Gen AI in Business - Global Trends Report 2024.pdf
Estimating SMOS Error Structure Using Triple Collocation
1. Estimating SMOS error structure using triple collocation Delphine Leroux, CESBIO, France Yann Kerr, CESBIO, France Philippe Richaume, CESBIO, France
2. Soil moisture products at global scale How to evaluate SMOS ??? AMSR-E (NSIDC) ERS-ASCAT (TU Wien) Model output (ECMWF) AMSR-E (VUA) TMI (VUA) SSM/I (VUA) Aquarius SMAP SMOS ?
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7. Datasets AMSR-E soil moisture derived with the VUA algorithm (Vrije University of Amsterdam) ECMWF product from SMOS Level 2 product (at SMOS resolution and crossing time) 2) Datasets Chosen datasets Number of triplets Frequency (GHz) Incidence angle (°) Instrument resolution (km) Crossing time (A/D) Grid resolution (km) SMOS 1.4 0-55 40 6am / 6pm 15 AMSR-E 6.9 – 10.7 - … 55 57-6.25 1:30pm/ 1:30am 25
8. Number of triplets for 2010 2) Datasets Chosen datasets Number of triplets Difficulties for regions with mountains, forests, wetlands, …
9. Std of SMOS errors 3) Global maps of … relative errors bias scaling factors Good results in North America, North Africa, Middle East, Australia. Land contamination in Asia (Richaume et al., RAQRS, 2010).
10. Std of AMSR-E(VUA) errors 3) Global maps of … relative errors bias scaling factors Good results in the same areas as SMOS.
11. Std of ECMWF errors 3) Global maps of … relative errors bias scaling factors
12. Comparison over continents 3) Global maps of … relative errors bias scaling factors RELATIVE ERRORS SMOS is often between or close to the two values except in Asia !
13. Bias : AMSR-E(VUA) - SMOS 3) Global maps of … relative errors bias scaling factors Very high bias for high latitudes (mainly due to the vegetation) Mean bias around 0.1
14. Bias : ECMWF - SMOS 3) Global maps of … relative errors bias scaling factors High bias for high latitudes but more homogeneous Mean bias around 0.2-0.3
15. Scale factor AMSR-E(VUA) 3) Global maps of … relative errors bias scaling factors Scale >1 higher dynamic than SMOS Scale <1 lower dynamic than SMOS
16. Scale factor ECMWF 3) Global maps of … relative errors bias scaling factors Unlike the bias maps, there is no obvious structure for the scale factor
There exist many soil moisture data sets at the global scale. Some are derived from satellite data such as AMSR-E, SSM/I, TMI or ERS-ASCAT. And there can be different algorithms deriving SM from the same satellite, VUA and NSIDC for AMSR-E. SMAP is scheduled to be launched in 2014. Soil moisture can also be an output from models such as ECMWF. … All these data sets can be compared with each other. Now we would like to know what the position of SMOS is : is it better ? Is it different ? How different ?
The first step in a validation process when there is a new soil moisture data set is to compare it to ground measurements to see if there exists a bias or if the dynamic is well followed. The second step would be to compare with other existing data sets and to identify the differences. Now what would it be at the global scale ? We cannot compare with ground measurements representing the truth so we need to use statistics !
3 datasets as linear functions of the truth theta (except the first dataset which is considered as the reference) R are the bias and S the scaling factors Epsilon are the errors between the dataset and the truth In order to get rid of the bias terms, we will talk about the anomalies of the variables, meaning that we will consider the variability or the dynamic instead of the variables The final results are the std of the errors, depending on averages of the anomalies. This final equation is not dependent on the chosen reference dataset. The goal is to have the smallest std. It is very important to understand that these errors are completely relative to the 3 chosen datasets. This is a comparison and not the absolute truth. From these equations, we are able to draw global maps of the std of the errors, maps of bias and scale factors between the datasets and the reference.
What I did not say in the last slide is that there are restrictions to apply triple collocation. 2 assumptions : mutually independent errors and no bias between the datasets Scipal et al. estimated that a minimum of 100 common dates is needed for getting reliable statistics. To satisfy the assumption of the independent errors, it is important to choose properly the 3 datasets (we can’t choose AMSR-E/VUA and AMSR-E/NSIDC, it would be too risky). Otherwise, this is not the same frequency. Concerning the no bias, we applied TC to the anomalies so there is no bias. We have only used data from 2010 and the condition of the 100 common dates is not fulfilled. So we have decided that for each point, the 6 closest nodes are considered as they were the point of interest. At the end, we add the common dates of the 7 points so that we have at least 100 dates.
In this presentation, I will only talk about results from SMOS, AMSR-E/VUA and ECMWF. Here is a table of the characteristics of the two satellites … We have used the ECMWF product from SMOS level 2 product. It used to compute a set of initial values but it has been proven there is no correlation between ECMWF and SMOS.
As I said before, TC required at least 100 common dates or triplets. On this map, the regions in color show where TC will be applied. White color means there is not enough data to apply statistics because of mountains, forests, water region, ice…
Here is the first map of SMOS relative errors compared to AMSR-E and ECMWF. Some regions give better results than others : North America, North Africa, middle Asia, Australia. Land contamination in Asia for sure.
Here is the map of the errors from AMSR-E/VUA. We can see that the regions where the std is the lowest are the same : North America, North Africa, Middle Asia, Australia + Extreme South America and Extreme South Africa. Moreover if we compare the two maps, we see that where AMSR-E is good, it is better than SMOS but where it is bad, it worse than SMOS. So we can say that maybe SMOS is more homogeneous. SMOS is better over Europe.
The ECMWF dataset is good almost everywhere
Here is a table of the mean std over each continent in order to compare the datasets. On bold are the best results. For all continents, either AMSR-E or ECMWF is the best but SMOS is sometimes between AMSR-E and VUA (North America, Europe, Eastern Asia), sometimes very close (South America, North Africa, Australia), and sometimes it can give bad results (South Africa, Central Asia). To its defense, SMOS is not as bad as it can be seen here as it is still a young algorithm on which many people work to improve it. These results are in fact encouraging for the future.
Now are the maps of the bias between AMSR-E and the reference dataset (SMOS). The highest bias can be found for high latitudes, Europe, Eastern USA, South America and Central Africa.
Except for the high latitude band, the bias is in general higher between ECMWF and SMOS.
The scale factor maps can be seen as how the dynamic is represented compared to SMOS dynamic, less than 1 meaning a lower dynamic and more than 1 a higher dynamic. Most of the time, it is around 1.
Most of the regions, the scale factor seems to be under 1.
