Science of Remote Sensing最新文献

{"title":"Coastal vertical land motion across Southeast Asia derived from combining tide gauge and satellite altimetry observations","authors":"Dongju Peng ,&nbsp;Grace Ng ,&nbsp;Lujia Feng ,&nbsp;Anny Cazenave ,&nbsp;Emma M. Hill","doi":"10.1016/j.srs.2024.100176","DOIUrl":"10.1016/j.srs.2024.100176","url":null,"abstract":"<div><div>Vertical land motion (VLM) is complex in Southeast Asia because this region is subject to a range of natural processes (e.g., earthquakes) and anthropogenic activities (e.g., groundwater withdrawal) that can change land heights. To aid in coastal management, long-term observations of VLM are as crucial as observations for climate-induced sea surface height changes; however, such long-term observations are sparse for Southeast Asian coasts. To fill this observational gap, here we derive monthly VLM time series from 1993 to 2020 at 50 coastal sites across Southeast Asia by combining tide-gauge records and newly generated satellite altimetry observations. These altimetry observations are reproduced sea-level products using new altimetry standards and more accurate geophysical corrections. Our 27-year-long VLM dataset shows high spatial variability and non-linear temporal changes in VLM across Southeast Asia. We identify several major sources that dominate the regional land-height changes, which include large subsidence due to groundwater extraction in Manila and Bangkok, land uplift in Indonesia and subsidence in Thailand from postseismic deformation resulting from the sequence of large Sumatran earthquakes since 2004, and land subsidence as a result of sediment compaction in Malaysia. Those signals are quantitatively or qualitatively consistent with observations from other sources. This VLM dataset can be used to advance our understanding of the physical mechanisms behind land-height changes and to improve sea level projections in the region.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"10 ","pages":"Article 100176"},"PeriodicalIF":5.7,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658607","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}

{"title":"Identifying thermokarst lakes using deep learning and high-resolution satellite images","authors":"Kuo Zhang ,&nbsp;Min Feng ,&nbsp;Yijie Sui ,&nbsp;Jinhao Xu ,&nbsp;Dezhao Yan ,&nbsp;Zhimin Hu ,&nbsp;Fei Han ,&nbsp;Earina Sthapit","doi":"10.1016/j.srs.2024.100175","DOIUrl":"10.1016/j.srs.2024.100175","url":null,"abstract":"<div><div>Thermokarst lakes play a critical role in hydrologic connectivity, permafrost stability, and carbon exchange from local to regional scales. Due to the typically small sizes and highly dynamic nature of thermokarst lakes, their identification in large regions remains challenging. This study presented a deep-learning model and applied it to high-resolution (1.2 m) satellite imagery to automatically delineate and inventory thermokarst lakes. The method was applied in the Yellow River source region in eastern Tibetan Plateau and identified 52,486 thermokarst lakes, with the majority (90.9%) smaller than 0.01 km². It's the most comprehensive survey of thermokarst lakes within the region and more than 45% of these lakes were not covered by any existing lake datasets, thereby leading to a possible underestimation of the amount and effects of thermokarst lakes. Validation with visually interpreted data reported MIoU of 0.97, F1 score of 0.96, and PA of 0.97, confirming that thermokarst lakes we detected were matched very well with the reference. The experiment demonstrated great potential for investigating the distribution and impacts of thermokarst lakes in borad regions, such as the entire Tibetan Plateau or even the globe, to provide critical knowledge for their response to climate change and effects from their dynamics.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"10 ","pages":"Article 100175"},"PeriodicalIF":5.7,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587189","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}

