cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.29148229.v1
title: Understanding discrepancies in soil moisture from SMAP and AMSR2: insights into performance and dry-down behavior

description: Understanding the dynamic changes in soil moisture (SM) is crucial for studying land–atmosphere interactions in hydrometeorology. While numerous SM datasets have been developed for passive microwave remote sensing systems operating at various frequencies, the consistency of SM dry-down patterns observed by different sensors remains uncertain. Additionally, the use of distinct algorithms across SM products complicates direct comparisons. This study addresses these issues by producing two new enhanced-resolution (approximately 10-km) SM datasets using the same multi-channel collaborative algorithm (MCCA). These datasets are retrieved from the L-band brightness temperature (Tb) from Soil Moisture Active Passive (SMAP) and the C/X/Ku-band Tb from Advanced Microwave Scanning Radiometer 2 (AMSR2), referred to as MCCA SMAP and MCCA AMSR2. The satellite-derived SM data are evaluated and compared using 40 globally distributed SM observation networks at both regional (dense network) and grid scales. The results indicate that the Pearson correlation coefficients (R) of MCCA SMAP and MCCA AMSR2 SM in 23 dense networks are 0.795 and 0.664, respectively, with unbiased root-mean-square deviation (ubRMSD) of 0.041 m<sup>3</sup>/m<sup>3</sup> and 0.048 m<sup>3</sup>/m<sup>3</sup>, respectively. Both datasets perform better at the regional scale than at the grid scale. The analysis shows that SMAP outperforms AMSR2 overall, although the sensing capabilities of both payloads decline with increasing vegetation water content (VWC). Further analysis of global SM dry-down patterns reveals minimal differences in the magnitude of SM dry-down between the two payloads, but slightly larger differences in the effective wilting point. A notable disparity was observed in SM memory, with SMAP exhibiting a significantly longer memory than AMSR2. Analysis of SM loss rates shows that SMAP has a lower loss rate compared to AMSR2, consistent with the theoretical expectation that lower-frequency observations, such as those from SMAP, observes deeper soil layers. These results highlight the importance of considering differences in payload configurations when using remote sensing SM products for studies of land–atmosphere interactions in hydrometeorology and for improving land surface models (LSMs).
DOI: 10.6084/m9.figshare.29253829
title: A virtual coastal band-driven optimization-based bathymetric approach (VOBA) for optically shallow water with multispectral imagery

description: Bathymetry mapping is important for detecting underwater topography. For optically shallow waters, the use of satellite-based high-spatial-resolution imagery (Such as Gaofen-2, Ziyuan-3, Worldview-2) and historical medium-spatial-resolution imagery (Landsat-5 thematic mapper (TM) and Landsat-7 enhanced thematic mapper plus (ETM+)) has great potential for detailed bathymetric information mapping and monitoring bathymetric temporal variations, respectively. However, some of the above-mentioned imagery provides only three visible bands (blue, green and red bands) for bathymetry mapping, such as Gaofen-2, Ziyuan-3, TM, ETM+. This limitation restricts the accuracy of optimization-based bathymetric approaches, which can estimate water depth without in situ bathymetric data. Therefore, a simple linear relationship was established using in situ reflectance data of optically shallow waters from four different sites in China to derive a virtual coastal band (~445 nm) of Landsat-8 from the adjacent blue band. Then, the virtual coastal band, along with the original three visible bands, was employed to estimate water depth using the optimization-based bathymetric approach. The new approach, called the virtual coastal band-driven optimization-based bathymetric approach (VOBA), was validated with in situ reflectance data, 3 simulated reflectance data sets, and 18 satellite-based images (6 high-spatial-resolution images of Gaofen-2, Ziyuan-3, and Worldview-2 for 4 study sites in China and 12 TM or ETM+ images for 12 different sites worldwide) that cover similar optically shallow waters. The comparison results indicate that VOBA can significantly improve the accuracy of the estimated water depth, especially for optically shallow water at depths greater than 5 m. Additionally, we showcased the potential of the VOBA for monitoring underwater topographic temporal changes by applying it to TM data (1987–2011) for two sites in China.
DOI: 10.6084/m9.figshare.29253829.v1
title: A virtual coastal band-driven optimization-based bathymetric approach (VOBA) for optically shallow water with multispectral imagery

