Pub Date : 2024-09-16DOI: 10.1016/j.srs.2024.100163
Anxin Ding , Shunlin Liang , Han Ma , Tao He , Aolin Jia , Qian Wang
Snow albedo is a key geophysical parameter that controls the energy exchanges between the atmosphere and Earth's surfaces and has been widely utilized in climatic and environmental change studies. However, recent studies have demonstrated that current albedo satellite products still have large uncertainties in snow-covered areas. In this study, we estimated the blue-sky shortwave albedo of snow surfaces using the eXtreme Gradient Boosting (XGBoost) algorithm with Moderate Resolution Imaging Spectroradiometer (MODIS) top-of-atmosphere (TOA) reflectance values, ERA-5 land reanalysis snow parameters (e.g., snow cover, snow density and snow depth water equivalent) and in situ measurements. In the XGBoost model, the MODIS MCD43 albedo values were input as prior knowledge, and the random sample validation results showed that the R2 and root mean square error (RMSE) values of this model were approximately 0.953 and 0.044, respectively. The typical sites for independent validation were subjected to in situ measurements at the UPE_L, AWS5, and CA_ARB sites. Finally, the retrieved XGBoost albedo values were compared with the official NASA MODIS (MCD43, collection 6), the Global Land Surface Satellite (GLASS), and the National Oceanic and Atmospheric Administration (NOAA) Visible Infrared Imaging Radiometer Suite (VIIRS) SURFALB albedo products. The validation results indicated that the proposed approach achieved much greater accuracy (RMSE = 0.052, bias = 0.002) than did the corresponding official MODIS (RMSE = 0.087, bias = −0.033), GLASS (RMSE = 0.089, bias = −0.031) and VIIRS SURFALB albedo (RMSE = 0.100, bias = −0.032) products. The improved shortwave albedo captured the rapid temporal changes in surface snow conditions.
{"title":"Improved estimation of daily blue-sky snow shortwave albedo from MODIS data and reanalysis information","authors":"Anxin Ding , Shunlin Liang , Han Ma , Tao He , Aolin Jia , Qian Wang","doi":"10.1016/j.srs.2024.100163","DOIUrl":"10.1016/j.srs.2024.100163","url":null,"abstract":"<div><p>Snow albedo is a key geophysical parameter that controls the energy exchanges between the atmosphere and Earth's surfaces and has been widely utilized in climatic and environmental change studies. However, recent studies have demonstrated that current albedo satellite products still have large uncertainties in snow-covered areas. In this study, we estimated the blue-sky shortwave albedo of snow surfaces using the eXtreme Gradient Boosting (XGBoost) algorithm with Moderate Resolution Imaging Spectroradiometer (MODIS) top-of-atmosphere (TOA) reflectance values, ERA-5 land reanalysis snow parameters (e.g., snow cover, snow density and snow depth water equivalent) and in situ measurements. In the XGBoost model, the MODIS MCD43 albedo values were input as prior knowledge, and the random sample validation results showed that the R<sup>2</sup> and root mean square error (RMSE) values of this model were approximately 0.953 and 0.044, respectively. The typical sites for independent validation were subjected to in situ measurements at the UPE_L, AWS5, and CA_ARB sites. Finally, the retrieved XGBoost albedo values were compared with the official NASA MODIS (MCD43, collection 6), the Global Land Surface Satellite (GLASS), and the National Oceanic and Atmospheric Administration (NOAA) Visible Infrared Imaging Radiometer Suite (VIIRS) SURFALB albedo products. The validation results indicated that the proposed approach achieved much greater accuracy (RMSE = 0.052, bias = 0.002) than did the corresponding official MODIS (RMSE = 0.087, bias = −0.033), GLASS (RMSE = 0.089, bias = −0.031) and VIIRS SURFALB albedo (RMSE = 0.100, bias = −0.032) products. The improved shortwave albedo captured the rapid temporal changes in surface snow conditions.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"10 ","pages":"Article 100163"},"PeriodicalIF":5.7,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666017224000476/pdfft?md5=e99e57265aeb05eddcd22708dbcf028e&pid=1-s2.0-S2666017224000476-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The photosynthetic rate has a nonlinear relationship with PAR during the day. We previously developed an algorithm for estimating GPP capacity, which is defined GPP under low-stress condition, using light response curves (LRCs). In this study, we studied the characteristics of LRC parameters of the initial slope and the maximum gross photosynthesis rate (Pmax), and formulas to calculate Pmax from the relationship between the chlorophyll index of the green and near-infrared (NIR) bands (CIgreen) and the GPP capacity at PAR = 2000 μmol m−2 s−1 (GP2000) for nine vegetation types spanning tropical to subarctic climates on the Eurasian and North American continents using eddy covariance flux measurements and Moderate Resolution Imaging Spectrometer (MODIS) data. The slope of the relationship between CIgreen and GP2000 was highest for sites dominated by herbaceous plants such as open shrubland, savanna, and cropland (rice paddy); it was lower at sites dominated by woody plants. The yearly GPP/GPP capacity ratio was close to one in flux data. When the method was applied to satellite data, the daily GPP capacity exhibited a similar seasonal pattern to that of the Flux GPP and MODIS GPP products. Under high dryness conditions, Flux GPP showed the drop from the GPP capacity estimated from CIgreen and diurnal PAR data around noon, and they were nearly identical during the early morning and late afternoon. The instantaneous GPP capacity could be considered the baseline of the instantaneous GPP with stress-free conditions and important for quantifying midday depression at the sub-day scale.
