SMOS-IC: An Alternative SMOS Soil Moisture and Vegetation Optical Depth Product
Abstract
:1. Introduction
- I
- The main objective of this product is to be as independent as possible from auxiliary data. The SMOS-IC algorithm does not take into consideration pixel land use and assumes the pixel to be homogeneous as suggested by Wigneron et al., 2012 [29]. The SM and τ retrieval is performed over the whole pixel rather than over the fraction designated as either low vegetation or forest. Note that this approach is similar to the one considered in the development of the AMSR-E and SMAP SM algorithms (O’Neill et al., 2012 [27]). By simplifying the retrieval approach, the SMOS-IC product becomes independent of the ECMWF soil moisture information currently used as auxiliary information to estimate TB in the subordinate pixel fractions of heterogeneous pixels in the operational SMOS L2 and L3 algorithms (Kerr et al., 2012 [1]).
- II
- In relation to the above point, in some cases, the Level 2 and Level 3 algorithms use values of LAI derived from MODIS [30] to initialize the value of optical depth in the inversion algorithm (Kerr et al., 2012 [1]). In SMOS-IC, this is not implemented, and the initialization of optical depth in the inversion algorithm is based on a very simple approach (given in the following) and is completely independent of the MODIS data.
- III
- SMOS-IC uses as input SMOS Level 3 fixed angle bins Brightness Temperature (TB) data at the top of the atmosphere and contains different flags allowing to filter SM retrievals accounting for the quality of the input TB data and for the TB angular range in the L-MEB inversion. SMOS-IC does not make use of the computationally expensive corrections based on angular antenna patterns to account for pixel heterogeneity as in the L2 and L3 retrieval algorithms.
- IV
- New values of the effective vegetation scattering albedo (ω) and soil roughness parameters (HR, NRV, and NRH) are considered in the SMOS-IC product. This change is based on the results of Fernandez-Moran et al. (2016) [31] who calibrated the L-MEB vegetation and soil parameters for different land cover types based on the International Geosphere-Biosphere Programme (IGBP) classes, as well as the findings of Parrens et al. (2016) [32] who computed a global map of the soil roughness HR values. The calibration of Fernandez-Moran et al. (2016) [31] was obtained by selecting the values of the parameters (HR, NRV, NRH, and ω) which optimized the SMOS SM retrievals, with respect to the in situ SM values measured over numerous sites obtained from ISMN (International Soil Moisture Network). The parameter values resulting from this new calibration differ from those used in the current SMOS L2 and L3 products. Values currently used in the SMOS L2 and L3 algorithms (Kerr et al., 2012 [1]) were defined before launch from literature. Over forested areas, values were updated but not over low vegetation. Consequently, in Version 620 of the L2 (and Version 300 for L3) algorithm, ω is still assumed to be zero over low vegetation canopies and ω ~ 0.06–0.08 over forests. Similarly, HR is equal to 0.3 for forests and HR = 0.1 for the rest of the cover types, whereas NRH and NRV are respectively set to 2 and 0 at global scale.
2. Materials and Methods
2.1. SMOSL3 Brightness Temperature, Soil Moisture and Vegetation Optical Depth
2.2. SMOS-IC
2.2.1 Model Description
2.2.2. Effective Vegetation Scattering Albedo, Soil Roughness and Soil Texture Parameters
2.2.3. Quality Flags
2.3. ECMWF and MODIS Data
2.4. Inter-Comparison
2.4.1 Data Filtering
2.4.2 Metrics
3. Results and Discussion
3.1. Soil Moisture
3.2. Vegetation Optical Depth
4. Summary and Conclusions
Supplementary Materials
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Class | ω (SMOS-IC) | ω (SMOSL3 V300) | HR (SMOS-IC) | HR (SMOSL3 V300) |
---|---|---|---|---|
1—Evergreen needle leaf forest | 0.06 | 0.06–0.08 * | 0.30 | 0.30 |
2—Evergreen broadleaf forest | 0.06 | 0.06–0.08 * | 0.30 | 0.30 |
3—Deciduous needle leaf forest | 0.06 | 0.06–0.08 * | 0.30 | 0.30 |
4—Deciduous broadleaf forest | 0.06 | 0.06–0.08 * | 0.30 | 0.30 |
5—Mixed forests | 0.06 | 0.06–0.08 * | 0.30 | 0.30 |
6—Closed shrublands | 0.10 | 0.00 | 0.27 | 0.10 |
7—Open shrublands | 0.08 | 0.00 | 0.17 | 0.10 |
8—Woody savannas | 0.06 | 0.00 | 0.30 | 0.10 |
9—Savannas | 0.10 | 0.00 | 0.23 | 0.10 |
10—Grasslands | 0.10 | 0.00 | 0.12 | 0.10 |
11—Permanent wetland | 0.10 | 0.00 | 0.19 | 0.10 |
12—Croplands | 0.12 | 0.00 | 0.17 | 0.10 |
13—Urban and built-up | 0.10 | 0.00 | 0.21 | 0.10 |
14—Cropland/Natural Vegetation Mosaic | 0.12 | 0.00 | 0.22 | 0.10 |
15—Snow and ice | 0.10 | 0.00 | 0.12 | 0.10 |
16—Barren and sparsely vegetated | 0.12 | 0.00 | 0.02 | 0.10 |
Scene Flags | Description |
---|---|
Presence of moderate topography | Same filter as SMOSL3 V300 |
Presence of strong topography | Same filter as SMOSL3 V300 |
Polluted scene | Water, urban and ice fractions (according to the IGBP classification) represent less than 10% of the pixel |
Frozen scene | Soil temperature < 273 K |
Processing Flags | Description |
---|---|
SM retrieved successfully | |
SM retrieved successfully but not recommended | RMSE < 12 K |
Failed retrieval | SM < 0 or SM > 1 m3/m3 |
© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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Fernandez-Moran, R.; Al-Yaari, A.; Mialon, A.; Mahmoodi, A.; Al Bitar, A.; De Lannoy, G.; Rodriguez-Fernandez, N.; Lopez-Baeza, E.; Kerr, Y.; Wigneron, J.-P. SMOS-IC: An Alternative SMOS Soil Moisture and Vegetation Optical Depth Product. Remote Sens. 2017, 9, 457. https://doi.org/10.3390/rs9050457
Fernandez-Moran R, Al-Yaari A, Mialon A, Mahmoodi A, Al Bitar A, De Lannoy G, Rodriguez-Fernandez N, Lopez-Baeza E, Kerr Y, Wigneron J-P. SMOS-IC: An Alternative SMOS Soil Moisture and Vegetation Optical Depth Product. Remote Sensing. 2017; 9(5):457. https://doi.org/10.3390/rs9050457
Chicago/Turabian StyleFernandez-Moran, Roberto, Amen Al-Yaari, Arnaud Mialon, Ali Mahmoodi, Ahmad Al Bitar, Gabrielle De Lannoy, Nemesio Rodriguez-Fernandez, Ernesto Lopez-Baeza, Yann Kerr, and Jean-Pierre Wigneron. 2017. "SMOS-IC: An Alternative SMOS Soil Moisture and Vegetation Optical Depth Product" Remote Sensing 9, no. 5: 457. https://doi.org/10.3390/rs9050457