Modeling SAR Observables by Combining a Crop-Growth Model With Machine Learning
Author:
Affiliation:
1. Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, Netherlands
2. European Space Agency (ESA/ESTEC), Noordwijk, Netherlands
Funder
Dutch network on MIcrowaves for a New Era of Remote sensing of Vegetation for Agricultural monitoring
Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Netherlands Space Office
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Atmospheric Science,Computers in Earth Sciences
Link
http://xplorestaging.ieee.org/ielx7/4609443/9973430/10201908.pdf?arnumber=10201908
Reference68 articles.
1. Joint Assimilation of Leaf Area Index and Soil Moisture from Sentinel-1 and Sentinel-2 Data into the WOFOST Model for Winter Wheat Yield Estimation
2. VICAL: Global Calculator to Estimate Vegetation Indices for Agricultural Areas with Landsat and Sentinel-2 Data
3. Sensitivity of Sentinel-1 backscatter to vegetation dynamics: An Austrian case study;vreugdenhil;Remote Sens,2018
4. How Universal Is the Relationship between Remotely Sensed Vegetation Indices and Crop Leaf Area Index? A Global Assessment
5. Spatial Rice Yield Estimation Using Multiple Linear Regression Analysis, Semi-Physical Approach and Assimilating SAR Satellite Derived Products with DSSAT Crop Simulation Model
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