DOI: 10.5281/zenodo.5579548
title: The Multi-Temporal Dual Channel Algorithm (MT-DCA)

description: I) SUMMARY This soil moisture and vegetation optical depth product is called the Multi-Temporal Dual Channel Algorithm (MT-DCA). It retrieves surface soil moisture and vegetation optical depth (directly related to total water volume in the vegetation canopy) from SMAP level 1C brightness temperature observations using a robust estimation technique. It is an in-house MIT algorithm and is not an official SMAP product. The data are freely available on 9km and 36km grids from April 2015 to July 2021 in daily time steps. No co-authorship is required for use of this data in publications. However, to properly acknowledge the dataset when publishing any research using the MT-DCA, we ask data users to (1) cite the DOI as an in-text citation and/or in the data acknowledgements in any publication and (2) reference Konings et al. (2017) when referring to the MT-DCA in the text. Feel free to send us an email at afeld24@mit.edu to let us know how you are using the data. The version 5 update is a re-implementation of the MT-DCA using the updated SMAP L1C brightness temperatures. It extends the data through July 2021. II) CONTACT For questions, please email Andrew Feldman at afeld24@mit.edu. III) ALGORITHM DESCRIPTION The algorithmic approach uses both horizontally and vertically polarized brightness temperatures to retrieve soil moisture and VOD simultaneously. The key innovation of the MT-DCA is that it recognizes that classical dual-channel algorithms are under-determined: brightness temperature observations are correlated and cannot retrieve two unknowns (soil moisture and VOD) (as illustrated in Konings et al, RSE 2016). This creates amplifying errors in retrievals from snapshot dual-channel algorithms. The MT-DCA uses a viable assumption that VOD changes more slowly than soil moisture between overpasses, and uses information from multiple SMAP overpasses to stabilize the retrieval. It is considered a regularization approach similar to the Sobolev Norm regularization. Specifically, this approach is applied to each temporally adjacent pair of overpasses (for SMAP, two overpasses approximately 2-3-days apart), which includes four brightness temperature measurements. For each overpass pair, the soil moisture at both overpasses is retrieved, along with a constant VOD for both overpasses. This leads to two retrievals of each of soil moisture and VOD at any given overpass time: one where the parameters are retrieved using additional TB information from the overpass before and one from the overpass after. Both retrievals of VOD and soil moisture values at each overpass are averaged. Ultimately, VOD is not held constant, but rather is slowed in time between overpasses. A second key innovation of the MT-DCA is that, because the retrievals are no longer under determined, it is also possible to retrieve a constant single scattering albedo for each pixel. The single scattering albedo is estimated through model selection of the value of the parameter that minimizes the sum of all overpass cost functions. The retrieved albedo is also included in the files here. VOD is reported at nadir. The single scattering albedo is assumed constant over the full record of SMAP data, as is currently accepted practice across approaches with SMAP, SMOS, and AMSR. There is a high amount of computational power required to retrieve an albedo over more than three years of SMAP data. Therefore, an adjustment was made: the single scattering albedo was retrieved over the third year of SMAP data (April 1st, 2017 to March 31st, 2018). This constant value was then applied to the other years without requiring the albedo optimization loop. Tests across many individual pixels revealed that albedo in the third year does not differ greatly from albedo over all years and the other individual years. The algorithm is described in more detail in Konings et al. (2017). The algorithm is based on principles explained in more detail in Konings et al. (2016), which describes the original algorithm development using Aquarius observations. See also the related Konings et al. (2015) publication for quantitative justification for the approach. While the dataset has not been officially validated, the MT-DCA soil moisture retrievals show in-situ comparison statistics similarly to the official baseline SMAP soil moisture product (SMAP soil moisture retrieval in-situ assessment can be found in Chan et al. (2016)). Finally, the MT-DCA vegetation optical depth retrievals are not validated due to only sparsely available ground information related to vegetation water content. Nevertheless, information about error propagation into the MT-DCA soil moisture and VOD retrievals as well as VOD error reductions using the MT-DCA regularization technique can be found in Feldman et al. (2021). Chan, S.K., Bindlish, R., O’Neill, P.E., Njoku, E., Jackson, T., Colliander, A., Chen, F., Burgin, M., Dunbar, S., Piepmeier, J., Yueh, S., Entekhabi, D., Cosh, M.H., Caldwell, T., Walker, J., Wu, X., Berg, A., Rowlandson, T., Pacheco, A., McNairn, H., Thibeault, M., Martinez-Fernandez, J., Gonzalez-Zamora, A., Seyfried, M., Bosch, D., Starks, P., Goodrich, D., Prueger, J., Palecki, M., Small, E.E., Zreda, M., Calvet, J.C., Crow, W.T., Kerr, Y., 2016. Assessment of the SMAP Passive Soil Moisture Product. IEEE Trans. Geosci. Remote Sens. 54, 4994–5007. https://doi.org/10.1109/TGRS.2016.2561938 Feldman, A.F., D. Chaparro, and D. Entekhabi (2021). Error propagation in microwave soil moisture and vegetation optical depth retrievals. