Observation of maritime traffic in southern Iran during the Covid-19 outbreak using Sentinel-1 images In the Google Earth Engine Platform

Vietnam wetland cover map: using hydro-periods Sentinel-2 images and Google Earth Engine to explore the mapping method of tropical wetland

The production of cloudless images from the optical satellite are critical in Earth surface monitoring. In 2015,Sentinel-2A was successfully launched into orbit by the European Space Agency. Sentinel-2 imagery is currently theprimary source of data for Earth monitoring. There are several ways to create cloudless images from multi-temporalSentinel-2 optical satellite imagery on the Google Earth Engine (GEE) platform. These include the Fmask (Function ofmask) method, the Fmask CDI (Cloud Displacement Index) method, and the Fmask CSP (Cloud Score Plus) method. Inthis paper, the authors build a program and evaluate the cloud masking methods on the GEE platform in Song Hinhdistrict, Phu Yen province, which is situated in the South-Central Coast region of Central Vietnam. The Song Hinh districtis a suitable study area for the evaluation of cloud masking methods on optical satellite images due to its diverse andcomplex terrain, which includes numerous peaks and valleys and a variety of climatic conditions. This article illustratesthe results of three cloud masking methods on Sentinel-2 images. In contrast to the Fmask method, the Fmask CDI andFmask CSP methods provide more benefits in detecting clouds and cloud shadows, resulting in more accurate outcomes

Nitrogen plays an important role in improving soil productivity and maintaining ecosystem stability. Mapping and monitoring the soil total nitrogen (STN) content is the basis for modern soil management. The Google Earth Engine (GEE) platform covers a wide range of available satellite remote sensing datasets and can process massive data calculations. We collected 6823 soil samples in Shandong Province, China. The random forest (RF) algorithm predicted the STN content in croplands from 2002 to 2016 in Shandong Province, China on the GEE platform. Our results showed that RF had the coefficient of determination (R2) (0.57), which can predict the spatial distribution of the STN and analyze the trend of STN changes. The remote sensing spectral reflectance is more important in model building according to the variable importance analysis. From 2002 to 2016, the STN content of cropland in the province had an upward trend of 35.6%, which increased before 2010 and then decreased slightly. The GEE platform provides an opportunity to map dynamic changes of the STN content effectively, which can be used to evaluate soil properties in the future long-term agricultural management.

With the growth of cloud computing, the use of the Google Earth Engine (GEE) platform to conduct research on water inversion, natural disaster monitoring, and land use change using long time series of Landsat images has also gradually become mainstream. Landsat images are currently one of the most important image data sources for remote sensing inversion. As a result of changes in time and weather conditions in single-view images, varying image radiances are acquired; hence, using a monthly or annual time scale to mosaic multi-view images results in strip color variation. In this study, the NDWI and MNDWI within 50 km of the coastline of the Yucatán Peninsula from 1993 to 2021 are used as the object of study on GEE platform, and mosaic areas with chromatic aberrations are reconstructed using Landsat TOA (top of atmosphere reflectance) and SR (surface reflectance) images as the study data. The DN (digital number) values and probability distributions of the reference image and the image to be restored are classified and counted independently using the random forest algorithm, and the classification results of the reference image are mapped to the area of the image to be restored in a histogram-matching manner. MODIS and Sentinel-2 NDWI products are used for comparison and validation. The results demonstrate that the restored Landsat NDWI and MNDWI images do not exhibit obvious band chromatic aberration, and the image stacking is smoother; the Landsat TOA images provide improved results for the study of water bodies, and the correlation between the restored Landsat SR and TOA images with the Sentinel-2 data is as high as 0.5358 and 0.5269, respectively. In addition, none of the existing Landsat NDWI products in the GEE platform can effectively eliminate the chromatic aberration of image bands.

Assessing the impact of unplanned settlements on urban renewal projects with GEE

The ecological quality of inland areas is an important aspect of the United Nations Sustainable Development Goals (UN SDGs). The ecological environment of Northwest China is vulnerable to changes in climate and land use/land cover, and the changes in ecological quality in this arid region over the last two decades are not well understood. This makes it more difficult to advance the UN SDGs and develop appropriate measures at the regional level. In this study, we used the Moderate Resolution Imaging Spectroradiometer (MODIS) products to generate remote sensing ecological index (RSEI) on the Google Earth Engine (GEE) platform to examine the relationship between ecological quality and environment in Xinjiang during the last two decades (from 2000 to 2020). We analyzed a 21-year time series of the trends and spatial characteristics of ecological quality. We further assessed the importance of different environmental factors affecting ecological quality through the random forest algorithm using data from statistical yearbooks and land use products. Our results show that the RSEI constructed using the GEE platform can accurately reflect the ecological quality information in Xinjiang because the contribution of the first principal component was higher than 90.00%. The ecological quality in Xinjiang has increased significantly over the last two decades, with the northern part of this region having a better ecological quality than the southern part. The areas with slightly improved ecological quality accounted for 31.26% of the total land area of Xinjiang, whereas only 3.55% of the land area was classified as having a slightly worsen (3.16%) or worsen (0.39%) ecological quality. The vast majority of the deterioration in ecological quality mainly occurred in the barren areas Temperature, precipitation, closed shrublands, grasslands and savannas were the top five environmental factors affecting the changes in RSEI. Environmental factors were allocated different weights for different RSEI categories. In general, the recovery of ecological quality in Xinjiang has been controlled by climate and land use/land cover during the last two decades and policy-driven ecological restoration is therefore crucial. Rapid monitoring of inland ecological quality using the GEE platform is projected to aid in the advancement of the comprehensive assessment of the UN SDGs.

