Remote Sensing of Environment最新文献

{"title":"Coordinated estimates of 4-day 500 m global land surface energy balance components","authors":"Junrui Wang , Ronglin Tang , Meng Liu , Yazhen Jiang , Lingxiao Huang , Zhao-Liang Li","doi":"10.1016/j.rse.2025.114795","DOIUrl":"10.1016/j.rse.2025.114795","url":null,"abstract":"<div><div>Accurate estimations of global land surface energy balance components [including net radiation (Rn), latent heat flux (LE), soil heat flux (G) and sensible heat flux (H)] are crucial for quantifying the exchange of heat and water between the land surface and atmosphere. In this study, a novel and practical model for Coordinated estimates of 4-day 500 m global land Surface Energy Balance components (CoSEB) was developed, using the multivariate random forest technique and a synthesis of remote sensing and reanalysis datasets, as well as the extensive observations at 336 eddy-covariance sites worldwide. The CoSEB model effectively balances the estimates of Rn, LE, H, and G by learning the physics of energy conservation embedded in these components within the training datasets. Its advantages include 1) the accurate estimation of the four energy components and their ratios [i.e. evaporation fraction, defined as <span><math><mi>LE</mi><mo>/</mo><mfenced><mrow><mi>Rn</mi><mo>−</mo><mi>G</mi></mrow></mfenced></math></span>, which reflects the proportion of surface available energy that is allocated to LE instead of H], and 2) the ability to maintain energy balance among the four energy components, i.e. <span><math><mi>Rn</mi><mo>−</mo><mi>G</mi><mo>−</mo><mi>LE</mi><mo>−</mo><mi>H</mi><mo>=</mo><mn>0</mn></math></span>. With the 10-fold cross-validation at 286 sites, the CoSEB model achieved the root mean square error (RMSE) of 16.42 W/m<sup>2</sup>, 16.40 W/m<sup>2</sup>, 16.49 W/m<sup>2</sup>, 4.79 W/m<sup>2</sup> and 0.22, and the coefficient of determination (R<sup>2</sup>) of 0.92, 0.86, 0.83, 0.55 and 0.59, respectively, for estimating Rn, LE, H, G and evaporative fraction, which were comparable or superior to those estimated by other typical data-driven uncoordinated and coordinated models. In the validation with test datasets at another 50 eddy-covariance sites, the CoSEB model, with the RMSE of 18.23 W/m<sup>2</sup>, 17.98 W/m<sup>2</sup>, and 19.17 W/m<sup>2</sup>, and the R<sup>2</sup> of 0.87, 0.72, 0.71 in estimating Rn, LE, and H, respectively, outperformed all six state-of-the-art algorithms/products, i.e. FLUXCOM, BESSV2.0, MOD16A2, PML_V2, ETMonitor and GLASS. Besides, the CoSEB model successfully maintained energy balance, exhibiting an energy imbalance ratio [EIR, defined as <span><math><mn>100</mn><mo>%</mo><mo>×</mo><mfenced><mrow><mi>Rn</mi><mo>−</mo><mi>G</mi><mo>−</mo><mi>LE</mi><mo>−</mo><mi>H</mi></mrow></mfenced><mo>/</mo><mi>Rn</mi></math></span>] of 0, in comparison to other coordinated and uncoordinated models/products presenting the EIRs of as high as 50%. The new model also produced consistent global patterns with the six state-of-the-art products for each of the Rn, LE, and H estimates at 8-day, monthly, and yearly scales. The CoSEB model is promising for operationally generating high-accuracy global balanced land surface energy components and their ratios, which is essential for rationally partitioning surface","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"326 ","pages":"Article 114795"},"PeriodicalIF":11.1,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913263","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}

{"title":"Refined risk assessment of probabilistic tsunami inundation assisted by SDGSAT-1 glimmer imagery","authors":"Zhipeng Sun,&nbsp;Xiaojing Niu","doi":"10.1016/j.rse.2025.114792","DOIUrl":"10.1016/j.rse.2025.114792","url":null,"abstract":"<div><div>Potential large seismic tsunamis could pose catastrophic damage to coastal cities, but conventional disaster risk assessment is difficult to realize precise management limited by the scale of publicly available data. This study aims to propose a refined probabilistic tsunami inundation risk assessment model assisted by high-resolution Sustainable Development Science Satellite 1 (SDGSAT-1) glimmer imagery, which consists of hazard, exposure and vulnerability assessment. Public exposure data are usually at regional scales, and further