scholarly journals Global maps of ecosystem functional properties with the SCOPE model on Google earth engine Sentinel-2 composites

2020 ◽  
Author(s):  
Egor Prikaziuk ◽  
Christiaan van der Tol ◽  
Mirco Migliavacca
Keyword(s):  
Functional Properties ◽  
Plant Traits ◽  
Google Earth ◽  
Weather Data ◽  
Area Index ◽  
Large Spatial Scale ◽  
Use Efficiency ◽  
Google Earth Engine ◽  
Scope Model ◽  
Sentinel 2

<p>To monitor ecosystems at large spatial scale multiple data sources are needed. We developed a methodology to simulate ecosystem functional properties (EFPs): light use efficiency (LUE), water use efficiency (WUE), and evaporative fraction (EF) with Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model at global scale using weather and optical satellite data.</p><p>EFPs, metrics that integrate ecosystem processes and environmental conditions, are calculated from ecosystem fluxes: gross primary productivity (GPP), sensible (H) and latent (LE) heat flux. These fluxes were simulated by SCOPE from weather parameters and plant traits (leaf area index (LAI), leaf chlorophyll content (Cab)). The weather data was taken from ECMWF ERA5-Land dataset, the plant traits were retrieved with look-up table (LUT) from Sentinel-2 Level 2 composites, exported from Google Earth engine at 10 km resolution.</p><p>LUT retrieval was optimized on a synthetic dataset to reach acceptable quality for the key drivers of GPP flux: LAI (R<sup>2</sup> = 0.75) and Cab (R<sup>2</sup> = 0.62). The global retrieved LAI showed some discrepancies with MODIS LAI product MCD15, especially in forest regions (RMSE = 1.73 m<sup>2</sup> m<sup>-2</sup>). As a consequence, SCOPE-simulated GPP was lower in those regions, compared to MODIS GPP product (MYD17) (RMSE = 0.81 kgC m<sup>-2</sup> yr<sup>-1</sup>). SCOPE-simulated heat fluxes were compared to ECMWF energy flux from ERA5-Land dataset (RMSE<sub>H</sub> = 35.4 W m<sup>-2</sup>, RMSE<sub>LE</sub> = 41.6 W m<sup>-2</sup>). EFPs validation is in progress.</p><p>The discrepancies in LAI can be explained by the fact that we did not use plant functional type information during LUT retrieval, in contrast to the MODIS algorithm. Significant overestimation of LE in dry areas is the consequence of the absence of water balance routine in SCOPE model. We consider SCOPE to be a promising tool for optical and weather data fusion.</p><p><em>The project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 721995.</em></p>

Mapping vegetation variables in Google Earth Engine using Gaussian Process Regression models.   

2021 ◽  
Author(s):  
Matías Salinero Delgado ◽  
Luca Pipia ◽  
Eatidal Amin ◽  
Santiago Belda ◽  
Jochem Verrelst
Keyword(s):  
Gaussian Process ◽  
Computational Efficiency ◽  
Gaussian Process Regression ◽  
Google Earth ◽  
Time Interval ◽  
Fluorescence Signal ◽  
Retrieval Models ◽  
Area Index ◽  
Google Earth Engine ◽  
Sentinel 2

