{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T18:51:20Z","timestamp":1735584680753},"reference-count":75,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T00:00:00Z","timestamp":1618963200000},"content-version":"am","delay-in-days":294,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Environmental Modelling & Software"],"published-print":{"date-parts":[[2020,7]]},"DOI":"10.1016\/j.envsoft.2020.104694","type":"journal-article","created":{"date-parts":[[2020,3,13]],"date-time":"2020-03-13T02:03:29Z","timestamp":1584065009000},"page":"104694","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":43,"special_numbering":"C","title":["AgKit4EE: A toolkit for agricultural land use modeling of the conterminous United States based on Google Earth Engine"],"prefix":"10.1016","volume":"129","author":[{"given":"Chen","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Liping","family":"Di","sequence":"additional","affiliation":[]},{"given":"Zhengwei","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Li","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Pengyu","family":"Hao","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.envsoft.2020.104694_bib1","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/S1161-0301(00)00076-9","article-title":"Effect of crop rotation and fertilisation on maize and wheat yields and yield stability in a long-term experiment","volume":"13","author":"Berzsenyi","year":"2000","journal-title":"Eur. J. Agron."},{"key":"10.1016\/j.envsoft.2020.104694_bib2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/rs8100807","article-title":"Collect earth: land use and land cover assessment through augmented visual interpretation","volume":"8","author":"Bey","year":"2016","journal-title":"Rem. Sens."},{"key":"10.1016\/j.envsoft.2020.104694_bib3","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1080\/10106049.2011.562309","article-title":"Monitoring US agriculture: the US department of agriculture, National agricultural statistics service, cropland data layer program","volume":"26","author":"Boryan","year":"2011","journal-title":"Geocarto Int."},{"key":"10.1016\/j.envsoft.2020.104694_bib4","series-title":"Agro-Geoinformatics, 2014 3rd International Conference. Presented at the International Conference on Agro-Geoinformatics","article-title":"US geospatial crop frequency data layers","author":"Boryan","year":"2014"},{"key":"10.1016\/j.envsoft.2020.104694_bib5","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.envsoft.2012.11.010","article-title":"Models as web services using the open geospatial consortium (OGC) web processing service (WPS) standard","volume":"41","author":"Castronova","year":"2013","journal-title":"Environ. Model. Software"},{"key":"10.1016\/j.envsoft.2020.104694_bib6","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.isprsjprs.2017.07.011","article-title":"A mangrove forest map of China in 2015: analysis of time series Landsat 7\/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform","volume":"131","author":"Chen","year":"2017","journal-title":"ISPRS J. Photogrammetry Remote Sens."},{"key":"10.1016\/j.envsoft.2020.104694_bib7","doi-asserted-by":"crossref","first-page":"108","DOI":"10.2134\/agronj1991.00021962008300010026x","article-title":"Rotational cropping sequence affects yield of corn and soybean","volume":"83","author":"Crookston","year":"1991","journal-title":"Agron. J."},{"key":"10.1016\/j.envsoft.2020.104694_bib8","series-title":"Big Data","article-title":"Building open environments to meet big data challenges in earth sciences","author":"Deng","year":"2014"},{"key":"10.1016\/j.envsoft.2020.104694_bib9","series-title":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Presented at the 2016 IEEE International Geoscience and Remote Sensing Symposium","first-page":"189","article-title":"Big data and its applications in agro-geoinformatics","author":"Di","year":"2016"},{"key":"10.1016\/j.envsoft.2020.104694_bib10","series-title":"Proceedings of the Earth Science Technology Office (ESTO)\/Advanced Information System Technology (AIST) Sensor Web Principal Investigator (PI) Meeting","first-page":"1","article-title":"Geospatial sensor web and self-adaptative earth predictive systems (SEPS)","author":"Di","year":"2007"},{"key":"10.1016\/j.envsoft.2020.104694_bib73","article-title":"CyberWay\u2013An integrated geospatial cyberinfrastructure to facilitate innovative Way of Inter-and Multi-disciplinary Geoscience Studies","volume":"21","author":"Di","year":"2019","journal-title":"Geophysical Research Abstracts"},{"key":"10.1016\/j.envsoft.2020.104694_bib11","series-title":"AGU Fall Meeting Abstracts","article-title":"Facilitating the easy use of earth observation data in earth system models through CyberConnector","author":"Di","year":"2017"},{"key":"10.1016\/j.envsoft.2020.104694_bib12","series-title":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Presented at the 2015 IEEE International Geoscience and Remote Sensing Symposium","first-page":"2739","article-title":"Remote sensing based crop growth stage estimation model","author":"Di","year":"2015"},{"key":"10.1016\/j.envsoft.2020.104694_bib13","doi-asserted-by":"crossref","first-page":"76","DOI":"10.2134\/agronj1988.00021962008000010018x","article-title":"Influence of tillage and crop rotation on yields of corn, soybean, and wheat","volume":"80","author":"Edwards","year":"1988","journal-title":"Agron. J."},{"key":"10.1016\/j.envsoft.2020.104694_bib14","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.envsoft.2018.03.025","article-title":"Integrating scientific cyberinfrastructures to improve reproducibility in computational hydrology: example for HydroShare and GeoTrust","volume":"105","author":"Essawy","year":"2018","journal-title":"Environ. Model. Software"},{"key":"10.1016\/j.envsoft.2020.104694_bib15","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.envsoft.2015.07.004","article-title":"Hydrologic and water quality impacts and biomass production potential on marginal land","volume":"72","author":"Feng","year":"2015","journal-title":"Environ. Model. Software"},{"key":"10.1016\/j.envsoft.2020.104694_bib16","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1016\/j.envsoft.2018.09.011","article-title":"Design and development of a web-based interface for the Agricultural Policy Environmental eXtender (APEX) model","volume":"111","author":"Feng","year":"2019","journal-title":"Environ. Model. Software"},{"key":"10.1016\/j.envsoft.2020.104694_bib17","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1007\/s00704-004-0061-8","article-title":"Changes in agro-meteorological indicators in the contiguous United States: 1951\u20132000","volume":"78","author":"Feng","year":"2004","journal-title":"Theor. Appl. Climatol."},{"key":"10.1016\/j.envsoft.2020.104694_bib18","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.compag.2019.02.016","article-title":"Site suitability analysis for tef (Eragrostis tef) within the contiguous United States","volume":"159","author":"Flynn","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.envsoft.2020.104694_bib19","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.envsoft.2013.03.019","article-title":"Coupling climate and hydrological models: interoperability through web services","volume":"46","author":"Goodall","year":"2013","journal-title":"Environ. Model. Software"},{"key":"10.1016\/j.envsoft.2020.104694_bib20","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google earth engine: planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.envsoft.2020.104694_bib21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.envsoft.2016.01.003","article-title":"Parameterization of the InVEST Crop Pollination Model to spatially predict abundance of wild blueberry (Vaccinium angustifolium Aiton) native bee pollinators in Maine, USA","volume":"79","author":"Groff","year":"2016","journal-title":"Environ. Model. Software"},{"key":"10.1016\/j.envsoft.2020.104694_bib22","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.compag.2012.03.005","article-title":"CropScape: a Web service based application for exploring and disseminating US conterminous geospatial cropland data products for decision support","volume":"84","author":"Han","year":"2012","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.envsoft.2020.104694_bib23","doi-asserted-by":"crossref","first-page":"4539","DOI":"10.1109\/JSTARS.2014.2315593","article-title":"Enhancing agricultural geospatial data dissemination and applications using geospatial web services","volume":"7","author":"Han","year":"2014","journal-title":"IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens."},{"key":"10.1016\/j.envsoft.2020.104694_bib24","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1244693","article-title":"High-resolution global maps of 21st-Century forest cover change","volume":"342","author":"Hansen","year":"2013","journal-title":"Science"},{"key":"10.1016\/j.envsoft.2020.104694_bib25","doi-asserted-by":"crossref","first-page":"1315","DOI":"10.3390\/rs9121315","article-title":"Google earth engine, open-access satellite data, and machine learning in support of large-area probabilistic wetland mapping","volume":"9","author":"Hird","year":"2017","journal-title":"Rem. Sens."},{"key":"10.1016\/j.envsoft.2020.104694_bib26","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.rse.2017.02.021","article-title":"Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine","volume":"202","author":"Huang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.envsoft.2020.104694_bib27","doi-asserted-by":"crossref","first-page":"1799","DOI":"10.2136\/sssaj1992.03615995005600060025x","article-title":"Light-fraction organic matter in soils from long-term crop rotations","volume":"56","author":"Janzen","year":"1992","journal-title":"Soil Sci. Soc. Am. J."},{"key":"10.1016\/j.envsoft.2020.104694_bib28","doi-asserted-by":"crossref","first-page":"484","DOI":"10.2134\/agronj2005.0098","article-title":"Crop rotation effects on soil quality at three Northern corn\/soybean belt locations","volume":"98","author":"Karlen","year":"2006","journal-title":"Agron. J."},{"key":"10.1016\/j.envsoft.2020.104694_bib29","series-title":"EarthCube: A Community-Driven Organization for Geoscience Cyberinfrastructure","author":"Katz","year":"2015"},{"key":"10.1016\/j.envsoft.2020.104694_bib30","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1002\/gdj3.36","article-title":"Cloud hosted real-time data services for the geosciences (CHORDS)","volume":"3","author":"Kerkez","year":"2016","journal-title":"Geosci. Data J."},{"key":"10.1016\/j.envsoft.2020.104694_bib31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5334\/dsj-2017-001","article-title":"Data and metadata brokering \u2013 theory and practice from the BCube project","volume":"16","author":"Khalsa","year":"2017","journal-title":"Data Sci. J."},{"key":"10.1016\/j.envsoft.2020.104694_bib32","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.isprsjprs.2018.12.011","article-title":"Participatory mapping of forest plantations with open foris and Google earth engine","volume":"148","author":"Koskinen","year":"2019","journal-title":"ISPRS J. Photogrammetry Remote Sens."},{"key":"10.1016\/j.envsoft.2020.104694_bib33","series-title":"The Semantic Web - ISWC 2015, Lecture Notes in Computer Science","first-page":"301","article-title":"The GeoLink modular oceanography ontology","author":"Krisnadhi","year":"2015"},{"key":"10.1016\/j.envsoft.2020.104694_bib34","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1109\/JSTARS.2011.2162643","article-title":"Recent developments in high performance computing for remote sensing: a review","volume":"4","author":"Lee","year":"2011","journal-title":"IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens."},{"key":"10.1016\/j.envsoft.2020.104694_bib35","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.envsoft.2018.11.004","article-title":"A Google Earth Engine-enabled software for efficiently generating high-quality user-ready Landsat mosaic images","volume":"112","author":"Li","year":"2019","journal-title":"Environ. Model. Software"},{"key":"10.1016\/j.envsoft.2020.104694_bib36","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.agrformet.2016.02.004","article-title":"Impacts of agricultural irrigation on ozone concentrations in the Central Valley of California and in the contiguous United States based on WRF-Chem simulations","volume":"221","author":"Li","year":"2016","journal-title":"Agric. For. Meteorol."