Tops from xml format

Rodriguez-Galiano, V., & Chica-Olmo, M. (2012). Land cover change analysis of a Mediterranean area in Spain using different sources of data: multi-seasonal Landsat images, land surface temperature, digital terrain models and texture. Applied Geography, 35(1), 208–218. Retrieved from http://www.sciencedirect.com/science/article/pii/S0143622812000707

Roy, D. P., Wulder, M. A., Loveland, T. R., CE, W., Allen, R. G., Anderson, M. C., … others. (2014). Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment, 145, 154–172. Retrieved from http://www.sciencedirect.com/science/article/pii/S003442571400042X

Running, S. W., Nemani, R. R., Heinsch, F. A., Zhao, M., Reeves, M., & Hashimoto, H. (2004). A continuous satellite-derived measure of global terrestrial primary production. Bioscience, 54(6), 547–560. Retrieved from http://bioscience.oxfordjournals.org/content/54/6/547.short

RVWHG, E. (n.d.). 3URJUDP IRU, QWHUIDFH.

Sasai, T., Saigusa, N., Nasahara, K. N., Ito, A., Hashimoto, H., Nemani, R., … others. (2011). Satellite-driven estimation of terrestrial carbon flux over Far East Asia with 1-km grid resolution. Remote Sensing of Environment, 115(7), 1758–1771. Retrieved from http://www.sciencedirect.com/science/article/pii/S0034425711000848

Schiffman, B., Basson, G., Lue, E., Ottman, D., Hawk, A., Ghosh, M., … Skiles, J. W. (2008). Estimation of Leaf Area Index (LAI) through the acquisition of ground truth data in Yosemite national park. In ASPRS 2008 Annual Conference, Portland, Oregon. Retrieved from http://www.asprs.org/a/publications/proceedings/portland08/0073.pdf

Setoyama, Y., & Sasai, T. (2013). Analyzing decadal net ecosystem production control factors and the effects of recent climate events in Japan. Journal of Geophysical Research: Biogeosciences, 118(1), 337–351. Retrieved from http://onlinelibrary.wiley.com/doi/10.1002/jgrg.20038/full

Sharif, H. O., Melton, F. S., & Nemani, R. R. (2010). The Relationship between Humidity and Influenza. In AGU Fall Meeting Abstracts (Vol. 1, p. 836). Retrieved from http://adsabs.harvard.edu/abs/2010AGUFM.H11D0836S

Smith, N. G., & Dukes, J. S. (2013). Plant respiration and photosynthesis in global-scale models: incorporating acclimation to temperature and CO2. Global Change Biology, 19(1), 45–63. Retrieved from http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2486.2012.02797.x/full

Song, C., Dannenberg, M. P., & Hwang, T. (2013). Optical remote sensing of terrestrial ecosystem primary productivity. Progress in Physical Geography, 37(6), 834–854. Retrieved from http://ppg.sagepub.com/content/37/6/834.short

Stephenson, J. B. (2010). Climate Change Adaptation: Information on Selected Federal Efforts to Adopt to a Changing Climate. DIANE Publishing. Retrieved from http://books.google.com/books?hl=en&lr=&id=Aeu0pB-OYV0C&oi=fnd&pg=PP1&dq=“Terrestrial+Observation+and+Prediction+System”&ots=pvYWvvu-sk&sig=6I98p9EW3EHC-kLrGQC6MOiYsas

Superior, L. (2011). Collaborative Super computing for Global Change Science. Eos, Transactions, American Geophysical Union, 92(13). Retrieved from http://onlinelibrary.wiley.com/doi/10.1029/eost2011EO13/abstract

Theobald, D., Jean, C., Network, G. Y., Daley, R., Schweiger, B., Network, R. M., … others. (n.d.). NASA Science Mission directorate Applied Sciences Program.

