Pub Date : 2024-12-14DOI: 10.1016/j.rse.2024.114559
Xiao Li , Zhongqiu Sun , Shan Lu , Kenji Omasa
Light photometric and polarimetric characteristics are crucial for describing the optical properties of leaf reflections, which play an essential role in investigating biochemical and surface structural trait inversion and radiative balance between vegetation and atmospheric system. Although several physical models are available, research on a comprehensive model that accounts for both photometric and polarimetric characteristics and incorporates biochemical and surface structural traits is still inadequate. In this study, we introduced PROPOLAR, a leaf model that considered leaf reflection in terms of polarized and unpolarized components and linked leaf reflection to leaf traits. PROPOLAR employed PROSPECT to simulate non-polarized component associated with biochemical traits, while used a three-parameter function (linear coefficient, refractive index factor, and roughness of leaf surface) to simulate the polarized component. The model was validated using a dataset (composed of both photometric and polarimetric measurements) collected from 533 samples of 13 plant species under various illumination-viewing geometries. The results showed that PROPOLAR outperformed PROSPECT and PROSPECULAR (a leaf model charactering BRF) in simulating light intensity (R2 = 0.98), and effectively simulated bidirectional polarization reflectance factor (BPRF) and degree of linear polarization (Dolp) across a wide spectral range (450–2300 nm) and species, with R2 = 0.92, and 0.80, respectively. Furthermore, PROPOLAR enhanced the accuracy of PROSPECT and showed comparable accuracy with PROSPECULAR in the inversion of biochemical traits from the multi-angular polarization measurements, including chlorophyll (R2 = 0.89, RMSE = 12.83 μg/cm2), equivalent water thickness (R2 = 0.90, RMSE = 0.0032 g/cm2), and leaf mass per area (R2 = 0.38, RMSE = 0.0031 g/cm2), due to the incorporation of polarization reflection and a linear coefficient during calibration. Notably, PROPOLAR can invert roughness and showed reasonable consistency with measured roughness (R2 = 0.61). These results demonstrated the effectiveness of PROPOLAR in simulating both photometric and polarimetric properties of leaf reflection, as well as its potential for biochemical and surface structural trait inversion. PROPOLAR may advance remote sensing applications in vegetation management by integrating photometric and polarimetric properties.
{"title":"A radiative transfer model for characterizing photometric and polarimetric properties of leaf reflection: Combination of PROSPECT and a polarized reflection function","authors":"Xiao Li , Zhongqiu Sun , Shan Lu , Kenji Omasa","doi":"10.1016/j.rse.2024.114559","DOIUrl":"10.1016/j.rse.2024.114559","url":null,"abstract":"<div><div>Light photometric and polarimetric characteristics are crucial for describing the optical properties of leaf reflections, which play an essential role in investigating biochemical and surface structural trait inversion and radiative balance between vegetation and atmospheric system. Although several physical models are available, research on a comprehensive model that accounts for both photometric and polarimetric characteristics and incorporates biochemical and surface structural traits is still inadequate. In this study, we introduced PROPOLAR, a leaf model that considered leaf reflection in terms of polarized and unpolarized components and linked leaf reflection to leaf traits. PROPOLAR employed PROSPECT to simulate non-polarized component associated with biochemical traits, while used a three-parameter function (linear coefficient, refractive index factor, and roughness of leaf surface) to simulate the polarized component. The model was validated using a dataset (composed of both photometric and polarimetric measurements) collected from 533 samples of 13 plant species under various illumination-viewing geometries. The results showed that PROPOLAR outperformed PROSPECT and PROSPECULAR (a leaf model charactering BRF) in simulating light intensity (R<sup>2</sup> = 0.98), and effectively simulated bidirectional polarization reflectance factor (BPRF) and degree of linear polarization (Dolp) across a wide spectral range (450–2300 nm) and species, with R<sup>2</sup> = 0.92, and 0.80, respectively. Furthermore, PROPOLAR enhanced the accuracy of PROSPECT and showed comparable accuracy with PROSPECULAR in the inversion of biochemical traits from the multi-angular polarization measurements, including chlorophyll (R<sup>2</sup> = 0.89, RMSE = 12.83 μg/cm<sup>2</sup>), equivalent water thickness (R<sup>2</sup> = 0.90, RMSE = 0.0032 g/cm<sup>2</sup>), and leaf mass per area (R<sup>2</sup> = 0.38, RMSE = 0.0031 g/cm<sup>2</sup>), due to the incorporation of polarization reflection and a linear coefficient during calibration. Notably, PROPOLAR can invert roughness and showed reasonable consistency with measured roughness (R<sup>2</sup> = 0.61). These results demonstrated the effectiveness of PROPOLAR in simulating both photometric and polarimetric properties of leaf reflection, as well as its potential for biochemical and surface structural trait inversion. PROPOLAR may advance remote sensing applications in vegetation management by integrating photometric and polarimetric properties.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114559"},"PeriodicalIF":11.1,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142820925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-14DOI: 10.1016/j.rse.2024.114562
Maquelle N. Garcia , Lucas B.S. Tameirão , Juliana Schietti , Izabela Aleixo , Tomas F. Domingues , K. Fred Huemmrich , Petya K.E. Campell , Loren P. Albert
Hydraulic traits mediate trade-offs between growth and mortality in plants yet characterizing these traits at the community level remains challenging, particularly in the Amazon, where they vary widely across species and environments. While previous studies have used reflectance-based estimates, hydraulic traits, which arise from wood and/or whole-plant anatomy and physiology, have not been comprehensively explored.
