Application of Google Earth Engine to Monitor Greenhouse Gases: A Review by Damar David Wilson, Gebrekidan Worku Tefera, Ram L. Ray

“…By leveraging GEE’s capabilities, researchers have developed tools and algorithms to analyze remotely sensed data and accurately quantify GHG emissions and uptakes. …”
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Evaluation of Three Satellite Precipitation Products TRMM 3B42, CMORPH, and PERSIANN over a Subtropical Watershed in China by Junzhi Liu, Zheng Duan, Jingchao Jiang, A-Xing Zhu

“…This study conducted a comprehensive evaluation of three satellite precipitation products (TRMM (Tropical Rainfall Measuring Mission) 3B42, CMORPH (the Climate Prediction Center (CPC) Morphing algorithm), and PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks)) using data from 52 rain gauge stations over the Meichuan watershed, which is a representative watershed of the Poyang Lake Basin in China. …”
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A 20 m spatial resolution peatland extent map of Alaska by Mark J. Lara, Roger Michaelides, Duncan Anderson, Wenqu Chen, Emma C. Hall, Caroline Ludden, Aiden I. G. Schore, Umakant Mishra, Sarah N. Scott

“…However, our best remotely sensed products describing the regional-scale distribution of peatland extents are constrained to 1 km2 pixels, often representing notable sub-pixel heterogeneity and local-scale uncertainties. …”
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Developing a semi-automated technique of surface water quality analysis using GEE and machine learning: A case study for Sundarbans by Sheikh Fahim Faysal Sowrav, Sujit Kumar Debsarma, Mohan Kumar Das, Khan Mohammad Ibtehal, Mahfujur Rahman, Noshin Tabassum Hridita, Atika Afia Broty, Muhammad Sajid Anam Hoque

“…This study presents a semi-automated approach for assessing water quality in the Sundarbans, a critical and vulnerable ecosystem, using machine learning (ML) models integrated with field and remotely-sensed data. Key water quality parameters—Sea Surface Temperature (SST), Total Suspended Solids (TSS), Turbidity, Salinity, and pH—were predicted through ML algorithms and interpolated using the Empirical Bayesian Kriging (EBK) model in ArcGIS Pro. …”
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Evaluation of the Operational Simplified Surface Energy Balance Model for Pastureland Evapotranspiration Mapping and Drought Monitoring in North Central Kentucky by Maheteme Gebremedhin, Ian Ries, Gabriel B. Senay, Martin Matisoff, Ibukun Amusan, Jeremy Sandifer, Buddhi Gyawali

“…The use of remotely sensed evapotranspiration (ET) for field applications in drought monitoring and assessment is gaining momentum, but meeting this need has been hampered by the absence of extensive ground-based measurement stations for ground validation across agricultural zones and natural landscapes. …”
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Mapping fruit tree dynamics using phenological metrics from optimal Sentinel-2 data and Deep Neural Network by Yingisani Chabalala, Elhadi Adam, Mahlatse Kganyago

“…The availability of remotely sensed data with high spatial and spectral resolutions makes it possible to use deep learning models to support decision-making in agriculture. …”
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Consistent patterns of LiDAR-derived measures of savanna vegetation complexity between wet and dry seasons by Zhengyang Wang, Jenia Singh, Andrew B. Davies

“…However, the acquisition of remotely-sensed data from the air and the surveying of biological information on the ground are often temporally disjunct and collected in different seasons. …”
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Spatial scales matter in designing buffer zones for coastal protected areas along the East Asian-Australasian Flyway by Roger H. Lee, Ivan H.Y. Kwong, Tom C.H. Li, Paulina P.Y. Wong, Yik-Hei Sung, Yat-Tung Yu

“…In this study, we focused on waterbirds as ecologically important wetland species to provide evidence of the significance of monitoring and managing buffer zones. By integrating remotely sensed parameters and 3-year monthly waterbird surveys in and around the Mai Po Inner Deep Bay Ramsar Site of Hong Kong, a key stopover of the East Asian Australasian Flyway, we mapped waterbird occurrences for all and different waterbird guilds during winter and summer using random forest models. …”
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Evaluation of Satellite Rainfall Products over the Mahaweli River Basin in Sri Lanka by Helani Perera, Shalinda Fernando, Miyuru B. Gunathilake, T. A. J. G. Sirisena, Upaka Rathnayake

“…Integrated MultisatellitE Retrievals for Global Precipitation Measurement (IMERG) outperformed among all SRPs, while Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products showed dire performances. …”
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The Effect of Spatial Scale on the Prediction of Tropical Forest Attributes from Image Texture by J. Alberto Gallardo-Cruz, Jonathan V. Solórzano, Edgar J. González, Jorge A. Meave

“…Therefore, understanding how ecological processes express themselves in a remotely sensed image becomes a critical task.…”
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Coastal Hazard and Vulnerability Assessment in Cameroon by Mesmin Tchindjang, Philippes Mbevo Fendoung, Casimir Kamgho

