Evaluating Yield Variability of Corn and Soybean Using Landsat-8, Sentinel-2 and Modis in Google Earth Engine

2019 ◽  
Author(s):  
Feng Gao ◽  
Martha Anderson
Keyword(s):  
Google Earth ◽  
Yield Variability ◽  
Landsat 8 ◽  
Google Earth Engine ◽  
Sentinel 2

scholarly journals Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine

2017 ◽  
Vol 9 (10) ◽  
pp. 1065 ◽  
Author(s):  
Jun Xiong ◽  
Prasad Thenkabail ◽  
James Tilton ◽  
Murali Gumma ◽  
Pardhasaradhi Teluguntla ◽  
...  
Keyword(s):  
Google Earth ◽  
Landsat 8 ◽  
Object Based ◽  
Google Earth Engine ◽  
Sentinel 2

scholarly journals Monitoring Oasis Cotton Fields Expansion in Arid Zones Using the Google Earth Engine: A Case Study in the Ogan-Kucha River Oasis, Xinjiang, China

2022 ◽  
Vol 14 (1) ◽  
pp. 225
Author(s):  
Lijing Han ◽  
Jianli Ding ◽  
Jinjie Wang ◽  
Junyong Zhang ◽  
Boqiang Xie ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Drip Irrigation ◽  
Google Earth ◽  
Cotton Field ◽  
Arid Zones ◽  
Landsat 8 ◽  
Land Resource ◽  
Google Earth Engine ◽  
Cotton Fields ◽  
Sentinel 2

Rapid and accurate mapping of the spatial distribution of cotton fields is helpful to ensure safe production of cotton fields and the rationalization of land-resource planning. As cotton is an important economic pillar in Xinjiang, accurate and efficient mapping of cotton fields helps the implementation of rural revitalization strategy in Xinjiang region. In this paper, based on the Google Earth Engine cloud computing platform, we use a random forest machine-learning algorithm to classify Landsat 5 and 8 and Sentinel 2 satellite images to obtain the spatial distribution characteristics of cotton fields in 2011, 2015 and 2020 in the Ogan-Kucha River oasis, Xinjiang. Unlike previous studies, the mulching process was considered when using cotton field phenology information as a classification feature. The results show that both Landsat 5, Landsat 8 and Sentinel 2 satellites can successfully classify cotton field information when the mulching process is considered, but Sentinel 2 satellite classification results have the best user accuracy of 0.947. Sentinel 2 images can distinguish some cotton fields from roads well because they have higher spatial resolution than Landsat 8. After the cotton fields were mulched, there was a significant increase in spectral reflectance in the visible, red-edge and near-infrared bands, and a decrease in the short-wave infrared band. The increase in the area of oasis cotton fields and the extensive use of mulched drip-irrigation water saving facilities may lead to a decrease in the groundwater level. Overall, the use of mulch as a phenological feature for classification mapping is a good indicator in cotton-growing areas covered by mulch, and mulch drip irrigation may lead to a decrease in groundwater levels in oases in arid areas.

Identifying multi-day to sub-daily supraglacial lake change in Greenland from Sentinel 2 and Landsat 8 imagery using Google Earth Engine

2021 ◽  
Author(s):  
Qiao Li ◽  
James Lea ◽  
Stephen Brough
Keyword(s):  
Google Earth ◽  
Landsat 8 ◽  
Lake Area ◽  
High Latitudes ◽  
Large Area ◽  
Large Lakes ◽  
North East ◽  
The North ◽  
Google Earth Engine ◽  
Sentinel 2

<p>Supraglacial lakes (SGLs) are a major component of Greenland’s surface hydrology and mass balance. Monitoring their evolution at multi-day to sub-daily timescales has traditionally been performed by relatively low-resolution sensors such as MODIS Terra, though opportunities exist for using higher spatial resolution sensors at high latitudes.</p><p>In this study, we take advantage of frequent orbital crossovers of Sentinel 2 and Landsat 8 imagery at high latitudes to monitor lakes at multi-day to sub-day temporal resolution, and spatial resolutions up to/over an order of magnitude higher than MODIS Terra (10 m to 30 m, compared to ~250 m for MODIS Terra). Through leveraging the cloud computing resources of Google Earth Engine (GEE), we have developed a workflow to track the evolution of lakes for all available Sentinel 2 and Landsat 8 images over a melt season.</p><p>Our workflow builds on the approach of Moussavi et al. (2020) that was developed for Antarctica, implementing it within GEE to explore its sensitivity and suitability for application to the catchment of the North East Greenland Ice Stream (NEGIS) for the 2019 melt season. To improve the efficiency of analysis, we analyse 282 large lakes (>0.125 km^2) that were previously identified through analysis of MODIS Terra imagery. All lake outlines are appended with image ID and lake area metadata to facilitate subsequent analysis, and allow each lake outline to be traced back to the original image that it was derived from. Our approach is able to monitor lake growth and drainage at unprecedented spatial and temporal resolutions over a large area, allowing the widespread characterization of seasonal lake evolution.</p>

scholarly journals 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

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.

scholarly journals Mapping the Natural Distribution of Bamboo and Related Carbon Stocks in the Tropics Using Google Earth Engine, Phenological Behavior, Landsat 8, and Sentinel-2

2020 ◽  
Vol 12 (18) ◽  
pp. 3109 ◽  
Author(s):  
Manjunatha Venkatappa ◽  
Sutee Anantsuksomsri ◽  
Jose Alan Castillo ◽  
Benjamin Smith ◽  
Nophea Sasaki
Keyword(s):  
Vegetation Index ◽  
Google Earth ◽  
Carbon Stocks ◽  
Landsat 8 ◽  
Threshold Values ◽  
Landsat 8 Oli ◽  
Natural Distribution ◽  
Siem Reap ◽  
Google Earth Engine ◽  
Sentinel 2

Although vegetation phenology thresholds have been developed for a wide range of mapping applications, their use for assessing the distribution of natural bamboo and the related carbon stocks is still limited, especially in Southeast Asia. Here, we used Google Earth Engine (GEE) to collect time-series of Landsat 8 Operational Land Imager (OLI) and Sentinel-2 images and employed a phenology-based threshold classification method (PBTC) to map the natural bamboo distribution and estimate carbon stocks in Siem Reap Province, Cambodia. We processed 337 collections of Landsat 8 OLI for phenological assessment and generated 121 phenological profiles of the average vegetation index for three vegetation land cover categories from 2015 to 2018. After determining the minimum and maximum threshold values for bamboo during the leaf-shedding phenology stage, the PBTC method was applied to produce a seasonal composite enhanced vegetation index (EVI) for Landsat collections and assess the bamboo distributions in 2015 and 2018. Bamboo distributions in 2019 were then mapped by applying the EVI phenological threshold values for 10 m resolution Sentinel-2 satellite imagery by accessing 442 tiles. The overall Landsat 8 OLI bamboo maps for 2015 and 2018 had user’s accuracies (UAs) of 86.6% and 87.9% and producer’s accuracies (PAs) of 95.7% and 97.8%, respectively, and a UA of 86.5% and PA of 91.7% were obtained from Sentinel-2 imagery for 2019. Accordingly, carbon stocks of natural bamboo by district in Siem Reap at the province level were estimated. Emission reductions from the protection of natural bamboo can be used to offset 6% of the carbon emissions from tourists who visit this tourism-destination province. It is concluded that a combination of GEE and PBTC and the increasing availability of remote sensing data make it possible to map the natural distribution of bamboo and carbon stocks.

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

2019 ◽  
Vol 11 (3) ◽  
pp. 288 ◽  
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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