For the first time, we comprehensively investigated the use of leaf reflectance to predict hydraulic traits alongside leaf functional traits in tropical evergreen and deciduous trees. For 196 Amazonian trees, we measured water potential, leaf mass per area (LMA), leaf reflectance, hydraulic conductivity curves (e.g., P50), and wood density (WD). We examined the relationships between leaf reflectance and traits using partial least square regression (PLSR).
Our findings indicate that leaf reflectance accurately predicts variation in LMA (R2 = 0.8), and reasonably estimates xylem water potential (R2 = 0.51) and WD (R2 = 0.52). However, P50 predictions were much less reliable (R2 = 0.27), with water absorption bands greatly influencing the PLSR model. Leaf phenological strategy had little impact on the results.
These findings suggest that reflectance-based remote sensing could monitor water status and forest carbon dynamics through water potential and wood density, respectively. However, our case study applying the PLSR approach to hyperspectral canopy spectra to predict wood density revealed challenges to upscaling. Despite these limitations, remote sensing of forest hydraulic traits at scale could enhance our understanding of drought vulnerability and carbon dynamics in Amazonian forests, with significant implications for conservation.
{"title":"Predicting drought vulnerability with leaf reflectance spectra in Amazonian trees","authors":"Maquelle N. Garcia , Lucas B.S. Tameirão , Juliana Schietti , Izabela Aleixo , Tomas F. Domingues , K. Fred Huemmrich , Petya K.E. Campell , Loren P. Albert","doi":"10.1016/j.rse.2024.114562","DOIUrl":"10.1016/j.rse.2024.114562","url":null,"abstract":"<div><div>Hydraulic traits mediate trade-offs between growth and mortality in plants yet characterizing these traits at the community level remains challenging, particularly in the Amazon, where they vary widely across species and environments. While previous studies have used reflectance-based estimates, hydraulic traits, which arise from wood and/or whole-plant anatomy and physiology, have not been comprehensively explored.</div><div>For the first time, we comprehensively investigated the use of leaf reflectance to predict hydraulic traits alongside leaf functional traits in tropical evergreen and deciduous trees. For 196 Amazonian trees, we measured water potential, leaf mass per area (LMA), leaf reflectance, hydraulic conductivity curves (e.g., P50), and wood density (WD). We examined the relationships between leaf reflectance and traits using partial least square regression (PLSR).</div><div>Our findings indicate that leaf reflectance accurately predicts variation in LMA (R<sup>2</sup> = 0.8), and reasonably estimates xylem water potential (R<sup>2</sup> = 0.51) and WD (R<sup>2</sup> = 0.52). However, P50 predictions were much less reliable (R<sup>2</sup> = 0.27), with water absorption bands greatly influencing the PLSR model. Leaf phenological strategy had little impact on the results.</div><div>These findings suggest that reflectance-based remote sensing could monitor water status and forest carbon dynamics through water potential and wood density, respectively. However, our case study applying the PLSR approach to hyperspectral canopy spectra to predict wood density revealed challenges to upscaling. Despite these limitations, remote sensing of forest hydraulic traits at scale could enhance our understanding of drought vulnerability and carbon dynamics in Amazonian forests, with significant implications for conservation.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114562"},"PeriodicalIF":11.1,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142820924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-13DOI: 10.1016/j.rse.2024.114558
Antti Kukkurainen , Antti Lipponen , Ville Kolehmainen , Antti Arola , Sergio Cogliati , Neus Sabater
Remote sensing of solar-induced vegetation chlorophyll fluorescence (SIF) has a rich history of more than 50 years of research covering active and passive techniques from leaf, canopy, and satellite scale. Current satellite-derived SIF products primarily focus on the far-red spectral range, with variations in techniques dependent on sensor capabilities. However, these retrieval methods often rely on parametric spectral models and are constrained to narrow absorption regions. In this paper, we introduce a novel Bayesian retrieval technique, referred to as SIFFI (Siffi Is For Fluorescence Inference), designed for the flexible and robust estimation of spectrally resolved fluorescence spectra. SIFFI utilizes spectral representations for both fluorescence and surface reflectance, enabling its application to distinct spectral ranges, e.g., red, far-red, and full spectral range. Also, its applicability extends to top-of-canopy (TOC) and top-of-atmosphere (TOA) measurements, with the latter being possible when auxiliary information about the atmospheric state is available. For the assessment of SIFFI, we performed an extensive proof-of-concept simulation exercise involving diverse scenarios that integrated measured leaf-level fluorescence and reflectance signals, propagated them to the TOC and TOA levels, and perturbed the resultant signal with instrument Gaussian noise to simulate realistic conditions. Additionally, we extend our assessment exercise to TOC measurements acquired by a fluorescence box (FloX) instrument during two diurnal cycles on sunlit and cloudy conditions. In all the TOC cases, simulations- and measured-based scenarios, we compared our SIF estimates with the results from two well-established methods: the improved Fraunhofer line discrimination method (iFLD) and the Spectral Fitting (SpecFit) method covering the full fluorescence spectra. Notably, our results highlight the versatility and accuracy of SIFFI in estimating spectrally resolved fluorescence, achieving Mean Absolute Error (MAE) values of 0.07 (0.09) in the TOC (TOA) simulation scenarios, improving the SpecFit method estimates, and being aligned with the iFLD method results at the oxygen bands. SIFFI represents a significant advancement in SIF retrieval, providing a robust approach that exploits the full spectral information from the red to the near-infrared regions.