{"title":"Use of light response curve parameters to estimate gross primary production capacity from chlorophyll indices of global observation satellite and flux data","authors":"Kanako Muramatsu , Emi Yoneda , Noriko Soyama , Ana López-Ballesteros , Juthasinee Thanyapraneedkul","doi":"10.1016/j.srs.2024.100164","DOIUrl":"10.1016/j.srs.2024.100164","url":null,"abstract":"<div><div>The photosynthetic rate has a nonlinear relationship with PAR during the day. We previously developed an algorithm for estimating GPP capacity, which is defined GPP under low-stress condition, using light response curves (LRCs). In this study, we studied the characteristics of LRC parameters of the initial slope and the maximum gross photosynthesis rate (P<sub>max</sub>), and formulas to calculate P<sub>max</sub> from the relationship between the chlorophyll index of the green and near-infrared (NIR) bands (CI<sub>green</sub>) and the GPP capacity at PAR = 2000 μmol m<sup>−2</sup> s<sup>−1</sup> (GP2000) for nine vegetation types spanning tropical to subarctic climates on the Eurasian and North American continents using eddy covariance flux measurements and Moderate Resolution Imaging Spectrometer (MODIS) data. The slope of the relationship between CI<sub>green</sub> and GP2000 was highest for sites dominated by herbaceous plants such as open shrubland, savanna, and cropland (rice paddy); it was lower at sites dominated by woody plants. The yearly GPP/GPP capacity ratio was close to one in flux data. When the method was applied to satellite data, the daily GPP capacity exhibited a similar seasonal pattern to that of the Flux GPP and MODIS GPP products. Under high dryness conditions, Flux GPP showed the drop from the GPP capacity estimated from CI<sub>green</sub> and diurnal PAR data around noon, and they were nearly identical during the early morning and late afternoon. The instantaneous GPP capacity could be considered the baseline of the instantaneous GPP with stress-free conditions and important for quantifying midday depression at the sub-day scale.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"10 ","pages":"Article 100164"},"PeriodicalIF":5.7,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142417827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-10DOI: 10.1016/j.srs.2024.100162
Saeid Aminjafari , Frédéric Frappart , Fabrice Papa , Ian Brown , Fernando Jaramillo
Lakes provide societies and natural ecosystems with valuable services such as freshwater supply and flood control. Water level changes in lakes reflect their natural responses to climatic and anthropogenic stressors; however, their monitoring is costly due to installation and maintenance requirements. With its advanced hardware and computational capabilities, altimetry has become a popular alternative to conventional in-situ gauging, although subject to the temporal availability of altimetric observations. To further improve the temporal resolution of altimetric measurements, we here combine radar altimetry data with Differential Interferometric Synthetic Aperture Radar (D-InSAR), using ten lakes in Sweden as a testing platform. First, we use Sentinel-1A and Sentinel-1B SAR images to generate consecutive six-day baseline interferograms across 2019. Then, we accumulate the phase change of coherent pixels to construct the time series of InSAR-derived water level anomalies. Finally, we retrieve altimetric observations from Sentinel-3, estimate their mean and standard deviation, and apply them to the D-InSAR standardized anomalies. In this way, we build a water-level time series with more temporal observations. In general, we find a strong agreement between water level estimates from the combination of D-InSAR and Satellite Altimetry (DInSAlt) and in-situ observations in eight lakes (Concordance Correlation Coefficient - CCC >0.8) and moderate agreement in two lakes (CCC >0.57). The applicability of DInSAlt is limited to lakes with suitable conditions for double-bounce scattering, such as the presence of trees or marshes. The accuracy of the water level estimates depends on the quality of the altimetry observations and the lake's width. These findings are important considering the recently launched Surface Water and Ocean Topography (SWOT) satellite, whose capabilities could expand our methodology's geographical applicability and reduce its reliance on ground measurements.
{"title":"Enhancing the temporal resolution of water levels from altimetry using D-InSAR: A case study of 10 Swedish Lakes","authors":"Saeid Aminjafari , Frédéric Frappart , Fabrice Papa , Ian Brown , Fernando Jaramillo","doi":"10.1016/j.srs.2024.100162","DOIUrl":"10.1016/j.srs.2024.100162","url":null,"abstract":"<div><p>Lakes provide societies and natural ecosystems with valuable services such as freshwater supply and flood control. Water level changes in lakes reflect their natural responses to climatic and anthropogenic stressors; however, their monitoring is costly due to installation and maintenance requirements. With its advanced hardware and computational capabilities, altimetry has become a popular alternative to conventional in-situ gauging, although subject to the temporal availability of altimetric observations. To further improve the temporal resolution of altimetric measurements, we here combine radar altimetry data with Differential Interferometric Synthetic Aperture Radar (D-InSAR), using ten lakes in Sweden as a testing platform. First, we use Sentinel-1A and Sentinel-1B SAR images to generate consecutive six-day baseline interferograms across 2019. Then, we accumulate the phase change of coherent pixels to construct the time series of InSAR-derived water level anomalies. Finally, we retrieve altimetric observations from Sentinel-3, estimate their mean and standard deviation, and apply them to the D-InSAR standardized anomalies. In this way, we build a water-level time series with more temporal observations. In general, we find a strong agreement between water level estimates from the combination of D-InSAR and Satellite Altimetry (DInSAlt) and in-situ observations in eight lakes (Concordance Correlation Coefficient - CCC >0.8) and moderate agreement in two lakes (CCC >0.57). The applicability of DInSAlt is limited to lakes with suitable conditions for double-bounce scattering, such as the presence of trees or marshes. The accuracy of the water level estimates depends on the quality of the altimetry observations and the lake's width. These findings are important considering the recently launched Surface Water and Ocean Topography (SWOT) satellite, whose capabilities could expand our methodology's geographical applicability and reduce its reliance on ground measurements.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"10 ","pages":"Article 100162"},"PeriodicalIF":5.7,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666017224000464/pdfft?md5=884dbde39217c179b32aa224366ef5ea&pid=1-s2.0-S2666017224000464-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142167929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-07DOI: 10.1016/j.srs.2024.100161
Xiaoxuan Li , Konrad Wessels , John Armston , Laura Duncanson , Mikhail Urbazaev , Laven Naidoo , Renaud Mathieu , Russell Main
Savannas cover more than 20% of the Earth and account for the third largest stock of global aboveground biomass yet estimates of their above ground biomass density (AGBD) are very inaccurate. The Global Ecosystem Dynamic Investigation (GEDI) sensor provides near-global full-waveform LiDAR data with 25 m footprints, from which various structural metrics are derived that are used to predict footprint level AGBD. The current GEDI L4A AGBD product uses a comprehensive Forest Structure and Biomass Database (FSBD) to develop models for specific plant functional types and geographic regions, but southern African savannas have been underrepresented in the reference data. The objectives of this study were to (i) validate GEDI L4A AGBD in South African savannas using field measurements and ALS datasets and (ii) develop and evaluate local GEDI footprint-level AGBD estimates from multiple L2A and L2B metrics. The local GEDI AGBD models outperformed GEDI L4A AGBD (R2 = 0.42, RMSE = 12 Mg/ha, %RMSE = 79.5%) with higher R2 and smaller error measures. The local GEDI AGBD using a random forest model (RF) had the highest R2 of 0.71 and lowest %RMSE of 53.3%, while the generalized linear model (GLM) results provided the lowest Relative Mean Systematic Deviation (RMSD) of 9.2%, which was half that of RF model. L4A significantly underestimated AGBD with an RMSD up to −37%. This highlights the importance and benefits of local calibration of biomass models to unlock the full potential of GEDI metrics for estimating AGBD. The field and ALS data have subsequently been contributed to the GEDI FSBD and should be used in calibration of future versions of GEDI L4A AGBD product. This research paves the way for the integration of the local GEDI AGBD estimates with other sensors, notable the eminent NISAR mission, to derive regional to global gridded AGBD products that will enable the monitoring of savanna carbon stocks.
