Remote Sensing of Environment最新文献

{"title":"Near real-time corn and soybean mapping at field-scale by blending crop phenometrics with growth magnitude from multiple temporal and spatial satellite observations","authors":"Yu Shen ,&nbsp;Xiaoyang Zhang ,&nbsp;Khuong H. Tran ,&nbsp;Yongchang Ye ,&nbsp;Shuai Gao ,&nbsp;Yuxia Liu ,&nbsp;Shuai An","doi":"10.1016/j.rse.2025.114605","DOIUrl":"10.1016/j.rse.2025.114605","url":null,"abstract":"<div><div>Timely and accurate crop mapping is essential for predicting crop production, estimating water use, and informing market forecasts. However, operational crop maps are typically accessible more than four months subsequent to harvest, rather than in real-time or near real-time (NRT). Recently, in-season crop mapping has emerged by leveraging rich satellite data sources at various scales in the United States (US) Corn Belt – a prominent food-producing agricultural region dominated by corn and soybeans. However, challenges persist due to inadequate clear-sky satellite observations and the absence of field-scale in-season crop phenometrics. Recognizing that SWIR (shortwave infrared reflectance) is able to reflect the asynchronous temporal variations in plant canopy water contents and that combining phenological shift and growth magnitude can enhance the classification of crop types, this study developed two canopy Greenness and Water (GW) content indices that are GW-I, which is a ratio of the kernel NDVI (normalized difference vegetation index) to SWIR to distinguish phenological shift of different crops, and GW-II, which is a product of kernel NDVI and SWIR to separate growth magnitude of different crops. To reconstruct gap-free field-scale GW-I and GW-II time series, historical and timely available multi-scale satellite observations, including Harmonized Landsat and Sentinel-2 (HLS), Visible Infrared Imaging Radiometer Suite (VIIRS), and Advanced Baseline Imager (ABI), are dynamically fused every week. The potential future GW-I and GW-II values are further predicted using a recently developed algorithm of Spatiotemporal Shape Matching Model (SSMM) and combined with the timely available time series for retrieving NRT phenometrics (greenup onset, mid-date of greenup phase, and maturity onset) every week during the crop greenup phase. Multiple Gaussian mixture models are used to independently estimate the weekly probability of corn and soybean types using three NRT crop phenometrics and the latest (≤3 days' latency) GW-II. Finally, the corn and soybean probabilities (estimated from GW-I phenometrics and GW-II crop growth magnitude together) are integrated to produce NRT corn and soybean mapping every week during the early growing season. The accuracy of NRT corn and soybean mapping is evaluated using the Cropland Data Layer (CDL). The result shows that our method can map corn and soybean in diverse croplands across the US Corn Belt with an overall accuracy of ∼90 % at a relatively early date (late July), although the local heterogeneity of agricultural landscapes potentially impacts the accuracy during the early stages. These findings underscore the feasibility of applying the developed method to produce near real-time corn and soybean mapping not only across the US Corn Belt but also in other countries and diverse agricultural regions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114605"},"PeriodicalIF":11.1,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142961948","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":"Consistency analysis of water diffuse attenuation between ICESat-2 and MODIS in Marginal Sea: A case study in China Sea","authors":"Zhenhua Zhang ,&nbsp;Siqi Zhang ,&nbsp;Michael J. Behrenfeld ,&nbsp;Cédric Jamet ,&nbsp;Paolo Di Girolamo ,&nbsp;Davide Dionisi ,&nbsp;Yongxiang Hu ,&nbsp;Xiaomei Lu ,&nbsp;Yuliang Pan ,&nbsp;Minzhe Luo ,&nbsp;Haiqing Huang ,&nbsp;Delu Pan ,&nbsp;Peng Chen","doi":"10.1016/j.rse.2025.114602","DOIUrl":"10.1016/j.rse.2025.114602","url":null,"abstract":"<div><div>Recent studies highlight the application of deriving the attenuation coefficient from spaceborne photon-counting lidar ATLAS/ICESat-2 over open oceans on global scales. However, its performance in the more optically complex and variable environments of marginal seas, which are more susceptible to human activity, has not been validated yet. In this study, we present an in-depth analysis of the consistency between diffuse attenuation coefficient (<span><math><msub><mi>K</mi><mi>d</mi></msub></math></span>) detection from MODIS and ICESat-2 in China's Marginal Seas. Findings demonstrate that ICESat-2 possesses strong capabilities for the retrieval of