土壤有机碳（SOC）是土壤健康的重要组成部分，影响土壤肥力、结构及其固碳能力，对减缓气候变化具有重大影响。准确估算和监测 SOC 对于可持续土地管理和农业实践至关重要。然而，传统的 SOC 评估方法可能是劳动密集型且成本高昂。遥感 (RS) 技术的进步，包括可见近红外 (VNIR) 和中红外 (MIR) 光谱等近端传感技术，与人工智能 (AI) 和机器学习 (ML) 相结合，为低成本、大规模的 SOC 估算和监测提供了新的机遇。
Soil organic carbon (SOC) is a critical component of soil health, influencing soil fertility, structure, and its ability to sequester carbon, which has significant implications for climate change mitigation. Accurate estimation and monitoring of SOC are essential for sustainable land management and agricultural practices. However, traditional methods of SOC assessment can be labor-intensive and costly. Advances in remote sensing (RS) technologies, including proximal sensing techniques like visible near-infrared (VNIR) and mid-infrared (MIR) spectroscopy, combined with artificial intelligence (AI) and machine learning (ML), offer new opportunities for low-cost, large-scale SOC estimation and monitoring.
本专刊的目的是重点介绍利用遥感数据估算土壤有机碳 (SOC) 的创新方法、工作流程和传感器。通过利用数字土壤测绘和人工智能技术，我们寻求提高 SOC 估算的准确性和成本效益。本期特刊旨在涵盖从数据采集和预处理到模型开发和应用的 SOC 估计的整个范围。我们欢迎原创研究文章、评论论文以及探索这些进步及其实际应用的交流和前沿动态。
The aim of this Special Issue is to highlight innovative methodologies, workflows, and sensors for estimating soil organic carbon (SOC) using remote sensing data. By leveraging digital soil mapping and AI techniques, we seek to enhance the accuracy and cost-effectiveness of SOC estimation. This Special Issue aims to cover the entire scope of SOC estimation from data acquisition and preprocessing to model development and application. We welcome both original research articles, review papers and communication and frontiers dynamics that explore these advancements and their practical applications.
我们邀请研究人员贡献专注于 SOC 远程（近端）传感的原创研究文章、评论和案例研究。感兴趣的主题包括但不限于以下内容：
We invite researchers to contribute original research articles, reviews, and case studies focusing on the remote (proximal) sensing of SOC. Topics of interest include, but are not limited to, the following:
（1）通过无人机 (UAV)、机载和卫星图像进行 SOC 估算：使用来自无人机、机载平台和卫星图像的数据（包括来自哥白尼等程序的数据）绘制 SOC 的技术。
SOC estimation from unmanned aerial vehicles (UAVs), airborne, and satellite imagery: Techniques for mapping SOC using data from UAVs, airborne platforms, and satellite imagery, including data from programs like Copernicus.
（2）用于 SOC 估算的 VNIR 和 MIR 光谱：利用可见光、近红外和中红外光谱进行准确且经济高效的 SOC 测量的方法。
VNIR and MIR spectroscopy for SOC estimation: Methods utilizing visible, near-infrared and mid-infrared spectroscopy for accurate and cost-effective SOC measurement.
（3）监测 SOC 动态：跟踪 SOC 随时间变化的方法，以评估土地利用、气候变化和管理实践的影响。
Monitoring SOC dynamics: Methods for tracking changes in SOC over time to assess the impact of land use, climate change, and management practices.
（4）土地管理对 SOC 的影响：评估不同的土地利用和管理实践如何影响 SOC 水平和土壤健康。
Impact of land management on SOC: Assessing how different land use and management practices affect SOC levels and soil health.
（5）减少农业碳足迹：遥感在促进可持续农业实践中的应用，以增强 SOC 并减少碳排放。
Reducing carbon footprint in agriculture: Applications of remote sensing in promoting sustainable agricultural practices that enhance SOC and reduce carbon emissions.
（6）先进的传感器和数据融合：利用光学高光谱数据、激光雷达、伽马辐射和新型传感器技术，包括数据融合技术。
Advanced sensors and data fusion: Utilization of optical hyperspectral data, LiDAR, gamma radiometric, and novel sensor technologies, including data fusion techniques.
（7）最小化 SOC 映射误差的策略：频带优化、误差源量化、不确定性分配和算法优化。
Strategies for minimizing errors in SOC mapping: Band optimization, error source quantification, uncertainty allocation and algorithm optimization.
（8）SOC 与大气碳之间的相互作用：研究 SOC 与大气碳之间的相互作用，重点关注反馈机制及其对全球气候动态的影响。
Interactions between SOC and atmospheric carbon: Investigating reciprocal interactions between SOC and atmospheric carbon, with a focus on feedback mechanisms and their impacts on global climate dynamics.
关键词 Keywords
soil fertility 土壤肥力
visible near-infrared (VNIR) spectroscopy
可见近红外 (VNIR) 光谱mid-infrared (MIR) spectroscopy
中红外 (MIR) 光谱climate change 气候变化
machine learning 机器学习
digital soil mapping 数字土壤测绘
land-use management 土地利用管理
unmanned aerial vehicles (UAVs)
无人机 (UAV)airborne, and satellite imagery
机载和卫星图像
Dr. Tong Li
School of Agriculture and Food Sustainability, The University of Queensland, St. Lucia, QLD 4072, Australia
Interests: land management; soil organic carbon; proximal sensing
Prof. Dr. Songchao Chen
Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 311200, China
Interests: proximal soil sensing; soil spectroscopy; digital soil mapping; carbon sequestration; soil biogeochemical modeling
Dr. Anquan Xia
Development and Research Center (National Geological Archives of China), China Geological Survey, Beijing, China
Interests: machine learning; soil organic carbon; biogeochemical cycling
Dr. Francesco Fava
Department of Environmental Science and Policy, Università degli Studi di Milano, Via Celoria 2, Milan, Italy
Interests: soil organic carbon; remote sensing dryland; grassland
Dr. Yash Dang
School of Agriculture and Food Sciences, The University of Queensland, Brisbane, QLD 4072, Australia
Interests: remote sensing; cropping soils; soil organic carbon
截止日期：2025年3月14日
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