{"title":"SIFFI: Bayesian solar-induced fluorescence retrieval algorithm for remote sensing of vegetation","authors":"Antti Kukkurainen , Antti Lipponen , Ville Kolehmainen , Antti Arola , Sergio Cogliati , Neus Sabater","doi":"10.1016/j.rse.2024.114558","DOIUrl":"10.1016/j.rse.2024.114558","url":null,"abstract":"<div><div>Remote sensing of solar-induced vegetation chlorophyll fluorescence (SIF) has a rich history of more than 50 years of research covering active and passive techniques from leaf, canopy, and satellite scale. Current satellite-derived SIF products primarily focus on the far-red spectral range, with variations in techniques dependent on sensor capabilities. However, these retrieval methods often rely on parametric spectral models and are constrained to narrow absorption regions. In this paper, we introduce a novel Bayesian retrieval technique, referred to as SIFFI (Siffi Is For Fluorescence Inference), designed for the flexible and robust estimation of spectrally resolved fluorescence spectra. SIFFI utilizes spectral representations for both fluorescence and surface reflectance, enabling its application to distinct spectral ranges, e.g., red, far-red, and full spectral range. Also, its applicability extends to top-of-canopy (TOC) and top-of-atmosphere (TOA) measurements, with the latter being possible when auxiliary information about the atmospheric state is available. For the assessment of SIFFI, we performed an extensive proof-of-concept simulation exercise involving diverse scenarios that integrated measured leaf-level fluorescence and reflectance signals, propagated them to the TOC and TOA levels, and perturbed the resultant signal with instrument Gaussian noise to simulate realistic conditions. Additionally, we extend our assessment exercise to TOC measurements acquired by a fluorescence box (FloX) instrument during two diurnal cycles on sunlit and cloudy conditions. In all the TOC cases, simulations- and measured-based scenarios, we compared our SIF estimates with the results from two well-established methods: the improved Fraunhofer line discrimination method (iFLD) and the Spectral Fitting (SpecFit) method covering the full fluorescence spectra. Notably, our results highlight the versatility and accuracy of SIFFI in estimating spectrally resolved fluorescence, achieving Mean Absolute Error (MAE) values of 0.07 (0.09) <span><math><mrow><mo>[</mo><mi>mW</mi><mo>/</mo><mrow><mo>(</mo><msup><mrow><mi>m</mi></mrow><mrow><mn>2</mn></mrow></msup><mspace></mspace><mi>sr</mi><mspace></mspace><mi>nm</mi><mo>)</mo></mrow><mo>]</mo></mrow></math></span> in the TOC (TOA) simulation scenarios, improving the SpecFit method estimates, and being aligned with the iFLD method results at the oxygen bands. SIFFI represents a significant advancement in SIF retrieval, providing a robust approach that exploits the full spectral information from the red to the near-infrared regions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114558"},"PeriodicalIF":11.1,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-13DOI: 10.1016/j.rse.2024.114564
Xiangyang Liu , Zhao-Liang Li , Si-Bo Duan , Pei Leng , Menglin Si
Soil and vegetation temperatures are crucial for various fields, including ecology, agriculture, and climate change. However, there remains a lack of entirely observation-based global datasets for these two component temperatures. To fill this gap, this study developed a multisource data Fusion-based global surface Soil and Vegetation Temperature retrieval method (FuSVeT). This novel method not only utilizes temporal and spatial information from MODIS data by adopting a temperature cycle model to capture temporal variation and using adjacent pixels to consider spatial differences and increase the number of equations solved, but also leverages ERA5-Land data to reduce unknown parameters, effectively compensating for the limitations of satellite observations. Its performances were comprehensively evaluated with simulated data, high-resolution satellite products, and in situ measurements, demonstrating competitive accuracy with root mean square errors below 2 K and Biases of under 1 K in most cases. Compared to previous retrieval method that relies solely on satellite-based temporal and spatial information, FuSVeT present enhanced accuracy, more complete spatial coverage, and improved computational efficiency, making it more applicable for global soil and vegetation temperature mapping. Using this method, we generated global 0.05° monthly mean soil and vegetation temperatures for January and July 2020. These data can capture more pronounced temperature heterogeneities within biomes than existing soil temperature products, indicating its superiority for global analyses. Importantly, FuSVeT can also be applied to satellite observations with higher spatiotemporal resolution, holding significant potential for providing accurate, long-term, global maps of surface soil and vegetation temperatures.