“…In addition, documents on climate change were consulted. Remotely sensed data, combined with GIS tools, helped to determine and assess the associated risks. …”
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Delineating urban flooding when incorporating community stormwater knowledge by Madeline Scolio, Peleg Kremer, Virginia Smith, Achira Amur, Bridget Wadzuk, Kate Homet, Eric Devlin, Md Abdullah Al Mehedi, Laura Moore

“…An array of data, including remotely sensed satellite imagery after major flooding events, Federal Emergency Management Agency’s Special Flood Hazard Areas, First Street Foundation’s Flood Factor, road closures, National Flood Insurance Program claims, and community surveys, were compared for the study areas. …”
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Evaluation and Correction of GPM IMERG Precipitation Products over the Capital Circle in Northeast China at Multiple Spatiotemporal Scales by Wei Sun, Yonghua Sun, Xiaojuan Li, Tao Wang, Yanbing Wang, Qi Qiu, Zhitian Deng

“…Accurate remote-sensed precipitation data are crucial to the effective monitoring and analysis of floods and climate change. …”
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Statistical Evaluation and Trend Analysis of ANN Based Satellite Products (PERSIANN) for the Kelani River Basin, Sri Lanka by Helani Perera, Miyuru B. Gunathilake, Ravindu Panditharathne, Najib Al-mahbashi, Upaka Rathnayake

“…Three SbPPs, precipitation estimation using remotely sensed information using artificial neural networks (PERSIANN), PERSIANN-cloud classification system (CCS), and PERSIANN-climate data record (CDR) and ground observed rain gauge daily rainfall data at nine locations were used for the analysis. …”
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Accounting for Australia’s threatened species: Estimating historical and recent change in terrestrial habitat by Katherine M. Giljohann, Karel Mokany, Simon Ferrier, Thomas D. Harwood, Chris Ware, Kristen J. Williams

“…This involves: 1) estimating the historical (pre-intensification) habitat distribution for listed threatened species, and 2) deriving a metric of habitat provision for threatened species by combining the historical habitat distributions with remotely sensed annual ecosystem condition data. We demonstrate the method for Australia using 1,518 nationally-listed threatened species and ecosystem condition data for 2017 and 2018, and present summaries by sub-national jurisdictions.Across Australia, intensively developed regions had the greatest number of threatened species based on estimated historical habitat. …”
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Unburned habitat essential for amphibian breeding persistence following wildfire by Larissa L. Bailey, Richard Henderson, Wendy A. Estes-Zumpf, Charles C. Rhoades, Ellie Miller, Dominique Lujan, Erin Muths

“…Importantly, we found that remotely sensed data typically produced to predict flooding and erosion at broad (catchment) scales is a poor representation of the amount and variation in soil burn severity surrounding small habitat features, suggesting that additional field sampling is necessary to understand wildfire responses for species that rely on these small habitat features. …”
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Hydrological Models and Artificial Neural Networks (ANNs) to Simulate Streamflow in a Tropical Catchment of Sri Lanka by Miyuru B. Gunathilake, Chamaka Karunanayake, Anura S. Gunathilake, Niranga Marasingha, Jayanga T. Samarasinghe, Isuru M. Bandara, Upaka Rathnayake

“…Results revealed that the simulated hydrological model based on observed rainfall outperformed those of based on remotely sensed SbPPs. BR algorithm-based ANN algorithm was found to be superior among the data-driven models in the context of ANN model simulations. …”
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Satellite-based Emission Inventory Adjustments Improve Simulations of Long-range Transport Events by Wei-Syun Huang, Stephen M. Griffith, Yi-Chun Lin, Ying-Chieh Chen, Chung-Te Lee, Charles C.-K. Chou, Ming-Tung Chuang, Sheng-Hsiang Wang, Neng-Huei Lin

“…This study adjusts a bottom-up emission inventory based on changes in remotely sensed NO2 column densities for a source region of East Asia, then with CMAQv5.2.1 simulates transport of LRT plumes to Taiwan. …”
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Hyperspectral imaging for precision nitrogen management: A comparative exploration of two methodological approaches to estimate optimal nitrogen rate in processing tomato by Vito Aurelio Cerasola, Francesco Orsini, Giuseppina Pennisi, Gaia Moretti, Stefano Bona, Francesco Mirone, Jochem Verrelst, Katja Berger, Giorgio Gianquinto

“…The direct biomass retrieval from spectral data was compared with the indirect biomass retrieval from the remotely sensed LAI applying empirical regressions. PLSR outperformed the other algorithms in estimating N uptake (Relative Root Mean Square Error, RRMSE=21.8 %), while SVR better estimated NNI (RRMSE=10.2 %) and direct biomass (RRMSE=19.4 %). …”
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