Synergy of optical and synthetic aperture radar data for early-stage crop yield estimation: a case study over a state of Germany-Reference-Cited by-同舟云学术

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Synergy of optical and synthetic aperture radar data for early-stage crop yield estimation: a case study over a state of Germany

Published:2022-02-20 Issue:25 Volume:37 Page:10743-10766
ISSN:1010-6049
Container-title:Geocarto International
language:en
Short-container-title:Geocarto International

Author:

Ghosh Parmita¹²,Mandal Dipankar¹^ORCID,Wilfling Sarah³,Hollberg Jens⁴,Bargiel Damian³⁵,Bhattacharya Avik¹^ORCID

Affiliation:

1. Microwave Remote Sensing Lab, Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai, India

2. CORTEVA Agriscience, Ascendas IT Park, Hyderabad, India

3. Institut für Geodäsie, Technische Universität Darmstadt, Darmstadt, Germany

4. Hessisches Statistisches Landesamt, Wiesbaden, Germany

5. Hochschule Geisenheim University, Geisenheim, Germany

Publisher

Informa UK Limited

Subject

Water Science and Technology,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/10106049.2022.2039306

Reference72 articles.

1. Soil Moisture Content Estimation Based on Sentinel-1 and Auxiliary Earth Observation Products. A Hydrological Approach

2. Estimation of Corn Yield by Assimilating SAR and Optical Time Series Into a Simplified Agro-Meteorological Model: From Diagnostic to Forecast

3. Changes in spectral reflectance of wheat leaves in response to specific macronutrient deficiency

4. A new method for crop classification combining time series of radar images and crop phenology information

5. Assimilation of LAI and Dry Biomass Data From Optical and SAR Images Into an Agro-Meteorological Model to Estimate Soybean Yield

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1. A comprehensive review on wheat yield prediction based on remote sensing;Multimedia Tools and Applications;2024-07-25

2. Ensemble learning-based crop yield estimation: a scalable approach for supporting agricultural statistics;GIScience & Remote Sensing;2024-06-28

3. Synergetic Use of Sentinel-1 and Sentinel-2 Data for Wheat-Crop Height Monitoring Using Machine Learning;AgriEngineering;2024-04-22

4. Enhanced Crop Discrimination and Monitoring Using Compact-Polarimetric SAR Signature Analysis From RADARSAT Constellation Mission;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

5. Machine learning for food security: current status, challenges, and future perspectives;Artificial Intelligence Review;2023-10-27

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