BUILDING OF TEMPERATURE MAPS OF NOVOSIBIRSK CITY BASED ON LANDSAT 8 DATA

An algorithm for constructing temperature maps of the underlying surface based on a multi-time series of atmospheric corrected satellite data from Landsat 8, implemented in the Google Earth Engine system, is presented. The results of the construction of temperature maps of Novosibirsk using this algorithm are discussed.

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Monitoring Cropland Phenology on Google Earth Engine Using Gaussian Process Regression

Remote Sensing ◽

10.3390/rs14010146 ◽

2021 ◽

Vol 14 (1) ◽

pp. 146

Author(s):

Matías Salinero-Delgado ◽

José Estévez ◽

Luca Pipia ◽

Santiago Belda ◽

Katja Berger ◽

...

Keyword(s):

Time Series ◽

Gaussian Process ◽

Satellite Data ◽

Gaussian Process Regression ◽

Google Earth ◽

Gap Filling ◽

Area Index ◽

Filling Time ◽

Google Earth Engine ◽

Mean Square Errors

Monitoring cropland phenology from optical satellite data remains a challenging task due to the influence of clouds and atmospheric artifacts. Therefore, measures need to be taken to overcome these challenges and gain better knowledge of crop dynamics. The arrival of cloud computing platforms such as Google Earth Engine (GEE) has enabled us to propose a Sentinel-2 (S2) phenology end-to-end processing chain. To achieve this, the following pipeline was implemented: (1) the building of hybrid Gaussian Process Regression (GPR) retrieval models of crop traits optimized with active learning, (2) implementation of these models on GEE (3) generation of spatiotemporally continuous maps and time series of these crop traits with the use of gap-filling through GPR fitting, and finally, (4) calculation of land surface phenology (LSP) metrics such as the start of season (SOS) or end of season (EOS). Overall, from good to high performance was achieved, in particular for the estimation of canopy-level traits such as leaf area index (LAI) and canopy chlorophyll content, with normalized root mean square errors (NRMSE) of 9% and 10%, respectively. By means of the GPR gap-filling time series of S2, entire tiles were reconstructed, and resulting maps were demonstrated over an agricultural area in Castile and Leon, Spain, where crop calendar data were available to assess the validity of LSP metrics derived from crop traits. In addition, phenology derived from the normalized difference vegetation index (NDVI) was used as reference. NDVI not only proved to be a robust indicator for the calculation of LSP metrics, but also served to demonstrate the good phenology quality of the quantitative trait products. Thanks to the GEE framework, the proposed workflow can be realized anywhere in the world and for any time window, thus representing a shift in the satellite data processing paradigm. We anticipate that the produced LSP metrics can provide meaningful insights into crop seasonal patterns in a changing environment that demands adaptive agricultural production.

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INVESTIGATING WATER STORAGE CHANGES IN IRAN USING GRACE AND CHIRPS DATA IN THE GOOGLE EARTH ENGINE SYSTEM

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w18-981-2019 ◽

2019 ◽

Vol XLII-4/W18 ◽

pp. 981-984

Author(s):

S. Shami ◽

Z. Ghorbani

Keyword(s):

Satellite Data ◽

Water Storage ◽

Google Earth ◽

Annual Precipitation ◽

Resources Management ◽

Total Annual Precipitation ◽

The World ◽

Grace Satellite ◽

Google Earth Engine ◽

Engine System

Abstract. Water storage in regions with the weather hot and arid or semi-arid such as Iran have many uses. Including these water storage, can be referred to groundwater. Groundwater is one of the sources of sweet waters in the world, and one of the factors is economical and social development. Hence, monitoring its changes in water resources management is of great importance. On the other hand, precipitation is one of the factors directly affecting the water storage level and groundwater level changes. In this study, water storage changes with GRACE satellite data and total annual precipitation with CHIRPS data in the Google Earth Engine system investigated for Iran during 2003–2017. The results obtained from the GRACE satellite data indicate over 10 cm reducing of the water storage levels in Iran during the period between 2008 to 2017. Also, the chart obtained from the CHIRPS data for the total annual precipitation shows that the amount of rainfall since 2008 has decreased in this region.

