Zhang, C., Yang, Z., Di, L., Yu, E.G., Zhang, B., Han, W, Lin, L., and Guo, L., 2022. Near-real-time MODIS-derived vegetation index data products and online services for CONUS based on NASA LANCE. Scientific Data, 9, 477. doi: 10.1038/s41597-022-01565-2

Zhang, C., Di, L., Lin, L., Li, H., Guo, L., Yang, Z., Yu, E.G., Di, Y. and Yang, A., 2022. Towards automation of in-season crop type mapping using spatiotemporal crop information and remote sensing data. Agricultural Systems, 201, 103462. doi: 10.1016/j.agsy.2022.103462

Zhang, C., Yang, Z., Zhao, H., Sun, Z., Di, L., Bindlish, R., Liu, P.W., Colliander, A., Mueller, R., Crow, W. and Reichle, R.H., 2022. Crop-CASMA: A web geoprocessing and map service based architecture and implementation for serving soil moisture and crop vegetation condition data over US Cropland. International Journal of Applied Earth Observation and Geoinformation, 112, 102902. doi: 10.1016/j.jag.2022.102902

Zhang, C., Di, L., Hao, P., Yang, Z., Lin, L., Zhao, H. and Guo, L., 2021. Rapid in-season mapping of corn and soybeans using machine-learned trusted pixels from Cropland Data Layer. International Journal of Applied Earth Observation and Geoinformation, 102, 102374. doi: 10.1016/j.jag.2021.102374

Zhang, C., Di, L., Yang, Z., Lin, L. and Hao, P., 2020. AgKit4EE: A toolkit for agricultural land use modeling of the conterminous United States based on Google Earth Engine. Environmental Modelling & Software, 129, 104694. doi: 10.1016/j.envsoft.2020.104694

Zhang, C., Di, L., Lin, L. and Guo, L., 2019. Machine-learned prediction of annual crop planting in the U.S. Corn Belt based on historical crop planting maps. Computers and Electronics in Agriculture, 166, 104989. doi: 10.1016/j.compag.2019.104989

Zhang, C., Di, L., Sun, Z., Lin, L., Yu, E.G. and Gaigalas, J., 2019. Exploring cloud-based Web Processing Service: A case study on the implementation of CMAQ as a Service. Environmental Modelling & Software, 113, pp. 29-41. doi: 10.1016/j.envsoft.2018.11.019

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