the attenuation coefficient across differing aquatic environments. However, discrepancies exist between the lidar system attenuation coefficient obtained from ICESat-2 and the diffuse attenuation coefficient determined by MODIS, influenced by factors such as multiple scattering. Implementation of a novel multiple scattering correction model demonstrates a notable ability in significantly reducing the inconsistency. Validation with in-situ Biogeochemical Argo float measurements reveals an enhancement in the accuracy of lidar-derived diffuse attenuation coefficients upon correction, with the mean absolute percent difference between lidar-derived <span><math><msub><mi>K</mi><mi>d</mi></msub></math></span> and Argo-<span><math><msub><mi>K</mi><mi>d</mi></msub></math></span> decreasing from 26 % to 15.7 %. The multiple scattering model developed can bridge the gap between the passive and active remote sensing detection and improve the reliability of lidar-derived attenuation coefficients. Fusing these two missions will greatly improve ocean observation capabilities, providing unprecedented opportunities for precise and comprehensive assessment of marine light environments. This approach has broad implications for ocean science and the application of satellite remote sensing in environmental studies.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114602"},"PeriodicalIF":11.1,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142961939","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":"A new machine-learning based cloud mask using harmonized data of two Meteosat generations shows a general decrease in cloudiness over Europe in recent decades","authors":"Sheetabh Gaurav ,&nbsp;Boris Thies ,&nbsp;Sebastian Egli ,&nbsp;Jörg Bendix","doi":"10.1016/j.rse.2025.114599","DOIUrl":"10.1016/j.rse.2025.114599","url":null,"abstract":"<div><div>Mid-latitude stratus clouds with large spatial extent are important cooling engines in a warming world, while other types of clouds may accelerate warming. However, our understanding of cloud feedback in a changing climate remains incomplete in both space and time. A key factor contributing to this knowledge gap is the lack of long-term observations with spatio-temporally continuous information over large areas. Satellite data from the geostationary orbit could help in this regard, but they were never originally intended for climatological studies and, as a result, provide inconsistent data between individual satellites of different satellite generations. However, for investigations on a time scale of 30 years and more, a homogeneous dataset of gross cloud occurrence is essential to assess changes in cloud cover over the last decades. In addition, such a dataset is the basis for further analyzing long-term changes in other cloud types such as fog and low stratus (FLS). The generation of temporally homogeneous cloud information over Europe requires a dataset that is consistent in space and time. The current study develops a new cloud detection scheme based on harmonized radiances obtained by cross-calibrating Meteosat First (MFG) MVIRI (Meteosat Visible Infra-Red Imager) and Second Generation (MSG) SEVIRI (Spinning Enhanced Visible and Infra-Red Imager) data. The harmonized data set consists of two MFG bands (thermal infrared IR and water vapour WV), which guarantee long-term (1991–2020) availability over the full diurnal cycle (24 h). The new cloud classification scheme is based on eXtreme Gradient Boosting (XGBoost) and uses the two MFG channels as primary predictors or features. While cloud detection using only two MFG channels is a challenging task, additional features such as temporal trends in brightness temperature (BT), its spatial heterogeneity, clear sky reference BTs, topographic variables, and solar and satellite angles are also considered in the XGBoost model. The EUMETSAT CM SAF SEVIRI cloud mask based on MSG SEVIRI is used in part as the binary target variable to train the XGBoost model (cloudy/clear-sky) and as a benchmark to test the performance of the newly developed cloud detection scheme. Test results show very good agreement with the benchmark CM SAF SEVIRI cloud mask, with an average Heidke Skill Score (HSS) of 0.83 for day-time and 0.8 for night-time cloud occurrence. Further testing shows that the new cloud mask clearly outperforms the existing EUMETSAT Optimal Cloud Analysis (OCA) dataset based on MSG visible and IR 10.8 μm channels. In particular, the FLS detection in our cloud mask was found to be superior to the OCA during night and boreal winter. Based on the trend analysis of the generated time series of cloud frequencies, we found a general decrease in cloudiness over the last 30 years in many parts of Europe.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114599"},"PeriodicalIF":11.1,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142961941","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}