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Drought Monitoring in Gresik Regency East Java using Satellite Image Time Series and Google Earth Engine

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/936/1/012003 ◽

2021 ◽

Vol 936 (1) ◽

pp. 012003

Author(s):

Rosmalisa Dwiyaniek ◽

Bangun Muljo Sukojo ◽

Filsa Bioresita

Keyword(s):

Time Series ◽

Satellite Image ◽

Data Retrieval ◽

Google Earth ◽

Landsat 8 ◽

Severe Drought ◽

Dry Land ◽

Aerial Photos ◽

Two Factors ◽

Google Earth Engine

Abstract Gresik is one of the areas with severe drought levels in East Java. This drought disaster caused by low rainfall and the high average surface temperature in an area. These two factors are currently difficult to predict due to uncertain climate change, this is also related to the global warming that is happening. This disaster cannot be completely avoided but can be minimized. This research was conducted to periodically check or time series of droughts by utilizing the Google Earth Engine platform. Drought identification obtained from multitemporal Landsat 8 satellite imagery with the TVDI (Temperature Vegetation Dryness Index) algorithm and field data retrieval in the form of aerial photos using a thermal camera from the DJI Mavic Enterprise Dual Thermal. From this study, it can be monitoring the distribution of drought in the 2015-2020 period in Gresik Regency occurred in 9 sub-districts, there are Wringinanom, Driyorejo, Kedamean, Balongpanggang, Benjeng, Menganti sub-districts as well as several areas in Duduksampeyan, Cerme and Panceng sub-districts. The identification of dry land also correlates well with the rainfall that occurs, namely?100 mm/month, which has low rainfall during drought events.

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A Google Earth Engine Tool to Investigate, Map and Monitor Volcanic Thermal Anomalies at Global Scale by Means of Mid-High Spatial Resolution Satellite Data

Remote Sensing ◽

10.3390/rs12193232 ◽

2020 ◽

Vol 12 (19) ◽

pp. 3232

Author(s):

Nicola Genzano ◽

Nicola Pergola ◽

Francesco Marchese

Keyword(s):

Spatial Resolution ◽

Satellite Data ◽

Hot Spot ◽

Global Scale ◽

Google Earth ◽

High Temporal Resolution ◽

Landsat 8 ◽

Thermal Anomalies ◽

Google Earth Engine ◽

Sentinel 2

Several satellite-based systems have been developed over the years to study and monitor thermal volcanic activity. Most of them use high temporal resolution satellite data, provided by sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) that if on the one hand guarantee a continuous monitoring of active volcanic areas on the other hand are less suited to map thermal anomalies, and to provide accurate information about their features. The Multispectral Instrument (MSI) and the Operational Land Imager (OLI), respectively, onboard the Sentinel-2 and Landsat-8 satellites, providing Short-Wave Infrared (SWIR) data at 20 m (MSI) and 30 m (OLI) spatial resolution, may make an important contribution in this area. In this work, we present the first Google Earth Engine (GEE) App to investigate, map and monitor volcanic thermal anomalies at global scale, integrating Landsat-8 OLI and Sentinel-2 MSI observations. This open tool, which implements the Normalized Hot spot Indices (NHI) algorithm, enables the analysis of more than 1400 active volcanoes, with very low processing times, thanks to the high GEE computational resources. Performance and limitations of the tool, such as its next upgrades, aiming at increasing the user-friendly experience and extending the temporal range of data analyses, are analyzed and discussed.

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30 m Resolution Global Annual Burned Area Mapping Based on Landsat Images and Google Earth Engine

Remote Sensing ◽

10.3390/rs11050489 ◽

2019 ◽

Vol 11 (5) ◽

pp. 489 ◽

Cited By ~ 25

Author(s):

Tengfei Long ◽

Zhaoming Zhang ◽

Guojin He ◽

Weili Jiao ◽

Chao Tang ◽

...

Keyword(s):

Time Series ◽

Land Cover ◽

Google Earth ◽

Burned Area ◽

Landsat 8 ◽

Landsat Images ◽

Burned Areas ◽

Random Decision Forests ◽

Burned Area Mapping ◽

Google Earth Engine

Heretofore, global Burned Area (BA) products have only been available at coarse spatial resolution, since most of the current global BA products are produced with the help of active fire detection or dense time-series change analysis, which requires very high temporal resolution. In this study, however, we focus on an automated global burned area mapping approach based on Landsat images. By utilizing the huge catalog of satellite imagery, as well as the high-performance computing capacity of Google Earth Engine, we propose an automated pipeline for generating 30-m resolution global-scale annual burned area maps from time-series of Landsat images, and a novel 30-m resolution Global annual Burned Area Map of 2015 (GABAM 2015) was released. All the available Landsat-8 images during 2014–2015 and various spectral indices were utilized to calculate the burned probability of each pixel using random decision forests, which were globally trained with stratified (considering both fire frequency and type of land cover) samples, and a seed-growing approach was conducted to shape the final burned areas after several carefully-designed logical filters (NDVI filter, Normalized Burned Ratio (NBR) filter, and temporal filter). GABAM 2015 consists of spatial extent of fires that occurred during 2015 and not of fires that occurred in previous years. Cross-comparison with the recent Fire_cci Version 5.0 BA product found a similar spatial distribution and a strong correlation ( R 2 = 0.74) between the burned areas from the two products, although differences were found in specific land cover categories (particularly in agriculture land). Preliminary global validation showed the commission and omission errors of GABAM 2015 to be 13.17% and 30.13%, respectively.

