By Alfredo Huete, Tomoaki Miura (auth.), Jonathan M. Hanes (eds.)
A number of biophysical functions (e.g. leaf quarter index and gross basic productiveness) were derived from measurements of the Earth method got remotely through NASA’s MODIS sensors and different satellite tv for pc systems. In Biophysical purposes of satellite tv for pc distant Sensing, the authors describe significant purposes of satellite tv for pc distant sensing for learning Earth's biophysical phenomena. the point of interest of the e-book lies at the vast palette of particular purposes (metrics) of biophysical task derived utilizing satellite tv for pc distant sensing. With in-depth discussions of satellite-derived biophysical metrics that attention particularly on thought, method, validation, significant findings, and instructions of destiny learn, this publication offers a very good source for distant sensing experts, ecologists, geographers, biologists, climatologists, and environmental scientists.
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IEEE Trans Geosci Remote Sens 30(2):261–270 Kaufman YJ, Remer LA, Tanre D, Li RR, Kleidman R, Mattoo S, Levy RC, Eck TF, Holben BN, Ichoku C, Martins JV, Koren I (2005) A critical examination of the residual cloud contamination and diurnal sampling effects on MODIS estimates of aerosol over ocean. IEEE Trans Geosci Remote Sens 43(12):2886–2897 Kawamura K, Akiyama T, Yokota H, Tsutsumi M, Yasuda T, Watanabe O, Wang G, Wang S (2005) Monitoring of forage conditions with MODIS imagery in the Xilingol steppe, Inner Mongolia.
Two methods for generating dense, synthetic time series of high spatial resolution imagery are the spatial and temporal adaptive reflectance fusion model (STARFM) algorithm (Gao et al. 2006) and the multi-temporal MODIS– Landsat data fusion method (Roy et al. 2008). Such methods to integrate multiresolution satellite sensor data provide better resolution properties than the individual data sources and are vital to better understand interactions and processes that influence carbon stocks, water resources, and land use activities.
Remote Sens Environ 101(2):212–229 Field CB, Randerson JT, Malmstrom CM (1995) Global net primary production—combining ecology and remote-sensing. Remote Sens Environ 51(1):74–88 Fischer A (1994) A model for the seasonal-variations of vegetation indexes in coarse resolution data and its inversion to extract crop parameters. Remote Sens Environ 48(2):220–230 Franklin KA, Lyons K, Nagler PL, Lampkin D, Glenn EP, Molina-Freaner F, Markow T, Huete AR (2006) Buffelgrass (Pennisetum ciliare) land conversion and productivity in the plains of Sonora, Mexico.