Land surface evapotranspiration estimation combining soil texture information and global reanalysis datasets in Google Earth Engine

The soiling of solar panels from dry deposition affects the overall efficiency of power output from solar power plants. This study focuses on the detection and monitoring of sand deposition (wind-blown dust) on photovoltaic (PV) solar panels in arid regions using multitemporal remote sensing data. The study area is located in Bhadla solar park of Rajasthan, India which receives numerous sandstorms every year, carried by westerly and north-westerly winds. This study aims to use Google Earth Engine (GEE) in monitoring the soiling phenomenon on PV panels. Optical imageries archived in the GEE platform were processed for the generation of various sand indices such as the normalized differential sand index (NDSI), the ratio normalized differential soil index (RNDSI), and the dry bare soil index (DBSI). Land surface temperature (LST) derived from Landsat 8 thermal bands were also used to correlate with sand indices and to observe the pattern of sand accumulation in the target region. Additionally, high-resolution PlanetScope images were used to quantitatively validate the sand indices. Our study suggests that the use of freely available satellite data with semiautomated processing on GEE can be a useful alternative to manual methods. The developed method can provide near real-time monitoring of soiling on PV panels cost-effectively. This study concludes that the DBSI method has a comparatively higher potential (89.6% Accuracy, 0.77 Kappa) in the detection of sand deposition on PV panels as compared to other indices. The findings of this study can be useful to solar energy companies in the development of an operational plan for the cleaning of PV panels regularly.

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Google Earth Engine for land surface albedo estimation: comparison among different algorithms

10.1117/12.2597666 ◽

2021 ◽

Author(s):

Alessandra Capolupo ◽

Cristina Monterisi ◽

Carlo Barletta ◽

Eufemia Tarantino

Keyword(s):

Land Surface ◽

Surface Albedo ◽

Google Earth ◽

Google Earth Engine

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Monitoring ecosystem restoration of multiple surface coal mine sites in China via Landsat images on Google Earth Engine

10.22541/au.160578567.78852046/v1 ◽

2020 ◽

Author(s):

Huihui Wangh ◽

Miaomiao Xie ◽

Hanting Li ◽

Qianqian Feng ◽

Cui Zhang

Keyword(s):

Coal Mine ◽

Land Surface ◽

Vegetation Index ◽

Ecosystem Restoration ◽

Google Earth ◽

Landsat Images ◽

Surface Coal Mine ◽

Google Earth Engine ◽

Mine Sites ◽

Mine Restoration

The restoration of surface mining is a key to meet the global ecosystem restoration target. With increased data accessibility and computing tool capabilities, it becomes possible to expand mine restoration monitoring from single mine sites to multiple mine sites on a large scale. This study constructed a new index, Mine Landscape Restoration Index (MLRI), by coupling Land Surface Temperature (LST) and Enhanced Vegetation Index (EVI) to simultaneously monitor the restoration of regional multiple mine sites. We analyze historical and future trends of restoration using Mann-Kendall test, Sen’ slope, and Hurst exponent for MLRI time series. The restoration effects of 46 surface coal mine sites located in the northwestern ecologically fragile region of China from 2000 to 2019 were assessed, based on 3675 Landsat images on Google Earth Engine. The results showed that MLRI was effective in identifying restoration areas and processes in surface mine sites, which was validated by high-resolution images and field investigation of mine samples. The restoration area overall percentage was significantly higher in mines started mining before 2000 than after 2000. According to the restoration effects, we clustered the 46 sites into high, medium, and low restoration area percentage clusters with 13, 11, and 22 mine sites, respectively. Individual clusters have aggregation characteristics within each mine region, but are distributed irregularly across the different six mine regions. This study provides a new approach to monitoring the restoration of surface coal mine sites and inform government managers in developing mine restoration programs and sustainable mining development plans.

