Center for Spatial Information Science and Systems

Journal Articles

2021

  • Arora, A., Pandey, M., Mishra, V. N., Kumar, R., Rai, P. K., Costache, R., Punia, M., & Di, L. (2021). Comparative evaluation of geospatial scenario-based land change simulation models using landscape metrics. Ecological Indicators, 128, 107810.
  • Di, L., & Yu, E. G. (2021). Spatial Search. Urban Informatics, 683–699.
  • Lin, L., Di, L., Zhang, C., Guo, L., & Di, Y. (2021). Remote Sensing of Urban Poverty and Gentrification. Remote Sensing, 13(20), 4022.
  • Rahman, M. S., Di, L., Yu, E., Lin, L., & Yu, Z. (2021). Remote Sensing Based Rapid Assessment of Flood Crop Damage Using Novel Disaster Vegetation Damage Index (DVDI). International Journal of Disaster Risk Science, 12(1), 90–110.
  • Sun, Z., Di, L., Cvetojevic, S., & Yu, Z. (2021). GeoFairy2: A Cross-Institution Mobile Gateway to Location-Linked Data for In-Situ Decision Making. ISPRS International Journal of Geo-Information, 10(1), 1.
  • Tan, X., Di, L., Zhong, Y., Yao, Y., Sun, Z., & Ali, Y. (2021). Spark-based adaptive Mapreduce data processing method for remote sensing imagery. International Journal of Remote Sensing, 42(1), 191–207.
  • Tan, X., Jiao, J., Chen, N., Huang, F., Di, L., Wang, J., Sha, Z., & Liu, J. (2021). Geoscience model service integrated workflow for rainstorm waterlogging analysis. International Journal of Digital Earth, 1–23.
  • Xiong, Q., Di, L., Feng, Q., Liu, D., Liu, W., Zan, X., Zhang, L., Zhu, D., Liu, Z., Yao, X., & others. (2021). Deriving Non-Cloud Contaminated Sentinel-2 Images with RGB and Near-Infrared Bands from Sentinel-1 Images Based on a Conditional Generative Adversarial Network. Remote Sensing, 13(8), 1512.
  • Zhang, C., Di, L., Hao, P., Yang, Z., Lin, L., Zhao, H., & 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.
  • Zhang, Q., Di, L., Liu, G., Guo, L., & Yu, H. (2021). Remote-Sensing-Based Analysis of Spatiotemporal Variation of ET and Related Parameters in Xilingol Steppe, China. Polish Journal of Environmental Studies, 30(3), 2891–2904.
  • Zhong, S., Sun, Z., & Di, L. (2021). Characteristics of vegetation response to drought in the CONUS based on long-term remote sensing and meteorological data. Ecological Indicators, 127, 107767.

2020

  • Di, L., Ustundag, B. B., Guo, L., Shang, J., & Yang, R. (2020). Foreword to the Special Issue on Digital Innovations in Agriculture Research and Applications. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 6519–6523.
  • Hao, P., Di, L., Zhang, C., & Guo, L. (2020). Transfer Learning for Crop classification with Cropland Data Layer data (CDL) as training samples. Science of The Total Environment, 733, 138869.
  • Jin, M., Bai, Y., Devys, E., & Di, L. (2020). Toward a Standardized Encoding of Remote Sensing Geo-Positioning Sensor Models. Remote Sensing, 12(9), 1530.
  • Jing, W., Di, L., Zhao, X., Yao, L., Xia, X., Liu, Y., Yang, J., Li, Y., & Zhou, C. (2020). A data-driven approach to generate past GRACE-like terrestrial water storage solution by calibrating the land surface model simulations. Advances in Water Resources, 143, 103683.
  • Jing, W., Zhao, X., Yao, L., Di, L., Yang, J., Li, Y., Guo, L., & Zhou, C. (2020). Can terrestrial water storage dynamics be estimated from climate anomalies? Earth and Space Science, 7(3), e2019EA000959.
  • Liang, L., Geng, D., Yan, J., Qiu, S., Di, L., Wang, S., Xu, L., Wang, L., Kang, J., & Li, L. (2020). Estimating crop LAI using spectral feature extraction and the hybrid inversion method. Remote Sensing, 12(21), 3534.
  • Liang, L., Huang, T., Di, L., Geng, D., Yan, J., Wang, S., Wang, L., Li, L., Chen, B., & Kang, J. (2020). Influence of different bandwidths on LAI estimation using vegetation indices. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 1494–1502.
  • Liu, Y., Yao, L., Jing, W., Di, L., Yang, J., & Li, Y. (2020). Comparison of two satellite-based soil moisture reconstruction algorithms: A case study in the state of Oklahoma, USA. Journal of Hydrology, 590, 125406.
  • Lu, L., Wu, C., & Di, L. (2020). Exploring the spatial characteristics of typhoon-induced vegetation damages in the southeast coastal area of China from 2000 to 2018. Remote Sensing, 12(10), 1692.
  • Rahman, M. S., & Di, L. (2020). A systematic review on case studies of remote-sensing-based flood crop loss assessment. Agriculture, 10(4), 131.
  • Sun, J., Di, L., Sun, Z., Wang, J., & Wu, Y. (2020). Estimation of GDP Using Deep Learning With NPP-VIIRS Imagery and Land Cover Data at the County Level in CONUS. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 1400–1415.
  • Sun, J., Lai, Z., Di, L., Sun, Z., Tao, J., & Shen, Y. (2020). Multilevel deep learning network for county-level corn yield estimation in the us corn belt. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5048–5060.
  • Sun, Z., Di, L., Burgess, A., Tullis, J. A., & Magill, A. B. (2020). Geoweaver: Advanced cyberinfrastructure for managing hybrid geoscientific AI workflows. ISPRS International Journal of Geo-Information, 9(2), 119.
  • Sun, Z., Di, L., Cash, B., & Gaigalas, J. (2020). Advanced cyberinfrastructure for intercomparison and validation of climate models. Environmental Modelling & Software, 123, 104559.
  • Sun, Z., Di, L., & Fang, H. (2020a). Machine Learning on Greenest Pixels for Crop Mapping.
  • Sun, Z., Di, L., & Fang, H. (2020b). Two Pixel Reference Algorithm.
  • Sun, Z., Di, L., Fang, H., & Burgess, A. (2020). Deep Learning Classification for Crop Types in North Dakota. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 2200–2213.
  • Sun, Z., Di, L., Sprigg, W., Tong, D., & Casal, M. (2020). Community venue exposure risk estimator for the COVID-19 pandemic. Health & Place, 66, 102450.
  • Xu, Z., Cao, L., Zhong, S., Liu, G., Yang, Y., Zhu, S., Luo, X., & Di, L. (2020). Trends in global vegetative drought from long-term satellite remote sensing data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 815–826.
  • Yu, Z., Di, L., Rahman, M., Tang, J., & others. (2020). Fishpond Mapping by Spectral and Spatial-Based Filtering on Google Earth Engine: A Case Study in Singra Upazila of Bangladesh. Remote Sensing, 12(17), 2692.
  • Zhang, C., Di, L., Yang, Z., Lin, L., & 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.
  • Zhang, C., Yang, Z., Di, L., Lin, L., & Hao, P. (2020). Refinement of Cropland Data Layer Using Machine Learning. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 161–164.
  • Zhang, C., Yang, Z., Di, L., Yu, E., Li, L., & Zhao, H. (2020). Web Geoprocessing Services for Disseminating and Analyzing SMAP Derived Soil Moisture Data Products. Earth and Space Science Open Archive ESSOAr.
  • Zhou, X., Wang, W., Di, L., Lu, L., & Guo, L. (2020). Estimation of Tree Height by Combining Low Density Airborne LiDAR Data and Images Using the 3D Tree Model: A Case Study in a Subtropical Forest in China. Forests, 11(12), 1252.