{"title":"A two-stage deep learning architecture for detection global coastal and offshore submesoscale ocean eddy using SDGSAT-1 multispectral imagery","authors":"Linghui Xia ,&nbsp;Baoxiang Huang ,&nbsp;Ruijiao Li ,&nbsp;Ge Chen","doi":"10.1016/j.srs.2024.100174","DOIUrl":"10.1016/j.srs.2024.100174","url":null,"abstract":"<div><div>Submesoscale ocean eddies are essential oceanic phenomenon that control and influence the ocean energy cascade. Most existing eddy detection methods rely on low-resolution satellite altimeter data, which fail to capture submesoscale ocean features and oceanographic phenomena in shallow water. Introducing high-resolution multispectral data can alleviate these problems, yet it has been largely overlooked. A generalized and efficient deep learning architecture that combines developments in deep learning with Sustainable Development Goals Science Satellite 1 (SDGSAT-1) multispectral data from earth observations offers a potential pathway for more fine detection of ocean eddies. Considering that oceanic eddy exhibits spatially sparse characteristics on high-resolution remote sensing scenes, the oceanic eddy detection (OED) model suitable for global coastal and offshore regions is divided into two stages: eddy information judgment and eddy position determination. Correspondingly, SDGSAT-1 multispectral data from November 2021 to December 2022 were carried out to construct two submesoscale eddy datasets for training and testing each stage model. The union validation of multiple metrics demonstrates that the proposed OED model and its stage models achieve state-of-the-art (SOTA) performance, especially in optically complex coastal and offshore waters. We applied the model to real-world scenes captured by SDGSAT-1 in 2023, and found that the detected results were mainly located at the water depth below 200 m. The authenticity of the recognition results is validated using sea surface chlorophyll concentration, temperature, and topography data, indicating that the OED model has achieved remarkable effectiveness under various sea conditions. In addition, the temporal distributions and statistical characteristics of detected submesoscale eddies are analyzed over an extended period (November 2021 to November 2023). Finally, HISEA-2, Landsat-9, and Sentinel-2 served as testing grounds to validate the generalization of the proposed methodology, with experimental results demonstrating that the OED model possesses significant developmental potential for multi-source remote sensing data. This paper presents a comprehensive deep learning framework for the global-scale detection of submesoscale eddies and underscores the pivotal role of high-resolution multispectral imagery as an innovative data source for global coastal and offshore eddy identification.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"10 ","pages":"Article 100174"},"PeriodicalIF":5.7,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658511","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}

{"title":"A comprehensive evaluation of satellite-based and reanalysis soil moisture products over the upper Blue Nile Basin, Ethiopia","authors":"Addis A. Alaminie ,&nbsp;Sofie Annys ,&nbsp;Jan Nyssen ,&nbsp;Mark R. Jury ,&nbsp;Giriraj Amarnath ,&nbsp;Muluneh A. Mekonnen ,&nbsp;Seifu A. Tilahun","doi":"10.1016/j.srs.2024.100173","DOIUrl":"10.1016/j.srs.2024.100173","url":null,"abstract":"<div><div>Soil moisture data is crucial for enhancing drought monitoring, optimizing water management, refining irrigation schedules, forecasting floods, and understanding climate change impacts. Despite the existence of long-term global satellite and reanalysis products, the performance of global satellite products in Ethiopia is underexplored, highlighting a need for comprehensive assessments to effectively utilize these resources and address critical environmental challenges. This research evaluates various operational satellites and reanalysis soil moisture datasets over the Gilgel Abay watershed. The datasets include the European Space Agency's Climate Change Initiative Soil Moisture (ESA-CCI SM), Soil Moisture and Ocean Salinity (SMOS), NASA's Soil Moisture Active Passive mission (SMAP Enhanced), the European Centre for Medium-Range Weather Forecasts Fifth Generation Reanalysis (ECMWF ERA5), Climate Forecast System reanalysis (CFSRv2), NASA's Short-term Prediction Research and Transition Center - Land Information System (SPoRT-LIS), and NASA's Global Land Data Assimilation System (GLDAS). After applying bias correction, the Kolmogorov-Smirnov two-sample t-tests, Bonferroni correction, and statistical error metrics, the evaluation reveals that all products, except NASA-GLDAS, effectively capture soil moisture dynamics. SMAP shows superior temporal dynamics, followed by SMOS, ESA-CCI, CFSRv2, LIS and ERA5. Using Spearman's rank correlation coefficient (r_s), SMAP (r_s = 0.68) and SMOS (r_s = 0.67) identified as the most accurate soil moisture products, with SMOS excelling in spatial representation and closely aligning with the Topographic Wetness Index (TWI). However, the lack of sufficient in situ monitoring networks limits the ability to perform a thorough evaluation. Establishing these networks is essential for improving satellite retrievals and modelling in the upper Blue Nile Basin, Ethiopia.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"10 ","pages":"Article 100173"},"PeriodicalIF":5.7,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142537797","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}

