Sangram Ganguly, Ph.D.

Affiliations: 
2008 Boston University, Boston, MA, United States 
Area:
Physical Geography
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"Sangram Ganguly"

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Ranga B. Myneni grad student 2008 Boston University
 (Earth System Data Records of vegetation Leaf Area Index from multiple satellite-borne sensors.)
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Publications

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Afzali Gorooh V, Kalia S, Nguyen P, et al. (2020) Deep Neural Network Cloud-Type Classification (DeepCTC) Model and Its Application in Evaluating PERSIANN-CCS Remote Sensing. 12: 316
Sebastian DE, Ganguly S, Krishnaswamy J, et al. (2019) Multi-Scale Association between Vegetation Growth and Climate in India: A Wavelet Analysis Approach Remote Sensing. 11: 2703
Hashimoto H, Nemani RR, Bala G, et al. (2019) Constraints to Vegetation Growth Reduced by Region-Specific Changes in Seasonal Climate Climate. 7: 27
Hashimoto H, Wang W, Melton FS, et al. (2019) High‐resolution mapping of daily climate variables by aggregating multiple spatial data sets with the random forest algorithm over the conterminous United States International Journal of Climatology. 39: 2964-2983
Shen C, Laloy E, Elshorbagy A, et al. (2018) HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community Hydrology and Earth System Sciences. 22: 5639-5656
Li S, Wang W, Ganguly S, et al. (2018) Radiometric Characteristics of the Landsat Collection 1 Dataset Ars. 7: 203-217
Wang W, Nemani R, Hashimoto H, et al. (2018) An Interplay between Photons, Canopy Structure, and Recollision Probability: A Review of the Spectral Invariants Theory of 3D Canopy Radiative Transfer Processes Remote Sensing. 10: 1805
Ferraz A, Saatchi S, Xu L, et al. (2018) Carbon storage potential in degraded forests of Kalimantan, Indonesia Environmental Research Letters. 13: 95001
Boyda E, Basu S, Ganguly S, et al. (2017) Deploying a quantum annealing processor to detect tree cover in aerial imagery of California. Plos One. 12: e0172505
Li S, Ganguly S, Dungan JL, et al. (2017) Sentinel-2 MSI Radiometric Characterization and Cross-Calibration with Landsat-8 OLI Advances in Remote Sensing. 6: 147-159
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