{"id":9,"date":"2020-01-18T02:23:56","date_gmt":"2020-01-18T07:23:56","guid":{"rendered":"https:\/\/cloud.csiss.gmu.edu\/center\/?page_id=9"},"modified":"2026-04-27T15:28:41","modified_gmt":"2026-04-27T20:28:41","slug":"publications","status":"publish","type":"page","link":"https:\/\/cloud.csiss.gmu.edu\/center\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"\n<p>Selected peer-reviewed journal papers published in the past 5 years.<\/p>\n\n\n\n<h3><strong>2026<\/strong><\/h3>\n\n\n\n<ul><li>Li, H., Di, L., Zhang, C., Guo, L., Yu, E.G., Shao, B., Liu, Z. and Li, H. (2026) \u2018Automated 10-m Resolution In-season Crop-type Data Layer Mapping for Contiguous United States\u2019, <em>Scientific Data<\/em>.<\/li><li>Liu, Z., Di, L., Yang, R., Guo, L., Zhang, C., Li, H. and Shao, B. (2026) \u2018In-season crop yield prediction: State of the art and future research direction\u2019, <em>International Journal of Applied Earth Observation and Geoinformation<\/em>, 146.<\/li><li>Li, H., Di, L., Yang, R., Qu, J.J., Tong, D.Q., Guo, L., Yu, E.G., Liu, Z., Shao, B. et al. (2026) \u2018In-season sugarcane mapping in the US and Brazil using time-invariant phenological features\u2019, <em>IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing<\/em>.<\/li><\/ul>\n\n\n\n<h3><strong>2025<\/strong><\/h3>\n\n\n\n<ul><li>Zhang, C., Kerner, H., Wang, S., Hao, P., Li, Z., Hunt, K.A., Abernethy, J., Zhao, H. et al. (2025) \u2018Remote sensing for crop mapping: A perspective on current and future crop-specific land cover data products\u2019, <em>Remote Sensing of Environment<\/em>, 330, p. 114995.<\/li><li>Hao, P., Di, L., Guo, L., Chen, Z. and Montgomery, L. (2025) \u2018A practical method for deriving all-weather ET from remote sensing and meteorological data\u2019, <em>International Journal of Applied Earth Observation and Geoinformation<\/em>, 144.<\/li><li>Zhang, H.K., Shen, Y., Zhang, X., Li, J., Yang, Z., Xu, Y., Zhang, C., Di, L. and Roy, D.P. (2025) \u2018Robust and timely within-season conterminous United States crop type mapping using Landsat Sentinel-2 time series and the transformer architecture\u2019, <em>Remote Sensing of Environment<\/em>, 329, p. 114950.<\/li><\/ul>\n\n\n\n<h3><strong>2024<\/strong><\/h3>\n\n\n\n<ul><li>Lin, L., Di, L., Zhang, C., Guo, L., Zhao, H., Islam, D., Li, H., Liu, Z. and Middleton, G. (2024) \u2018Modeling urban redevelopment: A novel approach using time-series remote sensing data and machine learning\u2019, <em>Geography and Sustainability<\/em>, 5(2), pp. 211-219.<\/li><li>Islam, M.D., Di, L., Zhang, C., Yang, R., Qu, J.J., Tong, D., Guo, L., Lin, L. and Pandey, A. (2024) \u2018A decision rule and machine learning-based hybrid approach for automated land-cover type local climate zones (LCZs) mapping using multi-source remote sensing data\u2019, <em>IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing<\/em>.<\/li><li>Li, H., Di, L., Zhang, C., Lin, L., Guo, L., Li, R. and Zhao, H. (2024) \u2018In-season mapping of sugarcane planting based on Sentinel-2 imagery\u2019, <em>IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing<\/em>.<\/li><li>Li, H., Di, L., Zhang, C., Lin, L., Guo, L., Yu, E.G. and Yang, Z. (2024) \u2018Automated in-season crop-type data layer mapping without ground truth for the conterminous United States based on multisource satellite imagery\u2019, <em>IEEE Transactions on Geoscience and Remote Sensing<\/em>, 62, pp. 1-14.<\/li><li>Lawler, S., Zhang, C., Siddiqui, A.R., Lindemer, C., Rosa, D., Lehman, W. et al. (2024) \u2018Leveraging OGC API for cloud-based flood modeling campaigns\u2019, <em>Environmental Modelling &amp; Software<\/em>, 171, p. 105855.<\/li><\/ul>\n\n\n\n<h3><strong>2023<\/strong><\/h3>\n\n\n\n<ul><li>Zhang, C., Di, L., Lin, L., Zhao, H., Li, H., Yang, A., Guo, L. and Yang, Z. (2023) \u2018Cyberinformatics tool for in-season crop-specific land cover monitoring: Design, implementation, and applications of iCrop\u2019, <em>Computers and Electronics in Agriculture<\/em>, 213, p. 108199.<\/li><li>Zhao, H., Di, L., Guo, L., Zhang, C. and Lin, L. (2023) \u2018An automated data-driven irrigation scheduling approach using model simulated soil moisture and evapotranspiration\u2019, <em>Sustainability<\/em>, 15(17), p. 12908.<\/li><li>Yu, Z., Di, L., Shrestha, S., Zhang, C., Guo, L., Qamar, F. and Mayer, T.J. (2023) \u2018Ricemapengine: a google earth engine-based web application for fast paddy rice mapping\u2019, <em>IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing<\/em>.