# Prolongation of SMAP to Spatio-temporally Seamless Coverage of Continental US Using a Deep Learning Neural Network

arXiv: https://arxiv.org/abs/1707.06611 · HTML: https://mlsanity.com/paper/1707.06611/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2017.

## Built on by (most cited first, 7 of 7)

- [Differentiable, learnable, regionalized process-based models with physical outputs can approach state-of-the-art hydrologic prediction accuracy](https://mlsanity.com/paper/2203.14827/) (2022)
- [The data synergy effects of time-series deep learning models in hydrology](https://mlsanity.com/paper/2101.01876/) (2021)
- [A Mass-Conserving-Perceptron for Machine Learning-Based Modeling of Geoscientific Systems](https://mlsanity.com/paper/2310.08644/) (2023)
- [Differentiable modeling to unify machine learning and physical models and advance Geosciences](https://mlsanity.com/paper/2301.04027/) (2023)
- [Temporal Fusion Transformers for Streamflow Prediction: Value of Combining Attention with Recurrence](https://mlsanity.com/paper/2305.12335/) (2023)
- [Capabilities of Deep Learning Models on Learning Physical Relationships: Case of Rainfall-Runoff Modeling with LSTM](https://mlsanity.com/paper/2106.07963/) (2021)
- [Comparative Assessment of Multimodal Earth Observation Data for Soil Moisture Estimation](https://mlsanity.com/paper/2602.18083/) (2026)

## Abstract (arXiv)

The Soil Moisture Active Passive (SMAP) mission has delivered valuable sensing of surface soil moisture since 2015. However, it has a short time span and irregular revisit schedule. Utilizing a state-of-the-art time-series deep learning neural network, Long Short-Term Memory (LSTM), we created a system that predicts SMAP level-3 soil moisture data with atmospheric forcing, model-simulated moisture, and static physiographic attributes as inputs. The system removes most of the bias with model simulations and improves predicted moisture climatology, achieving small test root-mean-squared error (<0.035) and high correlation coefficient >0.87 for over 75% of Continental United States, including the forested Southeast. As the first application of LSTM in hydrology, we show the proposed network avoids overfitting and is robust for both temporal and spatial extrapolation tests. LSTM generalizes well across regions with distinct climates and physiography. With high fidelity to SMAP, LSTM shows great potential for hindcasting, data assimilation, and weather forecasting.