2019

  • 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

2018

  • 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
  • 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
  • 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
  • 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
  • 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.
  • Lee, P., Saylor, R., McQueen, J., 2018. Air Quality Monitoring and Forecasting. Atmosphere 9, 89. https://doi.org/10.3390/atmos9030089
  • 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
  • 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
  • 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
  • Lu, L., Huang, Y., Di, L., Hang, D., 2018a. 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
  • Lu, L., Tao, Y., Di, L., 2018b. Object-Based Plastic-Mulched Landcover Extraction Using Integrated Sentinel-1 and Sentinel-2 Data. Remote Sensing 10, 1820. https://doi.org/10.3390/rs10111820
  • 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
  • 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
  • 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
  • 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
  • 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
  • Zhang, Xiang, 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
  • Zhang, Xiaochun, 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

2017

  • Bai, J., Yu, Y., Di, L., 2017. Comparison between TVDI and CWSI for drought monitoring in the Guanzhong Plain, China. Journal of Integrative Agriculture 16, 389–397. https://doi.org/10.1016/S2095-3119(15)61302-8
  • Boryan, C.G., Yang, Z., Willis, P., Di, L., 2017. Developing crop specific area frame stratifications based on geospatial crop frequency and cultivation data layers. Journal of Integrative Agriculture 16, 312–323. https://doi.org/10.1016/S2095-3119(16)61396-5
  • Bray, C.D., Battye, W., Aneja, V.P., Tong, D., Lee, P., Tang, Y., Nowak, J.B., 2017. Evaluating ammonia (NH3) predictions in the NOAA National Air Quality Forecast Capability (NAQFC) using in-situ aircraft and satellite measurements from the CalNex2010 campaign. Atmospheric Environment 163, 65–76. https://doi.org/10.1016/j.atmosenv.2017.05.032
  • Cao, X., Zhang, X., Tong, D.Q., Chen, W., Zhang, S., Zhao, H., Xiu, A., 2017. Review on physicochemical properties of pollutants released from fireworks: environmental and health effects and prevention. Environ. Rev. 26, 133–155. https://doi.org/10.1139/er-2017-0063
  • Chai, T., Kim, H.-C., Pan, L., Lee, P., Tong, D., 2017. Impact of Moderate Resolution Imaging Spectroradiometer Aerosol Optical Depth and AirNow PM2.5 assimilation on Community Multi-scale Air Quality aerosol predictions over the contiguous United States. Journal of Geophysical Research: Atmospheres 122, 5399–5415. https://doi.org/10.1002/2016JD026295
  • Chen, W., Tong, D.Q., Zhang, S., Zhang, X., Zhao, H., 2017. Local PM10 and PM2.5 emission inventories from agricultural tillage and harvest in northeastern China. Journal of Environmental Sciences 57, 15–23. https://doi.org/10.1016/j.jes.2016.02.024
  • Di, L., Üstündağ, B.B., Chen, Z., Yang, Z., 2017. Guest Editorial Foreword to the Special Issue on Agro-Geoinformatics: Monitoring, Prediction, and Decision Support. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10, 5331–5333. https://doi.org/10.1109/JSTARS.2017.2778858
  • Di, Liping, Yu, E.G., Kang, L., Shrestha, R., Bai, Y., 2017. RF-CLASS: A remote-sensing-based flood crop loss assessment cyber-service system for supporting crop statistics and insurance decision-making. Journal of Integrative Agriculture 16, 408–423. https://doi.org/10.1016/S2095-3119(16)61499-5
  • Hong, C., Zhang, Q., Zhang, Y., Tang, Y., Tong, D., He, K., 2017. Multi-year downscaling application of two-way coupled WRF v3. 4 and CMAQ v5. 0.2 over east Asia for regional climate and air quality modeling: model evaluation and aerosol direct effects. Geoscientific Model Development 10.
  • Huang, M., Carmichael, G.R., Crawford, J.H., Wisthaler, A., Zhan, X., Hain, C.R., Lee, P., Guenther, A.B., 2017a. Biogenic isoprene emissions driven by regional weather predictions using different initialization methods: Case studies during the SEAC4RS and DiSCOVER-AQ airborne campaigns. Geoscientific Model Development 10, 3085–3104. http://dx.doi.org/10.5194/gmd-10-3085-2017
  • Huang, M., Carmichael, G.R., Pierce, R.B., Jo, D.S., Park, R.J., Flemming, J., Emmons, L.K., Bowman, K.W., Henze, D.K., Davila, Y., Sudo, K., Jonson, J.E., Tronstad Lund, M., Janssens-Maenhout, G., Dentener, F.J., Keating, T.J., Oetjen, H., Payne, V.H., 2017b. Impact of intercontinental pollution transport on North American ozone air pollution: an HTAP phase 2 multi-model study. Atmospheric Chemistry and Physics 17, 5721–5750. https://doi.org/10.5194/acp-17-5721-2017
