Search results for "Infrared sounder"
showing 3 items of 3 documents
Comparison of Radiosonde and Remote Sensing Data to Evaluate Convective Forest Fire Risk: The Haines Index
2018
Haines Index (HI) has been associated with convective forest fires risk. Temperatures and humidities in low atmospheric levels are required to compute HI and usually, atmospheric sounding data are used for this purpose. However, spatial and temporal resolutions of these data are coarse and remote sensing data could improve them. Therefore, the aim of this work is to test remote sensing data from the Atmospheric Infrared Sounder (AIRS) instrument on board the EOS Aqua satellite, specifically the Level 2 V6 products (AIRX2RET and AIRS2RET), for this purpose. First, we validated the remote sensing data with radiosonde daytime and nighttime data located in the Iberian Peninsula in 2014. Signifi…
Comparison between different sources of atmospheric profiles for land surface temperature retrieval from single channel thermal infrared data
2012
Abstract Different sources of atmospheric water vapor and temperature profiles were used with a radiative transfer model for retrieving land surface temperature (LST) from thermal infrared remote sensing data with the so-called single channel (SC) method. Retrieved LSTs were compared to concurrent ground measurements over homogeneous rice fields to assess the accuracy of the atmospheric profiles. These included radiosonde balloons launched at the test site near-concurrently to satellite overpasses, re-analysis profiles from the National Centers for Environmental Prediction (NCEP), and satellite sounder products from the Atmospheric Infrared Sounder (AIRS) and the Moderate Imaging Spectrorad…
Deep Gaussian processes for biogeophysical parameter retrieval and model inversion
2020
Parameter retrieval and model inversion are key problems in remote sensing and Earth observation. Currently, different approximations exist: a direct, yet costly, inversion of radiative transfer models (RTMs); the statistical inversion with in situ data that often results in problems with extrapolation outside the study area; and the most widely adopted hybrid modeling by which statistical models, mostly nonlinear and non-parametric machine learning algorithms, are applied to invert RTM simulations. We will focus on the latter. Among the different existing algorithms, in the last decade kernel based methods, and Gaussian Processes (GPs) in particular, have provided useful and informative so…