Current projects using machine learning methods to:

1. improve downscaling of aerial soil moisture data products
2. develop site-specific crop-growth models from yield data and environmental covariates

Collaborators

• Prof. Robert Hardin (Biological and Agricultural Engineering)
• Hanzi Mao (PhD Student, Computer Science and Engineering)
• Prof. Binayak Mohanty (Biological and Agricultural Engineering)
• Prof. Nithya Rajan (Soil and Crop Sciences)
• Ruiwan Xu (MS student, Electrical and Computer Engineering)

Papers

• Gap Filling of High‐Resolution Soil Moisture for SMAP/Sentinel‐1: A Two‐Layer Machine Learning‐Based Framework, by Hanzi Mao, Dhruva Kathuria, Nick Duffield, Binayak P. Mohanty, Water Resources Research, July 2019, Also: https://eartharxiv.org/ce865/

Funding

• Sustain: Smart Use Of Site-Specific Technology With Artificial Intelligence Networks, Texas A&M T3 Grant, March 2018, PI R. Hardin (Texas A&M Biological and Agricultural Engineering), co-PIs N. Duffield, N. Rajan (Texas A&M Soil and Crop Sciences), $30,000
• Understanding Multi-Scale Hydrology: Fusion of BIG DATA from Ground Networks and Space-Based Satellites, Texas A&M Big Data Seed Grant, January 2016, PI: Binayak Mohanty (Texas A&M Biological and Agricultural Engineering); co-PI: N. Duffield. Total $50,000./ Duffield $25,000