{"title":"Nighttime light remote sensing image haze removal based on a deep learning model","authors":"Xiaofeng Ma,&nbsp;Qunming Wang,&nbsp;Xiaohua Tong","doi":"10.1016/j.rse.2024.114575","DOIUrl":"10.1016/j.rse.2024.114575","url":null,"abstract":"<div><div>Haze contamination is a quite common issue in nighttime light remote sensing (NTLRS) images. It significantly limits the application of NTLRS images, especially in human activity monitoring and socio-economic studies. Furthermore, NTLRS images usually struggle with noise. Although many remote sensing image haze removal methods have been developed, to the best of our knowledge, very few studies have been conducted on haze removal of NTLRS images. In this study, to address haze in NTLRS images, particularly the challenging issue of concomitant noise contamination, we developed a nighttime light haze removal network (NTLHR-Net). Specifically, to capture effective spatial structural information (dominated by sparse or spot-like shapes) and eliminate joint haze and noise contamination, an encoder-decoder structure coupled with a mixture attention block was developed. Moreover, a multiscale convolutional block was employed iteratively in the middle of the encoder-decoder structure to distill the spatial structural information in high-dimensional spaces. In the experiments, the NTLHR-Net method was compared with seven state-of-the-art haze removal methods for both simulated and real hazy NTLRS images with different spatial structures. The results demonstrate the feasibility of the proposed NTLHR-Net method in cases with various haze and noise contamination. This study provides a new solution for increasing the quality of the observed NTLRS images for downstream applications.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114575"},"PeriodicalIF":11.1,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142961938","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":"Logic combination and diagnostic rule-based method for consistency assessment and its application to cross-sensor calibrated nighttime light image products","authors":"Zihao Zheng ,&nbsp;Qiming Zheng ,&nbsp;Zhifeng Wu ,&nbsp;Zheng Cao ,&nbsp;Hong Zhu ,&nbsp;Yingbiao Chen ,&nbsp;Benyan Jiang ,&nbsp;Yingfeng Guo ,&nbsp;Dong Xu ,&nbsp;Francesco Marinello","doi":"10.1016/j.rse.2025.114598","DOIUrl":"10.1016/j.rse.2025.114598","url":null,"abstract":"<div><div>With observations from the Defense Meteorological Satellite Programme's Operational Line Scanning System (DMSP/OLS, 1992–2013) and the Suomi National Polar-Orbiting Partnership's Visible Infrared Imaging Radiometer Suite (NPP/VIIRS, 2012-), night-time light (NTL) imagery has become one of the most unique and widely-used data for understanding human activities due to its unique low-light detection capability and close correlation with socioeconomic development. Its capability for long-term observation has been further enhanced by the recent advancement in cross-sensor calibrated NTL products, which address the inconsistency between DMSP-OLS and VIIRS data and combine them together as extended NTL time series (ENTL). Despite the prosperity of cross-sensor calibration models, comprehensive and in-depth assessments of temporal consistency of their resulting ENTL products remain scarce or constrained at an aggregated scale. This study developed a new assessment scheme based on logical combinations and diagnosis rules for NTL intensity trends. Compared to previous schemes, the proposed scheme offers significant advantages in fine-grained, non-subjective intervention and semi-automation for NTL intensity consistency assessment, and its derived consistency profile layer of ENTL products can more effectively inform end-users in ENTL products selection of products and account for uncertainty in their analysis. Based on the assessment, we generated a standard light intensity dynamic trend layer (SNID) to illustrate the characteristics of global NTL intensity variations over the period from 1992 to 2020 and the applied this layer to validate the effectiveness and applicability of six most representative ENTL products. Our results showed that the scheme can automatically generate NTL intensity consistency features at a finer spatial scale than the previous TSOL-based method, and revealed for the first time a fact that has been neglected before, that is, there was a distinct