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LAND SURFACE TEMPERATURE AS AN INDICATOR OF URBAN HEAT ISLAND EFFECT: A GOOGLE EARTH ENGINE BASED WEB-APP

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

10.5194/isprs-archives-xliv-m-3-2021-57-2021 ◽

2021 ◽

Vol XLIV-M-3-2021 ◽

pp. 57-62

Author(s):

A. Galodha ◽

S. K. Gupta

Keyword(s):

Surface Temperature ◽

Urban Heat Island ◽

Land Surface Temperature ◽

Land Surface ◽

Google Earth ◽

Heat Island ◽

Rapid Urbanization ◽

Urban Heat ◽

Google Earth Engine ◽

Web App

Abstract. At least 2 billion urban occupants will be concentrated in Asia and Africa, amounting to 70% of the global population by 2050. This rapid urbanization has caused an innate effect on the ecology and environment, which further results in intense temperature variations in urban and rural areas, especially in India. According to a recent IPCC report, 8 out of the 15 hottest cities in the world are situated in India. The rising industrial work, construction activities, type of material used for construction, and other factors have reduced thermal cooling and created temperature imbalance, thereby creating a vicious effect called “urban heat island” (UHI) or “surface urban heat island” (SUHI). Several researchers have also related it with climate change due to their contribution to the greenhouse effect and global warming. In this study, we have particularly emphasized northern India, including Punjab, Rajasthan, Haryana, and Delhi. We created a Google Earth Engine (GEE) based Web-App to assess the UHI intensity over the past 15 years (2003–2018). We are using Moderate Resolution Imaging Spectroradiometer (MODIS) images, Landsat 5, 7, and 8 data for studying UHI. The land surface temperature (LST) based UHI intensity (day and night time) will be available for major metropolitan cities with their respective clusters. With feasibility in SUHI monitoring, we can address an increasing need for resilient, sustainable, and safe urban planning of our cities as portrayed under the Sustainable Development Goals (SDG 11 highlighted by United Nations).

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Investigating the Effect of Lockdown During COVID-19 on Land Surface Temperature Using Machine Learning Technique by Google Earth Engine: Analysis of Rajasthan, India

Communication and Intelligent Systems - Lecture Notes in Networks and Systems ◽

10.1007/978-981-16-1089-9_29 ◽

2021 ◽

pp. 355-364

Author(s):

Amita Jangid ◽

Mukesh Kumar Gupta

Keyword(s):

Machine Learning ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Google Earth ◽

Machine Learning Technique ◽

Learning Technique ◽

Google Earth Engine

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Google Earth Engine Open-Source Code for Land Surface Temperature Estimation from the Landsat Series

Remote Sensing ◽

10.3390/rs12091471 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1471 ◽

Cited By ~ 5

Author(s):

Sofia L. Ermida ◽

Patrícia Soares ◽

Vasco Mantas ◽

Frank-M. Göttsche ◽

Isabel F. Trigo

Keyword(s):

Surface Temperature ◽

Spatial Resolution ◽

Land Surface Temperature ◽

Land Surface ◽

High Spatial Resolution ◽

Urban Climate ◽

Google Earth ◽

Small Scale ◽

Temperature Estimation ◽

Google Earth Engine

Land Surface Temperature (LST) is increasingly important for various studies assessing land surface conditions, e.g., studies of urban climate, evapotranspiration, and vegetation stress. The Landsat series of satellites have the potential to provide LST estimates at a high spatial resolution, which is particularly appropriate for local or small-scale studies. Numerous studies have proposed LST retrieval algorithms for the Landsat series, and some datasets are available online. However, those datasets generally require the users to be able to handle large volumes of data. Google Earth Engine (GEE) is an online platform created to allow remote sensing users to easily perform big data analyses without increasing the demand for local computing resources. However, high spatial resolution LST datasets are currently not available in GEE. Here we provide a code repository that allows computing LSTs from Landsat 4, 5, 7, and 8 within GEE. The code may be used freely by users for computing Landsat LST as part of any analysis within GEE.