{"title":"Retrieval of global surface soil and vegetation temperatures based on multisource data fusion","authors":"Xiangyang Liu , Zhao-Liang Li , Si-Bo Duan , Pei Leng , Menglin Si","doi":"10.1016/j.rse.2024.114564","DOIUrl":"10.1016/j.rse.2024.114564","url":null,"abstract":"<div><div>Soil and vegetation temperatures are crucial for various fields, including ecology, agriculture, and climate change. However, there remains a lack of entirely observation-based global datasets for these two component temperatures. To fill this gap, this study developed a multisource data Fusion-based global surface Soil and Vegetation Temperature retrieval method (FuSVeT). This novel method not only utilizes temporal and spatial information from MODIS data by adopting a temperature cycle model to capture temporal variation and using adjacent pixels to consider spatial differences and increase the number of equations solved, but also leverages ERA5-Land data to reduce unknown parameters, effectively compensating for the limitations of satellite observations. Its performances were comprehensively evaluated with simulated data, high-resolution satellite products, and in situ measurements, demonstrating competitive accuracy with root mean square errors below 2 K and Biases of under 1 K in most cases. Compared to previous retrieval method that relies solely on satellite-based temporal and spatial information, FuSVeT present enhanced accuracy, more complete spatial coverage, and improved computational efficiency, making it more applicable for global soil and vegetation temperature mapping. Using this method, we generated global 0.05° monthly mean soil and vegetation temperatures for January and July 2020. These data can capture more pronounced temperature heterogeneities within biomes than existing soil temperature products, indicating its superiority for global analyses. Importantly, FuSVeT can also be applied to satellite observations with higher spatiotemporal resolution, holding significant potential for providing accurate, long-term, global maps of surface soil and vegetation temperatures.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114564"},"PeriodicalIF":11.1,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-12DOI: 10.1016/j.rse.2024.114570
John Xun Yang , Yong-Keun Lee , Shuyan Liu , Christopher Grassotti , Quanhua Liu (Mark) , William Blackwell , Robert Vincent Leslie , Tom Greenwald , Ralf Bennartz , Scott Braun
The NASA TROPICS mission encompasses a constellation of CubeSats equipped with microwave radiometers, dedicated to investigating tropical meteorology and storm systems. In a departure from traditional microwave sounders, the TROPICS Microwave Sounder (TMS) employs new frequencies at F-band near 118 GHz and features an additional G-band channel at 205 GHz. We have expanded the capabilities of the Microwave Integrated Retrieval System (MiRS), a state-of-the-art one-dimensional variational (1DVAR) algorithm, for the retrieval of geophysical variables with the TROPICS Pathfinder early-phase data. Here we focus on assessing the retrieved precipitation in terms of rainfall and graupel. TROPICS captures well the spatial distribution and temporal evolution of Hurricane Ida and Super Typhoon Mindulle. TROPICS depicted the eyewall replacement cycle of Mindulle as it weakened and reintensified. The global precipitation distribution and dynamics are well represented by TROPICS. We compare TROPICS with other precipitation datasets, including Global Precipitation Mission (GPM) GPM Microwave Imager (GMI) and Dual-Frequency Precipitation Radar (DPR) products. For example, when compared with GMI, MiRS TROPICS instantaneous precipitation yields a correlation coefficient of 0.5 and an RMSE of 2.0 mm/h. For graupel, MiRS TROPICS retrievals show a correlation of 0.52 and an RMSE of 0.53 kg/m2. The retrieval performance is comparable to other sensors such as the Advanced Technology Microwave Sounder (ATMS), while the higher number of channels of ATMS, including its low-frequency channels serve to better constrain retrievals. TMS observes at higher spectral frequencies than the coincident ATMS channels, showing higher sensitivity to rainfall and graupel. The TMS high-frequency channels and lower orbit allow for greater resolution of precipitation features, while lower-frequency ATMS channels excel at resolving hurricane warm-core structures. The results underscore the value of the TROPICS mission for precipitation measurement and demonstrate the successful integration of TROPICS processing capability within the MiRS retrieval algorithm framework.
{"title":"Evaluating rainfall and graupel retrieval performance of the NASA TROPICS pathfinder through the NOAA MiRS system","authors":"John Xun Yang , Yong-Keun Lee , Shuyan Liu , Christopher Grassotti , Quanhua Liu (Mark) , William Blackwell , Robert Vincent Leslie , Tom Greenwald , Ralf Bennartz , Scott Braun","doi":"10.1016/j.rse.2024.114570","DOIUrl":"10.1016/j.rse.2024.114570","url":null,"abstract":"<div><div>The NASA TROPICS mission encompasses a constellation of CubeSats equipped with microwave radiometers, dedicated to investigating tropical meteorology and storm systems. In a departure from traditional microwave sounders, the TROPICS Microwave Sounder (TMS) employs new frequencies at F-band near 118 GHz and features an additional G-band channel at 205 GHz. We have expanded the capabilities of the Microwave Integrated Retrieval System (MiRS), a state-of-the-art one-dimensional variational (1DVAR) algorithm, for the retrieval of geophysical variables with the TROPICS Pathfinder early-phase data. Here we focus on assessing the retrieved precipitation in terms of rainfall and graupel. TROPICS captures well the spatial distribution and temporal evolution of Hurricane Ida and Super Typhoon Mindulle. TROPICS depicted the eyewall replacement cycle of Mindulle as it weakened and reintensified. The global precipitation distribution and dynamics are well represented by TROPICS. We compare TROPICS with other precipitation datasets, including Global Precipitation Mission (GPM) GPM Microwave Imager (GMI) and Dual-Frequency Precipitation Radar (DPR) products. For example, when compared with GMI, MiRS TROPICS instantaneous precipitation yields a correlation coefficient of 0.5 and an RMSE of 2.0 mm/h. For graupel, MiRS TROPICS retrievals show a correlation of 0.52 and an RMSE of 0.53 kg/m<sup>2</sup>. The retrieval performance is comparable to other sensors such as the Advanced Technology Microwave Sounder (ATMS), while the higher number of channels of ATMS, including its low-frequency channels serve to better constrain retrievals. TMS observes at higher spectral frequencies than the coincident ATMS channels, showing higher sensitivity to rainfall and graupel. The TMS high-frequency channels and lower orbit allow for greater resolution of precipitation features, while lower-frequency ATMS channels excel at resolving hurricane warm-core structures. The results underscore the value of the TROPICS mission for precipitation measurement and demonstrate the successful integration of TROPICS processing capability within the MiRS retrieval algorithm framework.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114570"},"PeriodicalIF":11.1,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-12DOI: 10.1016/j.rse.2024.114563
