Unveiling Soil-Vegetation Interactions: Reflection Relationships and an Attention-Based Deep Learning Approach for Carbon Estimation-Reference-Cited by-同舟云学术

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Unveiling Soil-Vegetation Interactions: Reflection Relationships and an Attention-Based Deep Learning Approach for Carbon Estimation

Published:2024-07-15 Issue: Volume: Page:1-6
ISSN:
Container-title:2024 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)
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Author:

Datta Dristi¹,Paul Manoranjan¹,Murshed Manzur²,Teng Shyh Wei³,Schmidtke Leigh M.⁴

Affiliation:

1. School of Computing, Mathematics, and Engineering, Charles Sturt University,Bathurst,NSW,Australia,2795

2. School of Info Technology, Deakin University,Burwood,VIC,Australia,3125

3. Cooperative Research Centre for High Performance Soils,Callaghan,NSW,Australia,2308

4. Gulbali Institue, Charles Sturt University,Wagga Wagga,NSW,Australia,2678

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10645349/10645352/10645460.pdf?arnumber=10645460

Reference15 articles.

1. Remote sensing prediction and characteristic analysis of cultivated land salinization in different seasons and multiple soil layers in the coastal area

2. Soil Moisture, Organic Carbon, and Nitrogen Content Prediction with Hyperspectral Data Using Regression Models

3. Estimating Agricultural Soil Moisture Content through UAV-Based Hyperspectral Images in the Arid Region

4. Comparative Analysis of Machine and Deep Learning Models for Soil Properties Prediction from Hyperspectral Visual Band

5. Prediction of soil organic carbon and the C:N ratio on a national scale using machine learning and satellite data: A comparison between Sentinel-2, Sentinel-3 and Landsat-8 images

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