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Data driven approach on in-situ soil carbon measurement.
- Source :
- Carbon Management; Dec2022, Vol. 13 Issue 1, p401-419, 19p
- Publication Year :
- 2022
-
Abstract
- Soil carbon (C) plays a key role in mitigating and adapting to global climate change. In-situ soil C measurement has faced many challenges including those related to aerial coverage, economics, accuracy, and availability. The concept of paying for C credits to farmers and ranchers who sequester C has necessitated availability of improved methods for in-situ measurement of soil C at large scale. The objective of this review is to i) synthesize the existing knowledge on methods of soil C measurement, (ii) discuss their pros and cons (iii) review key factors affecting soil C measurement, and (iv) propose integrated data driven method of soil C measurement using Machine Learning (ML)/Artificial Intelligence (AI) approach. Lab and in-situ techniques of soil C determination are expensive, time consuming and lack scale. Although, remote sensing (RS) technique is used to predict soil C maps at large scale, it also lacks accuracy and requires high technical knowledge of image processing. Soil C measurements are affected by key soil physical properties such as color, texture, moisture content, bulk density etc. Thus, these factors must be considered while developing innovative methods for soil C determination. A prototype handheld device is proposed to measure these four properties along with Near Infrared (NIR) reflectance of soil that store data in cloud using Wi-Fi signals. A data driven model is proposed that can use the data from handheld devices and integrate with drone imagery to create soil C map of the entire field and satellite imagery for the entire region. This model uses data from in-situ soil C measurement technique in integrated form and soil C map can be updated every time the handheld device is used at different locations of the field. [ABSTRACT FROM AUTHOR]
- Subjects :
- CARBON in soils
CLIMATE change
REMOTE-sensing images
REMOTE sensing
IMAGE processing
Subjects
Details
- Language :
- English
- ISSN :
- 17583004
- Volume :
- 13
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Carbon Management
- Publication Type :
- Academic Journal
- Accession number :
- 160890737
- Full Text :
- https://doi.org/10.1080/17583004.2022.2106310