Water, Energy, and Carbon with Artificial Neural Networks (WECANN): A statistically-based estimate of global surface turbulent fluxes using solar-induced fluorescence.
WECANN is a monthly product, at 0.5-degree resolution, starting in 2007. It typically outperforms other global estimates.
- Read more about the flux dataset
- Data is publicly available on the NASA website
Our machine learning retrieval CU-NN-AP (Columbia U., Neural Network, Active Passive) of soil moisture based on active and passive microwave measurements (ASCAT and AMSR) is available here:
or via sftp at: cunix.columbia.edu (Anonymous)
in folder: /hmt/sirius1/skv0/u/3/p/pg2328/public_html/Website/CU_NN_AP
Description of the retrievals are available in
- Kolassa, J., P. Gentine, C. Prigent, and F. Aires. 2016. "Soil moisture retrieval from AMSR-E and ASCAT microwave observation synergy. Part 1: Satellite data analysis." Remote Sensing of Environment 173 1-14 [10.1016/j.rse.2015.11.011]
- Kolassa, J., P. Gentine, C. Prigent, F. Aires, and S. Alemohammad. 2017. "Soil moisture retrieval from AMSR-E and ASCAT microwave observation synergy. Part 2: Product evaluation." Remote Sensing of Environment 195 202-217 [10.1016/j.rse.2017.04.020]
The main author is Jana Kolassa (NASA GMAO). Please read the readme file!
The next release will include SMAP and is described here:
- Kolassa J., Reichle R.H., Liu Q., Alemohammad S.H, Gentine P., Estimating surface soil moisture from SMAP observations using a Neural Network, Remote Sens Env
Isohydricity Index Based on Vegetation Optical Depth
This dataset represents microwave vegetation optical depth-based calculations of ecosystem scale isohydricity. It is kindly hosted by my colleague Alexandra Konings
Reconstructed Solar Induced Fluorescence (RSIF)
A new MODIS-based product of vegetation productivity based on machine learning, with GOME-2 Solar Induced Fluorescence as a training dataset. The data shows very good interannual variability compared to FLUXNET GPP.
The data is available here