8A Supplementary Material

Transcription

1 8A Supplementary Material Supplementary material belonging to Chapter 2, derived from the following publication: Van der Schalie, R., Parinussa, R.M., Renzullo, L.J., Van Dijk, A.I.J.M., Su, C.H. and De Jeu, R.A.M. (2015), SMOS soil moisture retrievals using the land parameter retrieval model: Evaluation over the Murrumbidgee Catchment, southeast Australia, Remote Sensing of Environment, 163, pp

2 Table S8A.1, results per site, 45 ascending/descending/combined, using MERRA derived effective temperature 45 Ascending 45 Descending 45 Combined Site r ± CI RMSE ± CI Bias ± CI N r ± CI RMSE ± CI Bias ± CI N r ± CI RMSE ± CI Bias ± CI M ± M ± 0.18 M ± M ± M ± M ± M ± Y ± 0.16 Y ± Y ± Y ± ± 3 - ± 9 ± 2 - ± 1 ± 1 ± ± 4 ± 3 - ± 4 ± 4 - ± ± ± ± ± ± ± ± ± ± ± ± ± 0 - ± 3 ± 3 - ± 0 ± 0 ± 0 ± 4 ± 2 - ± 2 ± 9 - ± ± ± ± ± ± ± ± ± ± ± ± 3 ± 6 - ± 1 ± 3 - ± 6 ± 6 ± 8 ± 4 ± 8 - ± 8 ± 8 - ± 172 N

3 Y ± Y-B 0.82 ± Y ± K ± K ± Mean 0.70 ± ± 6 - ± 6 - ± 9 - ± ± 5 - ± ± ± ± ± ± ± ± 7 ± 1 - ± 0 - ± ± ± ± ± ± ± ± ± ± ± 9 - ± 9 - ± ± 2 - ±

4 Table S8A.2, results per site, 45 ascending/descending/combined, using ECMWF derived effective temperature 45 Ascending 45 Descending 45 Combined Site r ± CI RMSE ± CI Bias ± CI N r ± CI RMSE ± CI Bias ± CI N r ± CI RMSE ± CI Bias ± CI M ± M ± 0.20 M ± M ± M ± 0.19 M ± M ± Y ± 0.18 Y ± Y ± Y ± ± 2 - ± 0 ± 6 - ± 9 ± 6 ± 1 - ± 1 ± 1 - ± ± 0 - ± ± ± ± ± ± ± ± ± ± ± ± ± 3 - ± 2 ± 5 - ± 5 ± 0 ± ± 6 ± 0 - ± ± 3 - ± ± ± ± ± ± ± ± ± ± ± ± 0 ± 6 - ± 1 ± 0 - ± 3 ± 3 ± 0 - ± 9 ± 1 - ± ± 7 - ± 174 N

5 Y ± Y-B 0.83 ± Y ± K ± K ± Mean 0.72 ± ± 2 - ± 7 - ± 3 - ± ± 1 - ± ± ± ± ± ± ± ± ± 0 - ± 0 - ± ± 6 - ± ± ± ± ± ± ± ± ± 4 - ± 6 - ± ± 9 - ±

6 Table S8A.3, results per site, 52.5 ascending/descending/combined, using MERRA derived effective temperature 52.5 Ascending 52.5 Descending 52.5 Combined Site r ± CI RMSE ± CI Bias ± CI N r ± CI RMSE ± CI Bias ± CI N r ± CI RMSE ± CI Bias ± CI M ± M ± 0.19 M ± M ± M ± M ± M ± Y ± Y ± Y ± Y ± ± 8 - ± 8 ± 0 - ± 3 ± 1 ± 5 ± 1 ± 7 - ± 9 ± 6 - ± ± ± ± ± ± ± ± ± ± ± ± ± 7 - ± 7 ± 7 - ± 2 ± 9 ± 4 ± 1 ± 9 - ± 0 ± 5 - ± ± ± ± ± ± ± ± ± ± ± ± 8 ± 2 - ± 2 ± 9 - ± 2 ± 4 ± 4 ± 6 ± 3 - ± 9 ± 1 - ± 176 N