{"title":"Evaluation of GEDI footprint level biomass models in Southern African Savannas using airborne LiDAR and field measurements","authors":"Xiaoxuan Li , Konrad Wessels , John Armston , Laura Duncanson , Mikhail Urbazaev , Laven Naidoo , Renaud Mathieu , Russell Main","doi":"10.1016/j.srs.2024.100161","DOIUrl":"10.1016/j.srs.2024.100161","url":null,"abstract":"<div><p>Savannas cover more than 20% of the Earth and account for the third largest stock of global aboveground biomass yet estimates of their above ground biomass density (AGBD) are very inaccurate. The Global Ecosystem Dynamic Investigation (GEDI) sensor provides near-global full-waveform LiDAR data with 25 m footprints, from which various structural metrics are derived that are used to predict footprint level AGBD. The current GEDI L4A AGBD product uses a comprehensive Forest Structure and Biomass Database (FSBD) to develop models for specific plant functional types and geographic regions, but southern African savannas have been underrepresented in the reference data. The objectives of this study were to (i) validate GEDI L4A AGBD in South African savannas using field measurements and ALS datasets and (ii) develop and evaluate local GEDI footprint-level AGBD estimates from multiple L2A and L2B metrics. The local GEDI AGBD models outperformed GEDI L4A AGBD (R<sup>2</sup> = 0.42, RMSE = 12 Mg/ha, %RMSE = 79.5%) with higher R<sup>2</sup> and smaller error measures. The local GEDI AGBD using a random forest model (RF) had the highest R<sup>2</sup> of 0.71 and lowest %RMSE of 53.3%, while the generalized linear model (GLM) results provided the lowest Relative Mean Systematic Deviation (RMSD) of 9.2%, which was half that of RF model. L4A significantly underestimated AGBD with an RMSD up to −37%. This highlights the importance and benefits of local calibration of biomass models to unlock the full potential of GEDI metrics for estimating AGBD. The field and ALS data have subsequently been contributed to the GEDI FSBD and should be used in calibration of future versions of GEDI L4A AGBD product. This research paves the way for the integration of the local GEDI AGBD estimates with other sensors, notable the eminent NISAR mission, to derive regional to global gridded AGBD products that will enable the monitoring of savanna carbon stocks.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"10 ","pages":"Article 100161"},"PeriodicalIF":5.7,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666017224000452/pdfft?md5=73e6de19f0a4636ffb39019a392d7591&pid=1-s2.0-S2666017224000452-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142232395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-02DOI: 10.1016/j.srs.2024.100159
Gonzalo Gavilán-Acuna , Nicholas C. Coops , Piotr Tompalski , Pablo Mena-Quijada , Andrés Varhola , Dominik Roeser , Guillermo F. Olmedo
While Leaf Area Index (LAI) is critical for understanding forest canopy, photosynthesis and forest growth, traditional field-based LAI measurements are laborious and costly. Remote sensing offers a practical alternative for extensive assessments. Satellite imagery provides broad-scale, long-term monitoring; however, may lack detail needed to guide specific forest management actions. Conversely, Airborne Laser Scanning (ALS) provides accurate LAI estimates at fine spatial detail but is limited by cost and temporal monitoring constraints. Combining ALS data with satellite observations could enhance plantation management decisions by balancing extensive coverage with detailed observations. This study explores the integration of ALS and satellite remote sensing as a comprehensive alternative for assessing LAI and stand volume growth rate (m3/ha/year) in operational Pinus radiata plantations in central-south Chile. Our approach comprised four major steps. First, we applied the Beer-Lambert law using ALS vertical profiles to estimate LAI across a forest plantation (LAIALS). We found that ALS accurately estimated LAI across 121 plots (R2 = 0.82 and RMSE = 0.51). Second, we built a simple linear regression to link LAIALS with the Normalized Difference Moisture Index (NDMI) derived from surface reflectance information from the Landsat/Sentinel-2 satellites, resulting in an R2 of 0.53 and an RMSE of 1.17. This step showed a higher correlation with satellite data compared to using only ground-based LAI estimates (R2 = 0.38; RMSE = 1.18). Third, we transformed biweekly NDMI time series to LAI, then derived peak annual LAI as an indicator of mean annual increment (MAI) (R2 = 0.51; RMSE = 5.27 m³/ha/year). This allowed us to characterize stand growth and LAI on a yearly wall-to-wall basis. Throughout the modelling steps, we incorporated error propagation, allowing final estimates to be error bounded. This integrated approach serves as a tool for identifying and visualizing growth irregularities, guiding adaptive management strategies to maintain or enhance stand productivity over time.
