Removing cloud cover interference from Sentinel-2 imagery in Google Earth Engine by fusing Sentinel-1 SAR data with a CNN model

Accurate and timely urban land mapping is fundamental to supporting large area environmental and socio-economic research. Most of the available large-area urban land products are limited to a spatial resolution of 30 m. The fusion of optical and synthetic aperture radar (SAR) data for large-area high-resolution urban land mapping has not yet been widely explored. In this study, we propose a fast and effective urban land extraction method using ascending/descending orbits of Sentinel-1A SAR data and Sentinel-2 MSI (MultiSpectral Instrument, Level 1C) optical data acquired from 1 January 2015 to 30 June 2016. Potential urban land (PUL) was identified first through logical operations on yearly mean and standard deviation composites from a time series of ascending/descending orbits of SAR data. A Yearly Normalized Difference Vegetation Index (NDVI) maximum and modified Normalized Difference Water Index (MNDWI) mean composite were generated from Sentinel-2 imagery. The slope image derived from SRTM DEM data was used to mask mountain pixels and reduce the false positives in SAR data over these regions. We applied a region-specific threshold on PUL to extract the target urban land (TUL) and a global threshold on the MNDWI mean, and slope image to extract water bodies and high-slope regions. A majority filter with a three by three window was applied on previously extracted results and the main processing was carried out on the Google Earth Engine (GEE) platform. China was chosen as the testing region to validate the accuracy and robustness of our proposed method through 224,000 validation points randomly selected from high-resolution Google Earth imagery. Additionally, a total of 735 blocks with a size of 900 × 900 m were randomly selected and used to compare our product’s accuracy with the global human settlement layer (GHSL, 2014), GlobeLand30 (2010), and Liu (2015) products. Our method demonstrated the effectiveness of using a fusion of optical and SAR data for large area urban land extraction especially in areas where optical data fail to distinguish urban land from spectrally similar objects. Results show that the average overall, producer’s and user’s accuracies are 88.03%, 94.50% and 82.22%, respectively.

Download Full-text

Evaluating Combinations of Temporally Aggregated Sentinel-1, Sentinel-2 and Landsat 8 for Land Cover Mapping with Google Earth Engine

Remote Sensing ◽

10.3390/rs11030288 ◽

2019 ◽

Vol 11 (3) ◽

pp. 288 ◽

Cited By ~ 35

Author(s):

Luis Carrasco ◽

Aneurin O’Neil ◽

R. Morton ◽

Clare Rowland

Keyword(s):

Land Cover ◽

Satellite Data ◽

Cloud Cover ◽

Google Earth ◽

Land Cover Mapping ◽

Temporal Aggregation ◽

Landsat 8 ◽

Composite Approach ◽

Google Earth Engine ◽

Sentinel 2

Land cover mapping of large areas is challenging due to the significant volume of satellite data to acquire and process, as well as the lack of spatial continuity due to cloud cover. Temporal aggregation—the use of metrics (i.e., mean or median) derived from satellite data over a period of time—is an approach that benefits from recent increases in the frequency of free satellite data acquisition and cloud-computing power. This enables the efficient use of multi-temporal data and the exploitation of cloud-gap filling techniques for land cover mapping. Here, we provide the first formal comparison of the accuracy between land cover maps created with temporal aggregation of Sentinel-1 (S1), Sentinel-2 (S2), and Landsat-8 (L8) data from one-year and test whether this method matches the accuracy of traditional approaches. Thirty-two datasets were created for Wales by applying automated cloud-masking and temporally aggregating data over different time intervals, using Google Earth Engine. Manually processed S2 data was used for comparison using a traditional two-date composite approach. Supervised classifications were created, and their accuracy was assessed using field-based data. Temporal aggregation only matched the accuracy of the traditional two-date composite approach (77.9%) when an optimal combination of optical and radar data was used (76.5%). Combined datasets (S1, S2 or S1, S2, and L8) outperformed single-sensor datasets, while datasets based on spectral indices obtained the lowest levels of accuracy. The analysis of cloud cover showed that to ensure at least one cloud-free pixel per time interval, a maximum of two intervals per year for temporal aggregation were possible with L8, while three or four intervals could be used for S2. This study demonstrates that temporal aggregation is a promising tool for integrating large amounts of data in an efficient way and that it can compensate for the lower quality of automatic image selection and cloud masking. It also shows that combining data from different sensors can improve classification accuracy. However, this study highlights the need for identifying optimal combinations of satellite data and aggregation parameters in order to match the accuracy of manually selected and processed image composites.

Download Full-text

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

Applied Sciences ◽

10.3390/app11094258 ◽

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.

Download Full-text

The rapid Land Use and Land Cover change analysis using the Sentinel-2 images in Google Earth Engine: A Case Study of Xiong’an New Area from 2017 to 2020

10.1109/agro-geoinformatics50104.2021.9530306 ◽

2021 ◽

Author(s):

Jiansong Luo ◽

Rujin Yuan ◽

Yujia Cao ◽

Min Xie ◽

Yikai Wang

Keyword(s):

Land Use ◽

Land Cover ◽

Land Cover Change ◽

Google Earth ◽

Change Analysis ◽

Google Earth Engine ◽

Land Cover Change Analysis ◽

Sentinel 2

Download Full-text

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

Seminar Nasional Geomatika ◽

10.24895/sng.2020.0-0.1193 ◽

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.

Download Full-text

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

10.1109/igarss47720.2021.9554984 ◽

2021 ◽

Author(s):

Mohammad Ali Hemati ◽

Mahdi Hasanlau ◽

Masaud Mahdianpari ◽

Fariba Mohammadimanesh

Keyword(s):

Google Earth ◽

Wetland Mapping ◽

Google Earth Engine ◽

Sentinel 2

Download Full-text

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

10.1109/igarss47720.2021.9553506 ◽

2021 ◽

Author(s):

Carsten Montzka ◽

Bagher Bayat ◽

Andreas Tewes ◽

David Mengen ◽

Harry Vereecken

Keyword(s):

Google Earth ◽

Crown Transparency ◽

Google Earth Engine ◽

Sentinel 2

Download Full-text

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

10.5194/egusphere-egu21-5310 ◽

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>

Download Full-text

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

Remote Sensing ◽

10.3390/rs10060859 ◽

2018 ◽

Vol 10 (6) ◽

pp. 859 ◽

Cited By ~ 55

Author(s):

Dimosthenis Traganos ◽

Dimitris Poursanidis ◽

Bharat Aggarwal ◽

Nektarios Chrysoulakis ◽

Peter Reinartz

Keyword(s):

Google Earth ◽

Google Earth Engine ◽

Sentinel 2

Download Full-text

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

PLoS ONE ◽

10.1371/journal.pone.0253209 ◽

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.

Download Full-text