Abstract. We propose a simple scheme to estimate surface evaporation over large heterogeneous areas using remote sensing data. Our approach is based on an extension of the Priestley-Taylor equation and a relationship between remotely sensed surface temperature and vegetation index. The required parameters for this approach are derived from advanced very high resolution radiometer NOAA 14 data over the southern Great Plains (SGP) in 1997. Compared to ground flux observations by the Atmospheric Radiation Measurement program, our six case studies varying from early spring to late summer over the SGP domain show that the proposed method provides better estimation accuracy for surface evaporation than the original Priestley-Taylor method. It appears that the proposed method can estimate evaporation over large areas with significantly less complexity than that of the traditionally used residual method based on aerodynamic resistance. The uncertainty in the estimation of surface evaporation for the proposed approach is closely related to the inaccuracy in deriving net radiation and soil heat flux from remote sensing data. Results suggest that the proposed approach is more reliable and easily applicable for operational estimation of evaporation over large areas where ground-based data are not readily available.

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Abstract. We propose a simple scheme to estimate surface evaporation over large heterogeneous areas using remote sensing observations. Our approach is based on a relationship between easily measured surface parameters (e.g. radiometric surface temperature) and a surrogate for effective surface resistance. Preliminary results, using remotely sensed data sets from AVHRR NOAA-14 over the Southern Great Plains, show good agreement. The proposed approach appears to be more reliable and easily applicable for operational estimation of evaporation over large areas.

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One of the primary goals for the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar‐orbiting Partnership is to provide the science and user communities with the data continuity of the Environmental Data Records (EDR) (or Level‐2 products) over global oceanic waters for various research and applications, including assessment of climatic and environmental variations. The ocean color EDR is one of the most important products derived from VIIRS. Since ocean color EDR is processed from the upstream Sensor Data Records (SDR) (or Level‐1B data), the objective of this study is to evaluate the impact of the SDR on the VIIRS ocean color EDR. The quality of the SDR relies on prelaunch sensor characterizations as well as on‐orbit radiometric calibrations, which are used to develop the sensor F‐factor lookup tables (F‐LUTs). VIIRS F‐LUTs derived from solar and lunar calibrations have been used in processing data from the VIIRS Raw Data Records (or Level‐0 data) to SDR. In this study, three sets of F‐LUTs with different generation schemes have been used to reprocess the SDR and then the ocean color EDR for product evaluations. VIIRS ocean color products are compared with in situ data from the Marine Optical Buoy and products from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the satellite Aqua. It is found that the data quality of VIIRS operational ocean color products before 6 February 2012 is poor due to the inappropriate use of the at‐launch F‐LUTs for the SDR calibration, and that the recently updated VIIRS F‐LUTs have significantly improved the SDR and ocean color EDR. Using reprocessed SDR with updated F‐LUTs and including vicarious calibration, VIIRS ocean color EDR products are consistent with those from MODIS‐Aqua in global deep waters. Although there are still some significant issues with VIIRS ocean color EDR, e.g., poor data quality over coastal regions, our results demonstrate that VIIRS has great potential to provide the science and user communities with consistently high‐quality global ocean color data records that are established from heritage ocean color sensors such as MODIS‐Aqua.

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