{"title":"Rapid advancements in large language models for quantitative remote sensing: The case of water depth inversion","authors":"Zhongqiang Wu ,&nbsp;Wei Shen ,&nbsp;Zhihua Mao ,&nbsp;Shulei Wu","doi":"10.1016/j.srs.2024.100166","DOIUrl":"10.1016/j.srs.2024.100166","url":null,"abstract":"<div><div>This study presents a comparative analysis of two advanced AI models, ChatGPT and ERNIE, in the context of water depth inversion. Utilizing satellite spectral data and in-situ bathymetric measurements collected from Rushikonda Beach, India, we processed and analyzed the data to generate high-resolution bathymetric maps. Both models demonstrated significant accuracy, with ChatGPT slightly outperforming ERNIE in terms of mean absolute error. The study highlights the advantages of AI models, such as efficient data processing and the ability to integrate multi-modal inputs, while also discussing challenges related to data quality, interpretability, and computational demands. The findings suggest that while both models are highly effective for water depth inversion, ongoing improvements in data handling and model transparency are essential for their broader application in environmental monitoring. This research contributes to the understanding of AI capabilities in geospatial analysis and sets the stage for future enhancements in the field.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"10 ","pages":"Article 100166"},"PeriodicalIF":5.7,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579017","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}

{"title":"A comprehensive review of rice mapping from satellite data: Algorithms, product characteristics and consistency assessment","authors":"Husheng Fang ,&nbsp;Shunlin Liang ,&nbsp;Yongzhe Chen ,&nbsp;Han Ma ,&nbsp;Wenyuan Li ,&nbsp;Tao He ,&nbsp;Feng Tian ,&nbsp;Fengjiao Zhang","doi":"10.1016/j.srs.2024.100172","DOIUrl":"10.1016/j.srs.2024.100172","url":null,"abstract":"<div><div>With a growing global population and intensifying regional conflicts, the need for food is more urgent than ever. Rice, as one of the world's major staple crops especially in Asia, sustains over 50 percent of the global population. Accurate rice mapping is fundamental to ensuring global food security and sustainable agricultural development. Remote sensing has become an essential tool for mapping rice cultivation due to its ability to cover large areas and provide timely observation. Existing reviews mainly focus on the paddy rice mapping methods. However, it lacks a comprehensive understanding on the quality of different paddy rice maps from regional to global scales. This paper provides a comprehensive review of existing satellite-based rice mapping methods and products. Firstly, we categorized all previous methods into four classes: 1) spatial statistical method; 2) traditional machine learning method; 3) phenology-based method; and 4) deep learning method. Secondly, we summarized 25 products, including 3 global products and 22 regional products. Furthermore, we examined the consistency and discrepancy among different products in China, Heilongjiang China and Vietnam respectively and explored the underlying reasons. We found that 1) rice fields with simple cropping patterns and intensive cultivation can be correctly recognized using various algorithms; 2) different products share low consistency in fragmented rice fields 3) the prevalence of clouds and complicated rice cropping patterns or diverse growing environments in subtropical and tropical regions poses challenges to accurate rice mapping. Due to these challenges, currently it still lacks paddy rice maps with both large spatial coverage, high spatial resolution, and long time series. Moreover, deficiency of ground-truth samples impedes product development and validation. For improved paddy rice mapping at large scale, we suggest to apply sample-free rice mapping techniques and remote sensing foundation models to leverage the strengths of phenology-based methods and deep learning methods.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"10 ","pages":"Article 100172"},"PeriodicalIF":5.7,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142560900","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}

{"title":"Estimation of reference evapotranspiration in South Korea using GK-2A AMI channel data and a tree-based machine learning method","authors":"Bu-Yo Kim,&nbsp;Joo Wan Cha","doi":"10.1016/j.srs.2024.100171","DOIUrl":"10.1016/j.srs.2024.100171","url":null,"abstract":"<div><div>Changes in evapotranspiration can affect water availability and climate, leading to extreme weather and severe impact on ecosystems. In particular, increased water stress in farmland, forests, and mountainous areas with limited water resources can result in detrimental impacts such as droughts and wildfires. In this study, we utilized data from the Advanced Meteorological Imager (AMI) sensor on the Geostationary Korea Multi-Purpose Satellite 2A (GK-2A) and employed a tree-based machine learning method to accurately estimate reference evapotranspiration (ET_o) in South Korea. The estimated SAT ET_o was compared to the ASOS ET_o, which was estimated using meteorological variables from the Automated Synoptic Observing System (ASOS) and the Penman–Monteith method. The hourly SAT ET_o demonstrated an estimated accuracy with a relative bias (rBias) of −0.26%, a relative root mean square error (rRMSE) of 34.01%, and a coefficient of determination (R²) of 0.94, whereas the daily SAT ET_o exhibited an estimated accuracy with an rBias of −0.25%, an rRMSE of 8.30%, and an R² of 0.97. In this study, various cases were analyzed in detail, including daytime and nighttime, wet and dry conditions, and varying cloud cover. The highly accurate estimation of ET_o using data from the GK-2A satellite, which have high temporal and spatial resolution, can be effectively utilized as monitoring data for water resource management and natural disaster prevention.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"10 ","pages":"Article 100171"},"PeriodicalIF":5.7,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535584","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}