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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.

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DETEKSI SEBARAN MUATAN PADATAN TERSUSPENSI DENGAN MODEL EMPIRIS DAN MODEL SEMI-ANALITIK DI PERAIRAN BEKASI

Jurnal Ilmu dan Teknologi Kelautan Tropis ◽

10.29244/jitkt.v12i2.28138 ◽

2020 ◽

Vol 12 (2) ◽

pp. 341-351

Author(s):

Pingkan Mayestika Afgatiani ◽

Maryani Hartuti ◽

Syarif Budhiman

Keyword(s):

Time Series ◽

Mean Absolute Error ◽

Absolute Error ◽

Google Earth ◽

Landsat 8 ◽

Surface Reflectance ◽

Mean Square ◽

Itu Model ◽

Google Earth Engine

Salah satu parameter dalam kualitas air adalah muatan padatan tersuspensi (MPT). Muatan padatan tersuspensi terdiri dari lumpur, pasir dan jasad renik yang disebabkan pengikisan tanah yang terbawa ke badan air. Penelitian ini bertujuan untuk mendeteksi sedimen tersuspensi di perairan Bekasi. Landsat 8 digunakan untuk analisis padatan tersuspensi dengan platform Google Earth Engine dengan membandingkan antara model empiris dan semi-analitik. Alur studi ini meliputi deliniasi wilayah non air menggunakan data citra surface reflectance, analisis MPT, dan visualisasi. Selanjutnya dilakukan validasi dengan data in situ, pemilihan model dan implementasi time series. Hasil deteksi MPT tertampil dengan tampilan warna yang berbeda sesuai dengan konsentrasinya. Hasil uji validasi dengan data in situ menunjukkan nilai Normalized Mean Absolute Error (NMAE) model semi-analitik lebih mendekati syarat minimum yaitu sebesar 66,8%, berbeda jauh dengan model empiris sebesar 43768%. Nilai Root Mean Square Error (RMSE) pun terlihat bahwa model semi-analitik menghasilkan nilai yang jauh lebih kecil sebesar 51,4 dan model empiris sebesar 58577,2. Hal ini menunjukkan bahwa model semi-analitik memiliki nilai yang lebih baik dalam mendeteksi sebaran MPT. Analisis time series menunjukkan bahwa persebaran MPT tahun 2015 – 2019 di perairan pesisir memiliki sebaran MPT yang sangat tinggi, karena banyaknya tambak dan muara sungai. Oleh karena itu, model semi-analitik lebih direkomendasikan untuk mengestimasi konsentrasi MPT dibandingkan dengan model empiris.

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Mapping Tidal Flats with Landsat 8 Images and Google Earth Engine: A Case Study of the China’s Eastern Coastal Zone circa 2015

Remote Sensing ◽

10.3390/rs11080924 ◽

2019 ◽

Vol 11 (8) ◽

pp. 924 ◽

Cited By ~ 11

Author(s):

Zhang ◽

Dong ◽

Liu ◽

Gao ◽

Hu ◽

...

Keyword(s):

Time Series ◽

Coastal Zone ◽

Tidal Flat ◽

Large Scale ◽

Tidal Flats ◽

Google Earth ◽

Landsat 8 ◽

Intertidal Flats ◽

Multi Temporal ◽

Google Earth Engine

Accurate and up-to-date tidal flat mapping is of much importance to learning how coastal ecosystems work in a time of anthropogenic disturbances and rising sea levels, which will provide scientific instruction for sustainable management and ecological assessments. For large-scale and high spatial-resolution mapping of tidal flats, it is difficult to obtain accurate tidal flat maps without multi-temporal observation data. In this study, we aim to investigate the potential and advantages of the freely accessible Landsat 8 Operational Land Imager (OLI) imagery archive and Google Earth Engine (GEE) for accurate tidal flats mapping. A novel approach was proposed, including multi-temporal feature extraction, machine learning classification using GEE and morphological post-processing. The 50 km buffer of the coastline from Hangzhou Bay to Yalu River in China’s eastern coastal zone was taken as the study area. From the perspective of natural attributes and unexploited status of tidal flats, we delineated a broader extent comprising intertidal flats, supratidal barren flats and vegetated flats, since intertidal flats are major component of the tidal flats. The overall accuracy of the resultant map was about 94.4% from a confusion matrix for accuracy assessment. The results showed that the use of time-series images can greatly eliminate the effects of tidal level, and improve the mapping accuracy. This study also proved the potential and advantage of combining the GEE platform with time-series Landsat images, due to its powerful cloud computing platform, especially for large scale and longtime tidal flats mapping.

