Towards a continuous NASA cloud climate data record from MODIS to VIIRS

<p>The Suomi NPP and JPSS series VIIRS imagers provide an opportunity to extend the NASA EOS Terra (20+ year) and Aqua (18+ year) MODIS cloud climate data record into the new generation NOAA operational weather satellite era. However, while building a consistent, long-term cloud data record has proven challenging for the two MODIS sensors alone, the transition to VIIRS presents additional challenges due to its lack of key water vapor and CO<sub>2</sub> absorbing channels available on MODIS that are used for high cloud detection and cloud-top property retrievals, and a mismatch in the spectral location of the 2.2µm shortwave infrared channels on MODIS and VIIRS that has important implications on inter-sensor consistency of cloud optical/microphysical property retrievals and cloud thermodynamic phase. Moreover, sampling differences between MODIS and VIIRS, including spatial resolution and local observation time, and inter-sensor relative radiometric calibration pose additional challenges. To create a continuous, long-term cloud climate data record that merges the observational records of MODIS and VIIRS while mitigating the impacts of these sensor differences, a common algorithm approach was pursued that utilizes a subset of spectral channels available on each imager. The resulting NASA CLDMSK (cloud mask) and CLDPROP (cloud-top and optical/microphysical properties) products were publicly released for Aqua MODIS and SNPP VIIRS in early 2020, with NOAA-20 (JPSS-1) VIIRS following in early 2021. Here, we present an overview of the MODIS-VIIRS CLDMSK and CLDPROP common algorithm approach, discuss efforts to monitor and address relative radiometric calibration differences, and highlight early analysis of inter-sensor cloud product dataset continuity.</p>

AN OPTIMAL IMAGE SELECTION METHOD TO IMPROVE QUALITY OF RELATIVE RADIOMETRIC CALIBRATION FOR UAV MULTISPECTRAL IMAGES

Radiometric calibration has become important pre-processing with increasing use of unmanned aerial vehicle (UAV) images in various applications. In order to convert the digital number (DN) to reflectance, vicarious radiometric calibration is widely used including relative radiometric calibration. Some UAV sensor systems can measure irradiance for precise relative radiometric calibration. However, most of UAV sensor systems cannot measure irradiance and therefore precise relative radiometric calibration is needed to produce reflectance map with vicarious calibration. In this study, an optimal image selection method is proposed to improve quality of relative radiometric calibration. The method, relative calibration by the optimal path (RCOP), uses filtered tie points acquired in geometric calibration based on selection optimal image by Dijkstra algorithm. About 100 multispectral images were acquired with a RedEdge-M camera and a fixed-wing UAV. The reflectance map was produced using RCOP and vicarious calibration using ground reference panels. A validation data was processed using irradiance for precise relative radiometric calibration. As a result, the RCOP method showed root mean square error (RMSE) of 0.03–0.10 reflectance to validation data. Therefore, the proposed method can be used to produce precise reflectance map by vicarious calibration.

Relative Radiometric Calibration Using Tie Points and Optimal Path Selection for UAV Images

As the use of unmanned aerial vehicle (UAV) images rapidly increases so does the need for precise radiometric calibration. For UAV images, relative radiometric calibration is required in addition to the traditional vicarious radiometric calibration due to the small field of view. For relative radiometric calibration, some UAVs install irradiance sensors, but most do not. For UAVs without them, an intelligent scheme for relative radiometric calibration must be applied. In this study, a relative radiometric calibration method is proposed to improve the quality of a reflectance map without irradiance measurements. The proposed method, termed relative calibration by the optimal path (RCOP), uses tie points acquired during geometric calibration to define the optimal paths. A calibrated image from RCOP was compared to validation data calibrated with irradiance measurements. As a result, the RCOP method produces seamless mosaicked images with uniform brightness and reflectance patterns. Therefore, the proposed method can be used as a precise relative radiometric calibration method for UAV images.