Dataset

This dataset was created by Mengmeng Li

Released under World Bank Dataset Terms of Use

Contents

This dataset is an update of our previous dataset published on doi:10.1594/PANGAEA.912597. Based on 11 well-acknowledged global-scale microwave remote sensing-based surface soil moisture products, and with 9 main quality impact factors of microwave-based soil moisture retrieval incorporated, we developed the Remote Sensing-based global Surface Soil Moisture dataset (RSSSM, 2003~2020) through a complicated neural network approach. The spatial resolution of RSSSM is 0.1°, while the temporal resolution is approximately 10 days. The original dataset covered 2003~2018, but now it has been updated to 2020. RSSSM dataset is outstanding in terms of temporal continuity, and has full spatial coverage except for snow, ice and water bodies. The comparison against the global-scale in-situ soil moisture measurements indicates that RSSSM has a higher spatial and temporal accuracy than most of the frequently-used global/regional long-term surface soil moisture datasets. In addition, although RSSSM is remote sensing based, without the incorporation of any precipitation data or records, its interannual variation generally conforms with that of precipitation (e.g., the GPM IMERG precipitation data) and Standardized Precipitation Evapotranspiration Index (SPEI). Moreover, RSSSM can also reflect the impact of human activities, e.g., urbanization, cropland irrigation and afforestation on soil moisture changes to some degree. The data is in 'Tiff' format, and the size after compression is 2.44 GB. The relevant data describing paper has been published in the Journal 'Earth System Science Data' in 2021.

Report of Remote Sensing Satellites Market is covering the summarized study of several factors encouraging the growth of the market such as market size, market type, major regions and end user applications. By using the report customer can recognize the several drivers that impact and govern the market. The report is describing the several types of Remote Sensing Satellites Industry. Factors that are playing the major role for growth of specific type of product category and factors that are motivating the status of the market.

Tabular data from the manuscript "Monitoring standing herbaceous biomass and thresholds in semiarid rangelands from harmonized Landsat 8 and Sentinel-2 imagery to support within-season adaptive management" published in the journal Remote Sensing of Environment. Data are plot-scale values of (1) ground-sampled herbaceous standing biomass estimated using visual obstruction (VO) methods, (2) ground sampled percent cover by vegetation type using the line-point intercept (LPI) method, (3) percent midgrass derived from hyperspectral aerial imagery (1 m) collected by the NEON AOP (see Gaffney et al. 2021 cited within the manuscript), and (4) satellite-derived indices and bands. Only seasonal data used to develop the standing biomass model is included. The bounding box coordinates of each plot are also included. Resources in this dataset:Resource Title: Tabular ground and satellite-derived data. File Name: Kearney_Biomass_from_HLS_data.csvResource Description: Seasonal plot-scale tabular ground and satellite-derived data along with four fields (minx, miny, etc.) for the bounding box of the plots (EPSG:32613 - UTM 13N, WGS 84). Data includes (1) ground-sampled biomass estimate using visual obstruction (VO) poles, (2) ground sampled vegetation cover estimated using the line-point intercept (LPI) method, (3) percent mid-grass estimated from a plant community map derived from hyperspectral aerial imagery (1 m) acquired by the NEON AOP, (4) satellite-derived indices and bands interpolated daily from the Harmonized Landsat-Sentinel (HLS) dataset (30 m). See Metadata_column_headers.csv for descriptions of the fields (columns) in this dataset.Resource Title: Metadata: Description of column headers for tabular dataset. File Name: Kearney_Biomass_from_HLS_data_metadata.csvResource Description: Descriptions of each field (column) in the tabular dataset.

The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/).

This dataset is the MUSES global LAI product at 1km spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao et al., 2014; Xiao et al., 2016). The MUSES LAI product is spatially complete and temporally continuous.

This dataset is the MUSES LAI product in 2019. Please click here to download the MUSES LAI product in 2018, and click here to download the MUSES LAI product in 2020.

Dataset Characteristics:

• Spatial Coverage: Global
• Temporal Coverage: 2019
• Spatial Resolution: 1km
• Temporal Resolution: 8 days
• Projection: Sinusoidal
• Data Format: HDF
• Scale: 0.01
• Valid Range: 0 – 1000

Citation (Please cite this paper whenever these data are used):

1. Xiao Zhiqiang, et al. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. IEEE Transactions on Geoscience and Remote Sensing, 52, 209-223.
2. Xiao Zhiqiang, et al. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 54, 5301-5318.
3. Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. International Journal of Remote Sensing, 43(4), 1199-1225.
4. Xiao Zhiqiang, et al. (2017). Evaluation of four long time-series global leaf area index products. Agricultural and Forest Meteorology, 246, 218-230.