7 Y ± Y-B 0.75 ± Y ± K ± K ± Mean 0.71 ± ± ± 8 - ± 9 - ± ± 4 - ± ± ± ± ± ± ± ± 9 ± 0 - ± 3 - ± 1 - ± 1 ± ± ± ± ± ± ± ± 4 ± 5 - ± 9 - ± ± ±

8 Table S8A.4, results per site, 52.5 ascending/descending/combined, using ECMWF derived effective temperature 52.5 Ascending 52.5 Descending 52.5 Combined Site r ± CI RMSE ± CI Bias ± CI N r ± CI RMSE ± CI Bias ± CI N r ± CI RMSE ± CI Bias ± CI M ± M ± 0.17 M ± M ± M ± M ± M ± Y ± Y ± Y ± Y ± ± 3 - ± 5 ± 6 - ± 0 ± 6 ± ± 8 ± 5 - ± ± 4 - ± ± ± ± ± ± ± ± ± ± ± ± ± 0 - ± 6 ± 2 - ± 6 ± 4 ± 2 ± 3 ± 5 - ± 7 ± 6 - ± ± ± ± ± ± ± ± ± ± ± ± 5 ± 6 - ± 5 ± 8 - ± 8 ± 0 ± ± 0 ± 5 - ± ± 1 - ± 178 N

9 Y ± Y-B 0.75 ± Y ± K ± K ± Mean 0.73 ± ± ± 4 - ± 4 - ± ± 3 - ± ± ± ± ± ± ± ± 9 ± 7 - ± 5 - ± ± ± ± ± ± ± ± ± ± 0 ± 1 - ± 6 - ± ± 2 - ±

10 Table S8A.5, results per site, 60 ascending/descending/combined, using MERRA derived effective temperature 60 Ascending 60 Descending 60 Combined Site r ± CI RMSE ± CI Bias ± CI N r ± CI RMSE ± CI Bias ± CI N r ± CI RMSE ± CI Bias ± CI M ± M ± M ± M ± M ± M ± M ± Y ± Y ± Y ± Y ± ± 1 - ± 5 ± 8 - ± 1 ± 9 ± 4 ± 1 ± 8 - ± 8 ± 7 - ± ± ± ± ± ± ± ± ± ± ± ± ± ± 2 ± ± 0 ± 5 ± 0 ± 5 ± 4 - ± 8 ± 5 - ± ± ± ± ± ± ± ± ± ± ± ± 9 ± 1 - ± 7 ± 6 - ± 1 ± 6 ± 6 ± 3 ± 3 - ± 3 ± 7 - ± 180 N

11 Y ± Y-B 0.82 ± Y ± K ± K ± Mean 0.72 ± ± 5 ± 6 - ± 7 - ± ± ± ± ± ± ± ± ± ± 9 ± 0 - ± 7 - ± 4 - ± 3 ± ± ± ± ± ± ± ± 9 ± 8 - ± 9 - ± 9 - ± ±

12 Table S8A.6, results per site, 60 ascending/descending/combined, using ECMWF derived effective temperature 60 Ascending 60 Descending 60 Combined Site r ± CI RMSE ± CI Bias ± CI N r ± CI RMSE ± CI Bias ± CI N r ± CI RMSE ± CI Bias ± CI M ± 0.16 M ± M ± M ± M ± M ± M ± Y ± Y ± Y ± Y ± ± 8 - ± 9 ± 3 - ± 8 ± 4 ± ± 5 ± 7 - ± ± 6 - ± ± ± ± ± ± ± ± ± ± ± ± ± 5 - ± 3 ± 8 - ± 6 ± 3 ± 4 ± 7 ± 3 - ± 1 ± 5 - ± ± ± ± ± ± ± ± ± ± ± ± ± 9 - ± 0 ± 5 - ± 7 ± 2 ± ± 6 ± 6 - ± ± 9 - ± 182 N