{"title":"Integration of very high-resolution stereo satellite images and airborne or satellite Lidar for Eucalyptus canopy height estimation","authors":"Manizheh Rajab Pourrahmati ,&nbsp;Nicolas Baghdadi ,&nbsp;Henrique Ferraco Scolforo ,&nbsp;Clayton Alcarde Alvares ,&nbsp;Jose Luiz Stape ,&nbsp;Ibrahim Fayad ,&nbsp;Guerric le Maire","doi":"10.1016/j.srs.2024.100170","DOIUrl":"10.1016/j.srs.2024.100170","url":null,"abstract":"<div><div>Eucalyptus plantations cover extensive areas in tropical regions and require accurate growth monitoring for efficient management. Traditional in-situ measurements, while necessary, are labor-intensive and impractical for large-scale assessments. Very high-resolution satellite stereo imagery is playing an increasingly important role in the estimation of fine Digital Surface Models (DSMs) across landscapes. However, its ability to estimate canopy height models (CHMs) has not been widely investigated. This study investigates the integration of high-resolution satellite stereo imagery from the Pleiades sensor with airborne or satellite Lidar data to estimate canopy height over eucalyptus plantations. Two study sites were selected in Brazil, representing flat and semi-mountainous topographies, Mato Grosso do Sul (MS) and Sao Paulo (SP), respectively. Digital Surface Models generated from Pleiades images (DSM_P) were combined with Digital Terrain Models extracted from airborne Lidar data (DTM_ALS) to create Canopy Height Models (CHM_ALS). The evaluation of the CHM_ALS was based on two in situ canopy height measurements (H_max and H_mean). For the SP site, the CHM_ALSmax, which is the average height of top 10% pixel values within each plot, correlated well with in situ H_mean, which is the average height of 10 central trees (r = 0.98), showing a bias of 1.4 m, RMSE of 3.1 m, and rRMSE of 18.5%. At the MS site, CHM_ALSmax demonstrated a bias of 1.9 m, RMSE of 2.3 m, rRMSE of 17.3%, and r correlation of 0.92. Despite a tendency to underestimate heights below 20 m in young tree plantations with open canopy, the results indicate reliable canopy height estimation. The study also investigates the potential of Global Ecosystem Dynamics Investigation (GEDI) elevation data as an alternative to DTM_ALS in absence of airborne Lidar data. The resulting CHM_Gedi is promising but slightly less accurate than Lidar-based CHMs. The best GEDI-based CHM (CHM_Gedimax) showed a bias and rRMSE of 1.3 m and 20.5% for the SP site, and 2.2 m and 24.9% for the MS site. These findings highlight the potential for integrating Pleiades and Lidar data for efficient and accurate canopy height monitoring in eucalyptus plantations.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"10 ","pages":"Article 100170"},"PeriodicalIF":5.7,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442482","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}

