Susanna Karlqvist, Iuliia Burdun, Sini-Selina Salko, Jussi Juola, Miina Rautiainen
{"title":"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","authors":"Susanna Karlqvist, Iuliia Burdun, Sini-Selina Salko, Jussi Juola, Miina Rautiainen","doi":"10.1016/j.rse.2024.114415","DOIUrl":null,"url":null,"abstract":"<div><p>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 <em>Sphagnum</em> mosses, whose spectral signatures are known to be highly sensitive to changes in moisture content. In this study, we tested methods to estimate <em>Sphagnum</em> 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 <em>Sphagnum</em> 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 <em>Sphagnum</em> 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 (<span><math><msubsup><mi>R</mi><mtext>Marg</mtext><mn>2</mn></msubsup></math></span>= 0.72, ICC = 0.40), (ii) the exponential OPTRAM performed best for the mesotrophic species (<span><math><msubsup><mi>R</mi><mtext>Marg</mtext><mn>2</mn></msubsup></math></span>= 0.70, ICC = 0.08), and (iii) the Modified Moisture Stress Index (MMSI) yielded the best results (<span><math><msubsup><mi>R</mi><mtext>Marg</mtext><mn>2</mn></msubsup></math></span>= 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 <em>Sphagnum</em>-dominated peatlands over the span of the multispectral satellite era.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114415"},"PeriodicalIF":11.1000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0034425724004413/pdfft?md5=404a7f19d8c659b03f8de4e6e084a339&pid=1-s2.0-S0034425724004413-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Retrieval of moisture content of common Sphagnum peat moss species from hyperspectral and multispectral data\",\"authors\":\"Susanna Karlqvist, Iuliia Burdun, Sini-Selina Salko, Jussi Juola, Miina Rautiainen\",\"doi\":\"10.1016/j.rse.2024.114415\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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 <em>Sphagnum</em> mosses, whose spectral signatures are known to be highly sensitive to changes in moisture content. In this study, we tested methods to estimate <em>Sphagnum</em> 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 <em>Sphagnum</em> 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 <em>Sphagnum</em> 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 (<span><math><msubsup><mi>R</mi><mtext>Marg</mtext><mn>2</mn></msubsup></math></span>= 0.72, ICC = 0.40), (ii) the exponential OPTRAM performed best for the mesotrophic species (<span><math><msubsup><mi>R</mi><mtext>Marg</mtext><mn>2</mn></msubsup></math></span>= 0.70, ICC = 0.08), and (iii) the Modified Moisture Stress Index (MMSI) yielded the best results (<span><math><msubsup><mi>R</mi><mtext>Marg</mtext><mn>2</mn></msubsup></math></span>= 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 <em>Sphagnum</em>-dominated peatlands over the span of the multispectral satellite era.</p></div>\",\"PeriodicalId\":417,\"journal\":{\"name\":\"Remote Sensing of Environment\",\"volume\":\"315 \",\"pages\":\"Article 114415\"},\"PeriodicalIF\":11.1000,\"publicationDate\":\"2024-09-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0034425724004413/pdfft?md5=404a7f19d8c659b03f8de4e6e084a339&pid=1-s2.0-S0034425724004413-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Remote Sensing of Environment\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0034425724004413\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Remote Sensing of Environment","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0034425724004413","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
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 (= 0.72, ICC = 0.40), (ii) the exponential OPTRAM performed best for the mesotrophic species (= 0.70, ICC = 0.08), and (iii) the Modified Moisture Stress Index (MMSI) yielded the best results (= 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.
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.