refining exposure assessment results is the key to realize more refined risk assessment. SDGSAT-1 glimmer imagery is excellent in revealing social economic activities, and this study is the first attempted to use it to realize refined exposure assessment based on the correlation between apparent radiances and exposure assessment results. In addition, 1,380,000 tsunami scenarios are considered to fully assess the potential tsunami inundation hazards and double-layer nested mesh is used to improve the accuracy and efficiency of nearshore tsunami propagation and inundation simulations. The impact of various vulnerability indexes on the vulnerability curves of different land use categories is also assessed to explore the changed vulnerability curves. Finally, the refined probabilistic tsunami inundation risk assessment model has been applied in Macao, and precise management measures can be implemented by the government based on the refined probabilistic tsunami inundation risk assessment results. The application of SDGSAT-1 glimmer imagery in refining probabilistic tsunami inundation risk assessment provides a new insight for global coastal risk management.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"326 ","pages":"Article 114792"},"PeriodicalIF":11.1,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913264","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}

{"title":"Assessment of methods for mapping snow albedo from MODIS","authors":"Ross T. Palomaki ,&nbsp;Karl Rittger ,&nbsp;Sebastien J.P. Lenard ,&nbsp;Edward Bair ,&nbsp;Jeff Dozier ,&nbsp;S. McKenzie Skiles ,&nbsp;Thomas H. Painter","doi":"10.1016/j.rse.2025.114742","DOIUrl":"10.1016/j.rse.2025.114742","url":null,"abstract":"<div><div>We compare five daily MODIS-derived snow albedo products to terrain-corrected, in situ data from sites in California and Colorado, USA, and to snow albedo derived from airborne hyperspectral imagery over several basins in California and Colorado. The MODIS-derived products we consider are NASA standard products MOD10A1, MCD43A3, and MCD19A3D, along with STC-MODSCAG/MODDRFS and MODIS SPIReS. These products vary in their retrieval algorithms, including whether, for mixed pixels, they represent the albedo of snow within the pixel or the albedo of the whole pixel. When compared to in situ data, STC-MODSCAG/MODDRFS and SPIReS products have the highest accuracy (RMSE ≤0.093) and most spatially and temporally complete data records (∼99 %) because the algorithms each have independently developed gap filling and interpolation methods. The MOD10A1 and MCD43A3 products underestimate snow albedo (RMSE ≤0.248) because they incorporate non-snow land surfaces into their calculations and have less complete data records (∼76 %) due to less accurate snow detection and lack of interpolation. The MCD19A3D product has accuracy similar to STC-MODSCAG/MODDRFS and SPIReS (RMSE = 0.090) but the lowest data completeness of all datasets (56 %). We found similar performance trends when comparing the MODIS products to airborne hyperspectral data. Our analysis shows algorithms that account for fractional snow cover and incorporate all available spectral information result in the best snow albedo products across time and space. Similar algorithms applied to hyperspectral data can better resolve spectral features to retrieve optical properties of snow; hence we can expect improvements in snow albedo retrievals from future hyperspectral satellite missions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"326 ","pages":"Article 114742"},"PeriodicalIF":11.1,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903759","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}

