If you find our code to be useful, please cite the following papers:

K. Fang, M. Pan, and CP. Shen, [The value of SMAP for long-term soil moisture estimation with the help of deep learning], Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2018.2872131 (2018) https://ieeexplore.ieee.org/document/8497052

K. Fang, CP. Shen, D. Kifer and X. Yang, [Prolongation of SMAP to Spatio-temporally Seamless Coverage of Continental US Using a Deep Learning Neural Network], Geophysical Research Letters, doi: 10.1002/2017GL075619, preprint accessible at: arXiv:1707.06611 (2017) https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017GL075619

Shen, CP., [A trans-disciplinary review of deep learning research and its relevance for water resources scientists], Water Resources Research. 54(11), 8558-8593, doi: 10.1029/2018WR022643 (2018) https://doi.org/10.1029/2018WR022643

Two examples with sample data are wrapped up including

• train a LSTM network to learn SMAP soil moisture
• estimate uncertainty of a LSTM network

A demo for temporal test is here

├── CONUS
│   ├── 2000
│   │   ├── [Variable-Name].csv
│   │   ├── ...
│   │   ├── timeStr.csv
│   │   └── time.csv
│   ├── ...
│   ├── 2017
│   │   └── ...
│   ├── const
│   │   ├── [Constant-Variable-Name].csv
│   │   └── ...
│   └── crd.csv
├── CONUSv4f1
│   └── ...
├── Statistics
│   ├── [Variable-Name]_stat.csv
│   ├── ...
│   ├── const_[Constant-Variable-Name]_stat.csv
│   └── ...
├── Subset
│   ├── CONUS.csv
│   └── CONUSv4f1.csv
└── Variable
    ├── varConstLst.csv
    └── varLst.csv

Data folder contains all data including both training and testing, time-dependent variables and constant variables. In example data structure, there are two dataset folders - CONUS and CONUSv4f1. Those data are saved in:

• year/[Variable-Name].csv:

A csv file of size [#grid, #time], where each column is one grid and each row is one time step. This file saved data of a time-dependent variable of current year. For example, CONUS/2010/SMAP_AM.csv is SMAP data of 2002 on the CONUS.

Most time-dependent varibles comes from NLDAS, which included two forcing product (FORA, FORB) and three simulations product land surface models (NOAH, MOS, VIC). Variables are named as [variable]_[product]_[layer], and reference of variable can be found in NLDAS document. For example, SOILM_NOAH_0-10 refers to soil moisture product simulated by NOAH model at 0-10 cm.

Other than NLDAS, SMAP data are also saved in same format but always used as target. In level 3 database, there are two SMAP csv files which are only available after 2015: SMAP_AM.csv and SMAP_PM.csv.

-9999 refers to NaN.

• year/time.csv & timeStr.csv

Dates csv file of current year folder, of size [#date]. time.csv recorded Matlab datenum and timeStr.csv recorded date in format of yyyy-mm-dd.

Notice that each year start from and end before April 1st. For example data in folder 2010 is actually data from 2010-04-01 to 2011-03-31. The reason is that SMAP launched at April 1st.

• const/[Constant Variable Name].csv

csv file for constant variables of size [#grid].

• crd.csv

Coordinate of all grids. First Column is latitude and second column is longitude. Each row refers a grid.

Stored statistics of variables in order to do data normalization during training. Named as:

• Time dependent variables-> [variable name].csv
• Constant variables-> const_[variable name].csv

Each file wrote four statistics of variable:

• 90 percentile
• 10 percentile
• mean
• std

During training we normalize data by (data - mean) / std

Subset refers to a subset of grids from the complete dataset (CONUS or Global). For example, a subset only contains grids in Pennsylvania. All subsets (including the CONUS or Global dataset) will have a [subset name].csv file in the Subset folder. [subset name].csv is wrote as:

• line 1 -> root dataset
• line 2 - end -> indexs of subset grids in rootset (start from 1)

If the index is -1 means all grid, from example CONUS dataset.

Stored csv files contains a list of variables. Used as input to training code. Time-dependent variables and constant variables should be stored seperately. For example:

• varLst.csv -> a list of time-dependent variables used as training predictors.
• varLst.csv -> a list of constant variables used as training predictors.