},{"key":"10.1016\/j.envsoft.2020.104694_bib74","doi-asserted-by":"crossref","DOI":"10.1109\/Agro-Geoinformatics.2017.8047069","article-title":"Developing a Web Service Based Application for Demographic Information Modeling and Analyzing","author":"Lin","year":"2017","journal-title":"2017 6th International Conference on Agro-Geoinformatics"},{"key":"10.1016\/j.envsoft.2020.104694_bib37","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.envsoft.2015.03.018","article-title":"CyberGIS-enabled decision support platform for biomass supply chain optimization","volume":"70","author":"Lin","year":"2015","journal-title":"Environ. Model. Software"},{"key":"10.1016\/j.envsoft.2020.104694_bib38","doi-asserted-by":"crossref","first-page":"1283","DOI":"10.3390\/rs10081283","article-title":"Flood prevention and emergency response system powered by Google earth engine","volume":"10","author":"Liu","year":"2018","journal-title":"Rem. Sens."},{"key":"10.1016\/j.envsoft.2020.104694_bib39","doi-asserted-by":"crossref","first-page":"963","DOI":"10.14358\/PERS.70.8.963","article-title":"Uncertainty and confidence in land cover classification using a hybrid classifier approach","volume":"70","author":"Liu","year":"2004","journal-title":"Photogramm. Eng. Rem. Sens."},{"key":"10.1016\/j.envsoft.2020.104694_bib40","doi-asserted-by":"crossref","first-page":"5701","DOI":"10.1016\/j.scitotenv.2009.07.009","article-title":"The spatial and temporal distribution of crop residue burning in the contiguous United States","volume":"407","author":"McCarty","year":"2009","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.envsoft.2020.104694_bib41","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/j.envsoft.2014.10.009","article-title":"An integrated crop and hydrologic modeling system to estimate hydrologic impacts of crop irrigation demands","volume":"72","author":"McNider","year":"2015","journal-title":"Environ. Model. Software"},{"key":"10.1016\/j.envsoft.2020.104694_bib42","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0184926","article-title":"Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing","volume":"12","author":"Midekisa","year":"2017","journal-title":"PloS One"},{"key":"10.1016\/j.envsoft.2020.104694_bib43","series-title":"Proceedings of the International Conference on Agricultural Statistics VI. Presented at the Sixth International Conference on Agricultural Statistics","first-page":"9","article-title":"Reported uses of CropScape and the National cropland data layer program","author":"Mueller","year":"2013"},{"key":"10.1016\/j.envsoft.2020.104694_bib44","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.envsoft.2012.03.007","article-title":"Environmental model access and interoperability: the GEO Model Web initiative","volume":"39","author":"Nativi","year":"2013","journal-title":"Environ. Model. Software"},{"key":"10.1016\/j.envsoft.2020.104694_bib45","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.cageo.2015.06.023","article-title":"Using Google's cloud-based platform for digital soil mapping","volume":"83","author":"Padarian","year":"2015","journal-title":"Comput. Geosci."},{"key":"10.1016\/j.envsoft.2020.104694_bib46","doi-asserted-by":"crossref","first-page":"2253","DOI":"10.1002\/cpe.3287","article-title":"FluMapper: a cyberGIS application for interactive analysis of massive location-based social media","volume":"26","author":"Padmanabhan","year":"2014","journal-title":"Concurrency Comput. Pract. Ex."},{"key":"10.1016\/j.envsoft.2020.104694_bib47","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.envsoft.2016.06.010","article-title":"Contributing to the GEO Model Web implementation: a brokering service for business processes","volume":"84","author":"Santoro","year":"2016","journal-title":"Environ. Model. Software"},{"key":"10.1016\/j.envsoft.2020.104694_bib48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/feart.2017.00017","article-title":"Exploring Google earth engine platform for big data processing: classification of multi-temporal satellite imagery for crop mapping","volume":"5","author":"Shelestov","year":"2017","journal-title":"Front. Earth Sci."},{"key":"10.1016\/j.envsoft.2020.104694_bib49","doi-asserted-by":"crossref","DOI":"10.1117\/1.JRS.6.063590","article-title":"Changes of crop rotation in Iowa determined from the United States Department of Agriculture, National Agricultural Statistics Service cropland data layer product","volume":"6","author":"Stern","year":"2012","journal-title":"J. Appl. Remote Sens."