TROUT, T. O. M., MELTON, F., & JOHNSON, L. E. E. (2014). A web-based tool that combines satellite and weather station observations to support irrigation scheduling. In Proceedings of the Central Plains Irrigation Conference. Retrieved from https://www.ksre.ksu.edu/irrigate/OOW/P14/Trout14.pdf

Vaccari, D. F., Genesio, D. L., & FIRENZE, I.-C. (n.d.). 6.3 LINEA A2-VALUTAZIONE MULTISCALA DELLA POTENZIALITA’ENOLOGICA DEL VIGNETO. Retrieved from http://www.consorziotuscania.it/data/files/Linea_ricerca_A2_IBIMET.pdf

Votava, P., Golden, K., & Nemani, R. (n.d.). Planning for Distributed Earth Science Data Processing.

Votava, P., Michaelis, A., Hashimoto, H., & Nemani, R. (n.d.). Anomaly Detection and Analysis Framework for Terrestrial Observation and Prediction System (TOPS). Retrieved from http://esto.nasa.gov/2012test/conferences/estf2011/papers/Votava_ESTF2011.pdf

Votava, P., Michaelis, A., Ichii, K., Melton, F., Nemani, R. R., Milesi, C., & Hashimoto, H. (2006). Rapid prototyping of ecological nowcasts and forecasts by extending the Terrestrial Observation and Prediction System. In AGU Fall Meeting Abstracts (Vol. 1, p. 1335). Retrieved from http://adsabs.harvard.edu/abs/2006AGUFMIN33B1335V

Votava, P., Morris, R., Dungan, J., & Khatib, L. (n.d.). SCIENCE WORKFLOW MANAGEMENT SYSTEM FOR THE TERRESTRIAL OBSERVATION AND PREDICTION SYSTEM (TOPS). Retrieved from http://geodesy.unr.edu/hanspeterplag/library/IGARSS2010/pdfs/5015.pdf

Votava, P., Nemani, R., Bowker, C., Michaelis, A., & Coughlan, J. (2002). Distributed application framework for Earth Science data processing. In Proceedings of the 2002 International Geoscience and Remote Sensing Symposium (IGARSS), Toronto, Canada. Retrieved from http://geo.arc.nasa.gov/sge/ecocast/publications/pubs/IGARSS-2002-final.pdf

Votava, P., Nemani, R. R., Ganguly, S., Michaelis, A., & Hashimoto, H. (2011). Applications of TOPS Anomaly Detection Framework to Amazon Drought Analysis. In AGU Fall Meeting Abstracts (Vol. 1, p. 1312). Retrieved from http://adsabs.harvard.edu/abs/2011AGUFMIN11C1312V

Votava, P., Nemani, R. R., & Michaelis, A. (2009). Extending TOPS: Knowledge Management System for Anomaly Detection and Analysis. In AGU Fall Meeting Abstracts (Vol. 1, p. 1017). Retrieved from http://adsabs.harvard.edu/abs/2009AGUFMIN31C1017V

Votava, P., Nemani, R. R., & Michaelis, A. (2010). Extending TOPS: Ontology-driven Anomaly Detection and Analysis System. In AGU Fall Meeting Abstracts (Vol. 1, p. 1371). Retrieved from http://adsabs.harvard.edu/abs/2010AGUFMIN41C1371V

Votava, P., Nemani, R. R., & Srivastava, A. N. (2008). Extending TOPS: A Prototype MODIS Anomaly Detection Architecture. In AGU Fall Meeting Abstracts (Vol. 1, p. 1048). Retrieved from http://adsabs.harvard.edu/abs/2008AGUFMIN21A1048V

Wang, W., Dungan, J., Hashimoto, H., Michaelis, A. R., Milesi, C., Ichii, K., & Nemani, R. R. (2011a). Diagnosing and assessing uncertainties of terrestrial ecosystem models in a multimodel ensemble experiment: 1. Primary production. Global Change Biology, 17(3), 1350–1366. Retrieved from http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2486.2010.02309.x/full