{"title":"Non-uniform climatic responses of land surface phenology derived from optical-, fluorescence-, and microwave-based satellite observations","authors":"Wendi Qu ,&nbsp;Lu Hu ,&nbsp;Josep Peñuelas ,&nbsp;Xiaoyu Liang ,&nbsp;Yang Li ,&nbsp;Wenjun He ,&nbsp;Chaoyang Wu","doi":"10.1016/j.rse.2025.114793","DOIUrl":"10.1016/j.rse.2025.114793","url":null,"abstract":"<div><div>Satellite remote sensing has greatly advanced the study of land surface phenology, providing crucial insights into large-scale vegetation dynamics in the context of climate change. Multi-sensor satellite-derived vegetation proxies, such as the normalized difference vegetation index (NDVI) in optical mode, solar-induced chlorophyll fluorescence (SIF) in fluorescence mode, and vegetation optical depth (VOD) in microwave mode, are effective indicators of vegetation dynamics in greenness, biomass, and productivity. However, a comprehensive understanding of phenological patterns derived from these proxies and their climatic responses remains limited. In this study, spanning 1988–2021, we analyzed spatio-temporal patterns and climatic responses of spring green-up dates (GUDs) and autumn dates of foliar senescence (DFSs) across the three vegetation proxies. We found broadly consistent trends of advancing GUDs (−2.4 to −1.1 days decade⁻¹) across proxies, while DFSs exhibited divergent temporal patterns: VOD-derived DFSs showed significant delays (+1.1 days decade⁻¹), whereas NDVI- and SIF-derived DFSs displayed no clear trends. Rising temperatures were the primary driver of earlier GUDs, while precipitation and insolation had limited influence. In autumn, warming delayed VOD-based DFSs but had minimal effects on NDVI- and SIF-based estimates. Spatial attribution analyses indicated that both biotic and abiotic factors contributed to the spatial variability in climatic responses, with species richness and tree density modulating temperature effects. These findings highlight the importance of proxy selection in phenological studies and underscore the need for integrated investigations into the biophysical mechanisms driving non-uniform climatic responses across vegetation proxies.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"325 ","pages":"Article 114793"},"PeriodicalIF":11.1,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903743","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}

{"title":"Angular effect correction of remotely sensed land surface temperature by integrating geostationary and polar-orbiting satellite data","authors":"Ran Wei ,&nbsp;Si-Bo Duan ,&nbsp;Xiangyang Liu ,&nbsp;Niantang Liu ,&nbsp;Xiaoxiao Min ,&nbsp;Zhao-Liang Li","doi":"10.1016/j.rse.2025.114788","DOIUrl":"10.1016/j.rse.2025.114788","url":null,"abstract":"<div><div>Satellite-derived land surface temperature (LST) is a directional variable and has significant angular anisotropy. This characteristic contributes to enhance the differences among different satellite-derived LST products, and therefore increases the challenge of using multi-sensor and multi-decadal data to provide a long-term and angle-consistent LST climate data record. The kernel-driven model can balance the interpretability and operability well, so that it is suitable for angular normalization of LST products. The calibration of the kernel-driven model depends on multi-angle data which is difficult to obtain due to the spatial-temporal heterogeneity of LST. In this study, a novel LST angular normalization method based on the kernel-driven model was proposed to correct the angular effect of satellite-derived LST product by constructing multi-angle LST dataset from one geostationary satellite (GOES-R/ABI) and four polar-orbiting satellites (Terra/MODIS, Aqua/MODIS, Metop/AVHRR, and S-NPP/VIIRS). The dataset gathered more abundant angle information, i.e., LSTs from three different observation geometries for the same pixel. The kernel-driven model was calibrated using the multi-angle LST dataset in the Continental United States (CONUS) during the year 2020. The discrepancies of the root mean square difference between LST before and after angular normalization range from 0.14 K to 1.10 K over nine land cover types in the four seasons. Similar results are obtained when the calibrated kernel-driven model was further expanded to other years and areas (i.e., the CONUS in 2021 and East Asia in 2020). The LST angular normalization method was applied to correct the angular effect of MODIS LST product. The results indicate that there is a strong correlation between the spatial distribution of LST differences (LST before and after angular normalization) and view zenith angle (VZA). MODIS LSTs before and after angular normalization were compared with Landsat 8 LST and Sentinel-3 A LST in near-nadir viewing for January, April, July, and October 2020. The angular normalization reduced the root mean square error (RMSE) between MODIS LST and Landsat 8 LST by 0.94–2.06 K in different months and by 0.13–2.61 K over various land cover types. For Sentinel-3 A, the RMSE decreased by 0.30–0.64 K in different months. The accuracies of MODIS LST before and after angular normalization were further validated using in situ measurements at the six SURFRAD sites. There are large discrepancies between the RMSE of MODIS LST before and after angular normalization versus in situ LST for VZA ≥ 45°. The largest discrepancy is up to approximately 1.3 K at the GWN site. The LST angular normalization method has the potential to provide an angle-consistent LST climate data record.