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Analisis Fenomena Pulau Panas Perkotaan Kota Bandung Menggunakan Google Earth Engine

10.31227/osf.io/bej3n ◽

2019 ◽

Author(s):

Muhammad Malik Ar-Rahiem ◽

Muhamad Riza Fakhlevi

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Google Earth ◽

Landsat 8 ◽

Heat Island ◽

Urban Heat ◽

Google Earth Engine

Pulau Panas Perkotaan (Urban Heat Island) adalah fenomena antropogenik akibat pengaruh urbanisasi. Kawasan perkotaan yang terbangun memiliki temperatur yang lebih hangat dibandingkan kawasan sekitarnya. Fenomena Pulau Panas Perkotaan di Kota Bandung diteliti menggunakan data Suhu Permukaan Tanah (Land Surface Temperature) yang diakuisisi dari satelit Landsat 8. Lima tahun data satelit dianalisis menggunakan piranti daring Google Earth Engine untuk menganalisis variasi temporal Pulau Panas Perkotaan di Kota Bandung dan sekitarnya. Suhu yang diakuisisi dari satelit dikonversi menjadi estimasi suhu permukaan dengan mempertimbangkan nilai Normalized Difference Vegetation Index. Hasil dari penelitian ini adalah peta persebaran rata-rata dan median suhu permukaan di Cekungan Bandung tahun 2013-2018, serta grafik seri waktu suhu permukaan di 3 jenis tata guna lahan yang mewakili daerah kota (sekitar Jalan Sudirman), hutan kota (Hutan Babakan Siliwangi), dan hutan (Tamah Hutan Raya Djuanda). Suhu rata-rata Kota Bandung pada tahun 2013-2018 adalah 26,93 oC (median seluruh data) dan 25,57oC (rata-rata seluruh data). Sementara perbandingan berdasarkan tata guna lahan; daerah kota memiliki suhu permukaan rata-rata 27,30 oC, daerah hutan kota memiliki suhu 21,31oC, dan daerah hutan memiliki suhu 18,60oC. Peta persebaran suhu panas permukaan dari citra Landsat 8 menunjukkan bahwa daerah hutan secara konsisten memiliki suhu paling rendah, diikuti dengan hutan kota, dan kemudian daerah kota menjadi area yang paling panas dengan suhu maksimal hingga 33,73oC. Penggunaan Google Earth Engine yang berbasis komputasi awan sangat memudahkan pengolahan data citra satelit dalam jumlah besar yang selama ini tidak memungkinkan dilakukan dengan cara konvensional (mengunduh dan memproses di komputer).

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Angular-Based Radiometric Slope Correction for Sentinel-1 on Google Earth Engine

Remote Sensing ◽

10.3390/rs12111867 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1867 ◽

Cited By ~ 2

Author(s):

Andreas Vollrath ◽

Adugna Mullissa ◽

Johannes Reiche

Keyword(s):

Land Surface ◽

Large Scale ◽

Google Earth ◽

Urban Environments ◽

Mountainous Area ◽

Backscatter Signal ◽

Wide Range ◽

Slope Correction ◽

Google Earth Engine ◽

Sar Imagery

This article provides an angular-based radiometric slope correction routine for Sentinel-1 SAR imagery on the Google Earth Engine platform. Two established physical reference models are implemented. The first model is optimised for vegetation applications by assuming volume scattering on the ground. The second model is optimised for surface scattering, and therefore targeted at urban environments or analysis of soil characteristics. The framework of both models is extended to simultaneously generate masks of invalid data in active layover and shadow affected areas. A case study, using openly available and reproducible code, exemplarily demonstrates the improvement of the backscatter signal in a mountainous area of the Austrian Alps. Furthermore, suggestions for specific use cases are discussed and drawbacks of the method with respect to pixel-area based methods are highlighted. The radiometrically corrected radar backscatter products are overcoming current limitations and are compliant with recent CEOS specifications for SAR backscatter over land. This improves a wide range of potential usage scenarios of the Google Earth Engine platform in mapping various land surface parameters with Sentinel-1 on a large scale and in a rapid manner. The provision of an openly accessible Earth Engine module allows users a smooth integration of the routine into their own workflows.

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