Estimating soil moisture using remote sensing data: A machine learning approach

By Sajjad Ahmad, Ajay Kalra, H. Stephen. 2010

485 citations recorded by the source corpus.

Subfields: Atmospheric Science, Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/6b96f4daa52f7fe7e6334bf2b2e3d8e245f652c7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Percy Liang
2. Tengyu Ma
3. Tomislav Hengl
4. Ananya Kumar
5. Chaopeng Shen

In Environmental Engineering

1. Tomislav Hengl
2. Budiman Minasny
3. Alex B. McBratney
4. Gerard B. M. Heuvelink
5. Bas Kempen
6. José Padarian
7. Chaopeng Shen
8. Sabine Grunwald

In Water Science and Technology

1. Gerard B. M. Heuvelink
2. José Padarian
3. Budiman Minasny
4. Alex B. McBratney
5. Tomislav Hengl
6. Chaopeng Shen
7. Bas Kempen
8. Attila Nemes

Citations in the corpus

• Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data (2015, methodology): “Ahmad et al. , [128] tested an SVM model on 10 sites for soil moisture estimation in the Lower Colorado River Basin (LCRB) in the western United States by using backscatter and incidence angle from the Tropical Rainfall Measuring Mission (TRMM) and the Normalized Difference Vegetation Index (NDVI)…”
• Machine learning and soil sciences: a review aided by machine learning tools (2019, background): “Water: Articles mostly focused on soil water content and its changes over time (Ahmad et al., 2010; Coopersmith et al., 2014; Greifeneder et al., 2018; Han et al., 2018).”
• A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists (2017, methodology): “…comparatively well in predictions of reservoir inflow (Lin et al., 2006), soil moisture retrieval using vegetation indices and precipitation (Ahmad et al., 2010), streamflow (Asefa et al., 2006), evapotranspiration (Tabari et al., 2012), lake level (Khan & Coulibaly, 2006), groundwater…”
• In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness (2020, background): “Developing machine learning models trained on remote sensing data is currently a popular line of work for practical problems such as typhoon rainfall estimation, monitoring reservoir water quality, and soil moisture estimation [29, 32, 1].”
• Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions (2015, background): “First, it has been proven in numerous studies [31, 37, 38] that the random forests algorithm can outperform linear regression.”