Sustainable Agriculture-Based Climate Change Training Models using Remote Hyperspectral Image with Machine Learning Model-Reference-Cited by-同舟云学术

Sustainable Agriculture-Based Climate Change Training Models using Remote Hyperspectral Image with Machine Learning Model

Published:2024-08-23 Issue: Volume: Page:
ISSN:2520-8195
Container-title:Remote Sensing in Earth Systems Sciences
language:en
Short-container-title:Remote Sens Earth Syst Sci

Author:

Durairaj M.,Rubenraju Kasapaka,Krishna B. V. Rama,Yajid Mohd Shukri Ab,Jackson Beulah^ORCID,Babu Jampani Satish,Padmavathi Kodali Lakshmi

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s41976-024-00118-y.pdf

Reference20 articles.

1. Pande CB, Moharir KN (2023) Application of hyperspectral remote sensing role in precision farming and sustainable agriculture under climate change: a review. In: Pande CB, Moharir KN, Singh SK, Pham QB, Elbeltagi A (eds) Climate Change Impacts on Natural Resources, Ecosystems and Agricultural Systems. Springer Climate. Springer, Cham. https://doi.org/10.1007/978-3-031-19059-9_21

2. Asadollah SBHS, Jodar-Abellan A, Pardo MÁ (2024) Optimizing machine learning for agricultural productivity: a novel approach with RScv and remote sensing data over Europe. Agric Syst 218:103955

3. Pande CB, Egbueri JC, Costache R, Sidek LM, Wang Q, Alshehri F, … Pal SC (2024) Predictive modeling of land surface temperature (LST) based on Landsat-8 satellite data and machine learning models for sustainable development. J Clean Prod 444:141035

4. Liu J, Yang K, Tariq A, Lu L, Soufan W, El Sabagh A (2023) Interaction of climate, topography and soil properties with cropland and cropping pattern using remote sensing data and machine learning methods. Egypt J Remote Sens Space Sci 26(3):415–426

5. Alkaraki KF, Hazaymeh K (2023) A comprehensive remote sensing-based Agriculture Drought Condition Indicator (CADCI) using machine learning. Environ Challenges 11:100699