<p>The aim of ESA's forthcoming FLuorescence EXplorer (FLEX) is to achieve a global monitoring of the vegetation's chlorophyll fluorescence by means of an imaging spectrometer, FLORIS. For the retrieval of the fluorescence signal measured from space, other vegetation variables need to be retrieved simultaneously, such as (1) Leaf Area Index (LAI), (2) Leaf Chlorophyll content (Cab), and (3) Fractional Vegetation cover (FCover), among others. The undergoing SENTIFLEX ERC project has already demonstrated the feasibility to operationally infer these variables by hybrid retrieval approaches, which combine the generalization capabilities offered by radiative transfer models (RTMs) and computational efficiency of machine learning methods. Reflectance spectra corresponding to a large variety of canopy realizations served as input to train a Gaussian Process Regression (GPR) algorithm for each targeted variable. Following this approach, sets of GPR retrieval models have been trained for Sentinel-2 and -3 reflectance images.</p><p>In that direction, we started to explore the potential of Google Earth Engine (GEE) to facilitate regional to global mapping.  GEE is a platform with multi-petabyte satellite imagery catalog and geospatial datasets with planetary-scale analysis capabilities, which is freely available for scientific purposes. Among the different EO archives, it is possible to access the whole collection of Sentinel-2 ground reflectance data. In this work, we present the results of an efficient implementation of the GPR-based vegetation models developed for Sentinel-2 in the framework of SENSAGRI H2020 project in GEE. By taking advantage of GEE cloud-computing power, we are able to avoid the typical bottleneck of downloading and process large amounts of data locally and generate results of GPR-based retrieval models developed for Sentinel-2 in a fast and efficient way, covering large areas in matter of seconds. As a first step in that direction we present here an open web-based GEE application able to generate LAI Green and LAI Brown maps from Sentinel-2- imagery at 20m in a tile-wise manner all over the world, and time series of selected pixels during user-defined time interval.</p><p>To illustrate this functionalities and have better understanding of the phenology, we targeted a region in Castilla y León (Spain) from where we will present results for 2018 classified per crop type. This land cover classification was generated by the ITACYL (<span>Instituto Tecnológico Agrario de Castilla y León</span>) during SENSAGRI.</p><p>Future development will tackle the possibility to extend our analysis capability to additional variables, such as FCover and Cab, maintaining the computational efficiency as the main driver to ensure that the GEE application continues to be an agile and easy tool for spatiotemporal Earth observation studies.</p>

scholarly journals Mapping National Mangrove Cover for Belize Using Google Earth Engine and Sentinel-2 Imagery

2021 ◽  
Vol 11 (9) ◽  
pp. 4258
Author(s):  
Jordan R. Cissell ◽  
Steven W. J. Canty ◽  
Michael K. Steinberg ◽  
Loraé T. Simpson
Keyword(s):  
Google Earth ◽  
Mangrove Forests ◽  
Random Forest Classification ◽  
High Resolution Mapping ◽  
Mangrove Ecosystems ◽  
Forest Classification ◽  
Resolution Mapping ◽  
Google Earth Engine ◽  
Terrestrial Biodiversity ◽  
Sentinel 2

In this paper, we present the highest-resolution-available (10 m) national map of the mangrove ecosystems of Belize. These important ecosystems are increasingly threatened by human activities and climate change, support both marine and terrestrial biodiversity, and provide critical ecosystem services to coastal communities in Belize and throughout the Mesoamerican Reef ecoregion. Previous national- and international-level inventories document Belizean mangrove forests at spatial resolutions of 30 m or coarser, but many mangrove patches and loss events may be too small to be accurately mapped at these resolutions. Our 10 m map addresses this need for a finer-scale national mangrove inventory. We mapped mangrove ecosystems in Belize as of 2020 by performing a random forest classification of Sentinel-2 Multispectral Instrument imagery in Google Earth Engine. We mapped a total mangrove area of 578.54 km2 in 2020, with 372.04 km2 located on the mainland and 206.50 km2 distributed throughout the country’s islands and cayes. Our findings are substantially different from previous, coarser-resolution national mangrove inventories of Belize, which emphasizes the importance of high-resolution mapping efforts for ongoing conservation efforts.

ANALISIS TEMPORAL PERUBAHAN HUTAN MANGROVE MENGGUNAKAN CITRA SATELIT SENTINEL-2 (Studi Kasus di Pulau Tanakeke, Kabupaten Takalar)

2021 ◽  
pp. 777
Author(s):  
Andi Tenri Waru ◽  
Athar Abdurrahman Bayanuddin ◽  
Ferman Setia Nugroho ◽  
Nita Rukminasari
Keyword(s):  
Random Forest ◽  
Google Earth ◽  
Multi Temporal ◽  
Google Earth Engine ◽  
Sentinel 2