  • Lee, P., McQueen, J., Stajner, I., Huang, J., Pan, L., Tong, D., Kim, H., Tang, Y., Kondragunta, S., Ruminski, M., Lu, S., Rogers, E., Saylor, R., Shafran, P., Huang, H.-C., Gorline, J., Upadhayay, S., Artz, R., 2017. NAQFC Developmental Forecast Guidance for Fine Particulate Matter (PM2.5). Wea. Forecasting 32, 343–360. https://doi.org/10.1175/WAF-D-15-0163.1
  • Liang, L., Sun, Q., Luo, X., Wang, J., Zhang, L., Deng, M., Di, L., Liu, Z., 2017. Long-term spatial and temporal variations of vegetative drought based on vegetation condition index in China. Ecosphere 8, e01919. https://doi.org/10.1002/ecs2.1919
  • Liu, Z., Ostrenga, D., Vollmer, B., Deshong, B., Macritchie, K., Greene, M., Kempler, S., 2017. Global Precipitation Measurement Mission Products and Services at the NASA GES DISC. Bull. Amer. Meteor. Soc. 98, 437–444. https://doi.org/10.1175/BAMS-D-16-0023.1
  • Lu, L., Huang, Y., Di, L., Hang, D., 2017. A New Spatial Attraction Model for Improving Subpixel Land Cover Classification. Remote Sensing 9, 360. https://doi.org/10.3390/rs9040360
  • Pan, L., Kim, H.C., Lee, P., Saylor, R., Tang, Y., Tong, D., Baker, B., Kondragunta, S., Xu, C., Ruminski, M.G., Chen, W., Mcqueen, J., Stajner, I., 2017. Evaluating a fire smoke simulation algorithm in the National Air Quality Forecast Capability (NAQFC) by using multiple observation data sets during the Southeast Nexus (SENEX) field campaign. Geoscientific Model Development Discussions 1–45. https://doi.org/10.5194/gmd-2017-207
  • Rahman, Md.S., Ahmed, B., Di, L., 2017. Landslide initiation and runout susceptibility modeling in the context of hill cutting and rapid urbanization: a combined approach of weights of evidence and spatial multi-criteria. J. Mt. Sci. 14, 1919–1937. https://doi.org/10.1007/s11629-016-4220-z
  • Rahman, M.S., Di, L., 2017. The state of the art of spaceborne remote sensing in flood management. Natural Hazards 85, 1223–1248. https://doi.org/10.1007/s11069-016-2601-9
  • Shrestha, R., Di, L., Yu, E.G., Kang, L., Shao, Y., Bai, Y., 2017. Regression model to estimate flood impact on corn yield using MODIS NDVI and USDA cropland data layer. Journal of Integrative Agriculture 16, 398–407. https://doi.org/10.1016/S2095-3119(16)61502-2
  • Song, J., Di, L., 2017. Near-Real-Time OGC Catalogue Service for Geoscience Big Data. ISPRS International Journal of Geo-Information 6, 337. https://doi.org/10.3390/ijgi6110337
  • Sun, Z., Di, L., Heo, G., Zhang, C., Fang, H., Yue, P., Jiang, L., Tan, X., Guo, L., Lin, L., 2017. GeoFairy: Towards a one-stop and location based Service for Geospatial Information Retrieval. Computers, Environment and Urban Systems 62, 156–167. https://doi.org/10.1016/j.compenvurbsys.2016.11.007
  • Tan, X., Guo, S., Di, L., Deng, M., Huang, F., Ye, X., Sun, Z., Gong, W., Sha, Z., Pan, S., 2017. Parallel Agent-as-a-Service (P-AaaS) Based Geospatial Service in the Cloud. Remote Sensing 9, 382. https://doi.org/10.3390/rs9040382
  • Tang, J., Di, L., Xiao, J., Lu, D., Zhou, Y., 2017. Impacts of land use and socioeconomic patterns on urban heat Island. International Journal of Remote Sensing 38, 3445–3465. https://doi.org/10.1080/01431161.2017.1295485
  • Tang, Y., Pagowski, M., Chai, T., Pan, L., Lee, P., Baker, B., Kumar, R., Monache, L.D., Tong, D., Kim, H.-C., 2017. A case study of aerosol data assimilation with the Community Multi-scale Air Quality Model over the contiguous United States using 3D-Var and optimal interpolation methods. Geoscientific Model Development 10, 4743–4758. https://doi.org/10.5194/gmd-10-4743-2017
  • Teng, B., Zhang, X., Yi, C., Zhang, Y., Ye, S., Wang, Y., Tong, D.Q., Lu, B., 2017. The Association between Ambient Air Pollution and Allergic Rhinitis: Further Epidemiological Evidence from Changchun, Northeastern China. International Journal of Environmental Research and Public Health 14, 226. https://doi.org/10.3390/ijerph14030226
  • Tong, D.Q., Wang, J.X.L., Gill, T.E., Lei, H., Wang, B., 2017. Intensified dust storm activity and Valley fever infection in the southwestern United States. Geophysical Research Letters 44, 4304–4312. https://doi.org/10.1002/2017GL073524
  • Yang, Z., Wu, W., Di, L., Üstündağ, B., 2017. Remote sensing for agricultural applications. Journal of Integrative Agriculture 16, 239–241. https://doi.org/10.1016/S2095-3119(16)61549-6
  • Zhao, H., Zhang, X., Zhang, S., Chen, W., Tong, D.Q., Xiu, A., 2017. Effects of Agricultural Biomass Burning on Regional Haze in China: A Review. Atmosphere 8, 88. https://doi.org/10.3390/atmos8050088

