County Level Crop Yield Prediction Using Smap Derived Data Products and Deep Learning Model-Reference-Cited by-同舟云学术

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County Level Crop Yield Prediction Using Smap Derived Data Products and Deep Learning Model

Published:2024-07-07 Issue: Volume: Page:3222-3225
ISSN:
Container-title:IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Yang Zhengwei¹,Wang Xiaoyu²,Huang Jingyi³,Zhang Zhou²

Affiliation:

1. USDA National Agricultural Statistics Service, USDA,Washington DC,USA,20250

2. University of Wisconsin - Madison,Department of Biological Systems Engineering,USA,WI 53706

3. University of Wisconsin - Madison,Department of Soil Science,USA,WI 53706

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10640349/10640352/10640545.pdf?arnumber=10640545

Reference10 articles.

1. Soil-dependent responses of US crop yields to climate variability and depth to groundwater

2. Intercomparison of Soil Moisture, Evaporative Stress, and Vegetation Indices for Estimating Corn and Soybean Yields Over the U.S.

3. Improving crop yield forecasts with satellite-based soil moisture estimates: An example for township level canola yield forecasts over the Canadian Prairies

4. Assimilation of remotely sensed soil moisture and vegetation with a crop simulation model for maize yield prediction

5. A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: A case study of the US Corn Belt at the county level

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