Derived Land Surface Emissivity From Suomi NPP CrIS

Presented here is the land surface IR spectral emissivity retrieved from the Cross-track Infrared Sounder (CrIS) measurements. The CrIS is aboard the Suomi National Polar-orbiting Partnership (NPP) satellite launched on October 28, 2011. We describe the retrieval algorithm, demonstrate the surface emissivity retrieved with CrIS measurements, and inter-comparison with the Infrared Atmospheric Sounding Interferometer (IASI) emissivity. We also demonstrate that surface emissivity from satellite measurements can be used in assistance of monitoring global surface climate change, as a long-term measurement of IASI and CrIS will be provided by the series of EUMETSAT MetOp and US Joint Polar Satellite System (JPSS) satellites. Monthly mean surface properties are produced using last 5-year IASI measurements. A temporal variation indicates seasonal diversity and El Nino/La Nina effects not only shown on the water but also on the land. Surface spectral emissivity and skin temperature from current and future operational satellites can be utilized as a means of long-term monitoring of the Earth's environment. CrIS spectral emissivity are retrieved and compared with IASI. The difference is small and could be within expected retrieval error; however it is under investigation

Land use land cover change and land surface emissivity in Ibadan, Nigeria

There are many drivers of climate change. The urbanization process has been adjudged to be one of the major factors influencing spatial variation in land use and land cover change (LULC), land surface temperature (LST), land surface emissivity (LSE), increasing greenhouse gases emission, and climate change. This article uses a multispectral satellite remote sensing and survey-based approach to examine the nexus of LULC and LSE in the Ibadan city region, Nigeria. The spectral reflectance, the sun angle spectral radiance of the Landsat imageries (2000, 2010, 2018) was corrected and converted from digital number. The LULC, Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI), LSE and LST were obtained from the analysis of Landsat imageries. From the findings, temperature increase was identified as a peculiar environmental issue. Analysis of the Landsat imageries revealed that the NDVI value increased from 0.44 in 2000 to 0.47 in 2018. The NDBI values showed that built-up areas in the core of the urban areas have the highest NDBI values (0.023-0.602). The spatio-temporal trends of LST were related to the changes in LULC, and the built-up area had the highest LSE. The maximum LST (43°C) was observed in the year 2018 at the core area of the city where building density was highest. The study suggests an application of cool pavements, green development, and urban forest regeneration for sustainable development

Global Land Surface Emissivity Estimation From AMSR2 Observations

A reliable estimate of emissivity is critical for a wide range of applications for the atmosphere, the biosphere, the lithosphere, the cryosphere, and the hydrosphere. This study uses three years (August 2012 – July 2015) of data from the Advanced Microwave Scanning Radiometer-2 (AMSR2) sensor that is onboard the Global Change Observation Mission 1st Water (GCOM-W1) satellite to explore estimates of instantaneous global land emissivity. A method is adopted to remove the known inconsistency in penetration depths between microwave brightness temperatures and infrared-based ancillary data that could cause differences between day and night emissivity estimates. After removing the diurnal atmospheric effects, the resulting retrieved cloud-free land emissivities realistically represent well-known large-scale features. As expected, the polarization differences of estimated emissivities show noticeable seasonal variations over the deciduous woodland and grassland regions due to changes in vegetation density. The potential of estimated emissivities for high-latitude snow detection and freeze/thaw states identification is also demonstrated

Remote Sensing Monitoring of Land Surface Temperature (LST)

This book is a collection of recent developments, methodologies, calibration and validation techniques, and applications of thermal remote sensing data and derived products from UAV-based, aerial, and satellite remote sensing. A set of 15 papers written by a total of 70 authors was selected for this book. The published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation techniques, improvements in long-term consistency in satellite LST, downscaling of LST, and LST applications and land surface emissivity research

Grassland Recognition with the Usage of Thermal Weights

In this paper we apply the usage of thermal weights, a new variable for geostatistical analysis and we present the method for their determination. In the case study we tested a data fusion between Sentinel-2 and Landsat 7/8 data, to incorporate also the thermal factor in the detection of land cover changes. The process distinguishes grasslands from other crops with similar vegetative appearance and offers us the possibility to create a new statistical sample with just grasslands. The data fusion is incorporated in the calculation of Land Surface Temperature (LSTFU) by combining the Sentinel-2 derived Normalized Difference Vegetation Index (NDVI), and from it derived land surface emissivity, with the Landsat 7/8 derived Top of Atmosphere Brightness Temperature (TOABT). The experimental LSTFU is modified into a normalized assessment variable by a time-series analysis. The result is a thermal weight layer which can help us in further object-based image analyses and classification. The thermal weight is calculated from Sentinel-2 and Landsat 7/8 datasets that has small acquisition time gaps between them. The accuracy assessment due to time gaps and sensor differences was evaluated with Cohens’s kappa (κ) and correlation matrix validation. The data fusion is made to test if a Sentinel-2 fusion approach could improve the Thermal Weight created just by Landsat imagery. The purpose was to evaluate the importance of thermal bands for LU/LC cover

Monitoring Surface Climate With its Emissivity Derived From Satellite Measurements

Satellite thermal infrared (IR) spectral emissivity data have been shown to be significant for atmospheric research and monitoring the Earth fs environment. Long-term and large-scale observations needed for global monitoring and research can be supplied by satellite-based remote sensing. Presented here is the global surface IR emissivity data retrieved from the last 5 years of Infrared Atmospheric Sounding Interferometer (IASI) measurements observed from the MetOp-A satellite. Monthly mean surface properties (i.e., skin temperature T(sub s) and emissivity spectra epsilon(sub v) with a spatial resolution of 0.5x0.5-degrees latitude-longitude are produced to monitor seasonal and inter-annual variations. We demonstrate that surface epsilon(sub v) and T(sub s) retrieved with IASI measurements can be used to assist in monitoring surface weather and surface climate change. Surface epsilon(sub v) together with T(sub s) from current and future operational satellites can be utilized as a means of long-term and large-scale monitoring of Earth 's surface weather environment and associated changes

Estimation of land surface Temperature of Maiduguri Metropolitan Area: A Case Study of Jere & some part of Mafa using Landsat 8 Operational Land Image (OLI)

Estimation of land surface Temperature of Maiduguri Metropolitan Area: A Case Study of Jere & some part of Mafa using Landsat 8 Operational Land Image (OLI) the research revealedmean solar radiance as 10.54 W/m2/str/μm the calculated minimum and maximum NDVI values are respectively -0.01674 and 0.5489, used to obtain proportion to vegetation which is then used to determine the emmissivity of the area having minimum emmissivity of 0.986 and maximum of 0.99, the minimum and maximum land surface temperature of Maiduguri Metropolitan areas are respectively 41.16 oC and 26.11 oC. KEY WORDS:- Landsat 8 satellite images, Temperature, Solar Radiance and Emmissivity DOI: 10.7176/APTA/83-06 Publication date: March 31st, 202