description: Bathymetry mapping is important for detecting underwater topography. For optically shallow waters, the use of satellite-based high-spatial-resolution imagery (Such as Gaofen-2, Ziyuan-3, Worldview-2) and historical medium-spatial-resolution imagery (Landsat-5 thematic mapper (TM) and Landsat-7 enhanced thematic mapper plus (ETM+)) has great potential for detailed bathymetric information mapping and monitoring bathymetric temporal variations, respectively. However, some of the above-mentioned imagery provides only three visible bands (blue, green and red bands) for bathymetry mapping, such as Gaofen-2, Ziyuan-3, TM, ETM+. This limitation restricts the accuracy of optimization-based bathymetric approaches, which can estimate water depth without in situ bathymetric data. Therefore, a simple linear relationship was established using in situ reflectance data of optically shallow waters from four different sites in China to derive a virtual coastal band (~445 nm) of Landsat-8 from the adjacent blue band. Then, the virtual coastal band, along with the original three visible bands, was employed to estimate water depth using the optimization-based bathymetric approach. The new approach, called the virtual coastal band-driven optimization-based bathymetric approach (VOBA), was validated with in situ reflectance data, 3 simulated reflectance data sets, and 18 satellite-based images (6 high-spatial-resolution images of Gaofen-2, Ziyuan-3, and Worldview-2 for 4 study sites in China and 12 TM or ETM+ images for 12 different sites worldwide) that cover similar optically shallow waters. The comparison results indicate that VOBA can significantly improve the accuracy of the estimated water depth, especially for optically shallow water at depths greater than 5 m. Additionally, we showcased the potential of the VOBA for monitoring underwater topographic temporal changes by applying it to TM data (1987–2011) for two sites in China.
DOI: 10.6084/m9.figshare.29306975
title: Forest cover and canopy health mapping in Australian subalpine landscape: supervised machine learning models for Sentinel-2 and Landsat images

description: Forests and woodlands worldwide appear to be increasingly vulnerable to decline and mortality, endangering forest-reliant biodiversity and ecosystem services. Consistent with this vulnerability, increased fire frequency and widespread insect-induced dieback has been reported across the subalpine woodlands of southeastern Australia. High-quality spatiotemporal information on the occurrence of the dieback is, however, scarce. This study aimed to develop a method to map distribution of subalpine woodlands and assess their canopy health using Sentinel-2 and Landsat series multispectral satellite imagery. Three-month growing-season geometric median surface reflectance was used for pixel-based supervised classification and regression. Forest masking models were trained on aerial LiDAR-derived forest cover. Canopy condition regression models were trained on ground-based assessment data surveyed at 85 vegetation plots. We tested random-forest (RF), support vector machine (SVM), and multiple linear regression (MLR) to find the algorithm that provides the best accuracy. Cross-validation experiments were undertaken to optimize the model configurations. Using Sentinel-2 surface reflectance, the best forest masking model was SVM (OA = 0.849 ± 0.003), while MLR performed best for canopy condition (<i>r</i><sup><i>2</i></sup> = 0.73 ± 0.07). The models built on Landsat surface reflectance performed similarly. The accuracy within our study area is greater than those of existing global/continental satellite-derived forest cover maps. In conclusion, constraining the region of interest and optimizing model configurations can improve forest cover mapping. Integrated with canopy condition regression, our method can identify forest cover and assess canopy condition of Australian subalpine woodlands with reasonable accuracies.
DOI: 10.6084/m9.figshare.29306975.v1
title: Forest cover and canopy health mapping in Australian subalpine landscape: supervised machine learning models for Sentinel-2 and Landsat images