If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

This dataset contains data used in the associated publication in the International Journal of Remote Sensing.Wilson, Natalie R., and Laura M. Norman. 2018. “Analysis of Vegetation Recovery Surrounding a Restored Wetland Using the Normalized Difference Infrared Index (NDII) and Normalized Difference Vegetation Index (NDVI).” International Journal of Remote Sensing 39 (10): 3243–74. https://doi.org/10.1080/01431161.2018.1437297.The geodatabase contains four feature classes: AOI, MajorZone, MinorZone, and Green2007.Publication abstract: Watershed restoration efforts seek to rejuvenate vegetation, biological diversity, and land productivity at Cienega San Bernardino, an important wetland in southeastern Arizona and northern Sonora, Mexico. Rock detention and earthen berm structures were built on the Cienega San Bernardino over the course of four decades, beginning in 1984 and continuing to the present. Previous research findings show that restoration supports and even increases vegetation health despite ongoing drought conditions in this arid watershed. However, the extent of restoration impacts is still unknown despite qualitative observations of improvement in surrounding vegetation amount and vigor. We analyzed spatial and temporal trends in vegetation greenness and soil moisture by applying the normalized difference vegetation index (NDVI) and normalized difference infrared index (NDII) to one dry summer season Landsat path/row from 1984 to 2016. The study area was divided into zones and spectral data for each zone was analyzed and compared with precipitation record using statistical measures including linear regression, Mann– Kendall test, and linear correlation. NDVI and NDII performed differently due to the presence of continued grazing and the effects of grazing on canopy cover; NDVI was better able to track changes in vegetation in areas without grazing while NDII was better at tracking changes in areas with continued grazing. Restoration impacts display higher greenness and vegetation water content levels, greater increases in greenness and water content through time, and a decoupling of vegetation greenness and water content from spring precipitation when compared to control sites in nearby tributary and upland areas. Our results confirm the potential of erosion control structures to affect areas up to 5 km downstream of restoration sites over time and to affect 1 km upstream of the sites.

This is a supplement to the journal article titled Uncertainty in Remote Sensing of Streams using Noncontact Radars by Khan et al., submitted to the Journal of Hydrology. Dataset includes time series of variables collected by 8 stream radars. A kmz file is included for the stream radar locations.

This dataset was created by Marc Schwaerzel (marc.schwaerzel@empa.ch) and is intended to get along with the Schwaerzel et al. (2021) AMT publication (amt-2020-146) . The data and the data structure is described in the readme.md text file.

The dataset contains:

- libRadtran output (radiances and AMFs)

