A deep learning method to predict soil organic carbon content at a regional scale using satellite-based phenology variables-Reference-Cited by-同舟云学术

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A deep learning method to predict soil organic carbon content at a regional scale using satellite-based phenology variables

Published:2021-10 Issue: Volume:102 Page:102428
ISSN:0303-2434
Container-title:International Journal of Applied Earth Observation and Geoinformation
language:en
Short-container-title:International Journal of Applied Earth Observation and Geoinformation

Author:

Yang Lin,Cai Yanyan,Zhang Lei,Guo Mao,Li Anqi,Zhou Chenghu

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Computers in Earth Sciences,Earth-Surface Processes,Global and Planetary Change

Reference54 articles.

1. Identification of representative samples from existing samples for digital soil mapping;An;Geoderma,2018

2. Remote sensing techniques for soil organic carbon estimation: A review;Angelopoulou;Remote Sens.,2019

3. Phenologic metrics derived from MODIS NDVI as indicators for Plant Available Water-holding Capacity;Araya;Ecol. Indic.,2016

4. Classification of soil aggregates: A novel approach based on deep learning;Azizi;Soil Tillage Res.,2020

5. Multi-scale digital soil mapping with deep learning;Behrens;Sci. Rep.,2018

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