Eni G. Njoku

Bio: Eni G. Njoku is an academic researcher from California Institute of Technology. The author has contributed to research in topics: Radiometer & Water content. The author has an hindex of 51, co-authored 177 publications receiving 12982 citations. Previous affiliations of Eni G. Njoku include Jet Propulsion Laboratory.

The Soil Moisture Active Passive (SMAP) Mission

Abstract: The Soil Moisture Active Passive (SMAP) mission is one of the first Earth observation satellites being developed by NASA in response to the National Research Council's Decadal Survey SMAP will make global measurements of the soil moisture present at the Earth's land surface and will distinguish frozen from thawed land surfaces Direct observations of soil moisture and freeze/thaw state from space will allow significantly improved estimates of water, energy, and carbon transfers between the land and the atmosphere The accuracy of numerical models of the atmosphere used in weather prediction and climate projections are critically dependent on the correct characterization of these transfers Soil moisture measurements are also directly applicable to flood assessment and drought monitoring SMAP observations can help monitor these natural hazards, resulting in potentially great economic and social benefits SMAP observations of soil moisture and freeze/thaw timing will also reduce a major uncertainty in quantifying the global carbon balance by helping to resolve an apparent missing carbon sink on land over the boreal latitudes The SMAP mission concept will utilize L-band radar and radiometer instruments sharing a rotating 6-m mesh reflector antenna to provide high-resolution and high-accuracy global maps of soil moisture and freeze/thaw state every two to three days In addition, the SMAP project will use these observations with advanced modeling and data assimilation to provide deeper root-zone soil moisture and net ecosystem exchange of carbon SMAP is scheduled for launch in the 2014-2015 time frame

Soil moisture retrieval from AMSR-E

Abstract: The Advanced Microwave Scanning Radiometer (AMSR-E) on the Earth Observing System (EOS) Aqua satellite was launched on May 4, 2002. The AMSR-E instrument provides a potentially improved soil moisture sensing capability over previous spaceborne radiometers such as the Scanning Multichannel Microwave Radiometer and Special Sensor Microwave/Imager due to its combination of low frequency and higher spatial resolution (approximately 60 km at 6.9 GHz). The AMSR-E soil moisture retrieval approach and its implementation are described in this paper. A postlaunch validation program is in progress that will provide evaluations of the retrieved soil moisture and enable improved hydrologic applications of the data. Key aspects of the validation program include assessments of the effects on retrieved soil moisture of variability in vegetation water content, surface temperature, and spatial heterogeneity. Examples of AMSR-E brightness temperature observations over land are shown from the first few months of instrument operation, indicating general features of global vegetation and soil moisture variability. The AMSR-E sensor calibration and extent of radio frequency interference are currently being assessed, to be followed by quantitative assessments of the soil moisture retrievals.

Retrieval of land surface parameters using passive microwave measurements at 6-18 GHz

Abstract: An approach is evaluated for retrieval of land surface parameters (soil moisture, vegetation water content, and surface temperature) using satellite microwave radiometer data in the 6-18 GHz frequency range. The approach is applicable to data that will be acquired by the Advanced Microwave Scanning Radiometer (AMSR), planned for launch on the Japanese Advanced Earth Observing Satellite (ADEOS)-II and Earth Observing System (EOS) PM-1 platforms in 1999 and 2000, respectively. The retrieval method is based on a radiative transfer (RT) model for land-surface and atmospheric emission, with model coefficients that can be tuned over specific calibration regions and applied globally. The method uses an iterative, least-squares algorithm, based on six channels of radiometric data. Simulations using this algorithm indicate that, for an assumed sensor noise of 0.3 K in all channels, soil moisture and vegetation water content retrieval accuracies of 0.06 g cm/sup -3/ and 0.15 kg m/sup -2/, respectively, should be achievable in regions of vegetation water content less than approximately 1.5 kg m/sup -2/. A surface temperature accuracy of 2 C should be achievable, except for bare soils, where discrimination between moisture and temperature variability is difficult using this algorithm. These accuracies are for retrievals averaged over the sensor footprint, and they exclude conditions of precipitation, open water, snow cover, frozen ground, or high topographic relief within the footprint. The algorithm has been tested using data from the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) for the years 1982-1985, over the African Sahel, and the retrieval results compared to output from an operational numerical weather prediction model.

The Ensemble Kalman Filter: Theoretical formulation and practical implementation

Abstract: The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it. This paper reviews the important results from these studies and also presents new ideas and alternative interpretations which further explain the success of the EnKF. In addition to providing the theoretical framework needed for using the EnKF, there is also a focus on the algorithmic formulation and optimal numerical implementation. A program listing is given for some of the key subroutines. The paper also touches upon specific issues such as the use of nonlinear measurements, in situ profiles of temperature and salinity, and data which are available with high frequency in time. An ensemble based optimal interpolation (EnOI) scheme is presented as a cost-effective approach which may serve as an alternative to the EnKF in some applications. A fairly extensive discussion is devoted to the use of time correlated model errors and the estimation of model bias.

The Soil Moisture Active Passive (SMAP) Mission

Abstract: The Soil Moisture Active Passive (SMAP) mission is one of the first Earth observation satellites being developed by NASA in response to the National Research Council's Decadal Survey SMAP will make global measurements of the soil moisture present at the Earth's land surface and will distinguish frozen from thawed land surfaces Direct observations of soil moisture and freeze/thaw state from space will allow significantly improved estimates of water, energy, and carbon transfers between the land and the atmosphere The accuracy of numerical models of the atmosphere used in weather prediction and climate projections are critically dependent on the correct characterization of these transfers Soil moisture measurements are also directly applicable to flood assessment and drought monitoring SMAP observations can help monitor these natural hazards, resulting in potentially great economic and social benefits SMAP observations of soil moisture and freeze/thaw timing will also reduce a major uncertainty in quantifying the global carbon balance by helping to resolve an apparent missing carbon sink on land over the boreal latitudes The SMAP mission concept will utilize L-band radar and radiometer instruments sharing a rotating 6-m mesh reflector antenna to provide high-resolution and high-accuracy global maps of soil moisture and freeze/thaw state every two to three days In addition, the SMAP project will use these observations with advanced modeling and data assimilation to provide deeper root-zone soil moisture and net ecosystem exchange of carbon SMAP is scheduled for launch in the 2014-2015 time frame