- Synthetic SCDs

✅ International Journal of Engineering and Advanced Technology ISSN - ResearchHelpDesk - International Journal of Engineering and Advanced Technology (IJEAT) is having Online-ISSN 2249-8958, bi-monthly international journal, being published in the months of February, April, June, August, October, and December by Blue Eyes Intelligence Engineering & Sciences Publication (BEIESP) Bhopal (M.P.), India since the year 2011. It is academic, online, open access, double-blind, peer-reviewed international journal. It aims to publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. All submitted papers will be reviewed by the board of committee of IJEAT. Aim of IJEAT Journal disseminate original, scientific, theoretical or applied research in the field of Engineering and allied fields. dispense a platform for publishing results and research with a strong empirical component. aqueduct the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. seek original and unpublished research papers based on theoretical or experimental works for the publication globally. publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. impart a platform for publishing results and research with a strong empirical component. create a bridge for a significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. solicit original and unpublished research papers, based on theoretical or experimental works. Scope of IJEAT International Journal of Engineering and Advanced Technology (IJEAT) covers all topics of all engineering branches. Some of them are Computer Science & Engineering, Information Technology, Electronics & Communication, Electrical and Electronics, Electronics and Telecommunication, Civil Engineering, Mechanical Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. The main topic includes but not limited to: 1. Smart Computing and Information Processing Signal and Speech Processing Image Processing and Pattern Recognition WSN Artificial Intelligence and machine learning Data mining and warehousing Data Analytics Deep learning Bioinformatics High Performance computing Advanced Computer networking Cloud Computing IoT Parallel Computing on GPU Human Computer Interactions 2. Recent Trends in Microelectronics and VLSI Design Process & Device Technologies Low-power design Nanometer-scale integrated circuits Application specific ICs (ASICs) FPGAs Nanotechnology Nano electronics and Quantum Computing 3. Challenges of Industry and their Solutions, Communications Advanced Manufacturing Technologies Artificial Intelligence Autonomous Robots Augmented Reality Big Data Analytics and Business Intelligence Cyber Physical Systems (CPS) Digital Clone or Simulation Industrial Internet of Things (IIoT) Manufacturing IOT Plant Cyber security Smart Solutions – Wearable Sensors and Smart Glasses System Integration Small Batch Manufacturing Visual Analytics Virtual Reality 3D Printing 4. Internet of Things (IoT) Internet of Things (IoT) & IoE & Edge Computing Distributed Mobile Applications Utilizing IoT Security, Privacy and Trust in IoT & IoE Standards for IoT Applications Ubiquitous Computing Block Chain-enabled IoT Device and Data Security and Privacy Application of WSN in IoT Cloud Resources Utilization in IoT Wireless Access Technologies for IoT Mobile Applications and Services for IoT Machine/ Deep Learning with IoT & IoE Smart Sensors and Internet of Things for Smart City Logic, Functional programming and Microcontrollers for IoT Sensor Networks, Actuators for Internet of Things Data Visualization using IoT IoT Application and Communication Protocol Big Data Analytics for Social Networking using IoT IoT Applications for Smart Cities Emulation and Simulation Methodologies for IoT IoT Applied for Digital Contents 5. Microwaves and Photonics Microwave filter Micro Strip antenna Microwave Link design Microwave oscillator Frequency selective surface Microwave Antenna Microwave Photonics Radio over fiber Optical communication Optical oscillator Optical Link design Optical phase lock loop Optical devices 6. Computation Intelligence and Analytics Soft Computing Advance Ubiquitous Computing Parallel Computing Distributed Computing Machine Learning Information Retrieval Expert Systems Data Mining Text Mining Data Warehousing Predictive Analysis Data Management Big Data Analytics Big Data Security 7. Energy Harvesting and Wireless Power Transmission Energy harvesting and transfer for wireless sensor networks Economics of energy harvesting communications Waveform optimization for wireless power transfer RF Energy Harvesting Wireless Power Transmission Microstrip Antenna design and application Wearable Textile Antenna Luminescence Rectenna 8. Advance Concept of Networking and Database Computer Network Mobile Adhoc Network Image Security Application Artificial Intelligence and machine learning in the Field of Network and Database Data Analytic High performance computing Pattern Recognition 9. Machine Learning (ML) and Knowledge Mining (KM) Regression and prediction Problem solving and planning Clustering Classification Neural information processing Vision and speech perception Heterogeneous and streaming data Natural language processing Probabilistic Models and Methods Reasoning and inference Marketing and social sciences Data mining Knowledge Discovery Web mining Information retrieval Design and diagnosis Game playing Streaming data Music Modelling and Analysis Robotics and control Multi-agent systems Bioinformatics Social sciences Industrial, financial and scientific applications of all kind 10. Advanced Computer networking Computational Intelligence Data Management, Exploration, and Mining Robotics Artificial Intelligence and Machine Learning Computer Architecture and VLSI Computer Graphics, Simulation, and Modelling Digital System and Logic Design Natural Language Processing and Machine Translation Parallel and Distributed Algorithms Pattern Recognition and Analysis Systems and Software Engineering Nature Inspired Computing Signal and Image Processing Reconfigurable Computing Cloud, Cluster, Grid and P2P Computing Biomedical Computing Advanced Bioinformatics Green Computing Mobile Computing Nano Ubiquitous Computing Context Awareness and Personalization, Autonomic and Trusted Computing Cryptography and Applied Mathematics Security, Trust and Privacy Digital Rights Management Networked-Driven Multicourse Chips Internet Computing Agricultural Informatics and Communication Community Information Systems Computational Economics, Digital Photogrammetric Remote Sensing, GIS and GPS Disaster Management e-governance, e-Commerce, e-business, e-Learning Forest Genomics and Informatics Healthcare Informatics Information Ecology and Knowledge Management Irrigation Informatics Neuro-Informatics Open Source: Challenges and opportunities Web-Based Learning: Innovation and Challenges Soft computing Signal and Speech Processing Natural Language Processing 11. Communications Microstrip Antenna Microwave Radar and Satellite Smart Antenna MIMO Antenna Wireless Communication RFID Network and Applications 5G Communication 6G Communication 12. Algorithms and Complexity Sequential, Parallel And Distributed Algorithms And Data Structures Approximation And Randomized Algorithms Graph Algorithms And Graph Drawing On-Line And Streaming Algorithms Analysis Of Algorithms And Computational Complexity Algorithm Engineering Web Algorithms Exact And Parameterized Computation Algorithmic Game Theory Computational Biology Foundations Of Communication Networks Computational Geometry Discrete Optimization 13. Software Engineering and Knowledge Engineering Software Engineering Methodologies Agent-based software engineering Artificial intelligence approaches to software engineering Component-based software engineering Embedded and ubiquitous software engineering Aspect-based software engineering Empirical software engineering Search-Based Software engineering Automated software design and synthesis Computer-supported cooperative work Automated software specification Reverse engineering Software Engineering Techniques and Production Perspectives Requirements engineering Software analysis, design and modelling Software maintenance and evolution Software engineering tools and environments Software engineering decision support Software design patterns Software product lines Process and workflow management Reflection and metadata approaches Program understanding and system maintenance Software domain modelling and analysis Software economics Multimedia and hypermedia software engineering Software engineering case study and experience reports Enterprise software, middleware, and tools Artificial intelligent methods, models, techniques Artificial life and societies Swarm intelligence Smart Spaces Autonomic computing and agent-based systems Autonomic computing Adaptive Systems Agent architectures, ontologies, languages and protocols Multi-agent systems Agent-based learning and knowledge discovery Interface agents Agent-based auctions and marketplaces Secure mobile and multi-agent systems Mobile agents SOA and Service-Oriented Systems Service-centric software engineering Service oriented requirements engineering Service oriented architectures Middleware for service based systems Service discovery and composition Service level agreements