2016

  • Cohen, J.P., Ding, W., Kuhlman, C., Chen, A., Di, L., 2016. Rapid building detection using machine learning. Appl Intell 45, 443–457. https://doi.org/10.1007/s10489-016-0762-6
  • Han, W., Di, L., Yu, G., Shao, Y., Kang, L., 2016. Investigating metrics of geospatial web services: The case of a CEOS federated catalog service for earth observation data. Computers & Geosciences 92, 1–8. https://doi.org/10.1016/j.cageo.2016.04.005
  • Huang, J., McQueen, J., Wilczak, J., Djalalova, I., Stajner, I., Shafran, P., Allured, D., Lee, P., Pan, L., Tong, D., Huang, H.-C., DiMego, G., Upadhayay, S., Delle Monache, L., 2016. Improving NOAA NAQFC PM2.5 Predictions with a Bias Correction Approach. Wea. Forecasting 32, 407–421. https://doi.org/10.1175/WAF-D-16-0118.1
  • Liang, L., Qin, Z., Zhao, S., Di, L., Zhang, C., Deng, M., Lin, H., Zhang, L., Wang, L., Liu, Z., 2016. Estimating crop chlorophyll content with hyperspectral vegetation indices and the hybrid inversion method. International Journal of Remote Sensing 37, 2923–2949. https://doi.org/10.1080/01431161.2016.1186850
  • Sun, Z., Fang, H., Di, L., Yue, P., 2016a. Realizing parameterless automatic classification of remote sensing imagery using ontology engineering and cyberinfrastructure techniques. Computers & Geosciences 94, 56–67. https://doi.org/10.1016/j.cageo.2016.06.004
  • Sun, Z., Fang, H., Di, L., Yue, P., Tan, X., Bai, Y., 2016b. Developing a web-based system for supervised classification of remote sensing images. Geoinformatica 20, 629–649. https://doi.org/10.1007/s10707-016-0252-3
  • Tan, X., Di, L., Deng, M., Huang, F., Ye, X., Sha, Z., Sun, Z., Gong, W., Shao, Y., Huang, C., 2016. Agent-as-a-service-based geospatial service aggregation in the cloud: A case study of flood response. Environmental Modelling & Software 84, 210–225. https://doi.org/10.1016/j.envsoft.2016.07.001
  • Xue, X., Di, L., Guo, L., Lin, L., 2016. Unsupervised Classification of Fully Polarimetric SAR Image Based on Polarimetric Features and Spatial Features. TELKOMNIKA (Telecommunication Computing Electronics and Control) 14, 244. https://doi.org/10.12928/telkomnika.v14i3A.4403

2015

  • Di, L., Yang, Z., 2015. Special Section Guest Editorial: Remote Sensing and Sensor Networks for Promoting Agro-Geoinformatics. JARS 8, 085101. https://doi.org/10.1117/1.JRS.8.085101
  • Guo, L., Di, L., Li, G., Luo, Q., Gao, M., 2015. GIS-based detection of land use transformation in the Loess Plateau: A case study in Baota District, Shaanxi Province, China. J. Geogr. Sci. 25, 1467–1478. https://doi.org/10.1007/s11442-015-1246-z
  • He, L., Yue, P., Di, L., Zhang, M., Hu, L., 2015. Adding Geospatial Data Provenance into SDI—A Service-Oriented Approach. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8, 926–936. https://doi.org/10.1109/JSTARS.2014.2340737
  • Liang, L., Di, L., Zhang, L., Deng, M., Qin, Z., Zhao, S., Lin, H., 2015. Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method. Remote Sensing of Environment 165, 123–134. https://doi.org/10.1016/j.rse.2015.04.032
  • Liu, Z., 2015a. Comparison of Integrated Multisatellite Retrievals for GPM (IMERG) and TRMM Multisatellite Precipitation Analysis (TMPA) Monthly Precipitation Products: Initial Results. J. Hydrometeor. 17, 777–790. https://doi.org/10.1175/JHM-D-15-0068.1
  • Liu, Z., 2015b. Evaluation of Precipitation Climatology Derived from TRMM Multi-Satellite Precipitation Analysis (TMPA) Monthly Product over Land with Two Gauge-Based Products. Climate 3, 964–982. https://doi.org/10.3390/cli3040964
  • Lu, L., Hang, D., Di, L., 2015. Threshold model for detecting transparent plastic-mulched landcover using moderate-resolution imaging spectroradiometer time series data: a case study in southern Xinjiang, China. JARS 9, 097094. https://doi.org/10.1117/1.JRS.9.097094
  • Mantas, V.M., Liu, Z., Caro, C., Pereira, A.J.S.C., 2015. Validation of TRMM multi-satellite precipitation analysis (TMPA) products in the Peruvian Andes. Atmospheric Research, 6th Workshop of the International Precipitation Working Group 163, 132–145. https://doi.org/10.1016/j.atmosres.2014.11.012
  • Peng, C., Deng, M., Di, L., Han, W., 2015. Delivery of agricultural drought information via web services. Earth Sci Inform 8, 527–538. https://doi.org/10.1007/s12145-014-0198-7
  • Sun, Z., Fang, H., Deng, M., Chen, A., Yue, P., Di, L., 2015. Regular Shape Similarity Index: A Novel Index for Accurate Extraction of Regular Objects from Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing 53, 3737–3748. https://doi.org/10.1109/TGRS.2014.2382566
  • Tan, X., Di, L., Deng, M., Chen, A., Huang, F., Peng, C., Gao, M., Yao, Y., Sha, Z., 2015. Cloud-and Agent-Based Geospatial Service Chain: A Case Study of Submerged Crops Analysis During Flooding of the Yangtze River Basin. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8, 1359–1370. https://doi.org/10.1109/JSTARS.2014.2376475
  • Tan, Xicheng, Di, L., Deng, M., Chen, A., Sun, Z., Huang, C., Shao, Y., Ye, X., 2015a. Agent-and Cloud-Supported Geospatial Service Aggregation for Flood Response. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences.
  • Tan, Xicheng, Di, L., Deng, M., Fu, J., Shao, G., Gao, M., Sun, Z., Ye, X., Sha, Z., Jin, B., 2015b. Building an Elastic Parallel OGC Web Processing Service on a Cloud-Based Cluster: A Case Study of Remote Sensing Data Processing Service. Sustainability 7, 14245–14258. https://doi.org/10.3390/su71014245
  • Yagci, A.L., Di, L., Deng, M., 2015. The effect of corn–soybean rotation on the NDVI-based drought indicators: a case study in Iowa, USA, using Vegetation Condition Index. GIScience & Remote Sensing 52, 290–314. https://doi.org/10.1080/15481603.2015.1038427