{"title":"Large-scale inventory in natural forests with mobile LiDAR point clouds","authors":"Jinyuan Shao ,&nbsp;Yi-Chun Lin ,&nbsp;Cameron Wingren ,&nbsp;Sang-Yeop Shin ,&nbsp;William Fei ,&nbsp;Joshua Carpenter ,&nbsp;Ayman Habib ,&nbsp;Songlin Fei","doi":"10.1016/j.srs.2024.100168","DOIUrl":"10.1016/j.srs.2024.100168","url":null,"abstract":"<div><div>Large-scale forest inventory at the individual tree level is critical for natural resource management decision making. Terrestrial Laser Scanning (TLS) has been used for individual tree level inventory at plot scale However, due to the inflexibility of TLS and the complex scene of natural forests, it is still challenging to localize and measure every tree at large scale. In this paper, we present a framework to conduct large-scale natural forest inventory at the individual tree level by taking advantage of deep learning models and Mobile Laser Scanning (MLS) systems. First, a deep learning model, ForestSPG, was developed to perform large-scale semantic segmentation on MLS LiDAR data in natural forests. Then, the forest segmentation results were used for individual stem mapping. Finally, Diameter at Breast Height (DBH) was measured for each individual stem. Two natural forests mapped with backpack and Unmanned Aerial Vehicle (UAV) LiDAR systems were tested. The results showed that the proposed ForestSPG is able to segment large-scale forest LiDAR data into multiple ecologically meaningful classes. The proposed framework was able to localize and measure all 5838 stems at individual tree level in a 20 ha natural forest in less than 20 min using UAV LiDAR. DBH measurement results on trees’ DBH larger than 38.1 cm (15 in) showed that backpack LiDAR was able to achieve 1.82 cm of Root Mean Square Error (RMSE) and UAV LiDAR was able to achieve 3.13 cm of RMSE. The proposed framework can not only segment complex forest components with LiDAR data from different platforms but also demonstrate good performance on stem mapping and DBH measurement. Our research provides and automatic and scalable solution for large-scale natural forest inventory at individual tree level, which can be the basis for large-scale estimation of wood volume and biomass.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"10 ","pages":"Article 100168"},"PeriodicalIF":5.7,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142444877","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}

{"title":"Design and performance of the Climate Change Initiative Biomass global retrieval algorithm","authors":"Maurizio Santoro ,&nbsp;Oliver Cartus ,&nbsp;Shaun Quegan ,&nbsp;Heather Kay ,&nbsp;Richard M. Lucas ,&nbsp;Arnan Araza ,&nbsp;Martin Herold ,&nbsp;Nicolas Labrière ,&nbsp;Jérôme Chave ,&nbsp;Åke Rosenqvist ,&nbsp;Takeo Tadono ,&nbsp;Kazufumi Kobayashi ,&nbsp;Josef Kellndorfer ,&nbsp;Valerio Avitabile ,&nbsp;Hugh Brown ,&nbsp;João Carreiras ,&nbsp;Michael J. Campbell ,&nbsp;Jura Cavlovic ,&nbsp;Polyanna da Conceição Bispo ,&nbsp;Hammad Gilani ,&nbsp;Frank Martin Seifert","doi":"10.1016/j.srs.2024.100169","DOIUrl":"10.1016/j.srs.2024.100169","url":null,"abstract":"<div><div>The increase in Earth observations from space in recent years supports improved quantification of carbon storage by terrestrial vegetation and fosters studies that relate satellite measurements to biomass retrieval algorithms. However, satellite observations are only indirectly related to the carbon stored by vegetation. While ground surveys provide biomass stock measurements to act as reference for training the models, they are sparsely distributed. Here, we addressed this problem by designing an algorithm that harnesses the interplay of satellite observations, modeling frameworks and field measurements, and generated global estimates of above-ground biomass (AGB) density that meet the requirements of the scientific community in terms of accuracy, spatial and temporal resolution. The design was adapted to the amount, type and spatial distribution of satellite data available around the year 2020. The retrieval algorithm estimated AGB annually by merging estimates derived from C- and L-band Synthetic Aperture Radar (SAR) backscatter observations with a Water Cloud type of model and does not rely on AGB reference data at the same spatial scale as the SAR data. This model is integrated with functions relating to forest structural variables that were trained on spaceborne LiDAR observations and sub-national AGB statistics. The yearly estimates of AGB were successively harmonized using a cost function that minimizes spurious fluctuations arising from the moderate-to-weak sensitivity of the SAR backscatter to AGB. The spatial distribution of the AGB estimates was correctly reproduced when the retrieval model was correctly set. Over-predictions occasionally occurred in the low AGB range (&lt;50 Mg ha⁻¹) and under-predictions in the high AGB range (&gt;300 Mg ha⁻¹). These errors were a consequence of sometimes too strong generalizations made within the modeling framework to allow reliable retrieval worldwide at the expense of accuracy. The precision of the estimates was mostly between 30% and 80% relative to the estimated value. While the framework is well founded, it could be improved by incorporating additional satellite observations that capture structural properties of vegetation (e.g., from SAR interferometry, low-frequency SAR, or high-resolution observations), a dense network of regularly monitored high-quality forest biomass reference sites, and spatially more detailed characterization of all model parameters estimates to better reflect regional differences.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"10 ","pages":"Article 100169"},"PeriodicalIF":5.7,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142417829","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}