Record for source data hosted in the National Spectral Database (NSD) Aquatic Library

Citation: Leiper, I., Phinn, S. & Dekker, A.G. (2012): Spectral reflectance of coral reef benthos and substrate assemblages on Heron Reef, Australia, International Journal of Remote Sensing, 33:12, 3946-3965. http://dx.doi.org/10.1080/01431161.2011.637675

For further information and instructions to access the database go to the following URL: https://cmi.ga.gov.au/data-products/dea/643/australian-national-spectral-database

description: This dataset includes 1km resolution monthly timescale estimates of evapotranspiration (ET) for the 2000-2015 timespan. These new SSEBop-WB estimates were developed by combining a previously published long-term annual average evapotranspiration map based on water balance constraints with the SSEBop remote sensing ET product (see Associated Items). The combination aims to leverage the advantages of each approach in gaining both the temporal resolution of remote sensing data and the long-term magnitude constraints of ground-based data. This data release also includes other supporting data associated with the publication of these estimation methods in a concurrent journal article. Analyses in the journal article included comparisons between SSEBop ET, the MOD16 remote sensing ET product, and the new SSEBop-WB ET in a variety of settings against ET data from 119 flux towers across the U.S. Residuals between the remote sensing methods and the flux tower data were mapped spatially, and these maps are included in the data release as well. The methods are fully described in the forthcoming article accepted for publication in Remote Sensing as of November 2017; this dataset will be updated with its full citation when available. See also the metadata file for additional information, or contact the authors with questions.; abstract: This dataset includes 1km resolution monthly timescale estimates of evapotranspiration (ET) for the 2000-2015 timespan. These new SSEBop-WB estimates were developed by combining a previously published long-term annual average evapotranspiration map based on water balance constraints with the SSEBop remote sensing ET product (see Associated Items). The combination aims to leverage the advantages of each approach in gaining both the temporal resolution of remote sensing data and the long-term magnitude constraints of ground-based data. This data release also includes other supporting data associated with the publication of these estimation methods in a concurrent journal article. Analyses in the journal article included comparisons between SSEBop ET, the MOD16 remote sensing ET product, and the new SSEBop-WB ET in a variety of settings against ET data from 119 flux towers across the U.S. Residuals between the remote sensing methods and the flux tower data were mapped spatially, and these maps are included in the data release as well. The methods are fully described in the forthcoming article accepted for publication in Remote Sensing as of November 2017; this dataset will be updated with its full citation when available. See also the metadata file for additional information, or contact the authors with questions.

