1 |
Almomany, A., A. Alquraan, and L. Balachandran, 2014: GCC vs. ICC comparison using PARSEC Benchmarks. International Journal of Innovative Technology and Exploring Engineering 4(7).
|
2 |
Andrew, M. E., M. A. Wulder, and T. A. Nelson, 2014: Potential contributions of remote sensing to ecosystem service assessments. Progress in Physical Geography 38(3), 328-353.
DOI
|
3 |
Ban, H.-Y., K. S. Kim, N.-W. Park, and B.-W. Lee, 2016: Using MODIS data to predict regional corn yields. Remote Sensing 9(1), 16pp.
DOI
|
4 |
Cohen, W. B., and S. N. Goward, 2004: Landsat's role in ecological applications of remote sensing. Bioscience 54(6), 535-545.
DOI
|
5 |
Busetto, L., and L. Ranghetti, 2016: MODIStsp : An R package for automatic preprocessing of MODIS Land Products time series. Computers & Geosciences 97, 40-48.
DOI
|
6 |
Doraiswamy, P., J. Hatfield, T. Jackson, B. Akhmedov, J. Prueger, and A. Stern, 2004: Crop condition and yield simulations using Landsat and MODIS. Remote sensing of environment 92(4), 548-559.
DOI
|
7 |
Hong, S. Y., S.-I. Na, K.-D. Lee, Y.-S. Kim, and S.-C. Baek, 2015: A study on estimating rice yield in DPRK using MODIS NDVI and rainfall data. Korean Journal of Remote Sensing 31(5), 441-448.
DOI
|
8 |
Jobbagy, E. G., O. E. Sala, and J. M. Paruelo, 2002: Patterns and controls of primary production in the Patagonian steppe: a remote sensing approach. Ecology 83(2), 307-319.
DOI
|
9 |
Lee, K.-D., S.-I. Na, S.-Y. Hong, C.-W. Park, K.-H. So, and J.-M. Park, 2017: Estimating corn and soybean yield using MODIS NDVI and meteorological data in Illinois and Iowa, USA. Korean Journal of Remote Sensing 33(5), 741-750.
DOI
|
10 |
Lee, J.-H., S.-K. Kang, K.-C. Jang, J.-H. Ko, and S.-Y. Hong, 2011: The evaluation of meteorological inputs retrieved from MODIS for estimation of gross primary productivity in the US corn belt region. Korean Journal of Remote Sensing 27(4), 481-494.
DOI
|
11 |
Li, J., M. Humphrey, C. Van Ingen, D. Agarwal, K. Jackson, and Y. Ryu, 2010: escience in the cloud: A modis satellite data reprojection and reduction pipeline in the windows azure platform. In 2010 IEEE International Symposium on Parallel & Distributed Processing (IPDPS), IEEE, 1-10.
|
12 |
Lobell, D. B., G. P. Asner, J. I. Ortiz-Monasterio, and T. L. Benning, 2003: Remote sensing of regional crop production in the Yaqui Valley, Mexico: estimates and uncertainties. Agriculture, Ecosystems & Environment 94(2), 205-220.
DOI
|
13 |
Mourani, G.,2001: Securing and Optimizing Linux: The Ultimate Solution. Open Network Architecture, Inc., 855pp.
|
14 |
Turner, D. P., W. D. Ritts, W. B. Cohen, S. T. Gower, S. W. Running, M. Zhao, M. H. Costa, A. A. Kirschbaum, J. M. Ham, S. R. Saleska, and D. E. Ahl, 2006: Evaluation of MODIS NPP and GPP products across multiple biomes. Remote Sensing of Environment 102(3-4), 282-292.
DOI
|
15 |
Prasad, A. K., L. Chai, R. P. Singh, and M. Kafatos, 2006: Crop yield estimation model for Iowa using remote sensing and surface parameters. International Journal of Applied Earth Observation and Geoinformation 8(1), 26-33.
DOI
|
16 |
Tie, B., F. Huang, J. Tao, J. Lu, and D. Qiu, 2018: A parallel and optimization approach for Land-Surface Temperature retrieval on a Windows-Based PC cluster. Sustainability 10(3), 621pp.
DOI
|
17 |
Tristram, W., and K. Bradshaw, 2012: Performance optimisation of sequential programs on multi-core processors. Proceedings of the South African Institute for Computer Scientists and Information Technologists Conference, Pretoria, South Africa, ACM, 119-128.
|
18 |
Vancutsem, C., P. Ceccato, T. Dinku, and S. J. Connor, 2010: Evaluation of MODIS land surface temperature data to estimate air temperature in different ecosystems over Africa. Remote Sensing of Environment 114(2), 449-465.
DOI
|
19 |
Yoo, B. H., and K. S. Kim, 2017: Development of a gridded climate data tool for the COordinated Regional climate Downscaling EXperiment data. Computers and Electronics in Agriculture 133, 128-140.
DOI
|