Estimating soil organic carbon levels in cultivated soils from satellite image using parametric and data-driven methods-Reference-Cited by-同舟云学术

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Estimating soil organic carbon levels in cultivated soils from satellite image using parametric and data-driven methods

Published:2022-05-03 Issue:9 Volume:43 Page:3429-3449
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Koparan M. H.¹²^ORCID,Rekabdarkolaee H. M.³^ORCID,Sood K.⁴^ORCID,Westhoff S. M.¹,Reese C. L.¹,Malo D. D.¹

Affiliation:

1. Agronomy, Horticulture & Plant Science Department, South Dakota State University, Brookings, SD, USA

2. Soil, Fertilizer and Water Resources Central Research Institute, Ankara, Turkey

3. Mathematics and Statistics Department, South Dakota State University, Brookings, SD, USA

4. Centre for Geo-Informatics Research and Training, HP Agricultural University, Palampur, HP, India

Funder

USDA – AFRI

Republic of Turkey Ministry of Agriculture and Forestry

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2022.2093144

Reference53 articles.

1. The West Wide Drought Tracker: Drought Monitoring at Fine Spatial Scales

2. Neural Networks and Deep Learning

3. Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review

4. The soil line concept in remote sensing

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Artificial intelligence and satellite‐based remote sensing can be used to predict soybean (Glycine max) yield;Agronomy Journal;2023-10-12

2. Examining the influence of bare soil UAV imagery combined with auxiliary datasets to estimate and map soil organic carbon distribution in an erosion-prone agricultural field;Science of The Total Environment;2023-04

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