Zanpin Xing , Lin Zhao , Lei Fan , Gabrielle De Lannoy , Xiaojing Bai , Xiangzhuo Liu , Jian Peng , Frédéric Frappart , Kun Yang , Xin Li , Zhilan Zhou , Xiaojun Li , Jiangyuan Zeng , Defu Zou , Erji Du , Chong Wang , Lingxiao Wang , Zhibin Li , Jean-Pierre Wigneron
Most existing soil moisture (SM) products from earth observations and land surface models over the Qinghai-Tibetan Plateau (QTP) have coarse resolutions or are mostly generated with high spatial resolutions based on downscaling methods. The former could hinder the applications in hydrological and ecological analyses at the regional scale and the performance of the latter could be limited by the intricate relationship between SM and downscaling factors in regions with complex topography. To address this issue, this paper aims to retrieve a 1 km SM product from 2017 to 2021 using Sentinel-1 Synthetic Aperture Radar (SAR) observations based on a semi-empirical method specific to the QTP region (SMS-1) as different from the previous downscaled SM products. The main interest in our retrievals is that the semi-empirical modeling approach allows exploring the relationships between microwave backscatters and the soil and vegetation parameters spatially based on well-defined mathematics. The SMS-1 retrievals were evaluated against the observations from five in-situ networks over the QTP and against six other existing downscaled 1 km SM products. The temporal evaluation against in-situ measurements showed that SMS-1 retrievals performed better than most 1 km SM products obtained from Machine Learning methods (median R = 0.57, ubRMSD = 0.064 m3/m3, RMSD = −0.107 m3/m3 and bias = −0.042 m3/m3) except for SMSg. Furthermore, the SMS-1 retrievals presented reasonable spatial patterns that are consistent with the spatial distribution of the grassland-type map. Our Sentinel-1 SAR-based method can therefore potentially serve as a foundation for the advance of active microwave remote sensing SM algorithm to retrieve spatially high-resolution SM.
{"title":"Retrieval of 1 km surface soil moisture from Sentinel-1 over bare soil and grassland on the Qinghai-Tibetan Plateau","authors":"Zanpin Xing , Lin Zhao , Lei Fan , Gabrielle De Lannoy , Xiaojing Bai , Xiangzhuo Liu , Jian Peng , Frédéric Frappart , Kun Yang , Xin Li , Zhilan Zhou , Xiaojun Li , Jiangyuan Zeng , Defu Zou , Erji Du , Chong Wang , Lingxiao Wang , Zhibin Li , Jean-Pierre Wigneron","doi":"10.1016/j.rse.2024.114563","DOIUrl":"10.1016/j.rse.2024.114563","url":null,"abstract":"<div><div>Most existing soil moisture (SM) products from earth observations and land surface models over the Qinghai-Tibetan Plateau (QTP) have coarse resolutions or are mostly generated with high spatial resolutions based on downscaling methods. The former could hinder the applications in hydrological and ecological analyses at the regional scale and the performance of the latter could be limited by the intricate relationship between SM and downscaling factors in regions with complex topography. To address this issue, this paper aims to retrieve a 1 km SM product from 2017 to 2021 using Sentinel-1 Synthetic Aperture Radar (SAR) observations based on a semi-empirical method specific to the QTP region (SM<sub>S-1</sub>) as different from the previous downscaled SM products. The main interest in our retrievals is that the semi-empirical modeling approach allows exploring the relationships between microwave backscatters and the soil and vegetation parameters spatially based on well-defined mathematics. The SM<sub>S-1</sub> retrievals were evaluated against the observations from five <em>in-situ</em> networks over the QTP and against six other existing downscaled 1 km SM products. The temporal evaluation against <em>in-situ</em> measurements showed that SM<sub>S-1</sub> retrievals performed better than most 1 km SM products obtained from Machine Learning methods (median <em>R</em> = 0.57, ubRMSD = 0.064 m<sup>3</sup>/m<sup>3,</sup> RMSD = −0.107 m<sup>3</sup>/m<sup>3</sup> and bias = −0.042 m<sup>3</sup>/m<sup>3</sup>) except for SM<sub>Sg</sub>. Furthermore, the SM<sub>S-1</sub> retrievals presented reasonable spatial patterns that are consistent with the spatial distribution of the grassland-type map. Our Sentinel-1 SAR-based method can therefore potentially serve as a foundation for the advance of active microwave remote sensing SM algorithm to retrieve spatially high-resolution SM.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114563"},"PeriodicalIF":11.1,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142809979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-12DOI: 10.1016/j.rse.2024.114549
Yan Wang , Xiaolin Zhu , Tao Wei , Fei Xu , Trecia Kay-Ann Williams , Helin Zhang
Accurate and timely mapping of rural settlements using medium-resolution satellite imagery, such as Landsat data, is crucial for evaluating rural infrastructure, estimating ecological service values, assessing the quality of life for rural populations, and promoting sustainable rural development. Current mapping techniques, including pixel-based and object-based classifications, primarily focus on identifying artificial surfaces, often failing to capture the complete spatial footprint of rural settlements. These settlements consist of diverse land cover elements, such as houses, roads, agricultural buildings, ponds, parks, and woodlands, which together form entities with distinct local characteristics. To address this limitation, we introduce a novel classification strategy: Entity-Based Image Analysis (EBIA). Inspired by cognitive principles of human visual perception, EBIA groups related land cover elements and differentiates settlements from their background. The key innovation of EBIA lies in its ability to incorporate semantic features within rural settlements, transforming pixel-level land cover classification results (Phase 1) into entity-level settlement mapping results (Phase 2). Our results demonstrate that EBIA effectively maps the comprehensive footprint of rural settlement entities, achieving F1 scores ranging from 0.79 to 0.88 across five globally selected experimental areas. Furthermore, EBIA can be utilized to monitor changes in rural settlements using long-term Landsat imagery. As a new classification strategy, EBIA holds potential for mapping other geographic entities.
