Remote Sensing of Environment最新文献

{"title":"Towards a point cloud understanding framework for forest scene semantic segmentation across forest types and sensor platforms","authors":"Hao Lu ,&nbsp;Bowen Li ,&nbsp;Gang Yang ,&nbsp;Guangpeng Fan ,&nbsp;Han Wang ,&nbsp;Yong Pang ,&nbsp;Zuyuan Wang ,&nbsp;Yining Lian ,&nbsp;Haifeng Xu ,&nbsp;Huan Huang","doi":"10.1016/j.rse.2024.114591","DOIUrl":"10.1016/j.rse.2024.114591","url":null,"abstract":"<div><div>Foliage, wood (i.e., trunk, branch), ground, and lower objects (i.e., grass, shrubs), are key semantic components of forests that play different roles in the forest ecosystem. For understanding forest ecosystem structure and function, Light Detection And Ranging (LiDAR) point cloud is a valuable form of remote sensing observation. The understanding intensively relies on precisely performing semantic segmentation task to segment semantic components from forest point cloud data. However, the semantic segmentation of massive point cloud data from forest scenes remains a significant challenge. The forest environment is highly heterogeneous and complex due to tree species and terrain conditions. Different climate zones lead to varying canopy characteristics and the diversity of LiDAR platforms delivers inconsistent point cloud properties. Heuristic approaches and conventional machine learning approaches inevitably suffer from poor generalization. Additionally, most deep learning methods lack a dedicated network design to address the characteristics of forests. This paper introduces Sen-net, a point cloud understanding network specifically constructed for semantic component segmentation of forest scene point cloud. Sen-net implements three modules tailored for forest characteristics. First, a spatial context enhancement module (SCEM) is designed for providing both global and dataset-level perspectives to mine geometric information and robust features hidden in heterogeneous forest. Second, a semantic-driven detail enrichment module (SDEM) is incorporated to preserve rich geometric details and semantic information thereby enhancing the learning of complex structures in the forests. Finally, an adaptive guidance flow (AGF) is added to seamlessly fuse the semantic and detailed features. Comprehensive experiments were conducted on both self-built Lin3D dataset and public datasets. Sen-net achieved an OA of 97.6 % and 85.1 % MIoU on the Lin3D dataset, and an OA of 94.5 % and 78.2 % MIoU on the public dataset FOR-instance. Results show that Sen-net outperformed the representative forest scene point cloud semantic segmentation approaches and state-of-the-art deep learning networks, and it has the potential to generalize to point cloud data collected by LiDAR from other platforms. It is concluded that Sen-net is a powerful and robust framework with substantial potential for being widely and deeply explored in forest ecosystem studies.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114591"},"PeriodicalIF":11.1,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142981852","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 multi-layer perceptron approach for SIF retrieval in the O2-A absorption band from hyperspectral imagery of the HyPlant airborne sensor system","authors":"Jim Buffat ,&nbsp;Miguel Pato ,&nbsp;Kevin Alonso ,&nbsp;Stefan Auer ,&nbsp;Emiliano Carmona ,&nbsp;Stefan Maier ,&nbsp;Rupert Müller ,&nbsp;Patrick Rademske ,&nbsp;Bastian Siegmann ,&nbsp;Uwe Rascher ,&nbsp;Hanno Scharr","doi":"10.1016/j.rse.2024.114596","DOIUrl":"10.1016/j.rse.2024.114596","url":null,"abstract":"<div><div>Accurate estimation of solar-induced fluorescence (SIF) from passively sensed hyperspectral remote sensing data has been identified as fundamental in assessing the photosynthetic activity of plants for various scientific and ecological applications at different spatial scales. Different techniques to derive SIF have been developed over the last decades. In view of ESA’s upcoming Earth Explorer satellite mission FLEX aiming to provide high-quality global imagery for SIF retrieval an increased interest is placed in physical approaches. We present a novel method to retrieve SIF in the O<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>-A absorption band of hyperspectral imagery acquired by the HyPlant sensor system. It aims at a tight integration of physical radiative transfer principles and self-supervised neural network training. To this end, a set of spatial and spectral constraints and a specific loss formulation are adopted. In a validation study we find good agreement between our approach and established retrieval methods as well as with in-situ top-of-canopy SIF measurements. In two application studies, we additionally find evidence that the estimated SIF (i) satisfies a first-order model of diurnal SIF variation and (ii) locally adapts the estimated optical depth in topographically variable terrain.