Journal Articles

Zhang, C., Di, L., Lin, L., 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. https://doi.org/10.1016/j.compag.2019.104989

Li, R., Chen, W., Xiu, A., Zhao, H., Zhang, X., Zhang, S., Tong, D.Q., 2019. A comprehensive inventory of agricultural atmospheric particulate matters (PM10 and PM2.5) and gaseous pollutants (VOCs, SO2, NH3, CO, NOx and HC) emissions in China. Ecological Indicators 107, 105609. https://doi.org/10.1016/j.ecolind.2019.105609

Gaigalas, J., Di, L., Sun, Z., 2019. Advanced Cyberinfrastructure to Enable Search of Big Climate Datasets in THREDDS. ISPRS International Journal of Geo-Information 8, 494. https://doi.org/10.3390/ijgi8110494

Sun, Z., Di, L., Cash, B., Gaigalas, J., 2020. Advanced cyberinfrastructure for intercomparison and validation of climate models. Environmental Modelling & Software 123, 104559. https://doi.org/10.1016/j.envsoft.2019.104559

Walker, J.T., Beachley, G., Amos, H.M., Baron, J.S., Bash, J., Baumgardner, R., Bell, M.D., Benedict, K.B., Chen, X., Clow, D.W., Cole, A., Coughlin, J.G., Cruz, K., Daly, R.W., Decina, S.M., Elliott, E.M., Fenn, M.E., Ganzeveld, L., Gebhart, K., Isil, S.S., Kerschner, B.M., Larson, R.S., Lavery, T., Lear, G.G., Macy, T., Mast, M.A., Mishoe, K., Morris, K.H., Padgett, P.E., Pouyat, R.V., Puchalski, M., Pye, H.O.T., Rea, A.W., Rhodes, M.F., Rogers, C.M., Saylor, R., Scheffe, R., Schichtel, B.A., Schwede, D.B., Sexstone, G.A., Sive, B.C., Sosa Echeverría, R., Templer, P.H., Thompson, T., Tong, D., Wetherbee, G.A., Whitlow, T.H., Wu, Z., Yu, Z., Zhang, L., 2019. Toward the improvement of total nitrogen deposition budgets in the United States. Science of The Total Environment 691, 1328–1352. https://doi.org/10.1016/j.scitotenv.2019.07.058

Ma, S., Zhang, X., Gao, C., Tong, D.Q., Xiu, A., Wu, G., Cao, X., Huang, L., Zhao, H., Zhang, S., Ibarra-Espinosa, S., Wang, X., Li, X., Dan, M., 2019. Multimodel simulations of a springtime dust storm over northeastern China: implications of an evaluation of four commonly used air quality models (CMAQ v5.2.1, CAMx v6.50, CHIMERE v2017r4, and WRF-Chem v3.9.1). Geoscientific Model Development 12, 4603–4625. https://doi.org/10.5194/gmd-12-4603-2019

Jiang, L., Sun, Z., Qi, Q., Zhang, A., 2019. Spatial Correlation between Traffic and Air Pollution in Beijing. The Professional Geographer 71, 654–667. https://doi.org/10.1080/00330124.2019.1595060

Sun, Z., Di, L., Gaigalas, J., 2019. SUIS: Simplify the use of geospatial web services in environmental modelling. Environmental Modelling & Software 119, 228–241. https://doi.org/10.1016/j.envsoft.2019.06.005

Guo, L., Di, L., Tian, Q., 2019. Detecting spatio-temporal changes of arable land and construction land in the Beijing-Tianjin corridor during 2000–2015. J. Geogr. Sci. 29, 702–718. https://doi.org/10.1007/s11442-019-1622-1

Tang, J., Di, L., Rahman, M.S., Yu, Z., 2019. Spatial–temporal landscape pattern change under rapid urbanization. JARS 13, 024503. https://doi.org/10.1117/1.JRS.13.024503

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

Zhong, S., Di, L., Sun, Z., Xu, Z., Guo, L., 2019. Investigating the Long-Term Spatial and Temporal Characteristics of Vegetative Drought in the Contiguous United States. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12, 836–848. https://doi.org/10.1109/JSTARS.2019.2896159

Sun, Z., Di, L., Fang, H., 2019. Using long short-term memory recurrent neural network in land cover classification on Landsat and Cropland data layer time series. International Journal of Remote Sensing 40, 593–614. https://doi.org/10.1080/01431161.2018.1516313

Saylor, R.D., Baker, B.D., Lee, P., Tong, D., Pan, L., Hicks, B.B., 2019. The particle dry deposition component of total deposition from air quality models: right, wrong or uncertain? Tellus B: Chemical and Physical Meteorology 71, 1–22. https://doi.org/10.1080/16000889.2018.1550324