description: Forests and woodlands worldwide appear to be increasingly vulnerable to decline and mortality, endangering forest-reliant biodiversity and ecosystem services. Consistent with this vulnerability, increased fire frequency and widespread insect-induced dieback has been reported across the subalpine woodlands of southeastern Australia. High-quality spatiotemporal information on the occurrence of the dieback is, however, scarce. This study aimed to develop a method to map distribution of subalpine woodlands and assess their canopy health using Sentinel-2 and Landsat series multispectral satellite imagery. Three-month growing-season geometric median surface reflectance was used for pixel-based supervised classification and regression. Forest masking models were trained on aerial LiDAR-derived forest cover. Canopy condition regression models were trained on ground-based assessment data surveyed at 85 vegetation plots. We tested random-forest (RF), support vector machine (SVM), and multiple linear regression (MLR) to find the algorithm that provides the best accuracy. Cross-validation experiments were undertaken to optimize the model configurations. Using Sentinel-2 surface reflectance, the best forest masking model was SVM (OA = 0.849 ± 0.003), while MLR performed best for canopy condition (<i>r</i><sup><i>2</i></sup> = 0.73 ± 0.07). The models built on Landsat surface reflectance performed similarly. The accuracy within our study area is greater than those of existing global/continental satellite-derived forest cover maps. In conclusion, constraining the region of interest and optimizing model configurations can improve forest cover mapping. Integrated with canopy condition regression, our method can identify forest cover and assess canopy condition of Australian subalpine woodlands with reasonable accuracies.
DOI: 10.6084/m9.figshare.30921210
title: Remotely sensed monitoring of coastal geomorphology: coupling satellite-derived vegetation edges with other proxy metrics

description: Building an understanding of how coastal processes interact is key for modelling future change, particularly in a rapidly changing climate with rising sea levels. Coastal change indicators can offer a wealth of information on past and potential future shoreface trends, but have been traditionally difficult and costly for users to acquire. Novel, open-source, coastal monitoring tools using satellite imagery now present an exciting opportunity for generating diverse timeseries to fuel coastal digital twins, with previously unseen frequency. However, focus on satellite-derived waterlines has led to a monitoring bias towards the intertidal zone, and a lack of regular records of upper shoreface change. Coastal vegetation edges can offer a less noisy geomorphic proxy of the upper shoreface, but there has thus far been an absence of event–seasonal, satellite-driven analyses of the vegetation edge, and its process links to coastal metrics. Using the novel Python toolkits VedgeSat and CoastSat powered by cloud-based data processing, neural network pixel classifiers, and dynamic thresholding, we automatically extracted 467142 measurements of vegetation edge and instantaneous waterline positions from Sentinel-2 imagery. For the first time, near-weekly change from 2015–2025, and storm response and seasonal variability, are quantified remotely for waterlines and vegetation edges along ~1400 cross-shore transects at an environmentally varied Scottish coast. We also define here the vegetation transition zone, which represents a spectrally overlapping region between vegetation and bare sediment pixels, characterised by juvenile and/or patch vegetation cover. We found strong seasonal signals and wider transition zones where vegetation is accreting, and narrower transition zones across steeper dune slopes where vegetation edges and waterlines are eroding. These insights can help coastal managers understand longer-term geomorphic trends and upper shoreface resilience using simple, satellite-derived proxy indicators. Copernicus Marine Service wave hindcasts and public topography data were also incorporated via the new copernicusmarine API, to allow rapid investigations of hydrodynamic forcings on vegetation and sediment. We demonstrate the value of remote sensing for gathering large and varied coastal datasets near-globally with a multivariate analysis, capturing both the spatial distribution of coastal metrics over time and the temporal correlation between metrics on each transect. These remotely sensed, decade-long timeseries are used to quantify a strong link between wave-driven sediment transport, intertidal topography, and vegetation (with a mean Spearman rank of 0.7). The analysis is scalable and applicable to other complex environmental systems via the open-source Python toolkit CoasTrack, demonstrating the potential for global transferability. These relationships fuelled by Big Data can inform environmental model domains, for real-time coastal change predictions.
DOI: 10.6084/m9.figshare.30921210.v1
title: Remotely sensed monitoring of coastal geomorphology: coupling satellite-derived vegetation edges with other proxy metrics