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114596"},"PeriodicalIF":11.1,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142986893","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":"Mechanism and algorithm for addressing the impact of multiple scattering on surface elevation extraction in photon-counting LiDAR data","authors":"Zijia Wang ,&nbsp;Sheng Nie ,&nbsp;Xuebo Yang ,&nbsp;Cheng Wang ,&nbsp;Xiaohuan Xi ,&nbsp;Xiaoxiao Zhu ,&nbsp;Bisheng Yang","doi":"10.1016/j.rse.2025.114603","DOIUrl":"10.1016/j.rse.2025.114603","url":null,"abstract":"<div><div>The Ice, Cloud and Land Elevation Satellite-2 (ICESat-2), equipped with the advanced topographic laser altimeter system (ATLAS), utilizes an innovative photon-counting LiDAR technique to conduct precise global elevation measurements. While it offers significant advantages in surface elevation retrieval, its performance can be compromised by substantial multiple scattering in highly scattering turbid media, leading to surface elevation deviations. Despite this issue, the mechanisms and effective algorithms to address the impact of multiple scattering in photon-counting LiDAR data have remained largely unexplored. To fill this gap, this study employs the DART-Lux model to simulate the photon-counting LiDAR signals, incorporating laser multiple scattering, and conducts a comprehensive analysis of its effect on photon spatial distribution and surface elevation retrieval. Additionally, a novel method based on the photon distribution characteristics is proposed to remove multiple scattering photons for surface elevation retrieval in highly scattering turbid media, which combines 2D Cloth Simulation Filtering (CSF) and Empirical Mode Decomposition (EMD) with adaptive classification threshold. Experimental results reveal that multiple scattering photons predominantly accumulate below the surface, with photon density decreasing as elevation declines. This causes a downward shift in the elevation density peak, resulting in surface elevation underestimation. The proposed method in this study effectively mitigates the impact of multiple scattering, a challenge that conventional surface extraction algorithms struggle to address. Through analyzing the surface types in different scenarios including day/night and strong/weak beams, the results indicate that our proposed method outperforms other methods, with an average bias of 0.009 m, MAE of 0.032 m, RMSE of 0.059 m, and R² of 0.990. Our method demonstrates high robustness and precision, particularly over land ice. In summary, this study is the first to not only analyze the impact of multiple scattering on the spatial distribution of photons and surface elevation retrieval, but also provide an effective method for separating multiple scattering photons in photon-counting LiDAR data.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114603"},"PeriodicalIF":11.1,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142981851","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 parametric approach for global estimation of forest above-ground biomass with SMOS and SMAP L-band radiometer data","authors":"Oliver Cartus ,&nbsp;Maurizio Santoro ,&nbsp;Carlos Jimenez ,&nbsp;Catherine Prigent ,&nbsp;Mike Schwank ,&nbsp;Urs Wegmüller","doi":"10.1016/j.rse.2025.114601","DOIUrl":"10.1016/j.rse.2025.114601","url":null,"abstract":"<div><div>L-band radiometer data collected by the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions have shown potential for mapping the spatial distribution and temporal changes of the above-ground biomass (AGB) of forests. Most studies focussed on the relationships observed between AGB and estimates of the vegetation optical depth (VOD) derived from L-band radiometer data. We here present an approach for retrieving AGB from SMOS and SMAP brightness temperatures which builds upon existing AGB retrieval frameworks developed for active microwave data. A physically-based model was adapted to relate brightness temperatures to the percent canopy cover and height available from space-borne optical and LiDAR missions and, via modelled relationships between canopy cover, height, and AGB, to AGB. An initial set of 36 global AGB maps was produced from 10-days composites of a polarimetric index calculated from H and V polarization SMOS and SMAP brightness temperatures acquired in 2016. When compared to an ESA Climate Change Initiative Biomass AGB map, the AGB estimates produced from SMOS and SMAP presented a reasonable agreement with low systematic biases and explained, dependent on the type of forest, between 30 % and 80 % of the AGB variability in the reference map. A comparison with AGB reference information derived from plot-level inventory data for a limited number of sites across the major forest biomes indicated the merit of the suggested retrieval approach but also revealed a need for improving the retrieval algorithm locally.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114601"},"PeriodicalIF":11.1,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142961940","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":"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}