This data package is associated with the publication “Impact of Topography and Climate on Post-fire Vegetation Recovery Across Different Burn Severity and Land Cover Types through Machine Learning” submitted to Remote Sensing of Environment (Zahura et al. 2023). In this research, a machine learning algorithm, random forest (RF), was utilized to examine the impact of climate and topography on post-fire vegetation recovery. We used enhanced vegetation index (EVI) to examine varying burn severity and land cover types. The data package includes the input files for RF model training, outputs from model predictions and analysis, and python scripts to run the model, analyze the results to understand model performance and interpretability, and plot manuscript figures. This data package contains three folders (Data, Scripts, and Figures), a file-level metadata (FLMD) csv, and a data dictionary (dd) csv. Please see Postfire_recovery_flmd.csv for a list of all files contained in this data package and descriptions for each. The data dictionary (Postfire_recovery_dd.csv) describes the csv column headers. The “Data” folder provides all the inputs and outputs to train the RF model, evaluate performance, and interpret predictions. The “Scripts” folder contains python scripts and jupyter notebooks for model training and result analysis. The “Figures” folder includes the figures used in the manuscript in “.png” and “.jpg” format.

The global arborist software market was valued at USD 350.79 Million in 2022 and is projected to reach USD 881.04 Million by 2030, registering a CAGR of 12.2% for the forecast period 2023-2030. Factors Affecting Arborist Software Market Growth

Growing awareness of tree care coupled with benefits of arborist software

With increased awareness of environmental conservation and the importance of urban green spaces, there's a rising demand for professional tree care services. Growing environmental education coupled with technology adoption in tree management helps to drive the arborist software demand. Arborist software helps urban planners, municipalities, and property owners effectively manage and care for trees in cities and suburbs. Arborist software streamlines various tasks like tree inventory management, maintenance scheduling, and communication with clients. This leads to improved efficiency and productivity for arborists.

The Restraining Factor of Arborist Software:

Data Security, privacy concerns;

Data security and privacy concerns are indeed significant factors that can impact the adoption of arborist software. Arborist software often stores information about clients' properties, contact details, and potentially even financial information. Many arborist software solutions use location data to map and manage trees. This location data could be misused if it falls into the wrong hands.

Market Opportunity:

Rising need to improve tree inventory practices;

The rising need to improve tree inventory practices is driven by several factors, including urbanization, environmental awareness, and advancements in technology. As cities grow and expand, urban planners need accurate tree inventory data to ensure that trees are integrated into urban design. Tree inventory helps prevent conflicts between infrastructure development and tree preservation. Arborists software helps to create and maintain digital inventories of trees, including information about species, location, size, health, and maintenance history. In addition, features like Geographic Information Systems (GIS), remote sensing, and mobile data collection technologies have made it easier to create, update, and manage tree inventories.

The COVID-19 impact on Arborist Software Market

The COVID-19 pandemic had various impacts on industries and markets, including the arborist software market. During lockdowns and restrictions, some tree care activities might have been deprioritized due to the sudden focus on healthcare sector. However, the pandemic accelerated digital transformation across industries. Arborists who were previously reliant on manual processes might have recognized the benefits of adopting software for tasks like inventory management, reporting, and client communication. Introduction of Arborist Software

An arborist is a professional who specializes in the cultivation, management, and study of trees, shrubs, and other woody plants. Arborists are trained in tree care practices, including planting, pruning, disease and pest management, and overall tree health maintenance. Arborist software are tools used to assist arborists in their work. These software solutions can provide various functionalities to help arborists manage and maintain trees effectively. Arborists can use software to create and maintain digital inventories of trees, including information about species, location, size, health, and maintenance history. Some common features of arborist software include tree inventory management, health assessment, risk assessment, mapping and GIS integration etc.

