Remote quantification of soil composition characteristics using an integrated hyperspectral remote sensing approach

使用综合高光谱遥感方法远程量化土壤成分特征

• 批准号: NE/X011232/1
• 负责人: Graham Ferrier
• 金额: $ 10.26万
• 依托单位: University of Hull
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FX011232%2F1
• 关键词: Remote; quantification; soil; composition; characteristics

Soils are a key component of terrestrial ecosystems with Soil Organic Carbon (SOC) being a critical part of the carbon cycle and balance, the largest terrestrial carbon pool and an important indicator of soil fertility. Even a small change in the SOC can substantially affect not only the climate but also the stability of ecosystems, because of its decisive role in the exchange of carbon between the soil and atmosphere and plant growth/production. Therefore, understanding the spatio-temporal changes of SOC is of critical importance to evaluate the feedbacks between the terrestrial C cycle, climate change and the maintenance of ecosystem functions. Soils are a complex mixture of organic and inorganic constituents with different physical and chemical properties, that show large variability at site and field scales. The capability to accurately measure SOC at high spatial and temporal resolution over large areas is therefore essential in order to inform on the effectiveness of potential sustainable agricultural practices and soil carbon sequestration approaches. Current field-based approaches provide a limited understanding of the nature, scale & spatial variability of SOC loss resulting in empirical uncertainties that may be amplified when modelling Carbon dynamics at larger scales. Hence our ability to accurately assess the impacts of climate change & farming practices in a way that will mitigate against SOC loss, is severely limited.Remote Sensing based approaches using images covering the Visible-to-Near InfraRed (VNIR) and Short Wave InfraRed (SWIR) offer the potential to overcome these severe sampling and cost limitations and provide a low-cost, repeatable, accurate methodology that can measure SOC at site-to-landscape scales on an operational basis. There have been a large number of research projects that have identified that there is a spectral response to increasing amounts of SOC in the soil but the instruments and the methods that these studies have implemented have left a number of uncertainties including (i) how accurately can surface SOC be determined using VNIR and SWIR spectral measurements; (ii) how does the spectrally measured surface SOC composition relate the mean SOC composition of the top 30cm of the soil horizon (bulk composition); how is the spectral response of the soil affected by illumination and surface roughness variations.This project will use a highly novel, integrated laboratory and field-based remote sensing approach using a range of hyperspectral sensors covering the VNIR and SWIR wavelengths, supported by contact spectral measurements and traditional laboratory soil analyses methods to enable these measurement uncertainties to be resolved. Two fields with contrasting soil regimes, one using organic and the other modern, commercial, farming methods to maintain soil health and SOC have been identified. An initial field-based survey using ground spectroradiometers and UAV-mounted hyperspectral cameras will acquire datasets over these two fields. The array of hyperspectral sensors (VNIR, SWIR & LiDAR) mounted on a gantry will enable ultra-high imagery to be acquired under highly controlled conditions. These datasets will enable the uncertainties stated above to be determined and a robust, accurate relationship between remotely measured SOC composition and the real surface and bulk SOC measurements to be resolved.A large number of satellite-based hyperspectral sensors covering the VNIR and SWIR are currently being deployed. This will, for the first time, provide researchers and government agencies with the datasets they can use to derive SOC routinely, at low cost, at landscape-scales over the entire globe. The results from this project will be able to directly assist the processing of these satellite datasets in meaningful measurements of surface and bulk SOC composition which will be able to assist directly research into spatial and temporal variability in soil health.

土壤是陆地生态系统的重要组成部分，土壤有机碳（SOC）是陆地碳循环和平衡的重要组成部分，是陆地最大的碳库，也是土壤肥力的重要指标。即使是SOC的微小变化，也会对气候和生态系统的稳定性产生重大影响，因为它在土壤和大气之间的碳交换以及植物生长/生产中起着决定性作用。因此，了解土壤有机碳的时空变化规律，对于评价陆地碳循环、气候变化和生态系统功能维持之间的反馈作用具有重要意义。土壤是有机和无机成分的复杂混合物，具有不同的物理和化学性质，在场地和田间尺度上表现出很大的变异性。因此，在大面积区域以高时空分辨率准确测量SOC的能力至关重要，以便了解潜在的可持续农业实践和土壤碳固存方法的有效性。当前基于场的方法提供了对SOC损失的性质、规模和空间变异性的有限理解，从而导致在较大尺度上对碳动态进行建模时可能会放大的经验不确定性。因此，我们准确评估气候变化和农业实践的影响，以减轻SOC损失的能力受到严重限制。基于遥感的方法使用覆盖可见光到近红外（VNIR）和短波红外（SWIR）的图像，提供了克服这些严重的采样和成本限制的潜力，并提供了低成本，可重复，准确的方法，可以测量SOC在现场景观规模的业务基础上。已经有大量的研究项目已经确定了土壤中SOC含量增加会产生光谱响应，但是这些研究所采用的仪器和方法留下了许多不确定性，包括（i）使用VNIR和SWIR光谱测量确定地表SOC的准确性如何;（ii）光谱测量的表层有机碳组成如何与土壤层顶30 cm的平均有机碳组成相关联（散装组成）;光照和表面粗糙度变化如何影响土壤的光谱响应。该项目将使用一种非常新颖的，综合实验室和实地遥感方法，使用一系列覆盖VNIR和SWIR波长的高光谱传感器，由接触光谱测量和传统的实验室土壤分析方法支持，以解决这些测量不确定性。两个领域的对比土壤制度，一个使用有机和其他现代，商业，耕作方法，以保持土壤健康和SOC已被确定。使用地面分光辐射计和无人机安装的高光谱相机进行的初步实地调查将获得这两个领域的数据集。安装在机架上的高光谱传感器（VNIR，SWIR和LiDAR）阵列将使超高图像能够在高度受控的条件下获得。这些数据集将使上述的不确定性被确定和一个鲁棒的，准确的关系之间的远程测量的SOC组成和真实的表面和散装SOC测量被解决。大量的基于卫星的高光谱传感器覆盖VNIR和SWIR目前正在部署。这将第一次为研究人员和政府机构提供数据集，他们可以使用这些数据集以低成本在整个地球仪的小规模范围内常规推导SOC。该项目的结果将能够直接帮助处理这些卫星数据集，对地表和土壤有机碳组成进行有意义的测量，从而能够直接帮助研究土壤健康的时空变异性。