叶面积指数(LAI)对于了解森林冠层、光合作用和森林生长至关重要,但传统的实地叶面积指数测量既费力又昂贵。遥感技术为广泛评估提供了一种实用的替代方法。卫星图像可提供大范围的长期监测,但可能缺乏指导具体森林管理行动所需的细节。与此相反,机载激光扫描(ALS)可提供精确的 LAI 估计值,但受到成本和时间监测的限制。将 ALS 数据与卫星观测数据相结合,可以在广泛的覆盖范围与详细的观测数据之间取得平衡,从而加强人工林管理决策。本研究探讨了如何将 ALS 与卫星遥感结合起来,作为评估智利中南部辐射松人工林的 LAI 和林木体积增长率(立方米/公顷/年)的综合替代方法。我们的方法包括四个主要步骤。首先,我们利用 ALS 垂直剖面应用比尔-朗伯定律估算人工林的 LAI(LAIALS)。我们发现,ALS 能准确估算 121 个地块的 LAI(R2 = 0.82,RMSE = 0.51)。其次,我们建立了一个简单的线性回归,将 LAIALS 与根据 Landsat/Sentinel-2 卫星表面反射率信息得出的归一化差异水分指数 (NDMI) 联系起来,结果 R2 为 0.53,RMSE 为 1.17。与仅使用地面 LAI 估计值(R2 = 0.38;RMSE = 1.18)相比,这一步骤显示出与卫星数据更高的相关性。第三,我们将双周 NDMI 时间序列转换为 LAI,然后得出年 LAI 峰值,作为年平均增量 (MAI) 的指标(R2 = 0.51;RMSE = 5.27 m³/ha/年)。这样,我们就能以每年墙到墙的方式来描述林分生长和 LAI 的特征。在整个建模步骤中,我们纳入了误差传播,从而使最终估算结果具有误差约束。这种综合方法可作为一种工具,用于识别和直观显示生长的不规则性,从而指导适应性管理策略,随着时间的推移保持或提高林分生产力。
{"title":"Characterizing annual leaf area index changes and volume growth using ALS and satellite data in forest plantations","authors":"Gonzalo Gavilán-Acuna , Nicholas C. Coops , Piotr Tompalski , Pablo Mena-Quijada , Andrés Varhola , Dominik Roeser , Guillermo F. Olmedo","doi":"10.1016/j.srs.2024.100159","DOIUrl":"10.1016/j.srs.2024.100159","url":null,"abstract":"<div><p>While Leaf Area Index (LAI) is critical for understanding forest canopy, photosynthesis and forest growth, traditional field-based LAI measurements are laborious and costly. Remote sensing offers a practical alternative for extensive assessments. Satellite imagery provides broad-scale, long-term monitoring; however, may lack detail needed to guide specific forest management actions. Conversely, Airborne Laser Scanning (ALS) provides accurate LAI estimates at fine spatial detail but is limited by cost and temporal monitoring constraints. Combining ALS data with satellite observations could enhance plantation management decisions by balancing extensive coverage with detailed observations. This study explores the integration of ALS and satellite remote sensing as a comprehensive alternative for assessing LAI and stand volume growth rate (m<sup>3</sup>/ha/year) in operational <em>Pinus radiata</em> plantations in central-south Chile. Our approach comprised four major steps. First, we applied the Beer-Lambert law using ALS vertical profiles to estimate LAI across a forest plantation (LAI<sub>ALS</sub>). We found that ALS accurately estimated LAI across 121 plots (R<sup>2</sup> = 0.82 and RMSE = 0.51). Second, we built a simple linear regression to link LAI<sub>ALS</sub> with the Normalized Difference Moisture Index (NDMI) derived from surface reflectance information from the Landsat/Sentinel-2 satellites, resulting in an R<sup>2</sup> of 0.53 and an RMSE of 1.17. This step showed a higher correlation with satellite data compared to using only ground-based LAI estimates (R<sup>2</sup> = 0.38; RMSE = 1.18). Third, we transformed biweekly NDMI time series to LAI, then derived peak annual LAI as an indicator of mean annual increment (MAI) (R<sup>2</sup> = 0.51; RMSE = 5.27 m³/ha/year). This allowed us to characterize stand growth and LAI on a yearly wall-to-wall basis. Throughout the modelling steps, we incorporated error propagation, allowing final estimates to be error bounded. This integrated approach serves as a tool for identifying and visualizing growth irregularities, guiding adaptive management strategies to maintain or enhance stand productivity over time.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"10 ","pages":"Article 100159"},"PeriodicalIF":5.7,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666017224000439/pdfft?md5=490f7a068eafcc92083ee3697de5608a&pid=1-s2.0-S2666017224000439-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142147832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-01DOI: 10.1016/j.srs.2024.100158
Yu Li , Hongliang Fang , Yao Wang , Sijia Li , Tian Ma , Yunjia Wu , Hao Tang
Canopy cover (CC) quantifies the proportion of canopy materials projected vertically onto the ground surface. CC is a crucial canopy structural variable and is commonly used in many ecological and climatic models. The vertical CC profile product is currently available from the Global Ecosystem Dynamics Investigation (GEDI). However, detailed information about the accuracy and uncertainty of the GEDI vertical CC profile product remains limited. The objective of this study is to validate the GEDI CC product over selected forest sites using reference values derived from digital hemispherical photography (DHP), airborne laser scanning (ALS) point clouds, and simulated waveforms. The accuracy of CC was quantified and analyzed regarding GEDI observation conditions, waveform processing, and estimation methods. The results show that the GEDI total CC correlates well with those estimated from DHP, ALS, and simulated waveform data (r2 = 0.65, 0.71, and 0.71, respectively) but is systematically underestimated (bias = −0.05, −0.11, and −0.07, respectively) based on reference data. Compared with the ALS-estimated CC, needleleaf forest shows the highest correlation for vertical CC (r2 ≥ 0.65) and shrubland shows the lowest bias for total CC (bias = −0.13). The mean absolute error (MAE) of the GEDI CC decreases from 0.15 to 0.09 as the estimation height increases from ground to 35 m. The GEDI total CCs derived from the waveform interpretation algorithms A2 and A6 display the highest r2 (≥ 0.6) and smallest RMSE (≤ 0.23) compared to those of the other algorithms. The CC accuracy increases with beam sensitivity and decreases with increasing canopy cover. The GEDI CC was improved at moderate CC values using a canopy-to-ground backscattering coefficient ratio () determined with the regression method. The partial difference between GEDI CC and ALS CC is attributed to definitional discrepancies. Further improvement of the CC algorithm can be made by using vegetation-specific waveform processing algorithms and realistic values.
