Appraising the crop health response to water stress from enhanced crop and soil water estimates using SAR data and machine learning approaches-Reference-Cited by-同舟云学术

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Appraising the crop health response to water stress from enhanced crop and soil water estimates using SAR data and machine learning approaches

Published:2023-07-03 Issue:13 Volume:44 Page:4190-4216
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Gopi Neena M.¹,Periasamy Shoba¹²^ORCID

Affiliation:

1. Department of Civil Engineering, College of Engineering & Technology, SRM Institute of Science and Technology, Chengalpattu, Kattankulathur, India

2. Institute of Remote Sensing, Department of Civil Engineering, College of Engineering, Anna University, Chennai, India

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2023.2232549

Reference92 articles.

1. Machine Learning for Predicting Field Soil Moisture Using Soil, Crop, and Nearby Weather Station Data in the Red River Valley of the North

2. A COMPARISON OF TREE-BASED REGRESSION MODELS FOR SOIL MOISTURE ESTIMATION USING SAR DATA

3. A COMPARISON OF TREE-BASED REGRESSION MODELS FOR SOIL MOISTURE ESTIMATION USING SAR DATA

4. Decomposition-Based Soil Moisture Estimation Using UAVSAR Fully Polarimetric Images

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1. A statistical approach to map saline and fluoride (F) affected areas using optical and microwave remote sensing data simulations: A case study in Palacode Taluk, Tamil Nadu, India;Remote Sensing Applications: Society and Environment;2024-08

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