Battye, W.H., Bray, C.D., Aneja, V.P., Tong, D., Lee, P., Tang, Y., 2019. Evaluating Ammonia (NH3) Predictions in the NOAA NAQFC for Eastern North Carolina Using Ground Level and Satellite Measurements. Journal of Geophysical Research: Atmospheres 124, 8242–8259. https://doi.org/10.1029/2018JD029990 Lin, L., Di, L., Tang, J., Yu, E., Zhang, C., Rahman, M.S., Shrestha, R., Kang, L., 2019. Improvement and Validation of NASA/MODIS NRT Global Flood Mapping. Remote Sensing 11, 205. https://doi.org/10.3390/rs11020205

Qian, Y., Yang, Z., Di, L., Rahman, M.S., Tan, Z., Xue, L., Gao, F., Yu, E.G., Zhang, X., 2019. Crop Growth Condition Assessment at County Scale Based on Heat-Aligned Growth Stages. Remote Sensing 11, 2439. https://doi.org/10.3390/rs11202439

Qu, C., Hao, X., Qu, J.J., 2019. Monitoring Extreme Agricultural Drought over the Horn of Africa (HOA) Using Remote Sensing Measurements. Remote Sensing 11, 902. https://doi.org/10.3390/rs11080902

Rahman, M.S., Di, L., Yu, E., Lin, L., Zhang, C., Tang, J., 2019a. Rapid Flood Progress Monitoring in Cropland with NASA SMAP. Remote Sensing 11, 191. https://doi.org/10.3390/rs11020191

Rahman, M.S., Di, L., Yu, E., Zhang, C., Mohiuddin, H., 2019b. In-Season Major Crop-Type Identification for US Cropland from Landsat Images Using Crop-Rotation Pattern and Progressive Data Classification. Agriculture 9, 17. https://doi.org/10.3390/agriculture9010017

Sun, J., Di, L., Sun, Z., Shen, Y., Lai, Z., 2019. County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model. Sensors 19, 4363. https://doi.org/10.3390/s19204363

Tan, X., Di, L., Zhong, Y., Chen, N., Huang, F., Wang, J., Sun, Z., Khan, Y.A., 2019. Distributed Geoscience Algorithm Integration Based on OWS Specifications: A Case Study of the Extraction of a River Network. ISPRS International Journal of Geo-Information 8, 12. https://doi.org/10.3390/ijgi8010012

Tang, J., Di, L., 2019. Past and Future Trajectories of Farmland Loss Due to Rapid Urbanization Using Landsat Imagery and the Markov-CA Model: A Case Study of Delhi, India. Remote Sensing 11, 180. https://doi.org/10.3390/rs11020180

Walker, J.T., Beachley, G.M., Amos, H.M., Baron, J.S., Bash, J., Baumgardner, R., Bell, M.D., Benedict, K.B., Chen, X., Clow, D.W., Cole, A., Coughlin, J.G., Cruz, K., Daly, R.W., Decina, S.M., Elliott, E.M., Fenn, M.E., Ganzeveld, L., Gebhart, K., Isil, S.S., Kerschner, B.M., Larson, R.S., Lavery, T., Lear, G.G., Macy, T., Mast, M.A., Mishoe, K., Morris, K.H., Padgett, P.E., Pouyat, R.V., Puchalski, M., Pye, H.O.T., Rea, A.W., Rhodes, M.F., Rogers, C.M., Saylor, R., Scheffe, R., Schichtel, B.A., Schwede, D.B., Sexstone, G.A., Sive, B.C., Templer, P.H., Thompson, T., Tong, D., Wetherbee, G.A., Whitlow, T.H., Wu, Z., Yu, Z., Zhang, L., 2019. Science needs for continued development of total nitrogen deposition budgets in the United States. EPA Report EPA 601/R-19/001.

Rahman, Md.S., Mohiuddin, H., Kafy, A.-A., Sheel, P.K., Di, L., 2018. Classification of cities in Bangladesh based on remote sensing derived spatial characteristics. Journal of Urban Management. https://doi.org/10.1016/j.jum.2018.12.001

Zhang, X., Xiong, Q., Di, L., Tang, J., Yang, J., Wu, H., Qin, Y., Su, R., Zhou, W., 2018. Phenological metrics-based crop classification using HJ-1 CCD images and Landsat 8 imagery. International Journal of Digital Earth 11, 1219–1240. https://doi.org/10.1080/17538947.2017.1387296

Liang, L., Di, L., Huang, T., Wang, J., Lin, L., Wang, L., Yang, M., 2018. Estimation of Leaf Nitrogen Content in Wheat Using New Hyperspectral Indices and a Random Forest Regression Algorithm. Remote Sensing 10, 1940. https://doi.org/10.3390/rs10121940