树冠覆盖(CC)量化了垂直投射到地表的树冠材料比例。CC 是一个重要的冠层结构变量,常用于许多生态和气候模型。目前,全球生态系统动力学调查(GEDI)提供了垂直 CC 剖面产品。然而,有关 GEDI 垂直 CC 剖面产品的准确性和不确定性的详细信息仍然有限。本研究的目的是利用从数字半球摄影(DHP)、机载激光扫描(ALS)点云和模拟波形中获得的参考值,对 GEDI CC 产品在选定森林地点的应用进行验证。针对 GEDI 观测条件、波形处理和估算方法,对 CC 的准确性进行了量化和分析。结果表明,GEDI 总 CC 与 DHP、ALS 和模拟波形数据估算的 CC 相关性良好(r2 分别为 0.65、0.71 和 0.71),但根据参考数据,GEDI 总 CC 被系统性低估(偏差分别为 -0.05、-0.11 和 -0.07)。与 ALS 估算的 CC 相比,针叶林的垂直 CC 相关性最高(r2 ≥ 0.65),灌木林的总 CC 偏差最小(偏差 = -0.13)。波形解释算法 A2 和 A6 得出的 GEDI 总 CC 与其他算法相比,r2 最高(≥ 0.6),RMSE 最小(≤ 0.23)。CC 精确度随光束灵敏度的增加而增加,随冠层覆盖度的增加而降低。在中等 CC 值时,使用回归法确定的冠层与地面的后向散射系数比(ρv/ρg)可提高 GEDI CC 的精度。GEDI CC 和 ALS CC 之间的部分差异归因于定义上的差异。通过使用特定植被波形处理算法和真实的 ρv/ρg 值,可以进一步改进 CC 算法。
{"title":"Validation of the vertical canopy cover profile products derived from GEDI over selected forest sites","authors":"Yu Li , Hongliang Fang , Yao Wang , Sijia Li , Tian Ma , Yunjia Wu , Hao Tang","doi":"10.1016/j.srs.2024.100158","DOIUrl":"10.1016/j.srs.2024.100158","url":null,"abstract":"<div><p>Canopy cover (CC) quantifies the proportion of canopy materials projected vertically onto the ground surface. CC is a crucial canopy structural variable and is commonly used in many ecological and climatic models. The vertical CC profile product is currently available from the Global Ecosystem Dynamics Investigation (GEDI). However, detailed information about the accuracy and uncertainty of the GEDI vertical CC profile product remains limited. The objective of this study is to validate the GEDI CC product over selected forest sites using reference values derived from digital hemispherical photography (DHP), airborne laser scanning (ALS) point clouds, and simulated waveforms. The accuracy of CC was quantified and analyzed regarding GEDI observation conditions, waveform processing, and estimation methods. The results show that the GEDI total CC correlates well with those estimated from DHP, ALS, and simulated waveform data (r<sup>2</sup> = 0.65, 0.71, and 0.71, respectively) but is systematically underestimated (bias = −0.05, −0.11, and −0.07, respectively) based on reference data. Compared with the ALS-estimated CC, needleleaf forest shows the highest correlation for vertical CC (r<sup>2</sup> ≥ 0.65) and shrubland shows the lowest bias for total CC (bias = −0.13). The mean absolute error (MAE) of the GEDI CC decreases from 0.15 to 0.09 as the estimation height increases from ground to 35 m. The GEDI total CCs derived from the waveform interpretation algorithms A2 and A6 display the highest r<sup>2</sup> (≥ 0.6) and smallest RMSE (≤ 0.23) compared to those of the other algorithms. The CC accuracy increases with beam sensitivity and decreases with increasing canopy cover. The GEDI CC was improved at moderate CC values using a canopy-to-ground backscattering coefficient ratio (<span><math><mrow><msub><mi>ρ</mi><mi>v</mi></msub><mo>/</mo><msub><mi>ρ</mi><mi>g</mi></msub></mrow></math></span>) determined with the regression method. The partial difference between GEDI CC and ALS CC is attributed to definitional discrepancies. Further improvement of the CC algorithm can be made by using vegetation-specific waveform processing algorithms and realistic <span><math><mrow><msub><mi>ρ</mi><mi>v</mi></msub><mo>/</mo><msub><mi>ρ</mi><mi>g</mi></msub></mrow></math></span> values.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"10 ","pages":"Article 100158"},"PeriodicalIF":5.7,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666017224000427/pdfft?md5=3e507f3a7fd1dc415673a49b89eef211&pid=1-s2.0-S2666017224000427-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142163676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-31DOI: 10.1016/j.srs.2024.100160
Tommaso Trotto , Nicholas C. Coops , Alexis Achim , Sarah E. Gergel , Dominik Roeser
Characterizing the extent, severity, and persistence of natural disturbances in forests is crucial in areas as large and heterogeneous as the Canadian boreal forest. Non-stand replacing (NSR) disturbances, in particular, can produce subtle and lagged impacts to forest canopy and structure with mechanisms that remain elusive, and they are challenging to discern using typical remote sensing approaches including aerial photointerpretation and spectral analysis of satellite imagery. Consequently, there is a need for timely and accurate information on the structural modifications due to NSR disturbances to inform proactive forest management practices. To address these needs, we leveraged a unique bitemporal airborne laser scanning (ALS) dataset to characterize changes in the forest structure caused by eastern spruce budworm (ESB, Choristoneura fumiferana (Clem.)), responsible for one of the greatest tree mortality in Canada. A range of infestation severity with varying impacts to forest structure are examined in a mixedwood boreal forest in Lac-Saint Jean, Quebec, Canada. We derived 14 ALS structural change metrics at 10 m spatial resolution, including height, cover, and gappiness 7 years apart (2014–2020). Six distinct structural responses to cumulative ESB infestations severity were identified using cluster analysis from the combination of the 14 change metrics, with canopy cover, the 75th and 25th height percentiles (p75-25) driving cluster separability. Canopy cover and p25 consistently decreased as cumulative infestation severity increased, whereas p75 showed greater variability across the landscape. Photointerpretation of aerial imagery over the same period confirmed the validity of the structural characterization. Further, we studied the role of initial forest structures in modulating the severity of the infestation and found that sparser canopies with cover <65% and shorter trees (p75 < 7.5 m, p25 < 2.5 m) were associated with less severe ESB infestations after 7 years, and controlling for underlying environmental factors. These findings showed the potential of bitemporal ALS data in characterizing structural changes due to ESB infestations at fine scale based on canopy cover and height, relevant for forest management strategies to better target current and future infestations.