2014

  • Bai, J., Di, L., Bai, J., 2014. NDVI and Regional Climate Variation Since the Implementation of Revegetation Program in Northern Shaanxi Province, China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7, 4581–4588. https://doi.org/10.1109/JSTARS.2014.2365590
  • Boryan, C.G., Yang, Z., Di, L., Hunt, K., 2014. A New Automatic Stratification Method for U.S. Agricultural Area Sampling Frame Construction Based on the Cropland Data Layer. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7, 4317–4327. https://doi.org/10.1109/JSTARS.2014.2322584
  • Di, L., Yang, Z., 2015. Special Section Guest Editorial: Remote Sensing and Sensor Networks for Promoting Agro-Geoinformatics. JARS 8, 085101. https://doi.org/10.1117/1.JRS.8.085101
  • Di, L., Yang, Z., 2014. Foreword to the Special issue on Agro-Geoinformatics—The Applications of Geoinformatics in Agriculture. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7, 4315–4316. https://doi.org/10.1109/JSTARS.2014.2382411
  • Han, Weiguo, Yang, Z., Di, L., Yagci, A.L., Han, S., 2014a. Making Cropland Data Layer Data Accessible and Actionable in GIS Education. Journal of Geography 113, 129–138. https://doi.org/10.1080/00221341.2013.838286
  • Han, Weiguo, Yang, Z., Di, L., Yue, P., 2014b. A Geospatial Web Service Approach for Creating On-Demand Cropland Data Layer Thematic Maps. Transactions of the ASABE 239–247. https://doi.org/10.13031/trans.57.10020
  • Han, W., Yang, Z., Di, L., Zhang, B., Peng, C., 2014. Enhancing Agricultural Geospatial Data Dissemination and Applications Using Geospatial Web Services. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7, 4539–4547. https://doi.org/10.1109/JSTARS.2014.2315593
  • Kang, L., Di, L., Deng, M., Shao, Y., Yu, G., Shrestha, R., 2014. Use of Geographically Weighted Regression Model for Exploring Spatial Patterns and Local Factors Behind NDVI-Precipitation Correlation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7, 4530–4538. https://doi.org/10.1109/JSTARS.2014.2361128
  • Liu, Z, Ostrenga, D., Teng, W., Kempler, S., 2014. Developing online visualization and analysis services for NASA satellite-derived global precipitation products during the Big Geospatial Data era. Big Data: Techniques and Technologies in Geoinformatics; CRC Press: Boca Raton, FL, USA 91–116.
  • Liu, Zhong, Ostrenga, D., Teng, W., Kempler, S., Milich, L., 2014. Developing GIOVANNI-based online prototypes to intercompare TRMM-related global gridded-precipitation products. Computers & Geosciences 66, 168–181. https://doi.org/10.1016/j.cageo.2013.12.012
  • Lu, L., Di, L., Ye, Y., 2014. A Decision-Tree Classifier for Extracting Transparent Plastic-Mulched Landcover from Landsat-5 TM Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7, 4548–4558. https://doi.org/10.1109/JSTARS.2014.2327226
  • Luo, W., Li, X., Molloy, I., Di, L., Stepinski, T., 2014. Web Service for extracting stream networks from DEM data. GeoJournal 79, 183–193. https://doi.org/10.1007/s10708-013-9502-1
  • Peng, C., Deng, M., Di, L., 2014. Relationships Between Remote-Sensing-Based Agricultural Drought Indicators and Root Zone Soil Moisture: A Comparative Study of Iowa. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7, 4572–4580. https://doi.org/10.1109/JSTARS.2014.2344115
  • Sun, Z., Peng, C., Deng, M., Chen, A., Yue, P., Fang, H., Di, L., 2014. Automation of Customized and Near-Real-Time Vegetation Condition Index Generation Through Cyberinfrastructure-Based Geoprocessing Workflows. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7, 4512–4522. https://doi.org/10.1109/JSTARS.2014.2377248