Li, T., Zheng, W., Zhang, S., Jia, Y., Li, Y., Xu, X., 2018. Spatial variations in soil phosphorus along a gradient of central city-suburb-exurban satellite. CATENA 170, 150–158. https://doi.org/10.1016/j.catena.2018.06.011

Lu, L., Tao, Y., Di, L., 2018. Object-Based Plastic-Mulched Landcover Extraction Using Integrated Sentinel-1 and Sentinel-2 Data. Remote Sensing 10, 1820. https://doi.org/10.3390/rs10111820

Lu, L., Huang, Y., Di, L., Hang, D., 2018. Large-scale subpixel mapping of landcover from MODIS imagery using the improved spatial attraction model. JARS 12, 046017. https://doi.org/10.1117/1.JRS.12.046017

Zhang, X., Chen, N., Chen, Z., Wu, L., Li, X., Zhang, L., Di, L., Gong, J., Li, D., 2018. Geospatial sensor web: A cyber-physical infrastructure for geoscience research and application. Earth-Science Reviews 185, 684–703. https://doi.org/10.1016/j.earscirev.2018.07.006

Tong, D., Tang, Y., 2018. Advancing Air Quality Forecasting to Protect Human Health. The Magazine for Environmental Managers.

Zhang, C., Yue, P., Di, L., Wu, Z., Zhang, C., Yue, P., Di, L., Wu, Z., 2018. Automatic Identification of Center Pivot Irrigation Systems from Landsat Images Using Convolutional Neural Networks. Agriculture 8, 147. https://doi.org/10.3390/agriculture8100147

Bray, C.D., Battye, W., Aneja, V.P., Tong, D.Q., Lee, P., Tang, Y., 2018. Ammonia emissions from biomass burning in the continental United States. Atmospheric Environment 187, 50–61. https://doi.org/10.1016/j.atmosenv.2018.05.052

Tan, Z., Yue, P., Di, L., Tang, J., Tan, Z., Yue, P., Di, L., Tang, J., 2018. Deriving High Spatiotemporal Remote Sensing Images Using Deep Convolutional Network. Remote Sensing 10, 1066. https://doi.org/10.3390/rs10071066

Rahman, M.S., Yang, R., Di, L., 2018. Clustering Indian Ocean Tropical Cyclone Tracks by the Standard Deviational Ellipse. Climate 6, 39. https://doi.org/10.3390/cli6020039

Liu, P., Di, L., Du, Q., Wang, L., 2018. Remote Sensing Big Data: Theory, Methods and Applications. Remote Sensing 10, 711. https://doi.org/10.3390/rs10050711

Kondragunta, S., Zhang, H., Ciren, P., Laszlo, I., Tong, D., 2018. The Rapid Refresh GOES-16 Advanced Baseline Imager. The Magazine for Environmental Managers 6.

Sun, Z., Di, L., Hao, H., Wu, X., Tong, D.Q., Zhang, C., Virgei, C., Fang, H., Yu, E., Tan, X., Yue, P., Lin, L., 2018. CyberConnector: a service-oriented system for automatically tailoring multisource Earth observation data to feed Earth science models. Earth Sci Inform 11, 1–17. https://doi.org/10.1007/s12145-017-0308-4

Lee, P., Saylor, R., McQueen, J., 2018. Air Quality Monitoring and Forecasting. Atmosphere 9, 89. https://doi.org/10.3390/atmos9030089

Gao, C., Zhang, X., Wang, W., Xiu, A., Tong, D.Q., Chen, W., 2018. Spatiotemporal Distribution of Satellite-Retrieved Ground-Level PM2.5 and Near Real-Time Daily Retrieval Algorithm Development in Sichuan Basin, China. Atmosphere 9, 78. https://doi.org/10.3390/atmos9020078

Huang, M., Crawford, J.H., Diskin, G.S., Santanello, J.A., Kumar, S.V., Pusede, S.E., Parrington, M., Carmichael, G.R., 2018. Modeling Regional Pollution Transport Events During KORUS-AQ: Progress and Challenges in Improving Representation of Land-Atmosphere Feedbacks. Journal of Geophysical Research: Atmospheres 123, 10,732-10,756. https://doi.org/10.1029/2018JD028554

Geng, G., Murray, N.L., Tong, D., Fu, J.S., Hu, X., Lee, P., Meng, X., Chang, H.H., Liu, Y., 2018. Satellite-Based Daily PM2.5 Estimates During Fire Seasons in Colorado. Journal of Geophysical Research: Atmospheres 123, 8159–8171. https://doi.org/10.1029/2018JD028573