{"title":"Characterizing forest structural changes in response to non-stand replacing disturbances using bitemporal airborne laser scanning data","authors":"Tommaso Trotto , Nicholas C. Coops , Alexis Achim , Sarah E. Gergel , Dominik Roeser","doi":"10.1016/j.srs.2024.100160","DOIUrl":"10.1016/j.srs.2024.100160","url":null,"abstract":"<div><p>Characterizing the extent, severity, and persistence of natural disturbances in forests is crucial in areas as large and heterogeneous as the Canadian boreal forest. Non-stand replacing (NSR) disturbances, in particular, can produce subtle and lagged impacts to forest canopy and structure with mechanisms that remain elusive, and they are challenging to discern using typical remote sensing approaches including aerial photointerpretation and spectral analysis of satellite imagery. Consequently, there is a need for timely and accurate information on the structural modifications due to NSR disturbances to inform proactive forest management practices. To address these needs, we leveraged a unique bitemporal airborne laser scanning (ALS) dataset to characterize changes in the forest structure caused by eastern spruce budworm (ESB, <em>Choristoneura fumiferana</em> (Clem.)), responsible for one of the greatest tree mortality in Canada. A range of infestation severity with varying impacts to forest structure are examined in a mixedwood boreal forest in Lac-Saint Jean, Quebec, Canada. We derived 14 ALS structural change metrics at 10 m spatial resolution, including height, cover, and gappiness 7 years apart (2014–2020). Six distinct structural responses to cumulative ESB infestations severity were identified using cluster analysis from the combination of the 14 change metrics, with canopy cover, the 75th and 25th height percentiles (p75-25) driving cluster separability. Canopy cover and p25 consistently decreased as cumulative infestation severity increased, whereas p75 showed greater variability across the landscape. Photointerpretation of aerial imagery over the same period confirmed the validity of the structural characterization. Further, we studied the role of initial forest structures in modulating the severity of the infestation and found that sparser canopies with cover <65% and shorter trees (p75 < 7.5 m, p25 < 2.5 m) were associated with less severe ESB infestations after 7 years, and controlling for underlying environmental factors. These findings showed the potential of bitemporal ALS data in characterizing structural changes due to ESB infestations at fine scale based on canopy cover and height, relevant for forest management strategies to better target current and future infestations.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"10 ","pages":"Article 100160"},"PeriodicalIF":5.7,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666017224000440/pdfft?md5=43b2b88984cde901116c4e7c896d1146&pid=1-s2.0-S2666017224000440-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142147831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-28DOI: 10.1016/j.srs.2024.100157
Xingyu Xu , Lin Liu , Lingcao Huang , Yan Hu
Himalayan glacial lakes have been rapidly developing and expanding in recent decades under climate change and glacier mass loss. These growing glacial lakes can produce glacial lake outburst floods (GLOFs) events with far-reaching and devastating consequences. However, the latest spatial distribution and temporal evolution of the Himalayan glacial lakes is not timely updated due to the inaccessibility of high mountain areas and the lack of an effective automated mapping method that can leverage the availability of wide-ranging remote sensing data. To frequently update glacial lake inventory in GLOF-vulnerable regions, we developed the state-of-the-art glacial lake mapping approaches based on deep learning technique and multi-source remote sensing imagery. DeepLabv3+, an advanced semantic segmentation algorithm, was trained to delineate glacial lakes with areas larger than 0.005 km2 from multi-source imagery and their derivatives, including PlanetScope red-green-blue (RGB), PlanetScope-derived Normalized Difference Water Index (NDWI), Sentinel-2 RGB, Sentinel-2-derived NDWI, Sentinel-1 Synthetic Aperture Radar (SAR), and Landsat-8 RGB images. The well-trained deep learning models achieved high mapping accuracy in the northern Bhutan test region, with the F1 score varying from 0.74 (Sentinel-1) to 0.91 (Planet-RGB) among the six types of images. We applied the well-trained models to automatically map the glacial lakes from multi-source satellite imagery. After manually cataloging the mapping results, we compiled a glacial lake inventory for the Bhutan Himalaya in 2021 that includes 2563 glacial lakes with a total area of 153.85 ± 9.33 km2. Our results demonstrated the mapping capability of deep learning on multiple satellite imagery, the key roles of PlanetScope optical images for accurate glacial lake mapping, and the essential supplementary usage of SAR images and NDWI images to complement the glacial lake inventory over Bhutan Himalaya. This study provides an advanced and transferable workflow for inventorying glacial lakes from multi-source satellite imagery, as well as provides a high-quality and comprehensive glacial lake inventory for outburst flood studies.