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"325 ","pages":"Article 114788"},"PeriodicalIF":11.1,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143895527","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}

{"title":"Uncertainty quantification for forest attribute maps with conformal prediction and k-nearest neighbor method","authors":"M. Kuronen ,&nbsp;J. Räty ,&nbsp;P. Packalen ,&nbsp;M. Myllymäki","doi":"10.1016/j.rse.2025.114758","DOIUrl":"10.1016/j.rse.2025.114758","url":null,"abstract":"<div><div>Forest attribute maps relying on remotely sensed data are increasingly required for local decision-making related to the use of forest resources. Such maps always have uncertainty, which can be challenging to quantify. The objective of this work is to introduce the conformal prediction methodology to uncertainty quantification in forest attribute mapping, particularly for the <span><math><mi>k</mi></math></span>-NN method. We compare several conformal <span><math><mi>k</mi></math></span>-NN procedures for the mapping of total volume, broadleaved volume and Lorey’s height using Sentinel-2 satellite images and airborne laser scanning data. We show that all procedures produce valid prediction intervals in the sense that they contain the true value with the desired probability, for example 90%. We use multiple measures to quantify how well the prediction intervals adapt to the difficulty of prediction in different forest strata. We found that there are multiple methods for <span><math><mi>k</mi></math></span>-NN to produce prediction intervals competitive with those produced by conformal quantile regression. These methods include conformal prediction based on the standard deviation or quantiles of the <span><math><mi>k</mi></math></span> nearest neighbors with commonly used values of <span><math><mi>k</mi></math></span>. We present how to produce a forest attribute map with the proposed conformal prediction intervals. We also show a theoretical coverage guarantee for the jackknife conformal <span><math><mi>k</mi></math></span>-NN procedure. We recommend conformal prediction for unit-level uncertainty quantification of forest attribute maps.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"325 ","pages":"Article 114758"},"PeriodicalIF":11.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143895525","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}

{"title":"Spatio-Temporal reconstruction to fill spatial gaps in global satellite vegetation optical depth products","authors":"Hui Wang ,&nbsp;Zushuai Wei ,&nbsp;Linguang Miao ,&nbsp;Feng Tian ,&nbsp;Tianjie Zhao ,&nbsp;Lu Hu ,&nbsp;Lingkui Meng","doi":"10.1016/j.rse.2025.114787","DOIUrl":"10.1016/j.rse.2025.114787","url":null,"abstract":"<div><div>Vegetation Optical Depth (VOD) serves as a crucial tool for monitoring vegetation characteristics and plays a vital role in terrestrial ecosystems. The vegetation optical depth dataset from AMSR-E/2 using multi-channel collaborative algorithm (MCCA-AMSR VOD) possesses a longer time series spanning from 2002 to 2022. However, due to the limitations of satellite orbital scanning gap and retrieval algorithms, existing VOD datasets do not achieve complete coverage in global land. To address the need for a VOD dataset with high spatio-temporal coverage across different bands, we reconstruct the MCCA-AMSR VOD dataset using the 3D partial convolutional neural network. Three evaluation methods are employed to further assessthe reconstructed VOD dataset. The reconstructed VOD achieves a higher average spatial coverage (95.01 %) compared with the original dataset (72.55 %). The correlation coefficients (R) between original and reconstructed VOD dataset for all five simulated missing regions exceeded 0.99, indicating that the reconstructed VOD has a very high correlation with the original dataset at the regional scale. Furthermore, comparison with optical vegetation indices indicates that the reconstructed VOD can also capture vegetation water contentwell. Therefore, reconstructed VOD can provide valuable support for research on drought and vegetation monitoring.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"325 ","pages":"Article 114787"},"PeriodicalIF":11.1,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892226","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}