2013

  • Bai, Y., Di, L., 2012. Review of Geospatial Data Systems’ Support of Global Change Studies. International Journal of Environment and Climate Change 421–436. https://doi.org/10.9734/BJECC/2012/2726
  • Deng, M., Di, L., Han, W., Yagci, A.L., Peng, C., Heo, G., 2013. Web-service-based Monitoring and Analysis of Global Agricultural Drought. Photogrammetric Engineering & Remote Sensing 79. https://doi.org/10.14358/PERS.79.10.929
  • Di, L., Shao, Y., Kang, L., 2013a. Implementation of Geospatial Data Provenance in a Web Service Workflow Environment With ISO 19115 and ISO 19115-2 Lineage Model. IEEE Transactions on Geoscience and Remote Sensing 51, 5082–5089. https://doi.org/10.1109/TGRS.2013.2248740
  • Di, Liping, Yue, P., Ramapriyan, H.K., King, R.L., 2013. Introduction to the special issue on geoscience data provenance. IEEE Transactions on Geoscience and Remote Sensing 51, 5062–5064. https://doi.org/10.1109/TGRS.2013.2285999
  • Di, L., Yue, P., Ramapriyan, H.K., King, R.L., 2013b. Geoscience Data Provenance: An Overview. IEEE Transactions on Geoscience and Remote Sensing 51, 5065–5072. https://doi.org/10.1109/TGRS.2013.2242478
  • He, L.L., Di, L.P., Yue, P., Zhang, M.D., 2013. Fuzzy Logic-Supported Detection of Complex Geospatial Features in a Web Service Environment. ISPRS – International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-4/W2, 127–131. https://doi.org/10.5194/isprsarchives-XL-4-W2-127-2013
  • Shao, Y., Di, L., Bai, Y., Wang, H., Yang, C., 2013. Federated Catalogue for Discovering Earth Observation Data. Photogrammetrie-Fernerkundung-Geoinformation 2013, 43–52.
  • Shen, D., Deng, M., Di, L., Han, W., Peng, C., Yagci, A.L., Yu, G., Chen, Z., 2013. Study on generation and sharing of on-demand global seamless data—Taking MODIS NDVI as an example. Computers & Geosciences 54, 66–74. https://doi.org/10.1016/j.cageo.2012.11.011
  • Shen, Y., Di, L., Yu, G., Wu, L., 2013. Correlation Between Corn Progress Stages and Fractal Dimension From MODIS-NDVI Time Series. IEEE Geoscience and Remote Sensing Letters 10, 1065–1069. https://doi.org/10.1109/LGRS.2012.2228842
  • Shen, Yonglin, Wu, L., Di, L., Yu, Genong, Tang, H., Yu, Guoxian, Shao, Y., 2013. Hidden Markov Models for Real-Time Estimation of Corn Progress Stages Using MODIS and Meteorological Data. Remote Sensing 5, 1734–1753. https://doi.org/10.3390/rs5041734
  • Yagci, A.L., Di, L., Deng, M., 2013. The effect of land-cover change on vegetation greenness-based satellite agricultural drought indicators: a case study in the southwest climate division of Indiana, USA. International Journal of Remote Sensing 34, 6947–6968. https://doi.org/10.1080/01431161.2013.810824
  • Yue, P., Di, L., Wei, Y., Han, W., 2013. Intelligent services for discovery of complex geospatial features from remote sensing imagery. ISPRS Journal of Photogrammetry and Remote Sensing 83, 151–164. https://doi.org/10.1016/j.isprsjprs.2013.02.015

2012

  • Bai, Y., Di, L., 2012. Review of Geospatial Data Systems’ Support of Global Change Studies. International Journal of Environment and Climate Change 421–436. https://doi.org/10.9734/BJECC/2012/2726
  • Bai, Y., Di, L., Nebert, D.D., Chen, A., Wei, Y., Cheng, X., Shao, Y., Shen, D., Shrestha, R., Wang, H., 2012. GEOSS Component and Service Registry: Design, Implementation and Lessons Learned. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5, 1678–1686. https://doi.org/10.1109/JSTARS.2012.2215914
  • Chen, Z., Chen, N., Yang, C., Di, L., 2012. Cloud Computing Enabled Web Processing Service for Earth Observation Data Processing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5, 1637–1649. https://doi.org/10.1109/JSTARS.2012.2205372
  • Han, W., Di, L., Zhao, P., Shao, Y., 2012a. DEM Explorer: An online interoperable DEM data sharing and analysis system. Environmental Modelling & Software 38, 101–107. https://doi.org/10.1016/j.envsoft.2012.05.015
  • Han, W., Yang, Z., Di, L., Mueller, R., 2012b. CropScape: A Web service based application for exploring and disseminating US conterminous geospatial cropland data products for decision support. Computers and Electronics in Agriculture 84, 111–123. https://doi.org/10.1016/j.compag.2012.03.005
  • Qiu, F., Ni, F., Chastain, B., Huang, H., Zhao, P., Han, W., Di, L., 2012. GWASS: GRASS web application software system based on the GeoBrain web service. Computers & Geosciences, Towards a Geoprocessing Web 47, 143–150. https://doi.org/10.1016/j.cageo.2012.01.023
  • Sun, X., Shen, S., Leptoukh, G.G., Wang, P., Di, L., Lu, M., 2012. Development of a Web-based visualization platform for climate research using Google Earth. Computers & Geosciences, Towards a Geoprocessing Web 47, 160–168. https://doi.org/10.1016/j.cageo.2011.09.010
  • Sun, Z., Yue, P., Di, L., 2012. GeoPWTManager: a task-oriented web geoprocessing system. Computers & Geosciences, Towards a Geoprocessing Web 47, 34–45. https://doi.org/10.1016/j.cageo.2011.11.031
  • Yang, C., Chen, N., Di, L., 2012. RESTFul based heterogeneous Geoprocessing workflow interoperation for Sensor Web Service. Computers & Geosciences, Towards a Geoprocessing Web 47, 102–110. https://doi.org/10.1016/j.cageo.2011.11.010
  • Yu, G. (Eugene), Zhao, P., Di, L., Chen, A., Deng, M., Bai, Y., 2012. BPELPower—A BPEL execution engine for geospatial web services. Computers & Geosciences, Towards a Geoprocessing Web 47, 87–101. https://doi.org/10.1016/j.cageo.2011.11.029
  • Yue, P., Di, L., Han, W., Zhao, P., Yang, W., He, L., 2012a. Service-oriented approach for geospatial feature discovery. Earth Sci Inform 5, 153–165. https://doi.org/10.1007/s12145-012-0104-0
  • Yue, P., Gong, J., Di, L., He, L., 2012b. Automatic geospatial metadata generation for earth science virtual data products. Geoinformatica 16, 1–29. https://doi.org/10.1007/s10707-011-0123-x
  • Zhang, B., Di, L., Yu, G., Han, W., Wang, H., 2012. Towards Data and Sensor Planning Service for Coupling Earth Science Models and Earth Observations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5, 1628–1636. https://doi.org/10.1109/JSTARS.2012.2195639
  • Zhao, P., Di, L., Han, W., Li, X., 2012. Building a Web-Services Based Geospatial Online Analysis System. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5, 1780–1792. https://doi.org/10.1109/JSTARS.2012.2197372
  • Zhao, Peisheng, Di, L., Yu, G., 2012. Building asynchronous geospatial processing workflows with web services. Computers & Geosciences 39, 34–41. https://doi.org/10.1016/j.cageo.2011.06.006