{"title":"Combined use of multi-source satellite imagery and deep learning for automated mapping of glacial lakes in the Bhutan Himalaya","authors":"Xingyu Xu , Lin Liu , Lingcao Huang , Yan Hu","doi":"10.1016/j.srs.2024.100157","DOIUrl":"10.1016/j.srs.2024.100157","url":null,"abstract":"<div><p>Himalayan glacial lakes have been rapidly developing and expanding in recent decades under climate change and glacier mass loss. These growing glacial lakes can produce glacial lake outburst floods (GLOFs) events with far-reaching and devastating consequences. However, the latest spatial distribution and temporal evolution of the Himalayan glacial lakes is not timely updated due to the inaccessibility of high mountain areas and the lack of an effective automated mapping method that can leverage the availability of wide-ranging remote sensing data. To frequently update glacial lake inventory in GLOF-vulnerable regions, we developed the state-of-the-art glacial lake mapping approaches based on deep learning technique and multi-source remote sensing imagery. DeepLabv3+, an advanced semantic segmentation algorithm, was trained to delineate glacial lakes with areas larger than 0.005 km<sup>2</sup> from multi-source imagery and their derivatives, including PlanetScope red-green-blue (RGB), PlanetScope-derived Normalized Difference Water Index (NDWI), Sentinel-2 RGB, Sentinel-2-derived NDWI, Sentinel-1 Synthetic Aperture Radar (SAR), and Landsat-8 RGB images. The well-trained deep learning models achieved high mapping accuracy in the northern Bhutan test region, with the F1 score varying from 0.74 (Sentinel-1) to 0.91 (Planet-RGB) among the six types of images. We applied the well-trained models to automatically map the glacial lakes from multi-source satellite imagery. After manually cataloging the mapping results, we compiled a glacial lake inventory for the Bhutan Himalaya in 2021 that includes 2563 glacial lakes with a total area of 153.85 ± 9.33 km<sup>2</sup>. Our results demonstrated the mapping capability of deep learning on multiple satellite imagery, the key roles of PlanetScope optical images for accurate glacial lake mapping, and the essential supplementary usage of SAR images and NDWI images to complement the glacial lake inventory over Bhutan Himalaya. This study provides an advanced and transferable workflow for inventorying glacial lakes from multi-source satellite imagery, as well as provides a high-quality and comprehensive glacial lake inventory for outburst flood studies.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"10 ","pages":"Article 100157"},"PeriodicalIF":5.7,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666017224000415/pdfft?md5=b774ecb5b9dc4630c1dd34d70d190478&pid=1-s2.0-S2666017224000415-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142096131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-13DOI: 10.1016/j.srs.2024.100156
Yaxiong Ma , Sucharita Gopal , Magaly Koch , Les Kaufman
Lake Kyoga is a shallow, young, flooded basin just north of and about 30m lower than Lake Victoria. The catchment encompasses Lake Kyoga itself, and a constellation of several dozen small satellite lakes following valley contours mostly to its east. The Kyoga basin fish fauna shares many non-cichlid species plus a spectacular, partially endemic radiation of haplochromine cichlids most similar to but still largely distinct from those in Lake Victoria. This fish fauna is of high conservation concern, as it preserves remnants of the regional species flock that have disappeared from Lake Victoria and Lake Kyoga, leaving small remnant populations in some of the satellite lakes. Now, these too are imperiled by limnological dynamics, including fluctuations in the nature and extent of aquatic vegetation. The water bodies in the Kyoga Basin are highly dynamic due both to fluctuation in water level and large amplitude variation in marginal and floating vegetation. This variation has profound evolutionary and conservation implications, since it can create and destroy critical aquatic habitat. It can also alternately anneal and cleave gene flow over time, both between the main lake and its satellites, and among the satellite lakes. The aquatic vegetation cluttering these linkages can create a spatial refugium for many native fish species that are more tolerant of hypoxia than an introduced macropredator, the Nile perch. Anthropogenic impacts to this region have greatly increased in recent years, altering relationships between aquatic vegetation and endangered species, fisheries and other ecosystem services provided by the lake. Understanding these dynamics require a means of mapping aquatic vegetation, connectivity, and habitat through time. Here we develop a new and improved algorithm to map the spatial distribution and dynamics of floating and emergent aquatic vegetation via remote sensing. We utilize a time series of 440 Landsat images dating from 1986 to 2020. A series of water and vegetation indices are designed to reveal change in the aquascape over time. First, two types of water masks are derived using a majority rule - a separate water mask for each image and a composite water mask of the region over the study period. Second, the difference between the two masks is then used to delineate the potential location of macrophytes over the image. Third, an algorithm is developed to separate the floating vegetation from emergent vegetation; this algorithm uses Landsat spectral bands and two additional spatial and temporal metrics that considerably improve classification accuracy. A more extensive analysis of aquascape trajectories using remote sensing can inform fish conservation strategies and fisheries management and illuminate the role of landscape dynamics in macroevolutionary patterns of aquatic taxa.