<\/li><li>Islam, M.D., Di, L., Qamer, F.M., Shrestha, S., Guo, L., Lin, L., Mayer, T.J. and Phalke, A.R. (2023) \u2018Rapid rice yield estimation using integrated remote sensing and meteorological data and machine learning\u2019, <em>Remote Sensing<\/em>, 15(9), p. 2374.<\/li><li>Lin, L., Di, L., Zhang, C. and Guo, L. (2023) \u2018The global land surface temperature change in the 21st century\u2014A satellite remote sensing based assessment\u2019, <em>IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing<\/em>.<\/li><\/ul>\n\n\n\n<h3><strong>2022<\/strong><\/h3>\n\n\n\n<ul><li>Zhang, C., Yang, Z., Di, L., Yu, E.G., Zhang, B., Han, W., Lin, L. and Guo, L. (2022) \u2018Near-real-time MODIS-derived vegetation index data products and online services for CONUS based on NASA LANCE\u2019, <em>Scientific Data<\/em>, 9(1), p. 477.<\/li><li>Zhang, C., Di, L., Lin, L., Li, H., Guo, L., Yang, Z., Yu, E.G., Di, Y. and Yang, A. (2022) \u2018Towards automation of in-season crop type mapping using spatiotemporal crop information and remote sensing data\u2019, <em>Agricultural Systems<\/em>, 201, p. 103462.<\/li><li>Zhang, C., Yang, Z., Zhao, H., Sun, Z., Di, L., Bindlish, R., Liu, P.W., Colliander, A. et al. (2022) \u2018Crop-CASMA: A web geoprocessing and map service based architecture and implementation for serving soil moisture and crop vegetation condition data over US Cropland\u2019, <em>International Journal of Applied Earth Observation and Geoinformation<\/em>, 112.<\/li><li>Molla, A., Di, L., Guo, L., Zhang, C. and Chen, F. (2022) \u2018Spatio-temporal responses of precipitation to urbanization with Google Earth engine: A case study for Lagos, Nigeria\u2019, <em>Urban Science<\/em>, 6(2), p. 40.<\/li><li>Guo, L., Di, L., Zhang, C., Lin, L. and Di, Y. (2022) \u2018Influence of urban expansion on Lyme disease risk: A case study in the US I-95 Northeastern corridor\u2019, <em>Cities<\/em>, 125, p. 103633.<\/li><li>Zhao, H., Di, L. and Sun, Z. (2022) \u2018WaterSmart-GIS: A web application of a data assimilation model to support irrigation research and decision making\u2019, <em>ISPRS International Journal of Geo-Information<\/em>, 11(5), p. 271.<\/li><li>Huang, M., Fan, X., Jian, H., Zhang, H., Guo, L. and Di, L. (2022) \u2018Bibliometric analysis of OGC specifications between 1994 and 2020 based on Web of Science (WoS)\u2019, <em>ISPRS International Journal of Geo-Information<\/em>, 11(4), p. 251.<\/li><li>Lin, L., Di, L., Zhang, C., Guo, L., Di, Y., Li, H. and Yang, A. (2022) \u2018Validation and refinement of cropland data layer using a spatial-temporal decision tree algorithm\u2019, <em>Scientific Data<\/em>, 9(1), p. 63.<\/li><li>Guo, L., Di, L., Zhang, C., Lin, L., Chen, F. and Molla, A. (2022) \u2018Evaluating contributions of urbanization and global climate change to urban land surface temperature change: a case study in Lagos, Nigeria\u2019, <em>Scientific Reports<\/em>, 12(1), p. 14168.<\/li><li>Hao, P., Di, L. and Guo, L. (2022) \u2018Estimation of crop evapotranspiration from MODIS data by combining random forest and trapezoidal models\u2019, <em>Agricultural Water Management<\/em>, 259, p. 107249.<\/li><\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Selected peer-reviewed journal papers published in the past 5 years. 2026 Li, H., Di, L., Zhang, C., Guo, L., Yu, E.G., Shao, B., Liu, Z. and Li, H. (2026) \u2018Automated 10-m Resolution In-season Crop-type&#46;&#46;&#46;<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"spay_email":""},"_links":{"self":[{"href":"https:\/\/cloud.csiss.gmu.edu\/center\/wp-json\/wp\/v2\/pages\/9"}],"collection":[{"href":"https:\/\/cloud.csiss.gmu.edu\/center\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/cloud.csiss.gmu.edu\/center\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/cloud.csiss.gmu.edu\/center\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/cloud.csiss.gmu.edu\/center\/wp-json\/wp\/v2\/comments?post=9"}],"version-history":[{"count":3,"href":"https:\/\/cloud.csiss.gmu.edu\/center\/wp-json\/wp\/v2\/pages\/9\/revisions"}],"predecessor-version":[{"id":723,"href":"https:\/\/cloud.csiss.gmu.edu\/center\/wp-json\/wp\/v2\/pages\/9\/revisions\/723"}],"wp:attachment":[{"href":"https:\/\/cloud.csiss.gmu.edu\/center\/wp-json\/wp\/v2\/media?parent=9"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}