13 Y ± Y-B 0.84 ± Y ± K ± K ± Mean 0.75 ± ± 1 ± 3 - ± 2 - ± ± 4 - ± ± ± ± ± ± ± ± 1 ± 0 - ± 2 - ± 5 - ± ± ± ± ± ± ± ± ± 4 ± 6 - ± 4 - ± ± 4 - ±

14 Table S8A.7, results per site, average of the 3 incidence angles ascending/descending/combined, using MERRA derived effective temperature Ascending Descending Combined Site r ± CI RMSE ± CI Bias ± CI N r ± CI RMSE ± CI Bias ± CI N r ± CI RMSE ± CI Bias ± CI N M ± M ± 0.23 M ± M ± M ± M ± M ± Y ± Y ± Y ± Y ± ± 0 - ± 3 ± 8 - ± 7 ± 5 ± 9 ± 9 ± 8 - ± 8 ± 5 - ± ± ± ± ± ± ± ± ± ± ± ± ± 1 - ± 5 ± ± 0 ± 9 ± 4 ± 4 ± 3 - ± 7 ± 8 - ± ± ± ± ± ± ± ± ± ± ± ± 1 ± 5 - ± 8 ± 7 - ± 0 ± 6 ± 7 ± 1 ± 3 - ± 8 ± 2 - ±

15 Y ± Y-B 0.82 ± Y ± K ± K ± Mean 0.74 ± ± ± 4 - ± 7 - ± ± 4 - ± ± ± ± ± ± ± ± 9 ± 6 - ± 5 - ± 5 - ± 0 ± ± ± ± ± ± ± ± 7 ± 5 - ± 3 - ± ± ±

16 Table S8A.8, results per site, average of the 3 incidence angles ascending/descending/combined, using ECMWF derived effective temperature Ascending Descending Combined Site r ± CI RMSE ± CI Bias ± CI N r ± CI RMSE ± CI Bias ± CI N r ± CI RMSE ± CI Bias ± CI N M ± M ± 0.20 M ± M ± M ± 0.18 M ± M ± Y ± Y ± Y ± Y ± ± 5 - ± 6 ± 2 - ± 8 ± 0 ± 9 - ± 4 ± 8 - ± ± 3 - ± ± ± ± ± ± ± ± ± ± ± ± ± 9 - ± 6 ± 9 - ± 5 ± 6 ± 1 ± 6 ± 1 - ± 0 ± 4 - ± ± ± ± ± ± ± ± ± ± ± ± ± 6 - ± 0 ± 5 - ± 6 ± 2 ± ± 5 ± 6 - ± ± 4 - ±

17 Y ± Y-B 0.83 ± Y ± K ± K ± Mean 0.77 ± ± ± 7 - ± 2 - ± ± 2 - ± ± ± ± ± ± ± ± 3 ± 4 - ± 7 - ± ± ± ± ± ± ± ± ± ± ± 0 - ± 9 - ± ± 1 - ±

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19 8B Supplementary Material Supplementary material belonging to Chapter 4, derived from the following publication: Van der Schalie, R., Kerr, Y.H., Wigneron, J.P., Rodriguez-Fernandez, N.J., Al- Yaari, and De Jeu, R.A.M. (2016b), Passive Microwave Soil Moisture Data Fusion: Algorithm Theoretical Baseline Document for the Land Parameter Retrieval Fusion Approach, SMOS Fusion Project. 189