gap in NTL intensity consistency among different ENTL products, with the percentage of well-matched units fluctuating from 52.81 % to 84.46 %. These variations were particularly evident in regions with high light intensity, rural areas, and high-latitude regions, reflecting the influence of spatial heterogeneity and calibration strategies. In summary, this study refines the detection process for the consistency profile of ENTL products, significantly enhancing their reliability in socioeconomic analysis and urban expansion research. By revealing the intensity consistency differences among various products, it provides critical guidance for users in data selection and application, helping to better address uncertainty.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114598"},"PeriodicalIF":11.1,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142937518","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":"Unveiling year-round cropland cover by soil-specific spectral unmixing of Landsat and Sentinel-2 time series","authors":"Felix Lobert ,&nbsp;Marcel Schwieder ,&nbsp;Jonas Alsleben ,&nbsp;Tom Broeg ,&nbsp;Katja Kowalski ,&nbsp;Akpona Okujeni ,&nbsp;Patrick Hostert ,&nbsp;Stefan Erasmi","doi":"10.1016/j.rse.2024.114594","DOIUrl":"10.1016/j.rse.2024.114594","url":null,"abstract":"<div><div>Croplands are essential for food security but also impact the environment, biodiversity, and climate. Understanding, monitoring, modeling, and managing these impacts require accurate, comprehensive information on cropland vegetation cover.</div><div>This study aimed to continuously monitor the state and vegetative processes of cropland, focusing on the assessment of bare soil and its cover with photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV) at the national level. We employed regression-based unmixing techniques using time series of Sentinel-2 and Landsat imagery to quantify cover fractions of NPV, PV, and soil during the agricultural growing season. Our approach extends existing spectral unmixing methods by incorporating a novel soil-specific unmixing process based on a soil reflectance composite. The extension accounts for variations in the spectral characteristics of soils which is particularly relevant for large-scale monitoring of annually cultivated croplands, as the spectral soil properties can vary considerably at national level and periods of bare soil are frequent.</div><div>All cover fractions were predicted with mean absolute errors between 0.13 and 0.19. Introducing soil-specific unmixing reduced the mean absolute error of the predictions for soil by 11.3 % and NPV by 15.1 % without compromising PV predictions, particularly benefiting areas with bright soils. These findings demonstrate the efficacy of our method in accurately predicting crop cover throughout the cultivation period and underline the added value of incorporating the soil adjustment into the unmixing workflow.</div><div>The contributions of this research are twofold: first, it provides essential data for the continuous monitoring of cropland cover, supporting agricultural carbon cycle and soil erosion modeling. Second, it enables further investigation into cropland management practices, such as cover cropping and tillage, through time series analysis techniques. This work underscores the potential of advanced spectral unmixing methods for enhancing agricultural monitoring and management strategies.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114594"},"PeriodicalIF":11.1,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939606","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}

{"title":"Using an improved radiative transfer model to estimate leaf area index, fractional vegetation cover and leaf inclination angle from Himawari-8 geostationary satellite data","authors":"Yaoyao Chen ,&nbsp;Xihan Mu ,&nbsp;Tim R. McVicar ,&nbsp;Yuanyuan Wang ,&nbsp;Yuhan Guo ,&nbsp;Kai Yan ,&nbsp;Yongkang Lai ,&nbsp;Donghui Xie ,&nbsp;Guangjian Yan","doi":"10.1016/j.rse.2024.114595","DOIUrl":"10.1016/j.rse.2024.114595","url":null,"abstract":"<div><div>Quantitative vegetation structural parameters such as leaf area index (LAI), fractional vegetation cover (FVC), and leaf inclination angle (LIA) are important for controlling biophysical processes, such as carbon dynamics and transpiration. The generation of remote sensing vegetation structural products using geostationary satellite data may allow for near real-time monitoring of vegetation change and associated biophysical processes. However, operational algorithms for retrieving the vegetation structure from geostationary satellite imagery are rare. Herein we developed