{"title":"Entity-based image analysis: A new strategy to map rural settlements from Landsat images","authors":"Yan Wang , Xiaolin Zhu , Tao Wei , Fei Xu , Trecia Kay-Ann Williams , Helin Zhang","doi":"10.1016/j.rse.2024.114549","DOIUrl":"10.1016/j.rse.2024.114549","url":null,"abstract":"<div><div>Accurate and timely mapping of rural settlements using medium-resolution satellite imagery, such as Landsat data, is crucial for evaluating rural infrastructure, estimating ecological service values, assessing the quality of life for rural populations, and promoting sustainable rural development. Current mapping techniques, including pixel-based and object-based classifications, primarily focus on identifying artificial surfaces, often failing to capture the complete spatial footprint of rural settlements. These settlements consist of diverse land cover elements, such as houses, roads, agricultural buildings, ponds, parks, and woodlands, which together form entities with distinct local characteristics. To address this limitation, we introduce a novel classification strategy: Entity-Based Image Analysis (EBIA). Inspired by cognitive principles of human visual perception, EBIA groups related land cover elements and differentiates settlements from their background. The key innovation of EBIA lies in its ability to incorporate semantic features within rural settlements, transforming pixel-level land cover classification results (Phase 1) into entity-level settlement mapping results (Phase 2). Our results demonstrate that EBIA effectively maps the comprehensive footprint of rural settlement entities, achieving F1 scores ranging from 0.79 to 0.88 across five globally selected experimental areas. Furthermore, EBIA can be utilized to monitor changes in rural settlements using long-term Landsat imagery. As a new classification strategy, EBIA holds potential for mapping other geographic entities.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114549"},"PeriodicalIF":11.1,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142809720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-12DOI: 10.1016/j.rse.2024.114547
Francisco Mena , Deepak Pathak , Hiba Najjar , Cristhian Sanchez , Patrick Helber , Benjamin Bischke , Peter Habelitz , Miro Miranda , Jayanth Siddamsetty , Marlon Nuske , Marcela Charfuelan , Diego Arenas , Michaela Vollmer , Andreas Dengel
Accurate crop yield prediction is of utmost importance for informed decision-making in agriculture, aiding farmers, industry stakeholders, and policymakers in optimizing agricultural practices. However, this task is complex and depends on multiple factors, such as environmental conditions, soil properties, and management practices. Leveraging Remote Sensing (RS) technologies, multi-modal data from diverse global data sources can be collected to enhance predictive model accuracy. However, combining heterogeneous RS data poses a fusion challenge, like identifying the specific contribution of each modality in the predictive task. In this paper, we present a novel multi-modal learning approach to predict crop yield for different crops (soybean, wheat, rapeseed) and regions (Argentina, Uruguay, and Germany). Our multi-modal input data includes multi-spectral optical images from Sentinel-2 satellites and weather data as dynamic features during the crop growing season, complemented by static features like soil properties and topographic information. To effectively fuse the multi-modal data, we introduce a Multi-modal Gated Fusion (MMGF) model, comprising dedicated modality-encoders and a Gated Unit (GU) module. The modality-encoders handle the heterogeneity of data sources with varying temporal resolutions by learning a modality-specific representation. These representations are adaptively fused via a weighted sum. The fusion weights are computed for each sample by the GU using a concatenation of the multi-modal representations. The MMGF model is trained at sub-field level with 10 m resolution pixels. Our evaluations show that the MMGF outperforms conventional models on the same task, achieving the best results by incorporating all the data sources, unlike the usual fusion results in the literature. For Argentina, the MMGF model achieves an value of 0.68 at sub-field yield prediction, while at the field level evaluation (comparing field averages), it reaches around 0.80 across different countries. The GU module learned different weights based on the country and crop-type, aligning with the variable significance of each data source to the prediction task. This novel method has proven its effectiveness in enhancing the accuracy of the challenging sub-field crop yield prediction. Our investigation indicates that the gated fusion approach promises a significant advancement in the field of agriculture and precision farming.