20 8B.1 Roughness and angle bin update SMOS LPRM The results by Van der Schalie et al. (2016a) indicated that the roughness (h) derived from the global optimization for SMOS LPRM is incorrect in areas without or with very dense vegetation, leading to an overestimation of soil moisture (θθ, m 3 m -3 ) in sparsely vegetated areas and an underestimation over dense vegetation. In a short follow-up study, a new empirical method to estimate h was introduced, containing a correction for vegetation influences without using external sources of information on vegetation by first calculating SMOS LPRM as is done in Van der Schalie et al. (2016a) and then use the retrieved vegetation optical depth ( v ) in a second run to correct the h for vegetation influences following Equation 8B.1 (Same as Eq. 4.4). The v retrievals from the first run are used as a moving average of ± 1 month to reduce the otherwise introduced extra noise in the retrievals. Secondly, the impact is tested when the highest angle bin of 55 to 60 is dropped to increase the temporal resolution. An overview of difference in retrievals between the old and new method will be given in Section 8B.2. h=h 1 (AA vv (1 2θθθθθθ vv vv ) (8B.1) 8B.2 Evaluation of updates Figure 8B.2 shows the differences in the SMOS LPRM θθ retrievals when applying the new roughness approach with the vegetation correction. Figure 8B.2 A/B show the mean retrievals of the vegetation corrected SMOS LPRM dataset (ascending/descending), C/D show what the difference is, compared to the old SMOS LPRM dataset. Here you can see that the θθ has decreased over the desert areas, dropping down to values of m 3 m -3, while the biggest difference can be found in the more densely vegetated areas like the boreal forests and the at the edge of the tropical forests, where the θθ goes up with an average of around 0.1 m 3 m -3. However, as expected, when the vegetation gets really dense like in the center of the rainforests, no soil moisture of sufficient quality can be retrieved with SMOS LPRM, therefore the boundary of removing results with a retrieved v > 0.35 should still be 190

21 used as described by Van der Schalie et al. (2016). To get a better idea of the difference between the two methods, the average soil moisture Africa between 0 and 20 latitude is shown for different products and the SMOS LPRM ex- and including the new roughness update in Figure 8B.1. Here you can see that at the edge of the Sahara the retrievals drop, being more similar to the other products, and at the edges of the tropical forests there is an increase. In Figure 8B.2 E to H the differences are shown in correlation and ubrmse between the new and old SMOS LPRM against the official SMOS Level 3 product. The correlation hardly changed, with a small increase of for the ascending dataset (E) and for descending (F). Here you can see that for almost all areas the change in correlation is negligible, especially in the semi-arid areas. Differences can be found near the edges of the boreal forests in Alaska and Russia, showing a small increase in correlations, and in the very dry desert areas, giving mixed results, however this is mostly due to noise since the moisture retrievals are mostly stable year-round. For the ubrmse the small changes can also be found around the major forested areas, showing a small decrease around the tropical forests and a small increase around the boreal forest. This shows that the updated roughness with SMOS LPRM gives more realistic θθ retrievals, while the dataset has a negligible change in skill. By removing the highest angle bin from the retrievals, the total amount of retrievals is increased significantly with on average 22% for the ascending dataset and 20% for the descending set. This leads to a temporal resolution of once every five days for each of the dataset. Although it is an improvement, the temporal resolution is still lower than that of SMOS L3 which has a revisit time of once every three days; this is due to the necessity of the higher incidence angles in the LPRM model. Also with this step the skill against SMOS L3 hardly changes, with a decrease in correlation with SMOS L3 of - for both ascending as descending swaths, with the main areas where the change occurs are the desert areas due to differences in noise from the 191

22 observations and around areas contaminated with RFI. The ubrmse slightly increase with m 3 m -3 and m 3 m -3, which again is mainly around areas with RFI and at very dense vegetation. Figure S8B.1 Average θθ of different products over the period of July 2010 to December 2013 over African from a latitude of 0 to

23 Figure S8B.2 (part 1) Showing the results using the vegetation corrected roughness on SMOS LPRM, A and B are mean θθ retrievals over July 2010 to December 2013 (Asc/Desc), C and D the difference between the SMOS LPRM retrievals with and without vegetation correction (Asc/Desc), E and F its effect on the correlation against SMOS Level 3 retrievals (Asc/Desc) and G and H the difference in ubrmse against SMOS Level 3. Gray areas are not taken into consideration due to too dense vegetation. 193

24 Figure S8B.2 (part 2) Showing the results using the vegetation corrected roughness on SMOS LPRM, A and B are mean θθ retrievals over July 2010 to December 2013 (Asc/Desc), C and D the difference between the SMOS LPRM retrievals with and without vegetation correction (Asc/Desc), E and F its effect on the correlation against SMOS Level 3 retrievals (Asc/Desc) and G and H the difference in ubrmse against SMOS Level 3. Gray areas are not taken into consideration due to too dense vegetation. 194