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. In Press. Konings, A.G., M. Piles, N. Das, and D. Entekhabi (2017). L-band vegetation optical depth and effective scattering albedo estimation from SMAP. Remote Sensing of Environment, 198:460-470. https://doi.org/10.1016/j.rse.2017.06.037 Konings, A.G., M. Piles, K. Rötzer, K.A. McColl, S. Chan, and D. Entekhabi (2016). Vegetation optical depth and scattering albedo retrieval using time-series of dual-polarized L-band radiometer observations. Remote Sensing of Environment. 172, 178-189. https://doi.org/10.1016/j.rse.2015.11.009 Konings, A.G., K.A. McColl, M. Piles and D. Entekhabi (2015): How many parameters can be maximally estimated from a set of measurements? IEEE Geoscience and Remote Sensing Letters, 12(5), 1081-1085. https://doi.org/10.1109/LGRS.2014.2381641 IV) QUALITY CONTROL Several conditions can create uncertainty in the MT-DCA retrievals including surface water bodies (lakes, rivers, coastal areas, etc.), radio frequency interference (RFI), highly sloped surfaces (mountainous regions), dense vegetation, frozen ground, and others. The MT-DCA removes time periods of frozen ground and removes pixels with water body fractions of greater than 0.5. SMAP L1C brightness temperatures are adjusted considering RFI and surface water body information. Nevertheless, the MT-DCA retrievals are purposefully not substantially quality controlled to increase the range of science applications of the data. Therefore, the retrievals are subject to uncertainty in regions where and times when these aforementioned issues occur. We suggest the data user familiarize themselves with quality flags in the SMAP algorithm theoretical basis document in https://nsidc.org/data/SPL3SMP_E. Conservative quality control can be applied using SMAP quality flag information directly applicable to the dataset here. These quality flags can be downloaded from the SMAP official product files at https://nsidc.org/data/SPL3SMP_E. V) DATA FORMATTING AND FILE NAMES Data are provided in zipped folders in both netcdf4 (.nc) and matfile (.mat) formats. Each zipped folder contains soil moisture, vegetation optical depth, single scattering albedo, latitude, longitude, and time vector information. Note that as an update in Version 5, the zipped folders for 9km .mat files are separated into soil moisture and vegetation optical depth to reduce zip folder size. The other zipped folders still have all variables within them. These variables are provided at a 9km resolution as well as upscaled to 36km. For both .nc and .mat files, the 9km data are provided in 3-month periods with a naming convention of ‘YYYYMM_YYYYMM’ where YYYY is the 4-digit year, and MM is the 2-digit month. The first YYYYMM string represents the first month and the second YYYYMM string is the final month of the period. The 36km data are provided in 12-month periods with the same naming conventions in the file names. Retrievals are obtained from enhanced-resolution brightness temperatures from SMAP that are gridded at 9km. As such, they are on a 9km EASE2-grid. These retrievals are upscaled to 36km and gridded on a 36km EASE2 grid. Additional information and geolocation tools are available at https://nsidc.org/data/ease/ease_grid2.html. Information specific to folders with .nc and .mat formats is given below: a) NETCDF Files (.nc): The folders with netcdf files contain files with the convention MTDCA_YYYYMM_YYYYMM_Xkm_VX.nc where VX is the version number, Xkm is the grid scale, and YYYYMM strings are the first and last months of the range of data saved in the file. Soil moisture, vegetation optical depth, latitude, longitude, and time index information are provided in these files. A map of single scattering albedo for the full time series is saved in a separate file as MTDCA_OMEGA_Xkm_VX.nc along with latitude and longitude information. b) MATFILES (.mat): The folders with matfiles contain individual files for: Soil moisture: MTDCA_VX_SM_YYYYXX_YYYYXX_Xkm.mat Vegetation Optical Depth: MTDCA_VX_TAU_YYYYXX_YYYYXX_Xkm.mat Single Scattering Albedo: MTDCA_VX_OMEGA_Xkm.mat Latitude/Longitude: SMAPCenterCoordinatesXKM.mat A datevector variable in each soil moisture and vegetation optical depth file contains information on the year, month, and day corresponding to the timestep of each variable.