2011

  • Bai, Y., Di, L., 2011. Providing access to satellite imagery through OGC catalog service interfaces in support of the Global Earth Observation System of Systems. Computers & Geosciences 37, 435–443. https://doi.org/10.1016/j.cageo.2010.09.010
  • Chen, N., Chen, Z., Di, L., Gong, J., 2011. An Efficient Method for Near-Real-Time On-Demand Retrieval of Remote Sensing Observations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 4, 615–625. https://doi.org/10.1109/JSTARS.2011.2109035
  • Chen, Nengcheng, Chen, Z., Hu, C., Di, L., 2011. A capability matching and ontology reasoning method for high precision OGC web service discovery. International Journal of Digital Earth 4, 449–470. https://doi.org/10.1080/17538947.2011.553688
  • Chen, Z., Chen, N., Di, L., Gong, J., 2011. A Flexible Data and Sensor Planning Service for Virtual Sensors Based on Web Service. IEEE Sensors Journal 11, 1429–1439. https://doi.org/10.1109/JSEN.2010.2095839
  • Shao, Y., Guo, B., Hu, X., Di, L., 2011. Application of a Fast Linear Feature Detector to Road Extraction From Remotely Sensed Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 4, 626–631. https://doi.org/10.1109/JSTARS.2010.2094181
  • Yue, P., Gong, J., Di, L., He, L., Wei, Y., 2011a. Integrating semantic web technologies and geospatial catalog services for geospatial information discovery and processing in cyberinfrastructure. Geoinformatica 15, 273–303. https://doi.org/10.1007/s10707-009-0096-1
  • Yue, P., Wei, Y., Di, L., He, L., Gong, J., Zhang, L., 2011b. Sharing geospatial provenance in a service-oriented environment. Computers, Environment and Urban Systems 35, 333–343. https://doi.org/10.1016/j.compenvurbsys.2011.02.006
  • Zhao, H., Yang, Z., Li, L., Di, L., 2011. Improvement and comparative analysis of indices of crop growth condition monitoring by remote sensing. Transactions of the Chinese Society of Agricultural Engineering 27, 243–249.

2010

  • Chen, A., Di, L., Bai, Y., Wei, Y., Liu, Y., 2010. Grid computing enhances standards-compatible geospatial catalogue service. Computers & Geosciences 36, 411–421. https://doi.org/10.1016/j.cageo.2009.09.006
  • Chen, N., Di, L., Yu, G., Gong, J., 2010a. Geo-processing workflow driven wildfire hot pixel detection under sensor web environment. Computers & Geosciences 36, 362–372. https://doi.org/10.1016/j.cageo.2009.06.013
  • Chen, N., Gong, J., Di, L., Yu, G., 2010b. Automatic On-Demand Data Feed Service for AutoChem Based on Reusable Geo-Processing Workflow. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 3, 418–426. https://doi.org/10.1109/JSTARS.2010.2049094
  • Chen, N., Li, D., Di, L., Gong, J., 2010c. An automatic SWILC classification and extraction for the AntSDI under a Sensor Web environment. Canadian Journal of Remote Sensing 36, S1–S12. https://doi.org/10.5589/m10-011
  • Di, L., Moe, K., Zyl, T.L. van, 2010. Earth Observation Sensor Web: An Overview. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 3, 415–417. https://doi.org/10.1109/JSTARS.2010.2089575
  • Li, X., Di, L., Han, W., Zhao, P., Dadi, U., 2010. Sharing geoscience algorithms in a Web service-oriented environment (GRASS GIS example). Computers & Geosciences 36, 1060–1068. https://doi.org/10.1016/j.cageo.2010.03.004
  • Yu, E.G., Liping, D., Bei, Z., Huilin, W., 2010. Coordination Through Geospatial Web Service Workflow in the Sensor Web Environment. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 3, 433–441. https://doi.org/10.1109/JSTARS.2010.2049477
  • Yue, P., Gong, J., Di, L., 2010a. Augmenting geospatial data provenance through metadata tracking in geospatial service chaining. Computers & Geosciences 36, 270–281. https://doi.org/10.1016/j.cageo.2009.09.002
  • Yue, P., Gong, J., Di, L., Yuan, J., Sun, L., Sun, Z., Wang, Q., 2010b. GeoPW: Laying Blocks for the Geospatial Processing Web. Transactions in GIS 14, 755–772. https://doi.org/10.1111/j.1467-9671.2010.01232.x