Mapping land-cover/land-use (LCLU) and estimating forest biomass using satellite images is a challenge given the diversity of sensors available and the heterogeneity of forests. Copernicus program served by the Sentinel satellites family and the Google Earth Engine (GEE) platform, both with free and open services accessible to its users, present a good approach for mapping vegetation and estimate forest biomass on a global, regional, or local scale, periodically and in a repeated way. The Sentinel-2 (S2) systematically acquires optical imagery and provides global monitoring data with high spatial resolution (10–60 m) images. Given the novelty of information on the use of S2 data, this chapter presents a review on LCLU maps and forest above-ground biomass (AGB) estimates, in addition to exploring the efficiency of using the GEE platform. The Sentinel data have great potential for studies on LCLU classification and forest biomass estimates. The GEE platform is a promising tool for executing complex workflows of satellite data processing.

An integrated approach to obtain high-precision regional root water uptake maps

Objectives: National park is the main body of China's natural protected area system, and land cover mapping in national parks plays an important role in understanding the status of natural resources, identifying existing ecological security threats and responding to them quickly. This analysis attempts to develop an accurate and cost-effective model for mapping land cover dataset in national parks. Methods: Two land cover classification methods were developed based on Google Earth Engine (GEE) environment by combining Sentinel-1 and Sentinel-2 images, topographic and textural derivatives to map land cover types in the Qianjiangyuan National Park. One used pixel-based random forest (RF) classification algorithm, the other used object-oriented simple non-iterative clustering (SNIC) segmentation in partnership with RF algorithm, which indicated that the input featureswere first segmented into superpixel image objects then classified by RF algorithm. To optimize classification features, a method taking the advantages of cloud computing platform to design different groups of comparative experiments to finalize feature combination with the highest classification accuracy was proposed, followed by using a recursive feature elimination method to further screen the features. Results: Experimental results showed that both the pixel-based method and the object-oriented method had good performance in land cover classification, and the corresponding highest overall classification accuracies of the two methods were estimated at 92.37% and 93.98%, respectively. Furthermore, integration of synthetic aperture radar (SAR) data into the classifications could substantially improve the classification accuracy when using the pixel-based method, but there was no apparent escalating effect for the object-oriented classification. Conclusions: Experiments showed that the land cover classification map generated from the SNIC+RF algorithm in GEE platform was more complete and the algorithm requires fewer features (15 features, including multispectral bands and spectral indices) and runs quickly in the GEE platform. Thus, this algorithm deserves to be popularized in national park management practices.

Leveraging Google Earth Engine platform to characterize and map small seasonal wetlands in the semi-arid environments of South Africa

In tropical/subtropical monsoon regions, accurate rice mapping is hampered by the following factors: (1) The frequent occurrence of clouds in such areas during the rice-growing season interferes strongly with optical remote sensing observations; (2) The agro-landscape in such regions is fragmented and scattered. Rice maps produced using low spatial resolution data cannot well delineate the detailed distribution of rice, while pixel-based mapping using medium and high resolutions has significant salt-and-pepper noise. (3) The cropping system is complex, and rice has a rotation schedule with other crops. Therefore, the Phenology-, Object- and Double Source-based (PODS) paddy rice mapping algorithm is implemented, which consists of three steps: (1) object extraction from multi-temporal 10-m Sentinel-2 images where the extracted objects (fields) are the basic classification units; (2) specifying the phenological stage of transplanting from Savitzky–Golay filtered enhanced vegetation index (EVI) time series using the PhenoRice algorithm; and (3) the identification of rice objects based on flood signal detection from time-series microwave and optical signals of the Sentinel-1/2. This study evaluated the potential of the combined use of the Sentinel-1/2 mission on paddy rice mapping in monsoon regions with the Hangzhou-Jiaxin-Huzhou (HJH) plain in China as the case study. A cloud computing approach was used to process the available Sentinel-1/2 imagery from 2019 and MODIS images from 2018 to 2020 in the HJH plain on the Google Earth Engine (GEE) platform. An accuracy assessment showed that the resultant object-based paddy rice map has a high accuracy with a producer (user) accuracy of 0.937 (0.926). The resultant 10-m paddy rice map is expected to provide unprecedented detail, spatial distribution, and landscape patterns for paddy rice fields in monsoon regions.