Wang, W., Dungan, J., Hashimoto, H., Michaelis, A. R., Milesi, C., Ichii, K., & Nemani, R. R. (2011b). Diagnosing and assessing uncertainties of terrestrial ecosystem models in a multimodel ensemble experiment: 2. Carbon balance. Global Change Biology, 17(3), 1367–1378. Retrieved from http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2486.2010.02315.x/full

Wang, W., Dungan, J. L., Hashimoto, H., Michaelis, A., Milesi, C., Ichii, K., & Nemani, R. R. (2009). Diagnosing and Assessing Uncertainties of the Carbon Cycle in Terrestrial Ecosystem Models from a Multi-Model Ensemble Experiment. In AGU Fall Meeting Abstracts (Vol. 1, p. 7). Retrieved from http://adsabs.harvard.edu/abs/2009AGUFM.B23G..07W

Wang, W., Dungan, J., Nemani, R., Michaelis, A., Hashimoto, H., & Ichii, K. (2008). Ensemble Ecosystem Model Experiment and Intercomparison using the Terrestrial Observation and Prediction System (TOPS). In AGU Fall Meeting Abstracts (Vol. 1, p. 346). Retrieved from http://adsabs.harvard.edu/abs/2008AGUFM.B51A0346W

Wang, Y. (2011). Remote Sensing of Protected Lands. CRC Press. Retrieved from http://books.google.com/books?hl=en&lr=&id=w6MiVy5f_xUC&oi=fnd&pg=PP1&dq=“Terrestrial+Observation+and+Prediction+System”&ots=YWpMfyami2&sig=ubPJlLonF_gqNmQiy1Y4a83jdtc

Wang, Y., & Dieffenbach, F. (2010). OOS 50-3: A decision support system for monitoring and reporting the ecological condition of the Appalachian Trail. In The 95th ESA Annual Meeting. Retrieved from https://eco.confex.com/eco/2010/techprogram/P22160.HTM

Wang, Y., Nemani, R., Dieffenbach, F., Stolte, K., Holcomb, G., Robinson, M., … others. (2010). Development of a decision support system for monitoring, reporting and forecasting ecological conditions of the appalachian trail. In Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International (pp. 2095–2098). IEEE. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5651835

Wang, Y., Zhao, J., & Zhang, H. (2011). Remote sensing of Land Surface dynamics along the Appalachian Trail. In Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International (pp. 815–817). IEEE. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6049255

Weiss, J., White, M., & McKirdy, S. (n.d.). Utilising a Terrestrial Observation Predictive System for Emergency Plant Pest Incursion management. Retrieved from https://mssanz.org.au/MODSIM07/papers/21_s46/UtilizingATerrestrial_s46_Weiss_.pdf

White, M. A., & Nemani, R. R. (2004). Soil water forecasting in the continental United States: relative forcing by meteorology versus leaf area index and the effects of meteorological forecast errors. Canadian Journal of Remote Sensing, 30(5), 717–730. Retrieved from http://www.tandfonline.com/doi/abs/10.5589/m04-030

White, M. A., & Nemani, R. R. (2006). Real-time monitoring and short-term forecasting of land surface phenology. Remote Sensing of Environment, 104(1), 43–49. Retrieved from http://www.sciencedirect.com/science/article/pii/S0034425706001660

Winter, J., Young, C. A., Azarderakhsh, M., Ruane, A. C., & Rosenzweig, C. (2013). Climate Change Impacts on Water Resources and Irrigated Agriculture in the Central Valley of California. In AGU Fall Meeting Abstracts (Vol. 1, p. 7). Retrieved from http://adsabs.harvard.edu/abs/2013AGUFM.H41Q..07W

Wordell, T. A., & Brown, T. J. (n.d.). Forecasting Fire Danger Indices in the United States. In Wildfire conference materials.–2007.–Р (Vol. 12). Retrieved from http://www.fire.uni-freiburg.de/sevilla-2007/contributions/doc/cd/REGIONALES/B_AUSTRALASIA_NORTEAMERICA/Wordell_Brown_USA.pdf

