Data driven approach on in-situ soil carbon measurement

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.