Pulau Tanakeke merupakan salah satu pulau dengan hutan mangrove yang luas di pesisir Sulawesi Selatan. Hutan mangrove ini menjadi ekosistem penting bagi masyarakat sekitar karena nilai ekologi maupun ekonominya. Namun, dalam kurun waktu sekitar tahun 1980-2000, keberadaan mangrove tersebut terancam oleh perubahan penggunaan lahan dan juga pemanfaatan yang berlebihan. Penelitian ini bertujuan untuk menganalisis perubahan temporal luas dan tingkat kerapatan hutan mangrove di Pulau Tanakeke antara tahun 2016 dan 2019. Metode analisis perubahan luasan hutan mangrove menggunakan data citra satelit Sentinel-2 multi temporal berdasarkan hasil klasifikasi hutan mangrove dengan menggunakan random forest pada platform Google Earth Engine. Akurasi keseluruhan hasil klasifikasi hutan mangrove tahun 2016 dan 2019 sebesar 91% dan 98%. Berdasarkan hasil analisis spasial diperoleh perubahan penurunan luasan mangrove yang signifikan dari 800,21 ha menjadi 640,15 ha. Kerapatan mangrove di Pulau Tanakeke sebagian besar tergolong kategori dalam kerapatan tinggi.

Wetland Mapping of Northern Provinces of Iran Using Sentinel-1 and Sentinel-2 in Google Earth Engine

2021 ◽  
Author(s):  
Mohammad Ali Hemati ◽  
Mahdi Hasanlau ◽  
Masaud Mahdianpari ◽  
Fariba Mohammadimanesh
Keyword(s):  
Google Earth ◽  
Wetland Mapping ◽  
Google Earth Engine ◽  
Sentinel 2

Spruce Crown Transparency Levels Detected from Sentinel-2 Using Google Earth Engine

2021 ◽  
Author(s):  
Carsten Montzka ◽  
Bagher Bayat ◽  
Andreas Tewes ◽  
David Mengen ◽  
Harry Vereecken
Keyword(s):  
Google Earth ◽  
Crown Transparency ◽  
Google Earth Engine ◽  
Sentinel 2

An updated national-scale mangrove forest map in China Using Sentinel-1 and Sentinel-2 Time-Series Data with Google Earth Engine

2021 ◽  
Author(s):  
Luojia Hu ◽  
Wei Yao ◽  
Zhitong Yu ◽  
Yan Huang
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Mangrove Forest ◽  
Google Earth ◽  
Series Data ◽  
National Scale ◽  
Mangrove Area ◽  
Google Earth Engine ◽  
Forest Maps ◽  
Sentinel 2

<p>A high resolution mangrove map (e.g., 10-m), which can identify mangrove patches with small size (< 1 ha), is a central component to quantify ecosystem functions and help government take effective steps to protect mangroves, because the increasing small mangrove patches, due to artificial destruction and plantation of new mangrove trees, are vulnerable to climate change and sea level rise, and important for estimating mangrove habitat connectivity with adjacent coastal ecosystems as well as reducing the uncertainty of carbon storage estimation. However, latest national scale mangrove forest maps mainly derived from Landsat imagery with 30-m resolution are relatively coarse to accurately characterize the distribution of mangrove forests, especially those of small size (area < 1 ha). Sentinel imagery with 10-m resolution provide the opportunity for identifying these small mangrove patches and generating high-resolution mangrove forest maps. Here, we used spectral/backscatter-temporal variability metrics (quantiles) derived from Sentinel-1 SAR (Synthetic Aperture Radar) and sentinel-2 MSI (Multispectral Instrument) time-series imagery as input features for random forest to classify mangroves in China. We found that Sentinel-2 imagery is more effective than Sentinel-1 in mangrove extraction, and a combination of SAR and MSI imagery can get a better accuracy (F1-score of 0.94) than using them separately (F1-score of 0.88 using Sentinel-1 only and 0.895 using Sentinel-2 only). The 10-m mangrove map derived by combining SAR and MSI data identified 20,003 ha mangroves in China and the areas of small mangrove patches (< 1 ha) was 1741 ha, occupying 8.7% of the whole mangrove area. The largest area (819 ha) of small mangrove patches is located in Guangdong Province, and in Fujian the percentage of small mangrove patches in total mangrove area is the highest (11.4%). A comparison with existing 30-m mangrove products showed noticeable disagreement, indicating the necessity for generating mangrove extent product with 10-m resolution. This study demonstrates the significant potential of using Sentinel-1 and Sentinel-2 images to produce an accurate and high-resolution mangrove forest map with Google Earth Engine (GEE). The mangrove forest maps are expected to provide critical information to conservation managers, scientists, and other stakeholders in monitoring the dynamics of mangrove forest.</p>

scholarly journals Estimating Satellite-Derived Bathymetry (SDB) with the Google Earth Engine and Sentinel-2