Yang, F., White, M. A., Michaelis, A. R., Ichii, K., Hashimoto, H., Votava, P., … Nemani, R. R. (2006). Prediction of continental-scale evapotranspiration by combining MODIS and AmeriFlux data through support vector machine. Geoscience and Remote Sensing, IEEE Transactions on, 44(11), 3452–3461. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1717739

Zell, E., Huff, A. K., Carpenter, A. T., Friedl, L. A., Kuo, K. S., Lynnes, C. S., … others. (n.d.). SPECIAL ISSUE ON INTEROPERABILITY ARCHITECTURES AND ARRANGEMENTS FOR MULTI-DISCIPLINARY EARTH OBSERVATION SYSTEMS AND APPLICATIONS. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6397596

Zhang, G., Ganguly, S., White, M. A., Nemani, R. R., Hiatt, S. H., Hashimoto, H., … others. (2010). Assessment of Remotely Sensed Land Surface Phenology Data for North America: Inter-comparison and Forecasting. In AGU Fall Meeting Abstracts (Vol. 1, p. 472). Retrieved from http://adsabs.harvard.edu/abs/2010AGUFM.B23G0472Z

Zhang, G., Lowry, K., Nemani, R., Schmidt, C., & Skiles, J. (2008). Water Management in Cache County, Utah. In AGU Fall Meeting Abstracts (Vol. 1, p. 482). Retrieved from http://adsabs.harvard.edu/abs/2008AGUFM.B53B0482Z

Zhang, G., Lowry, K., Nemani, R., Skiles, J. W., & Schmidt, C. (2010). MODELING CURRENT AND FUTURE WATER USE IN UTAH WITH NASA’S TERRESTRIAL OBSERVATION AND PREDICTION SYSTEM. In ASPRS Annual Conference. www. asprs. org/publications/proceedings/baltimore09/0108. pdf. Accessed (Vol. 30). Retrieved from http://www.asprs.org/a/publications/proceedings/baltimore09/0108.pdf

Zhang, J., Li, W., & Zhai, L. (2013). Understanding geographical conditions monitoring: a perspective from China. International Journal of Digital Earth, (ahead-of-print), 1–20. Retrieved from http://www.tandfonline.com/doi/abs/10.1080/17538947.2013.846418

Zhang, J., Pu, R., Yuan, L., Huang, W., Nie, C., & Yang, G. (2013). Integrating Remotely Sensed and Meteorological Observations to Forecast Wheat Powdery Mildew at a Regional Scale. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6814816

Zhang, X., Goldberg, M. D., & Yu, Y. (2012). Prototype for monitoring and forecasting fall foliage coloration in real time from satellite data. Agricultural and Forest Meteorology, 158, 21–29. Retrieved from http://www.sciencedirect.com/science/article/pii/S0168192312000433

Zhang, Y. (2013). A Rangeland Predictive Phenological Model for the Upper Colorado River Basin and Its Web Delivery. The University of Utah. Retrieved from http://content.lib.utah.edu/utils/getfile/collection/etd3/id/2603/filename/2601.pdf

Zhao, J., Wang, Y., Hashimoto, H., Melton, F. S., Hiatt, S. H., Zhang, H., & Nemani, R. R. (2013). The variation of land surface phenology from 1982 to 2006 along the Appalachian Trail. Geoscience and Remote Sensing, IEEE Transactions on, 51(4), 2087–2095. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6339043

Zhao, J., Wang, Y., & Zhang, H. (2011). Automated batch processing of mass remote sensing and geospatial data to meet the needs of end users. In Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International (pp. 3464–3467). IEEE. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6049966

Zhu, L., & Southworth, J. (2013). Disentangling the relationships between net primary production and precipitation in southern Africa savannas using satellite observations from 1982 to 2010. Remote Sensing, 5(8), 3803–3825. Retrieved from http://www.mdpi.com/2072-4292/5/8/3803/htm