Journal Article

A review on digital mapping of soil carbon in cropland: progress, challenge, and prospect

Wu, Qi, Shen, Feixue, Yang, Lin, Zhou, Chenghu, Pu, Yue, Cai, Yanyan, Yang, Chenconghai, Zhang, Lei, Huang, Haili

2022

Overview

Cropland soil carbon not only serves food security but also contributes to the stability of the terrestrial ecosystem carbon pool due to the strong interconnection with atmospheric carbon dioxide. Therefore, the better monitoring of soil carbon in cropland is helpful for carbon sequestration and sustainable soil management. However, severe anthropogenic disturbance in cropland mainly in gentle terrain creates uncertainty in obtaining accurate soil information with limited sample data. Within the past 20 years, digital soil mapping has been recognized as a promising technology in mapping soil carbon. Herein, to advance existing knowledge and highlight new directions, the article reviews the research on mapping soil carbon in cropland from 2005 to 2021. There is a significant shift from linear statistical models to machine learning models because nonlinear models may be more efficient in explaining the complex soil-environment relationship. Climate covariates and parent material play an important role in soil carbon on the regional scale, while on a local scale, the variability of soil carbon often depends on topography, agricultural management, and soil properties. Recently, several kinds of agricultural covariates have been explored in mapping soil carbon based on survey or remote sensing technique, while, obtaining agricultural covariates with high resolution remains a challenge. Based on the review, we concluded several challenges in three categories: sampling, agricultural covariates, and representation of soil processes in models. We thus propose a conceptual framework with four future strategies: representative sampling strategies, establishing standardized monitoring and sharing system to acquire more efficient crop management information, exploring time-series sensing data, as well as integrating pedological knowledge into predictive models. It is intended that this review will support prospective researchers by providing knowledge clusters and gaps concerning the digital mapping of soil carbon in cropland.

• Publisher: IOP Publishing
• Subject: Agricultural land / Agricultural management / Anthropogenic factors / Atmospheric models / Carbon / Carbon dioxide / Carbon sequestration / Climate models / Crop management / cropland / Digital mapping / Digital maps / digital soil mapping / environmental covariates / Environmental monitoring / Food security / Information management / Machine learning / Mapping / Mathematical models / Monitoring / Prediction models / predictive models / Remote sensing / Sampling / soil carbon / soil information / Soil management / Soil mapping / Soil properties / Statistical analysis / Statistical models / Sustainability management