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Marco P. Maneta
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2020 – today
- 2022
- [j7]Thomas Colligan
, David Ketchum, Douglas Brinkerhoff, Marco P. Maneta:
A Deep Learning Approach to Mapping Irrigation Using Landsat: IrrMapper U-Net. IEEE Trans. Geosci. Remote. Sens. 60: 1-11 (2022) - 2021
- [j6]Colin Brust, John S. Kimball
DroughtCast: A Machine Learning Forecast of the United States Drought Monitor. Frontiers Big Data 4: 773478 (2021) - [c4]Emma Izquierdo-Verdiguier, Álvaro Moreno-Martínez
Global Upscaling of the MODIS Land Cover with Google Earth Engine and Landsat Data. IGARSS 2021: 309-312 - [i1]Thomas Colligan, David Ketchum, Douglas Brinkerhoff, Marco P. Maneta:
A Deep Learning Approach to Mapping Irrigation: IrrMapper-U-Net. CoRR abs/2103.03278 (2021) - 2020
- [j5]Marco P. Maneta, Kelly M. Cobourn, John S. Kimball
A satellite-driven hydro-economic model to support agricultural water resources management. Environ. Model. Softw. 134: 104836 (2020) - [j4]Patrick M. Wurster, Marco P. Maneta, John S. Kimball
Monitoring Crop Status in the Continental United States Using the SMAP Level-4 Carbon Product. Frontiers Big Data 3: 597720 (2020) - [j3]David Ketchum
IrrMapper: A Machine Learning Approach for High Resolution Mapping of Irrigated Agriculture Across the Western U.S. Remote. Sens. 12(14): 2328 (2020) - [c3]Álvaro Moreno-Martínez
Down-Scaling Modis Vegetation Products with Landsat GAP Filled Surface Reflectance in Google Earth Engine. IGARSS 2020: 2320-2323
2010 – 2019
- 2019
- [c2]John S. Kimball
Smap L4 Assessment of the Us Northern Plains 2017 Flash Drought. IGARSS 2019: 5366-5369 - 2018
- [j2]Sylvain Kuppel, Doerthe Tetzlaff
What can we learn from multi-data calibration of a process-based ecohydrological model? Environ. Model. Softw. 101: 301-316 (2018) - [j1]Mingzhu He
Regional Crop Gross Primary Productivity and Yield Estimation Using Fused Landsat-MODIS Data. Remote. Sens. 10(3): 372 (2018) - [c1]Álvaro Moreno-Martínez
Interpolation and Gap Filling of Landsat Reflectance Time Series. IGARSS 2018: 349-352
Coauthor Index
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