2009

  • Bai, Y., Di, L., Wei, Y., 2009. A taxonomy of geospatial services for global service discovery and interoperability. Computers & Geosciences 35, 783–790. https://doi.org/10.1016/J.CAGEO.2007.12.018
  • Chen, A., Di, L., Wei, Y., Bai, Y., Liu, Y., 2009. Use of grid computing for modeling virtual geospatial products. International Journal of Geographical Information Science 23, 581–604. https://doi.org/10.1080/13658810902733666
  • Chen, N., Di, L., Yu, G., Gong, J., Wei, Y., 2009a. Use of ebRIM-based CSW with sensor observation services for registry and discovery of remote-sensing observations. Computers & Geosciences 35, 360–372. https://doi.org/10.1016/j.cageo.2008.08.003
  • Chen, N., Di, L., Yu, G., Min, M., 2009b. A flexible geospatial sensor observation service for diverse sensor data based on Web service. ISPRS Journal of Photogrammetry and Remote Sensing 64, 234–242. https://doi.org/10.1016/j.isprsjprs.2008.12.001
  • Di, L., Moe, K.L., Yu, G. (Eugene), 2009. Metadata requirements analysis for the emerging Sensor Web This was orally presented at the European Geosciences Union General Assembly 2008, Vienna, Austria, 13–18 April 2008. International Journal of Digital Earth 2, 3–17. https://doi.org/10.1080/17538940902866195
  • Ramapriyan, H.K., Di, L., Bruzzone, L., 2009. Guest Editorial. IEEE Transactions on Geoscience and Remote Sensing 47, 17–19. https://doi.org/10.1109/TGRS.2008.2010483
  • Yue, P., Di, L., Yang, W., Yu, G., Zhao, P., Gong, J., 2009. Semantic Web Services‐based process planning for earth science applications. International Journal of Geographical Information Science 23, 1139–1163. https://doi.org/10.1080/13658810802032680
  • Zhao, P., Di, L., Yu, G., Yue, P., Wei, Y., Yang, W., 2009. Semantic Web-based geospatial knowledge transformation. Computers & Geosciences, Geoscience Knowledge Representation in Cyberinfrastructure 35, 798–808. https://doi.org/10.1016/j.cageo.2008.03.013

Before 2008

  • Deng, M., Di, L., 2008. Building an Online Learning and Research Environment to Enhance Use of Geospatial Data. International Journal of Spatial Data Infrastructures Research 4, 77–95–95. https://doi.org/10.2902/ijsdir.v4i4.116
  • Di, L., Chen, A., Yang, W., Liu, Y., Wei, Y., Mehrotra, P., Hu, C., Williams, D., 2008. The development of a geospatial data Grid by integrating OGC Web services with Globus-based Grid technology. Concurrency Computation Practice and Experience 20, 1617–1635. https://doi.org/10.1002/cpe
  • Zhang, D., Yu, L., Deng, C., Di, L., 2008. OGC WPS-based remote sensing image processing in Web environment. J Zhejiang Univ (Eng Sci) 7, 018.
  • ZHANG, D., YU, L., XIE, B., Yang, L., DI, L., 2008. A spatial information service technique based on WSRF. Journal of Zhejiang University (Science Edition) 6, 024.
  • Bai, Y., Di, L., Chen, A., Liu, Y., Wei, Y., 2007. Towards a geospatial catalogue federation service. Photogrammetric Engineering & Remote Sensing 73, 699–708. https://doi.org/10.14358/PERS.73.6.699
  • Chen, A., Leptoukh, G., Di, L., Kempler, S., Lynnes, C., 2007. Visualization of and Access to CloudSat Vertical Data through Google Earth. Nature Precedings. https://doi.org/10.1038/npre.2007.595.1
  • Dadi, U., Di, L., 2007. Data independence and geospatial web services. Geoinformatics.
  • Wei, Y., Di, L., Zhao, B., Liao, G., Chen, A., 2007. Transformation of HDF-EOS metadata from the ECS model to ISO 19115-based XML. Computers & Geosciences 33, 238–247. https://doi.org/10.1016/j.cageo.2006.06.006
  • Yue, P., Di, L., Yang, W., Yu, G., Zhao, P., 2007. Semantics-based automatic composition of geospatial Web service chains. Computers & Geosciences 33, 649–665. https://doi.org/10.1016/j.cageo.2006.09.003
  • Chen, A., Di, L., Wei, Y., Liu, Y., Bai, Y., Hu, C., Mehrotra, P., 2005. Grid Computing enabled geospatial catalogue web service. American Society for Photogrammetry and Remote Sensing 2005 7–11.
  • Di, L., 2005. A Framework for Developing Web-Service-Based Intelligent Geospatial Knowledge Systems. Geographic Information Sciences 11, 24–28. https://doi.org/10.1080/10824000509480597
  • Yang, W., Di, L., 2004. An Accurate and Automated Approach to Georectification of HDF-EOS Swath Data. Photogrammetric Engineering & Remote Sensing 70, 397–404. https://doi.org/10.14358/PERS.70.4.397
  • Di, L., 2003. Recent progresses on remote sensing monitoring of desertification. Annals of Arid Zone 42, 371–392.
  • Zhan, X., Miller, S., Chauhan, N., Di, L., Ardanuy, P., Running, S., 2002. Soil Moisture Visible/Infrared Imager/Radiometer Suite Algorithm Theoretical Basis Document. Raytheon Systems Company, Maryland, Version 5.
  • Li, Z., Hui, W., Di, L., 2001. The Protection of Heping Powder on Indomethacin-Induced Gastric Mucosal Damage in Rats. Journal of Chinese Physician.
  • Di, L., Rundquist, D.C., Han, L., 1994. Modelling relationships between ndvi and precipitation during vegetative growth cycles. International Journal of Remote Sensing 15, 2121–2136. https://doi.org/10.1080/01431169408954231