Nabin K. Malakar

The ASTER Global Emissivity Dataset (ASTER GED): Mapping Earth's emissivity at 100 meter spatial scale

Thermal infrared (TIR) data, acquired by instruments on several NASA satellite platforms, are primarily used to estimate the surface temperature/emissivity of the Earths land surface. One such instrument launched on NASAs Terra satellite in 1999 is the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), which has a spatial resolution of 90 m. Using ASTER data, NASA/Jet Propulsion Laboratory recently released the most detailed emissivity map of the Earth termed the ASTER Global Emissivity Dataset (ASTER GED) that was acquired by processing millions of cloud free ASTER scenes from 2000 to 2008. The ASTER GEDv3 provides an average emissivity at ~100 m and ~1 km, while GEDv4 provides a monthly emissivity from 2000 to 2015 at ~5 km spatial resolution in the wavelength range between 8 and 12 µm. Validation with lab spectra from four desert sites resulted in an average absolute band error of ~1%, compared to current heritage MODIS products that had average absolute errors of 2.4% (Collection 4) and 4.6% (Collection 5).

Bayesian evidence and model selection

In this paper we review the concepts of Bayesian evidence and Bayes factors, also known as log odds ratios, and their application to model selection. The theory is presented along with a discussion of analytic, approximate and numerical techniques. Specific attention is paid to the Laplace approximation, variational Bayes, importance sampling, thermodynamic integration, and nested sampling and its recent variants. Analogies to statistical physics, from which many of these techniques originate, are discussed in order to provide readers with deeper insights that may lead to new techniques. The utility of Bayesian model testing in the domain sciences is demonstrated by presenting four specific practical examples considered within the context of signal processing in the areas of signal detection, sensor characterization, scientific model selection and molecular force characterization.

A Physics-Based Algorithm for the Simultaneous Retrieval of Land Surface Temperature and Emissivity From VIIRS Thermal Infrared Data

Land surface temperature (LST) is a key climate variable for studying the energy and water balance of the earth surface and monitoring the effects of climate change. This paper presents a physics-based temperature emissivity separation (TES) algorithm for the simultaneous retrieval of LST and emissivity (LST&E) from the thermal infrared bands of the Suomi National Polar-Orbiting Partnerships Visible Infrared Imaging Radiometer Suite (VIIRS) payload. The new VIIRS LST&E product (VNP21) was developed to provide continuity with the Moderate-Resolution Imaging Spectroradiometer (MODIS) equivalent LST&E product (MxD21) product, which is available in Collection 6, and to address inconsistencies between the current MODIS and VIIRS split-window LST products. The TES algorithm uses full radiative transfer simulations to isolate the surface emitted radiance, and an emissivity calibration curve based on the variability in the surface radiance data to dynamically retrieve both LST and spectral emissivity. Furthermore, an improved water vapor scaling model was implemented to improve the accuracy and stability of the atmospheric correction for conditions with high atmospheric water vapor content. An independent assessment of the VIIRS LST retrievals was performed against in situ LST measurements over two dedicated validation sites at Lake Tahoe and Salton Sea in the Southwestern USA, while the VIIRS emissivity retrievals were evaluated with the latest ASTER Global Emissivity Dataset Version 3 (GEDv3). The bias and root-mean-square error (RMSE) in retrieved VIIRS LST were 0.50 and 1.40 K, respectively for the two sites combined, while mean emissivity differences between VIIRS and ASTER GEDv3 were 0.2%, 0.1%, and 0.3% for bands M14 (

Entropy‐Based Search Algorithm for Experimental Design

8.55~\mu \text{m}

Application of Landsat 8 for monitoring impacts of wastewater discharge on coastal water quality

), M15 (

The Spatial Sensitivity Function of a Light Sensor

10.76~\mu \text{m}

Modeling a Sensor to Improve Its Efficacy

), and M16 (

High spatial resolution imaging of methane and other trace gases with the airborne Hyperspectral Thermal Emission Spectrometer (HyTES)

12.01~\mu \text{m}

A water vapor scaling model for improved land surface temperature and emissivity separation of MODIS thermal infrared data

), respectively, with an RMSE of 1%. We further demonstrate close agreement between the MODIS and VIIRS TES algorithm LST products to within ~0.3 K difference, as opposed to the current MODIS and VIIRS split window products, which had an average difference of 3 K.

NASA's MODIS and VIIRS Land Surface Temperature and Emissivity Products: A Long-Term and Consistent Earth System Data Record

The scientific method relies on the iterated processes of inference and inquiry. The inference phase consists of selecting the most probable models based on the available data; whereas the inquiry phase consists of using what is known about the models to select the most relevant experiment. Optimizing inquiry involves searching the parameterized space of experiments to select the experiment that promises, on average, to be maximally informative. In the case where it is important to learn about each of the model parameters, the relevance of an experiment is quantified by Shannon entropy of the distribution of experimental outcomes predicted by a probable set of models. If the set of potential experiments is described by many parameters, we must search this high‐dimensional entropy space. Brute force search methods will be slow and computationally expensive. We present an entropy‐based search algorithm, called nested entropy sampling, to select the most informative experiment for efficient experimental desi...