{"title":"Mapping the dynamics of aquatic vegetation in Lake Kyoga and its linkages to satellite lakes","authors":"Yaxiong Ma , Sucharita Gopal , Magaly Koch , Les Kaufman","doi":"10.1016/j.srs.2024.100156","DOIUrl":"10.1016/j.srs.2024.100156","url":null,"abstract":"<div><p>Lake Kyoga is a shallow, young, flooded basin just north of and about 30m lower than Lake Victoria. The catchment encompasses Lake Kyoga itself, and a constellation of several dozen small satellite lakes following valley contours mostly to its east. The Kyoga basin fish fauna shares many non-cichlid species plus a spectacular, partially endemic radiation of haplochromine cichlids most similar to but still largely distinct from those in Lake Victoria. This fish fauna is of high conservation concern, as it preserves remnants of the regional species flock that have disappeared from Lake Victoria and Lake Kyoga, leaving small remnant populations in some of the satellite lakes. Now, these too are imperiled by limnological dynamics, including fluctuations in the nature and extent of aquatic vegetation. The water bodies in the Kyoga Basin are highly dynamic due both to fluctuation in water level and large amplitude variation in marginal and floating vegetation. This variation has profound evolutionary and conservation implications, since it can create and destroy critical aquatic habitat. It can also alternately anneal and cleave gene flow over time, both between the main lake and its satellites, and among the satellite lakes. The aquatic vegetation cluttering these linkages can create a spatial refugium for many native fish species that are more tolerant of hypoxia than an introduced macropredator, the Nile perch. Anthropogenic impacts to this region have greatly increased in recent years, altering relationships between aquatic vegetation and endangered species, fisheries and other ecosystem services provided by the lake. Understanding these dynamics require a means of mapping aquatic vegetation, connectivity, and habitat through time. Here we develop a new and improved algorithm to map the spatial distribution and dynamics of floating and emergent aquatic vegetation via remote sensing. We utilize a time series of 440 Landsat images dating from 1986 to 2020. A series of water and vegetation indices are designed to reveal change in the aquascape over time. First, two types of water masks are derived using a majority rule - a separate water mask for each image and a composite water mask of the region over the study period. Second, the difference between the two masks is then used to delineate the potential location of macrophytes over the image. Third, an algorithm is developed to separate the floating vegetation from emergent vegetation; this algorithm uses Landsat spectral bands and two additional spatial and temporal metrics that considerably improve classification accuracy. A more extensive analysis of aquascape trajectories using remote sensing can inform fish conservation strategies and fisheries management and illuminate the role of landscape dynamics in macroevolutionary patterns of aquatic taxa.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"10 ","pages":"Article 100156"},"PeriodicalIF":5.7,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666017224000403/pdfft?md5=3c55b1abdf343849640e800c87d754c3&pid=1-s2.0-S2666017224000403-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142048732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-12DOI: 10.1016/j.srs.2024.100155
Lena Katharina Jänicke, Rene Preusker, Jürgen Fischer
The Fluorescence Explorer (FLEX) satellite will carry the high-resolution Fluorescence Imaging Spectrometer (FLORIS) that measures the complete fluorescence spectrum emitted by chlorophyll of terrestrial vegetation. This small signal must be validated. One validation approach is comparing the fluorescence signal retrieved from satellite-based measurements with ground based measurements. However, the difference in spatial resolution of the satellite and ground-based instruments and a geolocation mismatch will result in differences in the detected signal and thus, in uncertainties of the validation strategy. In a case study, we identify a representative ground site for validating the fluorescence signal by analyzing surface reflectance measurements from an aeroplane.
We define requirements of representativeness for a validation ground site in vegetated areas. Based on those requirements, we identify a suitable position within a case study in central Italy using surface reflectance data from the airborne High-Performance Airborne Imaging Spectrometer (HyPlant) measured in summer 2018. The representativeness is quantified by the relative difference between the single HyPlant pixel representing a ground-based measurement and the averaged signal of several HyPlant pixels that mimics a FLORIS pixel. With this measure, we quantify the validation uncertainty due to spatial resolution and geolocation mismatch. The effect of the temporal evolution of the surface properties on the validation uncertainty due to spatial resolution is investigated.
We select the ground site position by minimizing the validation uncertainty due to spatial resolution. Especially for wavelengths larger than 700 nm, this uncertainty is smaller than 2 % for all different reference areas. The largest differences between ground-based like measurement and satellite-like measurement of the surface reflectance is due to geolocation mismatch. The uncertainty due the geolocation mismatch is very large for wavelengths smaller than 720 nm and moderate for wavelengths larger than 720 nm. Thus, the surface reflectance at the chosen position for the validation site is not homogeneous enough for validation purpose. Considering a reference area of 13.5 × 13.5 m2, we quantify temporal stable and small uncertainties for the spectral range between 720 and 800 nm. For an all-embracing validation of the surface reflectance of vegetated areas, the chosen site is not appropriate.
{"title":"Identification of an optimal ground-based validation site for FLEX and quantification of uncertainties using airborne HyPlant data - A case study in Italy","authors":"Lena Katharina Jänicke, Rene Preusker, Jürgen Fischer","doi":"10.1016/j.srs.2024.100155","DOIUrl":"10.1016/j.srs.2024.100155","url":null,"abstract":"<div><p>The Fluorescence Explorer (FLEX) satellite will carry the high-resolution Fluorescence Imaging Spectrometer (FLORIS) that measures the complete fluorescence spectrum emitted by chlorophyll of terrestrial vegetation. This small signal must be validated. One validation approach is comparing the fluorescence signal retrieved from satellite-based measurements with ground based measurements. However, the difference in spatial resolution of the satellite and ground-based instruments and a geolocation mismatch will result in differences in the detected signal and thus, in uncertainties of the validation strategy. In a case study, we identify a representative ground site for validating the fluorescence signal by analyzing surface reflectance measurements from an aeroplane.</p><p>We define requirements of representativeness for a validation ground site in vegetated areas. Based on those requirements, we identify a suitable position within a case study in central Italy using surface reflectance data from the airborne High-Performance Airborne Imaging Spectrometer (HyPlant) measured in summer 2018. The representativeness is quantified by the relative difference between the single HyPlant pixel representing a ground-based measurement and the averaged signal of several HyPlant pixels that mimics a FLORIS pixel. With this measure, we quantify the validation uncertainty due to spatial resolution and geolocation mismatch. The effect of the temporal evolution of the surface properties on the validation uncertainty due to spatial resolution is investigated.</p><p>We select the ground site position by minimizing the validation uncertainty due to spatial resolution. Especially for wavelengths larger than 700 nm, this uncertainty is smaller than 2 % for all different reference areas. The largest differences between ground-based like measurement and satellite-like measurement of the surface reflectance is due to geolocation mismatch. The uncertainty due the geolocation mismatch is very large for wavelengths smaller than 720 nm and moderate for wavelengths larger than 720 nm. Thus, the surface reflectance at the chosen position for the validation site is not homogeneous enough for validation purpose. Considering a reference area of 13.5 × 13.5 m<sup>2</sup>, we quantify temporal stable and small uncertainties for the spectral range between 720 and 800 nm. For an all-embracing validation of the surface reflectance of vegetated areas, the chosen site is not appropriate.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"10 ","pages":"Article 100155"},"PeriodicalIF":5.7,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666017224000397/pdfft?md5=a24cd18d7ea0ce7c56e2003d521fde23&pid=1-s2.0-S2666017224000397-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141985748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}