Multi-Temporal Dual Channel Algorithm (MT-DCA)

I currently manage the in-house MIT algorithm called the MT-DCA, which retrieves 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. The data is freely available in the link below on 9km and 36km grids from April 2015 to July 2021 (SM = soil moisture, TAU = vegetation optical depth). No co-authorship is required, though we ask data users to reference Konings et al. (2017) when publishing results using these datasets. Feel free to send us an email at afeld24@mit.edu to let us know how you are using the data.

Please note that this is not an official SMAP product and it did not undergo the same calibration-validation procedures. However, the soil moisture product has very similar in-situ comparison statistics to that of the baseline SMAP product. The vegetation optical depth product has been used in many recent studies revealing much about tropical forest, semi-arid grassland, and cropland behavior. VOD comparison with independent datasets and discussion of uncertainty can be found in Konings et al. (2017) and Feldman et al. (2018). An MT-DCA VOD uncertainty study is under review and forthcoming in 2021.

MT-DCA data is now available on Zenodo in this link

*Update August 2021* The latest MT-DCA implementation is now available which includes data until July 31st, 2021 using the newest update of the SMAP Level1C brightness temperatures.

When using the data, please reference Konings et al. (2017) and cite as:

Andrew F. Feldman, Alexandra G. Konings, Maria Piles, & Dara Entekhabi. (2021). The Multi-Temporal Dual Channel Algorithm (MT-DCA) (Version 4) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5579549