2018 ◽  
Vol 10 (6) ◽  
pp. 859 ◽  
Author(s):  
Dimosthenis Traganos ◽  
Dimitris Poursanidis ◽  
Bharat Aggarwal ◽  
Nektarios Chrysoulakis ◽  
Peter Reinartz
Keyword(s):  
Google Earth ◽  
Google Earth Engine ◽  
Sentinel 2

scholarly journals Accurate extraction of surface water in complex environment based on Google Earth Engine and Sentinel-2

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253209
Author(s):  
Jianfeng Li ◽  
Biao Peng ◽  
Yulu Wei ◽  
Huping Ye
Keyword(s):  
Surface Water ◽  
Google Earth ◽  
Water Extraction ◽  
Practical Significance ◽  
Commission Error ◽  
Complex Environment ◽  
Visual Interpretation ◽  
Water Index ◽  
Google Earth Engine ◽  
Sentinel 2

To realize the accurate extraction of surface water in complex environment, this study takes Sri Lanka as the study area owing to the complex geography and various types of water bodies. Based on Google Earth engine and Sentinel-2 images, an automatic water extraction model in complex environment(AWECE) was developed. The accuracy of water extraction by AWECE, NDWI, MNDWI and the revised version of multi-spectral water index (MuWI-R) models was evaluated from visual interpretation and quantitative analysis. The results show that the AWECE model could significantly improve the accuracy of water extraction in complex environment, with an overall accuracy of 97.16%, and an extremely low omission error (0.74%) and commission error (2.35%). The AEWCE model could effectively avoid the influence of cloud shadow, mountain shadow and paddy soil on water extraction accuracy. The model can be widely applied in cloudy, mountainous and other areas with complex environments, which has important practical significance for water resources investigation, monitoring and protection.

scholarly journals Avaliação da Acurácia Posicional do Mosaico Sentinel-2 para Análise de Aplicabilidade na Atualização da Base Cartográfica Contínua na escala 1:100.000 (BC100)

2021 ◽  
Vol 73 (3) ◽  
pp. 736-750
Author(s):  
Priscila Almeida Oliveira ◽  
Priscila de Lima Silva
Keyword(s):  
Rio De Janeiro ◽  
Google Earth ◽  
Google Earth Engine ◽  
Sentinel 2

A disponibilidade de geotecnologias tem estimulado a produção e o consumo de informações espaciais, aumentando a necessidade de validações para garantir a qualidade cartográfica. Este artigo tem como objetivo avaliar a aplicação de imagens do satélite Sentinel-2, que podem ser obtidas sem custos e em forma de mosaico temporal pela plataforma Google Earth Engine (GEE), para atualização da Base Cartográfica Contínua na escala de 1:100.000 (BC100) do Instituto Brasileiro de Geografia e Estatística (IBGE). Foi avaliada a acurácia posicional planimétrica, tomando como referência um ortomosaico do IBGE, com resolução espacial de 1 m, utilizou-se o método de feições lineares Buffer Duplo. Foram analisadas as vias da Universidade Federal Rural do Rio de Janeiro (UFRRJ), campus Seropédica, as quais foram vetorizadas na imagem de referência e no mosaico em teste. Em seguida foram avaliadas as discrepâncias obtidas, considerando o Padrão de Exatidão Cartográfica para Produtos Cartográficos Digitais (PEC-PCD) e o Erro Padrão (EP), para escala 1:100.000, obteve-se como resultado classe A. A aprovação do mosaico temporal formado por pixels de dias diferentes, disponibilizado pelo GEE, motivou a validação dos arquivos brutos e do mosaico obtido pela plataforma Sentinel Hub (EO Browser), ambos formados por pixels do mesmo dia. Aplicou-se a Análise de Variância (ANOVA) para comparar as discrepâncias entre as imagens e concluiu-se que estatisticamente não há diferenças. Mostrando que para a área de estudo, analisando as vias, as imagens brutas do Sentinel-2, o mosaico temporal GEE e mosaico Sentinel Hub são aplicáveis para a atualização da BC100.

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