{"title":"Use of close-range LiDAR devices and statistical inference approaches in operational stand-level forest inventories","authors":"Juan Alberto Molina-Valero ,&nbsp;Rorai Pereira Martins-Neto ,&nbsp;Adela Martínez-Calvo ,&nbsp;Joel Rodríguez-Ruiz ,&nbsp;Peter Surový ,&nbsp;Anika Seppelt ,&nbsp;César Pérez-Cruzado","doi":"10.1016/j.rse.2025.114773","DOIUrl":"10.1016/j.rse.2025.114773","url":null,"abstract":"<div><div>Close-range LiDAR devices are considered to have great potential for enhancing forest inventory (FI) estimates. However, this potential is still being explored in the case of ground-based LiDAR devices, especially when the target is focused on relatively large spatial scales, such as stand level. This study explored the performance of close-range LiDAR devices in terms of bias and error, particularly terrestrial laser scanning (TLS) instruments, as measurement tools in stand-level FIs. The main premise of the research is that close-range LiDAR devices provide auxiliary information that can be used to accurately and precisely predict the dependent variable of the target population, thereby reducing errors. To this end, this study compared the performance of different statistical inference approaches that can be implemented with these technologies, such as the simple expansion estimator (EXP), two-stage model-assisted regression (REG), conventional model-based (CMB) and three-phase hierarchical model-based (3pHMB) approaches. These approaches were used to compare the following types of data: field measurements and TLS single-scan data (EXP, REG); field measurements and unmanned aerial vehicles (UAV)-LiDAR data (CMB); and field measurements, TLS single-scan data and UAV-LiDAR data (3pHMB). The case study was carried out in a 16 ha experimental plot dominated by <em>Pinus radiata</em> and <em>Pinus pinaster</em> in northwest Spain, focusing on stand volume (<span><math><mi>V</mi></math></span>, m³ ha⁻¹) estimates. The findings showed that the use of close-range remote sensing devices as a source of auxiliary data provided lower error in <span><math><mi>V</mi></math></span> estimates than the EXP approach using a single data source. The findings also suggest that close-range LiDAR devices can potentially be used as FI instruments. Therefore, the transfer of these sampling techniques may play an important role in operationalizing the use of close-range LiDAR devices in FIs.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"325 ","pages":"Article 114773"},"PeriodicalIF":11.1,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143889676","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}

{"title":"Substantial increases in burned area in circumboreal forests from 1983 to 2020 captured by the AVHRR record and a new autoregressive burned area detection algorithm","authors":"Connor W. Stephens ,&nbsp;Anthony R. Ives ,&nbsp;Volker C. Radeloff","doi":"10.1016/j.rse.2025.114789","DOIUrl":"10.1016/j.rse.2025.114789","url":null,"abstract":"<div><div>Wildfire maintains boreal forest health by catalyzing nutrient cycling and forest succession. However, increased annual burned area due to climate warming may facilitate forest loss and soil carbon release, which makes it important to monitor circumboreal burned area. Our goal was to characterize regional changes in circumboreal burned area from 1983 to 2020 using Advanced Very High Resolution Radiometer (AVHRR) data, and to identify the ecoregions where increases in burned area represent significant trends. We accomplished this by developing and applying a new burned area mapping algorithm that is based on an autoregressive analysis of the AVHRR and MODIS MOD09CMGv061 time series. Our algorithm worked well, and