Radiometric calibration of all optical sensors is a key component to achieve high accuracy data, which is traceable to international standards and repeatable through time. This study introduces a Hyperspectral Imaging (HSI) system that RAL Space has developed and presents a thorough analysis of the radiometric reflectance calibration chain required for both static and aerial applications, where the HSI system is mounted on an Unoccupied Aerial System (UAS). RAL Space HSI system comprises two commercially available Ximea snapshot mosaic imagers, one sensitive in the visible (VIS) and the other in the near infrared (NIR) spectral ranges, that are coupled with a dedicated PC and customized LabVIEW code for the capture, process and storage of the raw hyperspectral information (hypercubes). Both imagers are optically characterized for their electrical gain and offset, the vignetting and stray light effects, while the spectral response of each band has also been determined. The corrected digital numbers (DN) are then translated into reflectance values with the use of reflectance standards. For the aerial surveys, an additional step has been developed into the calibration chain that corrects for any changes in ambient illumination during flight with the use of ground based upward-looking measured spectra. Future work will include any correction needed due to temperature dependencies of the calibration chain steps.
The USDA UV-B Monitoring and Research Program (UVMRP) comprises of 36 climatological sites along with 4 long-duration research sites, in 27 states, one Canadian province, and the south island of New Zealand. Each station is equipped with an Ultraviolet multi-filter rotating shadowband radiometer (UV-MFRSR) which can provide response-weighted irradiances at 7 wavelengths (300, 305.5, 311.4, 317.6, 325.4, and 368 nm) with a nominal full width at half maximun of 2 nm. These UV irradiance data from the long term monitoring station at Mauna Loa, Hawaii, are used as input to a retrieval algorithm in order to derive high time frequency total ozone columns. The sensitivity of the algorithm to the different wavelength inputs is tested and the uncertainty of the retrievals is assessed based on error propagation methods. For the validation of the method, collocated hourly ozone data from the Dobson Network of the Global Monitoring Division (GMD) of the Earth System Radiation Laboratory (ESRL) under the jurisdiction of the US National Oceanic & Atmospheric Administration (NOAA) for the period 2010-2015 were used.
The USDA UV-B Monitoring and Research Program (UVMRP) is an ongoing effort aiming to establish a valuable,
longstanding database of ground-based ultraviolet (UV) solar radiation measurements over the US. Furthermore, the
program aims to achieve a better understanding of UV variations through time, and develop a UV climatology for
the Northern American section. By providing high quality radiometric measurements of UV solar radiation,
UVMRP is also focusing on advancing science for agricultural, forest, and range systems in order to mitigate climate
impacts. Within these foci, the goal of the present study is to investigate, analyze, and validate the accuracy of the
measurements of the UV multi-filter rotating shadowband radiometer (UV-MFRSR) and Yankee (YES) UVB-1
sensor at the high altitude, pristine site at Mauna Loa, Hawaii. The response-weighted irradiances at 7 UV channels
of the UV-MFRSR along with the erythemal dose rates from the UVB-1 radiometer are discussed, and evaluated for
the period 2006-2015. Uncertainties during the calibration procedures are also analyzed, while collocated groundbased
measurements from a Brewer spectrophotometer along with model simulations are used as a baseline for the
validation of the data. Besides this quantitative research, the limitations and merits of the existing UVMRP methods
are considered and further improvements are introduced.
In neural networks, the training/predicting accuracy and algorithm efficiency can be improved significantly via accurate input feature extraction. In this study, some spatial features of several important factors in retrieving surface ultraviolet (UV) are extracted. An extreme learning machine (ELM) is used to retrieve the surface UV of 2014 in the continental United States, using the extracted features. The results conclude that more input weights can improve the learning capacities of neural networks.
Cloud screening is an essential procedure for in-situ calibration and atmospheric properties retrieval on (UV-)MultiFilter Rotating Shadowband Radiometer [(UV-)MFRSR]. Previous study has explored a cloud screening algorithm for direct-beam (UV-)MFRSR voltage measurements based on the stability assumption on a long time period (typically a half day or a whole day). To design such an algorithm requires in-depth understanding of radiative transfer and delicate data manipulation. Recent rapid developments on deep neural network and computation hardware have opened a window for modeling complicated End-to-End systems with a standardized strategy. In this study, a multi-layer dynamic bidirectional recurrent neural network is built for determining the cloudiness on each time point with a 17-year training dataset and tested with another 1-year dataset. The dataset is the daily 3-minute cosine corrected voltages, airmasses, and the corresponding cloud/clear-sky labels at two stations of the USDA UV-B Monitoring and Research Program. The results show that the optimized neural network model (3-layer, 250 hidden units, and 80 epochs of training) has an overall test accuracy of 97.87% (97.56% for the Oklahoma site and 98.16% for the Hawaii site). Generally, the neural network model grasps the key concept of the original model to use data in the entire day rather than short nearby measurements to perform cloud screening. A scrutiny of the logits layer suggests that the neural network model automatically learns a way to calculate a quantity similar to total optical depth and finds an appropriate threshold for cloud screening.
The difficulty of in-situ calibration on the 940 nm channel of Multi-Filter Rotating Shadowband Radiometer (MFRSR) stems from the distinctive non-linear relationship between the amount of precipitable water vapor (PW) and its optical depth (i.e. curve of growth) compared to the counterpart of aerosols. Previous approaches, the modified Langley methods (MLM), require exact aerosol optical depth (AOD) values and a constant PW value at all points participating the regression. Instead, we propose a new method that substitutes the PW optical depth derived from collocated GPS zenith wet delay retrieval in conjunction with meteorology data and requires a constant AOD value at all points participating the regression. The main benefits of the new method include: (1) Aerosol stability is easier to fulfill than PW stability; (2) AOD stability could be inferred from adjacent channels (e.g. 672 and 870 nm) of MFRSR itself without measurements of a collocated AERONET sun photometer; and (3) When applicable, the time interval of GPS derived PW (i.e. 3 minutes) is more compatible with the MFRSR sampling interval (i.e. 3 minutes) than AERONET interpolated AOD (i.e. 15 minutes). Both MLM and the new method were applied to the MFRSR of USDA UV-B Monitoring and Research Program at the station in Billings, Oklahoma (active for 18 years so far) on July 28, 2015. The performances of the two methods are compared in order to assess their accuracy and the advantages and disadvantages.
The stratospheric ozone depletion during the last two decades, the increase of UV-B irradiance levels at the ground and the possible impact on the biosphere has led scientists to develop and use instruments of high accuracy for UV measurements.
During the last two years, 9 UV stations have been established in different environments in Greece and Cyprus, with the aim to establish a long-term monitoring network. The instruments of the network (NILU-UV multichannel filter radiometers) can provide measurements of irradiance in the UV and the visible part of the solar spectrum.
In this study, first results from the calibration measurements and the quality assurance procedures are presented. The stability of the maximum of spectral response and the full width at half maximum was measured within 0.5 nm. Lamp tests were performed and downward drifts up to 40% in UVA channel sensitivity were observed. Calibration factors derived from lamp measurements could provide measurements of UV dose rate and total ozone with quite good agreement when compared with standard ultraviolet instruments.
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