RADARSAT-2 and Sentinel-1 differential interferograms and deformation time series and linear rates for Berezniki-1 potash mine (Perm Krai, Russia) during November 2011 – April 2014 and July 2016 – June 2020. See corresponding paper in Remote Sensing (https://www.mdpi.com/journal/remotesensing) for more information.

Results of atmospheric parameters of remote sensing images in each year.

Reducing uncertainty in data inputs at relevant spatial scales can improve tidal marsh forecasting models, and their usefulness in coastal climate change adaptation decisions. The Marsh Equilibrium Model (MEM), a one-dimensional mechanistic elevation model, incorporates feedbacks of organic and inorganic inputs to project elevations under sea-level rise (SLR) scenarios. We tested the feasibility of deriving two key MEM inputs – average annual suspended sediment concentration (SSC) and aboveground peak biomass – from remote sensing data in order to apply MEM across a broader geographic region. We analyzed the precision and representativeness (spatial distribution) of these remote sensing inputs to improve understanding of our study region, a brackish tidal marsh in San Francisco Bay, and to test the applicable spatial extent for coastal modeling. We compared biomass and SSC models derived from Landsat 8, Digital Globe World View-2 and hyperspectral airborne imagery. Landsat 8-derived inputs were evaluated in a MEM sensitivity analysis. Trend response surface analysis identified significant diversion (P < 0.05) between field and remote sensing-based model runs at 60 years due to model sensitivity at the marsh edge (80 – 140 cm NAVD88), though at 100 years, elevation forecasts differed less than 10 cm across 97% of the marsh surface (150 – 200 cm NAVD88). Results demonstrate the utility of Landsat 8 for landscape scale tidal marsh elevation projections due to its comparable performance with the other sensors, temporal frequency and cost. Integration of remote sensing data with MEM should advance regional projections of marsh vegetation change by better parameterizing MEM inputs spatially. Improving information for coastal modeling will support planning for ecosystem services, including habitat, carbon storage and flood protection.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

Examples of studies that categorically classified woody plant species in savanna using optical remote sensing.

The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/).

This dataset is the MUSES global LAI product at 0.05º spatial resolution and monthly temporal resolution. The MUSES LAI product is provided on Geographic grid and spans from 1981 to 2019 (continuously updated). It was generated from time-series Land Long-Term Data Record (LTDR) Advanced very high resolution radiometer (AVHRR) daily surface reflectance product (Version 4) using general regression neural networks (GRNNs) (Xiao et al., 2014; Xiao et al., 2016). The MUSES LAI product is spatially complete and temporally continuous.

Dataset Characteristics:

• Spatial Coverage: 180º W – 180º E, 90º S – 90º N
• Temporal Coverage: 1981 – 2019
• Spatial Resolution: 0.05º (approximately 5 km)
• Temporal Resolution: 1 month
• Projection: Geographic
• Data Format: HDF
• Scale: 0.01
• Valid Range: 0 – 1000

Citation (Please cite this paper whenever these data are used):

1. Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. International Journal of Remote Sensing, 43(4), 1199-1225.
2. Xiao Zhiqiang, et al. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. IEEE Transactions on Geoscience and Remote Sensing, 52, 209-223.
3. Xiao Zhiqiang, et al. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 54, 5301-5318.
4. Xiao Zhiqiang, et al. (2017). Evaluation of four long time-series global leaf area index products. Agricultural and Forest Meteorology, 246, 218-230.

If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

This dataset supports the findings of the research paper submitted to the journal Geophysical Research Letters that documents the rapid thaw of saline permafrost below a shallow thermokarst lake near Utqiagvik, Alaska. The lake, East Twin Lake, is located in the Barrow Environmental Observatory. We conducted repeat drilling-based surveys at East Twin Lake in the Barrow Environmental Observatory near Utqiagvik, Alaska between 2008 and 2023. These field data were integrated with transient electromagnetic (TEM) near-surface geophysics soundings in 2016 and 2022 and analysis of a time-series of wintertime Synthetic Aperture Radar (SAR) satellite imagery from 2015 to 2023 to assess changes in lake and sub-lake properties. Finally, we assessed the impact of thawing saline permafrost on shore erosion by quantifying a regime shift in the lateral expansion rate of East Twin Lake between 1948 and 2022. The datasets consist of a CSV file with the point measurements from the drilling campaign, processed TEM data along with the script, a table of SAR backscatter values extracted for three lakes, and a table with lake expansion rates for East Twin Lake.