description: Building an understanding of how coastal processes interact is key for modelling future change, particularly in a rapidly changing climate with rising sea levels. Coastal change indicators can offer a wealth of information on past and potential future shoreface trends, but have been traditionally difficult and costly for users to acquire. Novel, open-source, coastal monitoring tools using satellite imagery now present an exciting opportunity for generating diverse timeseries to fuel coastal digital twins, with previously unseen frequency. However, focus on satellite-derived waterlines has led to a monitoring bias towards the intertidal zone, and a lack of regular records of upper shoreface change. Coastal vegetation edges can offer a less noisy geomorphic proxy of the upper shoreface, but there has thus far been an absence of event–seasonal, satellite-driven analyses of the vegetation edge, and its process links to coastal metrics. Using the novel Python toolkits VedgeSat and CoastSat powered by cloud-based data processing, neural network pixel classifiers, and dynamic thresholding, we automatically extracted 467142 measurements of vegetation edge and instantaneous waterline positions from Sentinel-2 imagery. For the first time, near-weekly change from 2015–2025, and storm response and seasonal variability, are quantified remotely for waterlines and vegetation edges along ~1400 cross-shore transects at an environmentally varied Scottish coast. We also define here the vegetation transition zone, which represents a spectrally overlapping region between vegetation and bare sediment pixels, characterised by juvenile and/or patch vegetation cover. We found strong seasonal signals and wider transition zones where vegetation is accreting, and narrower transition zones across steeper dune slopes where vegetation edges and waterlines are eroding. These insights can help coastal managers understand longer-term geomorphic trends and upper shoreface resilience using simple, satellite-derived proxy indicators. Copernicus Marine Service wave hindcasts and public topography data were also incorporated via the new copernicusmarine API, to allow rapid investigations of hydrodynamic forcings on vegetation and sediment. We demonstrate the value of remote sensing for gathering large and varied coastal datasets near-globally with a multivariate analysis, capturing both the spatial distribution of coastal metrics over time and the temporal correlation between metrics on each transect. These remotely sensed, decade-long timeseries are used to quantify a strong link between wave-driven sediment transport, intertidal topography, and vegetation (with a mean Spearman rank of 0.7). The analysis is scalable and applicable to other complex environmental systems via the open-source Python toolkit CoasTrack, demonstrating the potential for global transferability. These relationships fuelled by Big Data can inform environmental model domains, for real-time coastal change predictions.
DOI: 10.6084/m9.figshare.28652546
title: A hybrid approach for enhanced tree volume estimation of complex trees using terrestrial LiDAR

