Spaceborne microwave remote sensing techniques have considerable capabilities for supporting investigations of the earth system. The coherent and consistent spatial and temporal observations…

This is a federal grant award to the Research Foundation of the City University of New York (RFCUNY), which serves as the fiscal administrator for external funding at the City University of New York (CUNY). The $362,904.09 grant, awarded by the National Aeronautics and Space Administration (NASA), funds the development of a multi-sensor calibration algorithm to improve the retrieval of land surface microwave emissivity. The goal is to create a well-calibrated, multi-year, multi-satellite product of instantaneous land surface emissivity across a range of microwave frequencies. This research aims to better separate the effects of the atmosphere and land surface on microwave brightness temperature measurements, enabling more accurate retrieval of geophysical parameters like soil moisture, snow cover, and vegetation. The award does not have a set-aside designation. As the parent organization, CUNY has a history of conducting federal contract and grant-funded research across scientific and technological domains.

Spaceborne microwave remote sensing techniques have considerable capabilities for supporting investigations of the earth system. The coherent and consistent spatial and temporal observations afforded by the all weather, day and night observational capabilities of microwave sensor systems bring a unique and powerful remote sensing tool to the study of geophysical phenomena at regional, continental, and global scales. The quantitative determination of trends and variability in the earths atmosphere, ocean, cryosphere, biosphere, and land surface depends significantly on the availability of contiguous multi instrument/multi platform data sets based on as synergistic observations from multiple sensors and platforms. Instantaneous measures of microwave brightness temperature, TB, are fundamental to estimation of column water vapor abundance, rainfall rate, soil moisture, snow cover, land surface temperature, and vegetation pattern. To understand the atmospheric phenomena such as rain rate, cloud liquid water, and total perceptible water the contribution of the surface should be accounted and be removed from the microwave signal. On the other hand, to extract geophysical information such as snow, soil moisture, vegetation structure, and sea ICE the effect of the atmosphere should be removed. Therefore disaggregating the effect of these two sources (i.e. atmosphere and the surface) is important, and directly using the brightness temperature in these applications without removing the effect of the other does not provide accurate results. This projects aims to develop a well calibrated, multiyear, multisatellite, instantaneous land surface emissivity product for frequencies ranging from 7 to 89.0 GHZ. Infrared thermal temperatures have been used as the physical temperature (surface temperature) to retrieve microwave emissivities, since there is no global information on physical temperature profiles over land. However, passive microwave emissions originate from deeper layers than the infrared. A difference in depth of origination can cause an inconsistency between diurnal variations of infrared and microwave brightness temperatures, resulting in significant differences of emissivities from day and night retrievals. In the proposed study, the diurnal cycle of the microwave brightness temperature will be constructed over the globe for different frequencies/polarizations using a multi sensor / multi platform algorithm. Advanced microwave scanning radiometer 2 (AMSR2) with its unique overpass time (1:30 A.M./P.M.) will provide valuable information at close daily maxima and minima temperatures. Interconsistency between AMSR2 and newly launched global precipitation mission (GPM) microwave imager (GMI) sensor and other non NASA microwave sensors will be investigated by accounting for differences in the spectral responses, incidence angles, and spatial resolution. Various methods such as the principal component analysis and non gaussian spatial texture analysis will be used to derive spatial variation of microwave TB diurnal cycles. A thorough error and uncertainty analysis will be conducted on all input and output parameters. The final emissivity product from different sensors will then be released to public.