a bidirectional model of reflectance and difference vegetation index (DVI) which requires LAI and other vegetation parameters as inputs, allowing these parameters to be estimated <em>via</em> an optimization scheme. The developed radiative transfer model specifically considers the high-frequency and multi-angle features of geostationary satellite data to separate the sun-angle related variables from the sun-angle independent variables. This parameterization facilitates the retrieval of vegetation structural products by reducing the number of variables while maintaining the generality of the model. The inversion of this physical radiative transfer model produced daily LAI and FVC with a spatial resolution of 1 km from the bidirectional reflectance factor (BRF) of Himawari-8 high-frequency observations for Australia. In contrast to most other readily available LAI products, this approach to generating Himawari-8 LAI did not rely on MODIS LAI or land cover data. Compared with field-measured data, the RMSE of Himawari-8 LAI was 1.009 and the bias was −0.354, and for FVC the RMSE was 0.132 and the bias was −0.014; these were more accurate than MODIS LAI and GLASS LAI, and GEOV3 FVC, respectively. The intercomparison of these products showed that the Himawari-8 LAI and FVC products performed well having realistic spatio-temporal distributions. For the first time, a mean leaf inclination angle (MLIA) product was generated only using satellite data. Similarity was found between the spatial patterns of MLIA and the land cover map over Australia. Independent validation data showed that the uncertainty of MLIA was generally less than 10°. The high-frequency nature of geostationary satellite imagery coupled with the radiative transfer model developed herein enables the derived vegetation structural products to facilitate improved monitoring of both short-term (<em>i.e.</em>, daily to weekly) and long-term (<em>i.e.</em>, seasonal to annual) vegetation dynamics.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114595"},"PeriodicalIF":11.1,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142936311","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":"Decoding primary forest changes in Haiti and the Dominican Republic using Landsat time series","authors":"Falu Hong ,&nbsp;S. Blair Hedges ,&nbsp;Zhiqiang Yang ,&nbsp;Ji Won Suh ,&nbsp;Shi Qiu ,&nbsp;Joel Timyan ,&nbsp;Zhe Zhu","doi":"10.1016/j.rse.2024.114590","DOIUrl":"10.1016/j.rse.2024.114590","url":null,"abstract":"<div><div>Forest loss has greatly reduced habitats and threatened Earth's biodiversity. Primary forest (PF) has an irreplaceable role in supporting biodiversity compared with secondary forest (SF). Therefore, distinguishing PF and SF using remote sensing observations is critical for evaluating the impact of forest loss on biodiversity. However, continuous monitoring of PF loss through remote sensing time series observations remains largely unexplored, particularly in developing tropical regions. In this study, we used the COLD algorithm (COntinuous monitoring of Land Disturbance) and Landsat time series data to quantify PF loss on the island of Hispaniola, comprising Haiti and the Dominican Republic, from 1996 to 2022. We considered the resilience of PF to different disturbance agents and identified the primary drivers of PF loss in Hispaniola through a sample-based approach. Accuracy assessment based on the stratified random sample shows that the overall accuracy of land cover classification is 80.5% (±5.2%) [95% confidence interval]. The user's, producer's, and overall accuracies of PF loss detection are 68.8% (±9.3%), 73.6% (±38%), and 99.4% (±0.5%), respectively. Map-based analysis reveals a more pronounced decline in PF coverage in Haiti (0.75% to 0.44% at 324 ha/year) compared to the Dominican Republic (7.14% to 5.67% at 2,704 ha/year), with substantial PF loss occurring both inside and outside protected areas. Furthermore, Haiti exhibits a higher degree of PF fragmentation, characterized by smaller and fewer PF patches, than the Dominican Republic, posing significant challenges for biodiversity conservation. The remaining PFs are found on steeper slopes in both Haiti and the Dominican Republic, suggesting that flatter, more accessible areas are more vulnerable to PF loss. Fire, tree-cutting, and hurricanes were identified as the primary drivers of PF loss, accounting for 65.7%, 20.9%, and 9.0% of the PF loss area in Hispaniola, respectively. These findings underscore the urgent need for conservation policies to protect remaining PF in Hispaniola, particularly in Haiti.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114590"},"PeriodicalIF":11.1,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142936310","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}

