitation. The reason why there might be such a relationship is that the contributing depths of the SMOS and in situ measurements are very likely to be different during and shortly after a rain event. The depth of soil that contributes to the radiometer observation becomes shallow when the near surface is wet, which may occur during and shortly after rainfall. After some elapsed time the soil moisture profile will become more uniform. The in situ observations are centered at 5 cm and include a surrounding volume. Thus, it is likely that when it is raining that the SMOS estimate of soil moisture will be higher than the in situ soil moisture. This is not actually an algorithm error, it is the result of the operational assumption we make; which is that the satellite sensor measures a certain depth on average.

14. Apply a Precipitation Flag? If we have a means of identifying precipitation events that can be readily integrated into the algorithm as a flag, will this improve the accuracy? Three different precipitation flags ECMWF precipitation (forecast product available to the algorithm) Rain gage data: +/- 0.5 hours of the SMOS over pass (ongoing or active precipitation) Rain gage: 6 hour antecedent rainfall prior to the SMOS over pass. Using the occurrence of rainfall as detected by these flags, we filtered these from the data set and repeated the statistical analyses of RMSE and bias.

15. Comparing these filtered data sets to the original for WG, we found a reduction in RMSE when ECMWF forecasts were used. Larger improvement for the pm product. The ECMWF and active rain flags performed ~ same ( good result ). Increasing the antecedent period to 6 hours reduced the am RMSE but had little effect on the pm. Interpretation: High intensity and short duration rain is more likely in afternoon/evening, therefore, the ongoing rain flag helps more in the afternoon. In the morning, using a longer antecedent period captures night time events. Effect of Precipitation Flagging on SMOS Soil Moisture Algorithm Performance Watershed Flag SMOS Asc. 0600 SMOS Des. 1800 RMSE Bias R N RMSE Bias R N Walnut Gulch, AZ None 0.033 -0.003 0.826 184 0.030 0.002 0.758 222 ECMWF 0.029 -0.007 0.749 170 0.023 -0.003 0.744 199 Active Rain 0.030 -0.006 0.834 177 0.022 -0.005 0.726 180 6-Hour Ant. 0.022 -0.011 0.714 162 0.021 -0.005 0.728 169 RMSE (Root mean square error), and Bias are in m 3 /m 3 . R=Linear correlation coefficient, N=Number of samples. 6-Hour Ant.=6-Hour Antecedent

16. Vegetation Parameter Tau SMOS provides a retrieval (or estimate) of the vegetation optical depth (tau). We compared the level and seasonal pattern of the SMOS tau to estimates based upon NDVI/VWC. Plots are only for the ascending overpass (similar behavior for descending). These show observed and retrieved SM, SMOS tau, and NDVI-based VWC.

17. There is very little vegetation in WG. The changes that occur during the year are associated with rainfall events, which are more common during the late summer period. Tau exhibits large day to day variability that does not appear to be associated with changes in the vegetation (reference NDVI based VWC). In addition, these deviations do not seem to be directly related to differences between the in situ and SMOS soil moisture since they occur during periods with both nominal as well as large errors and show no seasonal pattern Walnut Gulch Watershed

18. In Spring, winter wheat greens up. As wheat senesces in May, grasses and summer crops begin greening. Tau exhibits large day to day variability that does not appear to be associated with changes in the vegetation (reference NDVI based VWC). In addition, these deviations do not seem to be directly related to differences between the in situ and SMOS soil moisture since they occur during periods with both nominal as well as large errors and show no seasonal pattern Little Washita Watershed

19. Summer crops and trees dominate. Tau exhibits large day to day variability that does not appear to be associated with changes in the vegetation (reference NDVI based VWC). In addition, these deviations do not seem to be directly related to differences between the in situ and SMOS soil moisture since they occur during periods with both nominal as well as large errors and show no seasonal pattern Little River Watershed

20. Using the alternative method for estimating tau, the values are smaller than those retrieved by SMOS. This result raises concerns on the interpretation of SMOS tau parameter. Comparison of Tau Retrieved by SMOS (Asc.) and Estimated from MODIS NDVI (Jan. 2010-May 2011) Watershed SMOS Tau MODIS estimated Tau Walnut Gulch, AZ 0.19 0.06 Little Washita, OK 0.21 0.11 Little River, GA 0.30 0.14 Reynolds Creek, ID 0.22 0.08

21. Assessment of the SMOS Tau The level, the variability from day to day, and the lack of a seasonal response of tau indicated to us that these estimates may be dependent on other factors beyond the vegetation. These factors could include the quality of the brightness temperatures related to known problems with SMOS FOV. See Bindlish et al. on Wed. in the SMAP session.

22. Summary Evaluation of the SMOS soil moisture products over U.S. watershed sites. Approach: Combines in situ networks and AMSR-E soil moisture estimates. Confidence in the dense networks from previous field campaigns and AMSR-E validation studies. AMSR-E products should be a benchmark for performance Reasonable results, ~ AMSR-E, approaching target value. Focusing on some of the anomalous results can provide insights to algorithm issues. The retrieved tau values do not appear to be a reliable vegetation index, at this time.

23. SMOS FOV There are some problems with the SMOS FOV that impact brightness temperature estimates at certain angles (or positions within the snapshot) on some days. Snapshot images include areas that are either in the alias free (near the center) or extended alias free FOV (near the edges). Our hypothesis is that some of the variation in tau observed may be related to brightness temperature variations associated with this issue. Take another look at the RC watershed temporal plot.