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Assessment of Annual Composite Images Obtained by Google Earth Engine for Urban Areas Mapping Using Random Forest

Remote Sensing ◽

10.3390/rs13040748 ◽

2021 ◽

Vol 13 (4) ◽

pp. 748

Author(s):

Zhaoming Zhang ◽

Mingyue Wei ◽

Dongchuan Pu ◽

Guojin He ◽

Guizhou Wang ◽

...

Keyword(s):

Time Series ◽

Random Forest ◽

Urban Areas ◽

Google Earth ◽

Landsat 8 ◽

Annual Time Series ◽

Google Earth Engine ◽

Time Series Images ◽

The Impact ◽

Annual Time

Urban areas represent the primary source region of greenhouse gas emissions. Mapping urban areas is essential for understanding land cover change, carbon cycles, and climate change (urban areas also refer to impervious surfaces, i.e., artificial cover and structures). Remote sensing has greatly advanced urban areas mapping over the last several decades. At present, we have entered the era of big data. Long time series of satellite data such as Landsat and high-performance computing platforms such as Google Earth Engine (GEE) offer new opportunities to map urban areas. The objective of this research was to determine how annual time series images from Landsat 8 Operational Land Imager (OLI) can effectively be composed to map urban areas in three cities in China in support of GEE. Three reducer functions, ee.Reducer.min(), ee.Reducer.median(), and ee.Reducer.max() provided by GEE, were selected to construct four schemes to synthesize the annual intensive time series Landsat 8 OLI data for three cities in China. Then, urban areas were mapped based on the random forest algorithm and the accuracy was evaluated in detail. The results show that (1) the quality of annual composite images was improved significantly, particularly in reducing the impact of cloud and cloud shadows, and (2) the annual composite images obtained by the combination of multiple reducer functions had better performance than that obtained by a single reducer function. Further, the overall accuracy of urban areas mapping with the combination of multiple reducer functions exceeded 90% in all three cities in China. In summary, a suitable combination of reducer functions for synthesizing annual time series images can enhance data quality and ensure differences between characteristics and higher precision for urban areas mapping.

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Pixel- vs. Object-Based Landsat 8 Data Classification in Google Earth Engine Using Random Forest: The Case Study of Maiella National Park

Remote Sensing ◽

10.3390/rs13122299 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2299

Author(s):

Andrea Tassi ◽

Daniela Gigante ◽

Giuseppe Modica ◽

Luciano Di Martino ◽

Marco Vizzari

Keyword(s):

Land Cover ◽

National Park ◽

Time Window ◽

Central Italy ◽

Google Earth ◽

Data Cube ◽

Landsat 8 ◽

Change Analysis ◽

Object Based ◽

Google Earth Engine

With the general objective of producing a 2018–2020 Land Use/Land Cover (LULC) map of the Maiella National Park (central Italy), useful for a future long-term LULC change analysis, this research aimed to develop a Landsat 8 (L8) data composition and classification process using Google Earth Engine (GEE). In this process, we compared two pixel-based (PB) and two object-based (OB) approaches, assessing the advantages of integrating the textural information in the PB approach. Moreover, we tested the possibility of using the L8 panchromatic band to improve the segmentation step and the object’s textural analysis of the OB approach and produce a 15-m resolution LULC map. After selecting the best time window of the year to compose the base data cube, we applied a cloud-filtering and a topography-correction process on the 32 available L8 surface reflectance images. On this basis, we calculated five spectral indices, some of them on an interannual basis, to account for vegetation seasonality. We added an elevation, an aspect, a slope layer, and the 2018 CORINE Land Cover classification layer to improve the available information. We applied the Gray-Level Co-Occurrence Matrix (GLCM) algorithm to calculate the image’s textural information and, in the OB approaches, the Simple Non-Iterative Clustering (SNIC) algorithm for the image segmentation step. We performed an initial RF optimization process finding the optimal number of decision trees through out-of-bag error analysis. We randomly distributed 1200 ground truth points and used 70% to train the RF classifier and 30% for the validation phase. This subdivision was randomly and recursively redefined to evaluate the performance of the tested approaches more robustly. The OB approaches performed better than the PB ones when using the 15 m L8 panchromatic band, while the addition of textural information did not improve the PB approach. Using the panchromatic band within an OB approach, we produced a detailed, 15-m resolution LULC map of the study area.

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