},{"key":"10.1016\/j.envsoft.2020.104694_bib50","first-page":"1","article-title":"CyberConnector: a service-oriented system for automatically tailoring multisource Earth observation data to feed Earth science models","author":"Sun","year":"2017","journal-title":"Earth Sci. Inf"},{"key":"10.1016\/j.envsoft.2020.104694_bib76","doi-asserted-by":"crossref","DOI":"10.1109\/Agro-Geoinformatics.2017.8047055","article-title":"Building robust geospatial web services for agricultural information extraction and sharing","author":"Sun","year":"2017","journal-title":"2017 6th International Conference on Agro-Geoinformatics"},{"key":"10.1016\/j.envsoft.2020.104694_bib52","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.envsoft.2018.08.006","article-title":"A Bayesian total uncertainty analysis framework for assessment of management practices using watershed models","volume":"108","author":"Tasdighi","year":"2018","journal-title":"Environ. Model. Software"},{"key":"10.1016\/j.envsoft.2020.104694_bib53","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.isprsjprs.2018.07.017","article-title":"A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform","volume":"144","author":"Teluguntla","year":"2018","journal-title":"ISPRS J. Photogrammetry Remote Sens."},{"key":"10.1016\/j.envsoft.2020.104694_bib54","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.eja.2015.11.024","article-title":"Long-term effects of crop rotation, manure and mineral fertilisation on carbon sequestration and soil fertility","volume":"74","author":"Triberti","year":"2016","journal-title":"Eur. J. Agron."},{"key":"10.1016\/j.envsoft.2020.104694_bib55","doi-asserted-by":"crossref","first-page":"303","DOI":"10.4141\/cjss2013-093","article-title":"Long-term tillage and crop rotation effects on soil quality, organic carbon, and total nitrogen","volume":"94","author":"Van Eerd","year":"2014","journal-title":"Can. J. Soil Sci."},{"key":"10.1016\/j.envsoft.2020.104694_bib56","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.envsoft.2014.10.007","article-title":"Web technologies for environmental big data","volume":"63","author":"Vitolo","year":"2015","journal-title":"Environ. Model. Software"},{"key":"10.1016\/j.envsoft.2020.104694_bib57","doi-asserted-by":"crossref","first-page":"104528","DOI":"10.1016\/j.envsoft.2019.104528","article-title":"CoastSat: a Google Earth Engine-enabled Python toolkit to extract shorelines from publicly available satellite imagery","volume":"122","author":"Vos","year":"2019","journal-title":"Environ. Model. Software"},{"key":"10.1016\/j.envsoft.2020.104694_bib58","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1080\/00045601003791243","article-title":"A CyberGIS framework for the synthesis of cyberinfrastructure, GIS, and spatial analysis","volume":"100","author":"Wang","year":"2010","journal-title":"Ann. Assoc. Am. Geogr."},{"key":"10.1016\/j.envsoft.2020.104694_bib59","doi-asserted-by":"crossref","first-page":"2122","DOI":"10.1080\/13658816.2013.776049","article-title":"CyberGIS software: a synthetic review and integration roadmap","volume":"27","author":"Wang","year":"2013","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"10.1016\/j.envsoft.2020.104694_bib60","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.envsoft.2018.09.014","article-title":"Map based discovery of hydrologic data in the HydroShare collaboration environment","volume":"111","author":"Xue","year":"2019","journal-title":"Environ. Model. Software"},{"key":"10.1016\/j.envsoft.2020.104694_bib61","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compag.2016.08.008","article-title":"AgriSuit: a web-based GIS-MCDA framework for agricultural land suitability assessment","volume":"128","author":"Yalew","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.envsoft.2020.104694_bib62","series-title":"Spatial Cloud Computing: A Practical Approach","author":"Yang","year":"2013"},{"key":"10.1016\/j.envsoft.2020.104694_bib63","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1016\/j.compenvurbsys.2010.04.001","article-title":"Geospatial cyberinfrastructure: past, present and future","volume":"34","author":"Yang","year":"2010","journal-title":"Comput. Environ. Urban Syst. Geospatial Cyberinfrastructure"},{"key":"10.1016\/j.envsoft.2020.104694_bib64","first-page":"1","article-title":"A scalable cyberinfrastructure and cloud computing platform for forest aboveground biomass estimation based on the Google Earth Engine","author":"Yang","year":"2018","journal-title":"Int. J. Digit. Earth"},{"key":"10.1016\/j.envsoft.2020.104694_bib65","series-title":"AGU Fall Meeting Abstracts","first-page":"B23J","article-title":"Land Use\/Land Cover Classification and Change Analysis for Ganges River Basin from 2000 to 2010","author":"Yu","year":"2018"},{"key":"10.1016\/j.envsoft.2020.104694_bib66","first-page":"1373","article-title":"Towards intelligent GIServices","author":"Yue","year":"2015","journal-title":"Earth Sci. Inf."},{"key":"10.1016\/j.envsoft.2020.104694_bib67","doi-asserted-by":"crossref","first-page":"104989","DOI":"10.1016\/j.compag.2019.104989","article-title":"Machine-learned prediction of annual crop planting in the U.S. Corn Belt based on historical crop planting maps","volume":"166","author":"Zhang","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.envsoft.2020.104694_bib68","series-title":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). Presented at the 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","first-page":"1","article-title":"Extracting trusted pixels from historical cropland data layer using crop rotation patterns: a case study in Nebraska","author":"Zhang","year":"2019"},{"key":"10.1016\/j.envsoft.2020.104694_bib69","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.envsoft.2018.11.019","article-title":"Exploring cloud-based web processing service: a case study on the implementation of CMAQ as a service","volume":"113","author":"Zhang","year":"2019","journal-title":"Environ. Model. Software"},{"key":"10.1016\/j.envsoft.2020.104694_bib70","series-title":"2017 6th International Conference on Agro-Geoinformatics. Presented at the International Conference on Agro-Geoinformatics","article-title":"Integrating OGC web processing service with cloud computing environment for earth observation data","author":"Zhang","year":"2017"},{"key":"10.1016\/j.envsoft.2020.104694_bib71","series-title":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). Presented at the 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","first-page":"1","article-title":"Cloud environment for disseminating NASS cropland data layer","author":"Zhang","year":"2019"},{"key":"10.1016\/j.envsoft.2020.104694_bib75","doi-asserted-by":"crossref","first-page":"161","DOI":"10.5194\/isprs-archives-XLII-3-W11-161-2020","article-title":"Refinement of Cropland Data Layer Using Machine Learning","volume":"42","author":"Zhang","year":"2020","journal-title":"The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences"},{"key":"10.1016\/j.envsoft.2020.104694_bib72","article-title":"Geospatial web services: advances in information interoperability","author":"Zhao","year":"2010","journal-title":"IGI Global"}],"container-title":["Environmental Modelling & Software"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1364815219304670?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1364815219304670?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2021,4,25]],"date-time":"2021-04-25T11:11:38Z","timestamp":1619349098000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1364815219304670"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":75,"alternative-id":["S1364815219304670"],"URL":"https:\/\/doi.org\/10.1016\/j.envsoft.2020.104694","relation":{},"ISSN":["1364-8152"],"issn-type":[{"value":"1364-8152","type":"print"}],"subject":[],"published":{"date-parts":[[2020,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"AgKit4EE: A toolkit for agricultural land use modeling of the conterminous United States based on Google Earth Engine","name":"articletitle","label":"Article Title"},{"value":"Environmental Modelling & Software","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.envsoft.2020.104694","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2020 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"104694"}}