Google Earth Engine Applications Since Inception: Usage, Trends, and Potential

Kumar, Lalit; Mutanga, Onisimo

doi:10.3390/rs10101509

Cited by 540 publications

(342 citation statements)

References 19 publications

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“…Emerging cloud computing platforms such as the Google Earth Engine (GEE) (which has the planetary-scale archives of remote sensing data including L7, L8 and S2), reduces substantially the work for data management and speed up the analyzing process [26]. Built-in functions and algorithms within the GEE platform help simplify many pre-processing steps allowing to focus more on the interpretation of the core algorithms [27,28].…”

Section: Introductionmentioning

confidence: 99%

Harmonization of Landsat and Sentinel 2 for Crop Monitoring in Drought Prone Areas: Case Studies of Ninh Thuan (Vietnam) and Bekaa (Lebanon)

Nguyen

Baez-Villanueva

Bui³

et al. 2020

Remote Sensing

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Proper satellite-based crop monitoring applications at the farm-level often require near-daily imagery at medium to high spatial resolution. The combination of data from different ongoing satellite missions Sentinel 2 (ESA) and Landsat 7/8 (NASA) provides this unprecedented opportunity at a global scale; however, this is rarely implemented because these procedures are data demanding and computationally intensive. This study developed a robust stream processing for the harmonization of Landsat 7, Landsat 8 and Sentinel 2 in the Google Earth Engine cloud platform, connecting the benefit of coherent data structure, built-in functions and computational power in the Google Cloud. The harmonized surface reflectance images were generated for two agricultural schemes in Bekaa (Lebanon) and Ninh Thuan (Vietnam) during 2018–2019. We evaluated the performance of several pre-processing steps needed for the harmonization including the image co-registration, Bidirectional Reflectance Distribution Functions correction, topographic correction, and band adjustment. We found that the misregistration between Landsat 8 and Sentinel 2 images varied from 10 m in Ninh Thuan (Vietnam) to 32 m in Bekaa (Lebanon), and posed a great impact on the quality of the final harmonized data set if not treated. Analysis of a pair of overlapped L8-S2 images over the Bekaa region showed that, after the harmonization, all band-to-band spatial correlations were greatly improved. Finally, we demonstrated an application of the dense harmonized data set for crop mapping and monitoring. An harmonic (Fourier) analysis was applied to fit the detected unimodal, bimodal and trimodal shapes in the temporal NDVI patterns during one crop year in Ninh Thuan province. The derived phase and amplitude values of the crop cycles were combined with max-NDVI as an R-G-B false composite image. The final image was able to highlight croplands in bright colors (high phase and amplitude), while the non-crop areas were shown with grey/dark (low phase and amplitude). The harmonized data sets (with 30 m spatial resolution) along with the Google Earth Engine scripts used are provided for public use.

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Section: Introductionmentioning

confidence: 99%

Harmonization of Landsat and Sentinel 2 for Crop Monitoring in Drought Prone Areas: Case Studies of Ninh Thuan (Vietnam) and Bekaa (Lebanon)

Nguyen

Baez-Villanueva

Bui³

et al. 2020

Remote Sensing

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“…processing. The Google Earth Engine (GEE) portal is a powerful cloud-based computing platform for image processing [47,48]. The GEE archives massive, publicly-available remote sensing data, provides a programming environment, programming tools, and virtual machines for users with relatively simple code, and can process imagery data online.…”