description: Accurate forest biomass estimation is crucial for ecological research and policymaking, yet traditional methods relying on allometric equations often struggle with structural variability in diverse forest ecosystems. Quantitative Structure Models (QSMs) using terrestrial LiDAR have improved tree volume estimation, but existing models face challenges in accurately representing complex tree geometries, particularly buttressed trees with non-cylindrical trunks. This study evaluates four widely used QSMs – TreeQSM, AdQSM, AdTree, and SimpleForest – using terrestrial LiDAR point clouds from 191 trees. Results indicate that TreeQSM achieves the lowest Mean Absolute Percentage Deviation (MAPD) of 18.16%, followed by AdQSM (25.23%), while AdTree (31.22%) and SimpleForest (56.86%) exhibit higher errors. The consistent residuals and an R<sup>2</sup> value of 0.94 across a diverse dataset underscore the robustness of TreeQSM. Additionally, TreeQSM’s built-in noise filtering algorithm enhances its adaptability to noisy data, achieving a significant reduction in MAPD from 51.7% to 12.05% in datasets affected by noise. This study also investigates the challenges of modeling noisy data and the consequential loss of small branches by the noise filtering algorithm. Delving into the contribution and relevance of higher-order branches, the study reveals their marginal impact – accounting for approximately 5.2% of the total tree volume (including both stems and branches) – while also highlighting the increased uncertainty in volume estimations caused by their inclusion. These findings advocate for a focused modeling approach that emphasizes primary tree structures, potentially omitting detailed rendering of higher-order branches (i.e. those beyond the 2nd order) to maintain accuracy. In addressing the inadequacies of traditional QSM models in handling trees with complex geometries, this study introduces a novel hybrid approach, integrating Surface Reconstruction and QSM techniques. This approach significantly improves volume estimation for trees with complex geometries, such as buttresses. This method is fully automated, achieving a detection accuracy of 99% for buttressed trees and reducing the MAPD to 14.68%, a substantial improvement over the 21.35% MAPD observed with traditional TreeQSM applications. By advancing the understanding of the potential and limitations of different QSM models and introducing a hybrid modeling technique, this research contributes to enhanced forest carbon stock assessments. It offers a nuanced, precise, and adaptable strategy for volume estimation, thereby significantly aiding forestry management and ecological conservation efforts.
DOI: 10.6084/m9.figshare.28652546.v1
title: A hybrid approach for enhanced tree volume estimation of complex trees using terrestrial LiDAR

description: Accurate forest biomass estimation is crucial for ecological research and policymaking, yet traditional methods relying on allometric equations often struggle with structural variability in diverse forest ecosystems. Quantitative Structure Models (QSMs) using terrestrial LiDAR have improved tree volume estimation, but existing models face challenges in accurately representing complex tree geometries, particularly buttressed trees with non-cylindrical trunks. This study evaluates four widely used QSMs – TreeQSM, AdQSM, AdTree, and SimpleForest – using terrestrial LiDAR point clouds from 191 trees. Results indicate that TreeQSM achieves the lowest Mean Absolute Percentage Deviation (MAPD) of 18.16%, followed by AdQSM (25.23%), while AdTree (31.22%) and SimpleForest (56.86%) exhibit higher errors. The consistent residuals and an R<sup>2</sup> value of 0.94 across a diverse dataset underscore the robustness of TreeQSM. Additionally, TreeQSM’s built-in noise filtering algorithm enhances its adaptability to noisy data, achieving a significant reduction in MAPD from 51.7% to 12.05% in datasets affected by noise. This study also investigates the challenges of modeling noisy data and the consequential loss of small branches by the noise filtering algorithm. Delving into the contribution and relevance of higher-order branches, the study reveals their marginal impact – accounting for approximately 5.2% of the total tree volume (including both stems and branches) – while also highlighting the increased uncertainty in volume estimations caused by their inclusion. These findings advocate for a focused modeling approach that emphasizes primary tree structures, potentially omitting detailed rendering of higher-order branches (i.e. those beyond the 2nd order) to maintain accuracy. In addressing the inadequacies of traditional QSM models in handling trees with complex geometries, this study introduces a novel hybrid approach, integrating Surface Reconstruction and QSM techniques. This approach significantly improves volume estimation for trees with complex geometries, such as buttresses. This method is fully automated, achieving a detection accuracy of 99% for buttressed trees and reducing the MAPD to 14.68%, a substantial improvement over the 21.35% MAPD observed with traditional TreeQSM applications. By advancing the understanding of the potential and limitations of different QSM models and introducing a hybrid modeling technique, this research contributes to enhanced forest carbon stock assessments. It offers a nuanced, precise, and adaptable strategy for volume estimation, thereby significantly aiding forestry management and ecological conservation efforts.