Accurate regional identification of cropland quantities and spatial distributions is important for cropland monitoring, food security, and sustainable regional development. Various countries and organizations have produced series of land-cover products. However, variability among remote sensors, land-cover classification schemes, and classification methods has resulted in discrepancies. In this study, we develop a novel method to improve cropland data accuracy for the Belt and Road (B&R) region, by fusing and correcting four cropland products: CCI-LC, GFSAD30, MCD12Q1, and FROM-GLC. Spatial analysis techniques are implemented, including climate stratification, consistency assessment, and statistical filtering, to develop training samples for model correction. The Google Earth Engine (GEE) platform and random forest (RF) algorithm are executed with these training samples to correct fused multi-data product and generate a corrected 2015 cropland product. The corrected product indicates that cropland accounts for 14.94% of the B&R region, which is closer to the results found via FAO statistics than the results from any of the four individual land-cover products. On the national scale, the root mean square error between the corrected cropland product quantities and FAO statistics is 11.39% and the correlation coefficient value is 0.77. This indicates that the method exhibits better fitting characteristics. The accuracies of the areas of inconsistency among the four cropland products and our corrected product are assessed using 3112 visually interpreted samples and Google Earth. The overall accuracy of the corrected cropland product is 77.54% in inconsistent areas. The highest accuracy produced by the corrected cropland product indicates the effectiveness of our method, which can rapidly improve cropland data accuracy in heterogeneous regions. Combining the training samples produced by fusing existing cropland products and updating techniques with multi-source remote sensing data from the GEE platform, we foresee potential applications to update global cropland product.

Intertwined impacts of urbanization and land cover change on urban climate and agriculture in Aurangabad city (MS), India using google earth engine platform

Forest fires are major disturbances in forest ecosystems. The rapid detection of the spatial and temporal characteristics of fires is essential for formulating targeted post-fire vegetation restoration measures and assessing fire-induced carbon emissions. We propose an accurate and efficient framework for extracting the spatiotemporal characteristics of fires using vegetation change tracker (VCT) products and the Google Earth Engine (GEE) platform. The VCT was used to extract areas of persistent forest and forest disturbance patches from Landsat images of Xichang and Muli, Liangshan prefecture, Sichuan province in southwestern China and Huma, Heilongjiang province, in northeastern China. All available Landsat images in the GEE platform in a year were normalized using the VCT-derived persisting forest mask to derive three standardized vegetation indices (normalized burn ratio (NBRr), normalized difference moisture index (NDMIr), and normalized difference vegetation index (NDVIr)). Historical forest disturbance events in Xichang were used to train two decision trees using the C4.5 data mining tool. The differenced NBRr, NDMIr, and NDVIr (dNBRr, dNDMIr, and dNDVIr) were obtained by calculating the difference in the index values between two temporally adjacent images. The occurrence time of disturbance events were extracted using the thresholds identified by decision tree 1. The use of all available images in GEE narrowed the disturbance occurrence time down to 16 days. This period was extended if images were not available or had cloud cover. Fire disturbances were distinguished from other disturbances by comparing the dNBRr, dNDMIr, and dNDVIr values with the thresholds identified by decision tree 2. The results showed that the proposed framework performed well in three study areas. The temporal accuracy for detecting disturbances in the three areas was 94.33%, 90.33%, and 89.67%, the classification accuracy of fire and non-fire disturbances was 85.33%, 89.67%, and 83.67%, and the Kappa coefficients were 0.71, 0.74, and 0.67, respectively. The proposed framework enables the efficient and rapid extraction of the spatiotemporal characteristics of forest fire disturbances using frequent Landsat time-series data, GEE, and VCT products. The results can be used in forest fire disturbance databases and to implement targeted post-disturbance vegetation restoration practices.

Based on a total of 519 images, the composite images with the lowest possible cloud cover were generated at pixel level with image synthesis method on Google Earth Engine (GEE) platform. The Remote Sensing Ecological Index (RSEI) was adopted, and calculated in an efficient way with the assistance of parallel cloud computing of the GEE platform. The RSEI was used in this paper to evaluate and monitor the eco-environmental quality of the Lhasa Metropolitan Area. Results show that: (1) The ecological quality is better in the west than in the east of Lhasa Metropolitan Area, with Lhasa as an approximate dividing point. The ecological quality improved and then deteriorated dramatically before 2000, with the mean RSEI value dropping from 0.51 to 0.46; the trend was followed by a gradual increase up until 2017, with the mean RSEI value increased from 0.46 to 0.55. (2) The RSEI is weakly and positively correlated with socioeconomic indicators. This indicates that the population growth and economic development did not negatively influence the ecological quality, but actually boosted it. (3) The GEE can serve as an efficient computing platform for the assessment and monitoring of eco-environmental quality in vast regions.