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confidence: 99%

Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models

Yang

et al. 2020

Remote Sensing

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The forest stock volume (FSV) is one of the key indicators in forestry resource assessments on local, regional, and national scales. To date, scaling up in situ plot-scale measurements across landscapes is still a great challenge in the estimation of FSVs. In this study, Sentinel-2 imagery, the Google Earth Engine (GEE) cloud computing platform, three base station joint differential positioning technology (TBSJDPT), and three algorithms were used to build an FSV model for forests located in Hunan Province, southern China. The GEE cloud computing platform was used to extract the imagery variables from the Sentinel-2 imagery pixels. The TBSJDPT was put forward and used to provide high-precision positions of the sample plot data. The random forests (RF), support vector regression (SVR), and multiple linear regression (MLR) algorithms were used to estimate the FSV. For each pixel, 24 variables were extracted from the Sentinel-2 images taken in 2017 and 2018. The RF model performed the best in both the training phase (i.e., R 2 = 0.91, RMSE = 35.13 m 3 ha −1 , n = 321) and in the test phase (i.e., R 2 = 0.58, RMSE = 65.03 m 3 ha −1 , and n = 138). This model was followed by the SVR model (R 2 = 0.54, RMSE = 65.60 m 3 ha −1 , n = 321 in training; R 2 = 0.54, RMSE = 66.00 m 3 ha −1 , n = 138 in testing), which was slightly better than the MLR model (R 2 = 0.38, RMSE = 75.74 m 3 ha −1 , and n = 321 in training; R 2 = 0.49, RMSE = 70.22 m 3 ha −1 , and n = 138 in testing) in both the training phase and test phase. The best predictive band was Red-Edge 1 (B5), which performed well both in the machine learning methods and in the MLR method. The Blue band (B2), Green band (B3), Red band (B4), SWIR2 band (B12), and vegetation indices (TCW, NDVI_B5, and TCB) were used in the machine learning models, and only one vegetation index (MSI) was used in the MLR model. We mapped the FSV distribution in Hunan Province (3.50 × 10 8 m 3 ) based on the RF model; it reached a total accuracy of 63.87% compared with the official forest report in 2017 (5.48 × 10 8 m 3 ). The results from this study will help develop and improve satellite-based methods to estimate FSVs on local, regional and national scales.The forest stock volume (FSV, m 3 ha −1 ) is the sum of the stem volumes of all the living trees per unit area, and is one of key forest variables for forest resources management and assessments on local, region and country scales [1]. FSVs have a strong relationship with the aboveground biomass (AGB) and carbon stocks [2]. To understand the spatial distribution of carbon in forests and to derive predictions for monitoring carbon stock trends, the FSV must be quantified [3]. Traditionally, the FSV is estimated by sampling several plots, which involves substantial manpower, materials, and financial resources [4]. With the development of remote sensing technology, particularly the Landsat series since 1972, satellite imagery has played an important role in forest inventory. Various studies have been performed to estimate the forest variables ...

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“…Knowing the scope that can be achieved with GEE and the goals behind its development (Kumar & Mutanga, 2018), we hope that this work can set an antecedent towards a national accurate PA monitoring system and, ultimately, contribute to understanding rural changes and the promotion of sustainable rural management.…”

Section: Discussionmentioning

confidence: 99%

High-resolution mapping of protected agriculture in Mexico, through remote sensing data cloud geoprocessing

Perilla

Mas

2019

European Journal of Remote Sensing

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In recent years, plasticulture, which is broadly defined as the use of plastics in agriculture, including greenhouse, tunnels and plastic mulch has become a fast-growing production system. While in the 1970s, the system was used on 2.8 M ha worldwide, the area had grown to 38 M ha in 1996/97 and is still increasing at a rapid rate: In Asia and Africa, the use of plastic greenhouses is growing by 15-30% per year. Therefore, plasticulture is expected to rise in many agrarian countries such as Mexico. Nonetheless, there are negative effects of plasticulture, that need to be correctly addressed in order to have responsible rural landscape management. Ergo our study attempts, through cloud geoprocessing of Sentinel-2 imagery, to rapidly provide a reliable and updated high-resolution map of protected agriculture that employs plastics (mostly tunnels and greenhouses). This map can be used as a basis for a protected agriculture monitoring system in Mexico, and eventually help to effectively plan the upcoming rural development, e.g. by providing input for territorial planning, showing the municipal rural practice trends, and monitoring the markets and its economic impacts. ARTICLE HISTORY

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Google Earth Engine Applications Since Inception: Usage, Trends, and Potential

Cited by 540 publications

References 19 publications

Harmonization of Landsat and Sentinel 2 for Crop Monitoring in Drought Prone Areas: Case Studies of Ninh Thuan (Vietnam) and Bekaa (Lebanon)

Harmonization of Landsat and Sentinel 2 for Crop Monitoring in Drought Prone Areas: Case Studies of Ninh Thuan (Vietnam) and Bekaa (Lebanon)

Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models

High-resolution mapping of protected agriculture in Mexico, through remote sensing data cloud geoprocessing

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