{"title":"Adaptive fusion of multi-modal remote sensing data for optimal sub-field crop yield prediction","authors":"Francisco Mena , Deepak Pathak , Hiba Najjar , Cristhian Sanchez , Patrick Helber , Benjamin Bischke , Peter Habelitz , Miro Miranda , Jayanth Siddamsetty , Marlon Nuske , Marcela Charfuelan , Diego Arenas , Michaela Vollmer , Andreas Dengel","doi":"10.1016/j.rse.2024.114547","DOIUrl":"10.1016/j.rse.2024.114547","url":null,"abstract":"<div><div>Accurate crop yield prediction is of utmost importance for informed decision-making in agriculture, aiding farmers, industry stakeholders, and policymakers in optimizing agricultural practices. However, this task is complex and depends on multiple factors, such as environmental conditions, soil properties, and management practices. Leveraging Remote Sensing (RS) technologies, multi-modal data from diverse global data sources can be collected to enhance predictive model accuracy. However, combining heterogeneous RS data poses a fusion challenge, like identifying the specific contribution of each modality in the predictive task. In this paper, we present a novel multi-modal learning approach to predict crop yield for different crops (soybean, wheat, rapeseed) and regions (Argentina, Uruguay, and Germany). Our multi-modal input data includes multi-spectral optical images from Sentinel-2 satellites and weather data as dynamic features during the crop growing season, complemented by static features like soil properties and topographic information. To effectively fuse the multi-modal data, we introduce a Multi-modal Gated Fusion (MMGF) model, comprising dedicated modality-encoders and a Gated Unit (GU) module. The modality-encoders handle the heterogeneity of data sources with varying temporal resolutions by learning a modality-specific representation. These representations are adaptively fused via a weighted sum. The <em>fusion</em> weights are computed for each sample by the GU using a concatenation of the multi-modal representations. The MMGF model is trained at sub-field level with 10 m resolution pixels. Our evaluations show that the MMGF outperforms conventional models on the same task, achieving the best results by incorporating all the data sources, unlike the usual fusion results in the literature. For Argentina, the MMGF model achieves an <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> value of 0.68 at sub-field yield prediction, while at the field level evaluation (comparing field averages), it reaches around 0.80 across different countries. The GU module learned different weights based on the country and crop-type, aligning with the variable significance of each data source to the prediction task. This novel method has proven its effectiveness in enhancing the accuracy of the challenging sub-field crop yield prediction. Our investigation indicates that the gated fusion approach promises a significant advancement in the field of agriculture and precision farming.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114547"},"PeriodicalIF":11.1,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-12DOI: 10.1016/j.rse.2024.114518
Dan J. Dixon, Yunzhe Zhu, Yufang Jin
<div><div>Canopy height mapping is critical for assessing forest structure, forest resilience, carbon stocks, habitat, and biodiversity, all of which are threatened by changing climate and weather extremes. While current tools utilizing lidar (e.g., GEDI) and multispectral imagery (e.g., Landsat, Sentinel-2, airborne imagery) produce canopy height products, significant challenges remain, particularly in capturing fine-scale spatial details across large areas with high frequency. PlanetScope CubeSat imagery, with its 3 m spatial resolution and near-daily frequency, offers a unique opportunity to estimate woody plant structure by capturing fine-scale texture and temporal patterns that shift throughout the year. In this study, we adapted a 3D Spatio-Temporal Convolutional Neural Network (ST-CNN) to estimate canopy height at 3 m resolution, utilizing sequential PlanetScope time series over five months, summer Sentinel-1 radar imagery, and solar illumination layers as inputs. We generated a large and diverse reference database covering 2,296 sample scenes (each scene = 768 × 768 m, totaling <span><math><mo>∼</mo></math></span>135,000 ha) using a semi-automatic labeling process that leverages 23 aerial lidar surveys conducted in California between 2016 and 2021. Trained on a random selection of 2,046 scenes, the accuracy assessment on the remaining 250 scenes demonstrates strong performance across various ecoregions, capturing 80.8% of the observed variance in live canopy height with a mean absolute error (MAE) of 3.6 m and a bias of -0.53 m compared with aerial lidar. Analysis of all 681 GEDI footprints over the same testing scenes estimates the MAE of 6.5 m, bias of -1.82 m, and R<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> of 0.58 for the GEDI L2A Vector Canopy Top Height RH98 product. The ST-CNN model accurately identifies heterogeneous canopy structures, and shows sensitivity to canopies reaching 50 to 60 m in height. We found a major contribution from the PlanetScope time series, compared to a single PlanetScope image, and marginal benefits of including Sentinel-1 and terrain-based solar irradiance layers to improve performance on dense canopies or diverse topography. Example applications demonstrate the ability to generalize to different years, maintaining consistent predictions between years and capturing changes in canopy height over a seven year period (2017–2023) within 400 plots representing regrowth, minimal change, selective logging, and clear cut areas. We also demonstrate improved canopy height estimation compared to existing products from Landsat (MAE = 8.41 m) and Sentinel-2 (MAE = 7.19 m). A visualization tool displays our data alongside existing products for the Sierra Nevada in 2022. The Planet ST-CNN model, using a 15-day PlanetScope satellite time series, offers a scalable approach for annual canopy height estimation in California, achieving a high level of detail, often down to the resolution of in
{"title":"Canopy height estimation from PlanetScope time series with spatio-temporal deep learning","authors":"Dan J. Dixon, Yunzhe Zhu, Yufang Jin","doi":"10.1016/j.rse.2024.114518","DOIUrl":"10.1016/j.rse.2024.114518","url":null,"abstract":"<div><div>Canopy height mapping is critical for assessing forest structure, forest resilience, carbon stocks, habitat, and biodiversity, all of which are threatened by changing climate and weather extremes. While current tools utilizing lidar (e.g., GEDI) and multispectral imagery (e.g., Landsat, Sentinel-2, airborne imagery) produce canopy height products, significant challenges remain, particularly in capturing fine-scale spatial details across large areas with high frequency. PlanetScope CubeSat imagery, with its 3 m spatial resolution and near-daily frequency, offers a unique opportunity to estimate woody plant structure by capturing fine-scale texture and temporal patterns that shift throughout the year. In this study, we adapted a 3D Spatio-Temporal Convolutional Neural Network (ST-CNN) to estimate canopy height at 3 m resolution, utilizing sequential PlanetScope time series over five months, summer