resulting burned area totals were similar to those of the MODIS MCD64A1v61 burned area product for years where both were available (2001−2020); however, the advantage of our AVHRR burned area dataset is that it extends back to 1983. Based on the resulting burned area maps, we evaluated circumboreal burned area changes and tested for significant trends. Net changes were substantial: while only 5.37 % of the circumboreal biome burned in the 1980s, 8.22 % did during the 2010s, an increase of 2.85 % (= 8.22–5.37 %) that corresponds to a proportional increase in area burned of 0.53 (= 2.85/5.37). In one ecoregion, Muskwa Slave Lake Forests, burned area more than quadrupled from the 1980s to the 2010s, and in three it more than tripled (Northern Canadian Shield Taiga, Yukon Interior Dry Forests, and Northeast Siberian Taiga). Furthermore, despite interannual variability in burned area typically being high, we found statistically significant increasing trends in burned area in seven of the twenty-three boreal ecoregions, corresponding to 19.6 % of boreal forests <strong>(</strong>35 % of North American and 11 % of Eurasian boreal forests), while only one ecoregion (Eastern Canadian Shield Taiga) had a decreasing trend. By analyzing the long-term AVHRR record, we were able to capture much larger increases in burned area than from the shorter MODIS record, allowing us to quantify how widespread and substantial these increases have been. By analyzing ecoregions, we found that north-eastern Siberian, north-western Canadian, and Alaskan boreal forests have experienced the most increases in burned area. These increases in burned area may have implications for future forest persistence and carbon storage within Eurasian and North American boreal forests.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"325 ","pages":"Article 114789"},"PeriodicalIF":11.1,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886103","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}

{"title":"Tracking 35-year dynamics of retrogressive thaw slumps across permafrost regions of the Tibetan Plateau","authors":"Guoqing Yang ,&nbsp;Haijun Qiu ,&nbsp;Ninglian Wang ,&nbsp;Dongdong Yang ,&nbsp;Ya Liu","doi":"10.1016/j.rse.2025.114786","DOIUrl":"10.1016/j.rse.2025.114786","url":null,"abstract":"<div><div>Permafrost degradation on the Tibetan Plateau (TP) has triggered widespread retrogressive thaw slumps (RTSs), affecting hydrology, carbon sequestration and infrastructure stability. To date, there is still a lack of long-term monitoring of RTSs across the TP, the thaw dynamics and comprehensive driving factors remain unclear. Here, using time-series Landsat imagery and change detection algorithm, we identified RTSs on permafrost regions of the TP from 1986 to 2020. Existing RTSs inventories and high-resolution historical imagery were employed to verify the identified results, the temporal validation of RTSs disturbance pixels demonstrated a high accuracy. In the study area, a total of 3537 RTSs were identified, covering a total area of 5997 ha, representing a 26-fold increase since 1986, and 69.2 % of RTSs formed since 2010. Most RTSs are located on gentle slope (4–12°) at elevations between 4500 m and 5300 m, with a tendency to form in alpine grassland and alpine meadow. Annual variations in RTSs area exhibited a significant positive correlation with minimum air temperature, mean land surface temperature, and annual thawing index, while it showing a significant negative correlation with the decrease in downward shortwave radiation. Spatially, RTSs were more common in areas with higher soil water content and shallower active layer. Landsat imagery captured the vast majority of RTSs on the TP and revealed interannual disturbance details, but the 30 m resolution remains inadequate for delineating the refined boundaries of some micro-scale (&lt; 0.18 ha) RTSs. Detected RTSs disturbances on the TP will aid in hazard management and carbon feedback assessments, and our findings provide novel insights into the impacts of climate change and permafrost environments on RTSs formation.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"325 ","pages":"Article 114786"},"PeriodicalIF":11.1,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886102","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}