Prediction of peat depths using airborne radiometric data: optimization of ground surveys.

<p>The use of remote sensing data can lead to great efficiencies when mapping soil variables across broad regions. However, remote sensors rarely make direct measurements of the soil property of interest. Instead, an empirical model is required to relate the remote sensing data to ground measurements of the property of interest. We discuss how a survey of ground measurements required to calibrate such a model can be optimized. We make reference to the mapping of peat depth within the Dartmoor National Park (UK) using radiometric potassium data from an airborne survey of the region (http://www.tellusgb.ac.uk/). We expand the standard linear mixed model to accommodate nonlinear relationships between radiometric potassium and peat depths. The attenuation of the radiometric signal is seen to increase with peat depth, but the depth is particularly uncertain when the radiometric signal is small. When a spatial simulated annealing algorithm is used to optimize the locations for a survey of peat depth measurements to minimize the errors in the maps of peat depth upon use of the radiometric data, the complete range of the radiometric data are sampled but ground measurements are particularly focussed where the radiometric signal is small. We see that an optimized survey of 30 ground measurements combined with the radiometric data lead to more accurate maps than can be achieved from interpolation of more than 200 peat depth measurements.</p>

Accounting for Non-detects in Satellite Retrievals: Application Using CrIS Ammonia Observations

<p>For measurements from any instrument there is a minimum detection limit below which the sensor cannot measure (i.e., non-detects). Measurements of trace gases from satellite instruments can also suffer from a significant number of non-detects, especially for species with very low atmospheric concentrations  and that have a very weak or absent signals (signal-to-noise<1) in the spectral region used to detect the species (e.g., ammonia).  For ammonia, these non-signal conditions generally occur when thick clouds obscure the ammonia signal, or atmospheric conditions generates too weak of a radiometric signal to detect (e.g., very low concentrations). Presented is a robust approach to explicitly identify and account for cloud-free satellite observations that are below the detection limit of the sensor (which occur principally in  non-source regions) for the Cross-Track Infrared Sounder (CrIS) Fast Physical Retrieval (CFPR) ammonia (NH<sub>3</sub>) product. This approach uses the newly developed CrIS Ammonia Cloud Detection Algorithm (CACDA) to compute a cloud flag based on the CrIS IMG (CIMG) product . The CIMG product uses coincident Visible Infrared Imaging Radiometer Suite (VIIRS) brightness temperatures and cloud fractions mapped onto CrIS Field of Views (FOV). This cloud flag is used to separate CrIS FOVs without signal due to clouds from FOVs that are below the detection limit due to the atmospheric state (referred to as non-detects).  Survival data is generated from in-situ surface observations from non-emission source regions to produce ammonia concentration values under CrIS non-detect conditions. Accounting for these non-detects can be significant in reducing bias of averaged values (i.e., Level 3 products) in regions or conditions with low concentration amounts (e.g. wintertime, non-agriculture regions, etc.), with little impact on concentrations in emission regions. This presentation will provide examples and evaluations of the CACDA and the inclusion of non-detects in the CFPR generated ammonia product. This will include comparisons of annual and seasonal averages of surface level ammonia concentrations with and without survival data to demonstrate the reduction in bias.</p>

Evaluation of the structures size in the liquid-gas flow by gamma-ray absorption

The rapid development of tomography methods particularly electrical, X and gamma rays allows for a wide range of the information about flow structure. However, all of such methods are quite complicated. At the same time much simpler systems as the measuring system of gamma rays absorption, allows to obtain a all key flow information which describe the two-phase flow. In the article the results of analyzes of radiometric signal that not only allow to recognize the type of flow, but also the assessment of forming structures are presented. Calculation and interpretation of the data were based on the crosscorrelation and cross-spectral density function. In order to verify the calculations the photographic documentation made during the measurements was used.

Radio-frequency interference mitigating hyperspectral L-band radiometer

Radio-frequency interference (RFI) can significantly contaminate the measured radiometric signal of current spaceborne L-band passive microwave radiometers. These spaceborne radiometers operate within the protected passive remote sensing and radio-astronomy frequency allocation of 1400–1427 MHz but nonetheless are still subjected to frequent RFI intrusions. We present a unique surface-based and airborne hyperspectral 385 channel, dual polarization, L-band Fourier transform, RFI-detecting radiometer designed with a frequency range from 1400 through ≈ 1550 MHz. The extended frequency range was intended to increase the likelihood of detecting adjacent RFI-free channels to increase the signal, and therefore the thermal resolution, of the radiometer instrument. The external instrument calibration uses three targets (sky, ambient, and warm), and validation from independent stability measurements shows a mean absolute error (MAE) of 1.0 K for ambient and warm targets and 1.5 K for sky. A simple but effective RFI removal method which exploits the large number of frequency channels is also described. This method separates the desired thermal emission from RFI intrusions and was evaluated with synthetic microwave spectra generated using a Monte Carlo approach and validated with surface-based and airborne experimental measurements.

Radio frequency interference mitigating hyperspectral L-band radiometer

Radio Frequency Interference (RFI) can significantly contaminate the measured radiometric signal of current spaceborne L-band passive microwave radiometers. These spaceborne radiometers operate within the protected passive remote sensing and radio astronomy frequency allocation of 1400–1427 MHz, but despite this are still subjected to frequent RFI intrusions. We present a unique surface-based/airborne hyperspectral 385 channel, dual polarization, L-band Fourier transform, RFI detecting radiometer designed with a frequency range from 1400 through ≈ 1550 MHz. The extended frequency range was intended to increase the likelihood of detecting adjacent RFI-free channels to increase the signal, and therefore increase the thermal resolution, of the radiometer instrument. The external instrument calibration uses three targets (sky, ambient, and warm) and validation from independent stability measurements shows a mean absolute error (MAE) of 1.0 K for ambient and warm targets, while the MAE is 1.5 K for sky. A simple but effective RFI removal method which exploits the large number of frequency channels is also described. This method separates the desired thermal emission from RFI intrusions, and was evaluated with synthetic microwave spectra generated using a Monte Carlo approach and validated with surface-based and airborne experimental measurements.

Measurements relating fire radiative energy density and surface fuel consumption – RxCADRE 2011 and 2012

Small-scale experiments have demonstrated that fire radiative energy is linearly related to fuel combusted but such a relationship has not been shown at the landscape level of prescribed fires. This paper presents field and remotely sensed measures of pre-fire fuel loads, consumption, fire radiative energy density (FRED) and fire radiative power flux density (FRFD), from which FRED is integrated, across forested and non-forested RxCADRE 2011 and 2012 burn blocks. Airborne longwave infrared (LWIR) image time series were calibrated to FRFD and integrated to provide FRED. Surface fuel loads measured in clip sample plots were predicted across burn blocks from airborne lidar-derived metrics. Maps of surface fuels and FRED were corrected for occlusion of the radiometric signal by the overstorey canopy in the forested blocks, and FRED maps were further corrected for temporal and spatial undersampling of FRFD. Fuel consumption predicted from FRED derived from both airborne LWIR imagery and various ground validation sensors approached a linear relationship with observed fuel consumption, which matched our expectation. These field, airborne lidar and LWIR image datasets, both before and after calibrations and corrections have been applied, will be made publicly available from a permanent archive for further analysis and to facilitate fire modelling.