{"title":"A Sentinel-1 SAR-based global 1-km resolution soil moisture data product: Algorithm and preliminary assessment","authors":"Dong Fan ,&nbsp;Tianjie Zhao ,&nbsp;Xiaoguang Jiang ,&nbsp;Almudena García-García ,&nbsp;Toni Schmidt ,&nbsp;Luis Samaniego ,&nbsp;Sabine Attinger ,&nbsp;Hua Wu ,&nbsp;Yazhen Jiang ,&nbsp;Jiancheng Shi ,&nbsp;Lei Fan ,&nbsp;Bo-Hui Tang ,&nbsp;Wolfgang Wagner ,&nbsp;Wouter Dorigo ,&nbsp;Alexander Gruber ,&nbsp;Francesco Mattia ,&nbsp;Anna Balenzano ,&nbsp;Luca Brocca ,&nbsp;Thomas Jagdhuber ,&nbsp;Jean-Pierre Wigneron ,&nbsp;Jian Peng","doi":"10.1016/j.rse.2024.114579","DOIUrl":"10.1016/j.rse.2024.114579","url":null,"abstract":"<div><div>High spatial resolution of satellite-based soil moisture (SM) data are essential for hydrological, meteorological, ecological, and agricultural studies. Especially, for watershed hydrological simulation and crop water stress analysis, 1-km resolution SM data have attracted considerable attention. In this study, a dual-polarization algorithm (DPA) for SM estimation is proposed to produce a global-scale, 1-km resolution SM dataset (S1-DPA) using the Sentinel-1 synthetic aperture radar (SAR) data. Specifically, a forward model was constructed to simulate the backscatter observed by the Sentinel-1 dual-polarization SAR, and SM retrieval was achieved by minimizing the simulation error for different soil and vegetation states. The produced S1-DPA data products cover the global land surface for the period 2016–2022 and include both ascending and descending data with an observation frequency of 3–6 days for Europe and 6–12 days for the other regions. The validation results show that the S1-DPA reproduces the spatio-temporal variation characteristics of the ground-observed SM, with an unbiased root mean squared difference (ubRMSD) of 0.077 m³/m³. The generated 1-km SM product will facilitate the application of high-resolution SM data in the field of hydrology, meteorology and ecology.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114579"},"PeriodicalIF":11.1,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142936308","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}

{"title":"Abiotic influences on continuous conifer forest structure across a subalpine watershed","authors":"H. Marshall Worsham ,&nbsp;Haruko M. Wainwright ,&nbsp;Thomas L. Powell ,&nbsp;Nicola Falco ,&nbsp;Lara M. Kueppers","doi":"10.1016/j.rse.2024.114587","DOIUrl":"10.1016/j.rse.2024.114587","url":null,"abstract":"<div><div>Understanding the abiotic drivers of high-elevation forest physiognomy is essential for forecasting how mountain ecosystems will respond to emerging environmental pressures. Most prior studies of these relationships have relied on small samples of the full landscape, resulting in limited power to detect dominant covariates and their interactions. Here we report the first evaluation of abiotic influences on a complement of accurate, wall-to-wall estimates of conifer forest structure and composition at the watershed scale. In a subalpine conifer domain in the Colorado Rocky Mountains (USA), we developed a novel method for deriving stand structure metrics from waveform LiDAR data, which showed high fidelity with field inventory. We quantified the relationships between structural and compositional metrics and climate, topographic, edaphic, and geologic factors. Our results showed that peak snow water equivalent (SWE), snow disappearance rate, and elevation explained most of the variation in forest structure. The highest stand density, basal area, maximum canopy height, and quadratic mean diameter occurred in sites with SWE around one standard deviation below mean, but with long snow residence times. Stand density decreased linearly with elevation, while other metrics peaked between 3000 m.a.s.l. and 3200 m.a.s.l. Substrate properties had weaker influence. Continuous mapping of through-canopy forest structure enabled our novel findings of the dominant role of snowpack in explaining structural and compositional variation, and of elevation thresholds. Our reproducible approach facilitates assessment of forest-topoclimate relationships in other conifer-dominated landscapes and improves understanding of the baseline patterns controlling forest structure, which is needed for predicting long-term ecological change.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114587"},"PeriodicalIF":11.1,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142935356","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}