从高光谱和多光谱数据中检索常见泥炭藓物种的水分含量

IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES
Susanna Karlqvist, Iuliia Burdun, Sini-Selina Salko, Jussi Juola, Miina Rautiainen
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引用次数: 0

摘要

泥炭地在泥炭层中储存了大量的碳,而这些碳只有在积水的条件下才能形成和保存。监测泥炭地的水分状况至关重要,因为水分减少会导致泥炭氧化,并将积累的碳作为温室气体释放回大气中。光学遥感可通过识别植被光谱特征的湿度变化来间接监测泥炭地的湿度状况。北方泥炭地的植被以泥炭藓为主,众所周知,泥炭藓的光谱特征对水分含量的变化高度敏感。在这项研究中,我们测试了利用七种光谱水分指数、OPTRAM(光学梯形模型)和连续小波变换(CWT)从光谱数据估算泥炭藓水分含量的方法。这项研究基于从芬兰南部北方泥炭地的两个栖息地采集的代表九种泥炭藓的数据。研究结果表明,多光谱和高光谱数据都可用于估算泥炭藓的含水量。不过,最佳的检索方法取决于栖息地的特征。通过使用高光谱数据,我们发现(i) CWT 在所有研究的苔藓物种中都表现出卓越的性能(RMarg2= 0.72,ICC = 0.40),(ii) 指数 OPTRAM 在中营养物种中表现最佳(RMarg2= 0.70,ICC = 0.08),(iii) 改良水分压力指数(MMSI)在外养物种中产生了最佳结果(RMarg2= 0.68,ICC = 0.55)。此外,我们还证明了使用多光谱数据而不是高光谱数据作为 OPTRAM 或水分胁迫指数(MSI)的输入时,可提供相似的水分估算结果。在多光谱卫星时代,这种方法可使人们对泥炭藓为主的泥炭地的湿度动态有新的认识。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Retrieval of moisture content of common Sphagnum peat moss species from hyperspectral and multispectral data

Peatlands store enormous amounts of carbon in a peat layer, the formation and preservation of which can only occur under waterlogged conditions. Monitoring peatland moisture conditions is critically important because a decrease in moisture leads to peat oxidation and the release of accumulated carbon back into the atmosphere as a greenhouse gas. Optical remote sensing enables the indirect monitoring of peatland moisture conditions by identifying moisture-driven changes in vegetation spectral signatures. The vegetation of northern peatlands is dominated by Sphagnum mosses, whose spectral signatures are known to be highly sensitive to changes in moisture content. In this study, we tested methods to estimate Sphagnum moisture content from spectral data using seven spectral moisture indices, the OPtical TRApezoid Model (OPTRAM) and the Continuous Wavelet Transform (CWT). This study was based on data representing nine Sphagnum species sampled from two habitats in southern boreal peatlands in Finland. Our results showed that both multi- and hyperspectral data can be used to estimate the moisture content of Sphagnum mosses. Nevertheless, the optimal retrieval method depended on habitat characteristics. Using hyperspectral data, we found that: (i) the CWT exhibited superior performance for all studied moss species (RMarg2= 0.72, ICC = 0.40), (ii) the exponential OPTRAM performed best for the mesotrophic species (RMarg2= 0.70, ICC = 0.08), and (iii) the Modified Moisture Stress Index (MMSI) yielded the best results (RMarg2= 0.68, ICC = 0.55) for the ombrotrophic species. Furthermore, we demonstrated that using multispectral data instead of hyperspectral data provides comparable results in moisture estimation when used as input with OPTRAM or Moisture Stress Index (MSI). This approach could lead to new insights into the moisture dynamics in Sphagnum-dominated peatlands over the span of the multispectral satellite era.

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来源期刊
Remote Sensing of Environment
Remote Sensing of Environment 环境科学-成像科学与照相技术
CiteScore
25.10
自引率
8.90%
发文量
455
审稿时长
53 days
期刊介绍: Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing. The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques. RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.
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