Sentinel-1 radar imagery, and solar illumination layers as inputs. We generated a large and diverse reference database covering 2,296 sample scenes (each scene = 768 × 768 m, totaling <span><math><mo>∼</mo></math></span>135,000 ha) using a semi-automatic labeling process that leverages 23 aerial lidar surveys conducted in California between 2016 and 2021. Trained on a random selection of 2,046 scenes, the accuracy assessment on the remaining 250 scenes demonstrates strong performance across various ecoregions, capturing 80.8% of the observed variance in live canopy height with a mean absolute error (MAE) of 3.6 m and a bias of -0.53 m compared with aerial lidar. Analysis of all 681 GEDI footprints over the same testing scenes estimates the MAE of 6.5 m, bias of -1.82 m, and R<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> of 0.58 for the GEDI L2A Vector Canopy Top Height RH98 product. The ST-CNN model accurately identifies heterogeneous canopy structures, and shows sensitivity to canopies reaching 50 to 60 m in height. We found a major contribution from the PlanetScope time series, compared to a single PlanetScope image, and marginal benefits of including Sentinel-1 and terrain-based solar irradiance layers to improve performance on dense canopies or diverse topography. Example applications demonstrate the ability to generalize to different years, maintaining consistent predictions between years and capturing changes in canopy height over a seven year period (2017–2023) within 400 plots representing regrowth, minimal change, selective logging, and clear cut areas. We also demonstrate improved canopy height estimation compared to existing products from Landsat (MAE = 8.41 m) and Sentinel-2 (MAE = 7.19 m). A visualization tool displays our data alongside existing products for the Sierra Nevada in 2022. The Planet ST-CNN model, using a 15-day PlanetScope satellite time series, offers a scalable approach for annual canopy height estimation in California, achieving a high level of detail, often down to the resolution of in","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114518"},"PeriodicalIF":11.1,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142809719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-12DOI: 10.1016/j.rse.2024.114543
Fanghui Deng , Mark Zumberge
Space geodetic techniques have achieved centimeter to even millimeter precision in measuring earth surface deformation. However, a large data gap remains in the offshore area. Offshore man-made structures (e.g., oil/gas platforms) anchored to the ocean bottom provide an opportunity to study seafloor motion in some areas. Although satellite InSAR (Interferometric Synthetic Aperture Radar) has been widely used to study earth surface deformation, its application to offshore regions is extremely limited. Continuous GNSS (Global Navigation Satellite System) observations at several tens of offshore platforms in the Adriatic Sea have recently been released. Measuring the same platforms with InSAR provides a great opportunity to assess the feasibility of applying this technique to study seafloor motion on a regional scale using offshore structures. We processed a six-year-long time series of SAR images from the Sentinel-1A satellite using the Permanent Scatterer InSAR (PS-InSAR) method. We assessed the feasibility of phase unwrapping using synthetic data with different velocity fields and noise levels. Correct phase unwrapping could be achieved in the Adriatic Sea and two other large offshore oil/gas fields: the Gulf of Mexico and the North Sea. Different calibration strategies were applied, and we suggest that the InSAR results could be calibrated with limited and even no GNSS stations. Our InSAR results show good agreement with the GNSS measurements and the InSAR observations from the European Ground Motion Service. In addition, our InSAR results provide deformation measurements at about twenty offshore structures where GNSS stations are not present. Most of the offshore structures have a subsidence rate of no more than 5 mm/year, while a few of them reach about 10 mm/year. Our work demonstrates that it is feasible to apply the InSAR technique to measure displacement of discrete offshore man-made structures (fixed to the ocean bottom) on a regional scale but still on a case-by-case basis. Pre-acquired information including geological settings, existing geodetic observations, and human activity records (e.g., hydrocarbon production) are useful information to assess the feasibility and to validate the InSAR results.
{"title":"Seafloor motion from offshore man-made structures using satellite radar images – A case study in the Adriatic Sea","authors":"Fanghui Deng , Mark Zumberge","doi":"10.1016/j.rse.2024.114543","DOIUrl":"10.1016/j.rse.2024.114543","url":null,"abstract":"<div><div>Space geodetic techniques have achieved centimeter to even millimeter precision in measuring earth surface deformation. However, a large data gap remains in the offshore area. Offshore man-made structures (e.g., oil/gas platforms) anchored to the ocean bottom provide an opportunity to study seafloor motion in some areas. Although satellite InSAR (Interferometric Synthetic Aperture Radar) has been widely used to study earth surface deformation, its application to offshore regions is extremely limited. Continuous GNSS (Global Navigation Satellite System) observations at several tens of offshore platforms in the Adriatic Sea have recently been released. Measuring the same platforms with InSAR provides a great opportunity to assess the feasibility of applying this technique to study seafloor motion on a regional scale using offshore structures. We processed a six-year-long time series of SAR images from the Sentinel-1A satellite using the Permanent Scatterer InSAR (PS-InSAR) method. We assessed the feasibility of phase unwrapping using synthetic data with different velocity fields and noise levels. Correct phase unwrapping could be achieved in the Adriatic Sea and two other large offshore oil/gas fields: the Gulf of Mexico and the North Sea. Different calibration strategies were applied, and we suggest that the InSAR results could be calibrated with limited and even no GNSS stations. Our InSAR results show good agreement with the GNSS measurements and the InSAR observations from the European Ground Motion Service. In addition, our InSAR results provide deformation measurements at about twenty offshore structures where GNSS stations are not present. Most of the offshore structures have a subsidence rate of no more than 5 mm/year, while a few of them reach about 10 mm/year. Our work demonstrates that it is feasible to apply the InSAR technique to measure displacement of discrete offshore man-made structures (fixed to the ocean bottom) on a regional scale but still on a case-by-case basis. Pre-acquired information including geological settings, existing geodetic observations, and human activity records (e.g., hydrocarbon production) are useful information to assess the feasibility and to validate the InSAR results.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114543"},"PeriodicalIF":11.1,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142809718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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