Deriving high-quality surface emissivity spectra from atmospheric infrared sounder data using cumulative distribution function matching and principal component analysis regression

Land surface temperature and emissivity are essential variables in numerous environmental studies. This article proposes a multi-scale wavelet-based temperature and emissivity separation (MSWTES) a...

A multi-scale wavelet-based temperature and emissivity separation algorithm for hyperspectral thermal infrared data

Land surface temperature (LST) is a key parameter for surface water and energy budget calculations. To improve the accuracy of LST retrieval over bare soil surfaces, we proposed an alternat...

An alternative split-window algorithm for retrieving land surface temperature from Visible Infrared Imaging Radiometer Suite data

The reliable estimation of spatially distributed Land Surface Temperature (LST) is useful for monitoring regional land surface heat fluxes. A single-channel method is developed to derive the LST over the Heihe River Basin in China using data from the infrared sensor (IRS) onboard the Chinese “Environmental and Disaster Monitoring and Forecasting with a Small Satellite Constellation” (HJ-1B for short for one of the satellites), with ancillary water vapor information from Moderate Resolution Imaging Spectroradiometer (MODIS) products (MOD05) and in situ automatic sun tracking photometer CE318 data for the first time. In situ LST data for the period from mid-June to mid-September 2012 were acquired from automatic meteorological stations (AMS) that are part of Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project. MOD05-based LST and CE318-based LST are compared with in situ measurements at 16 AMS sites with land cover types of vegetable, maize and orchards. The results show that the use of the MOD05 product could achieve a comparable accuracy in LST retrieval with that achieved using the CE318 data. The largest difference between the MOD05-based LST and CE318-based LST is 0.84 K throughout the study period over the Heihe River Basin. The standard deviation (STD), root mean square error (RMSE), and correlation coefficient (R) of HJ-1B/IRS vs. the in situ measurements are 2.45 K, 2.78 K, and 0.67, respectively, whereas those for the MODIS 1 km LST product vs. the in situ measurements are 4.07 K, 2.98 K, and 0.79, respectively. The spatial pattern of the HJ-1B/LST over the study area in the Heihe River Basin generally agreed well with the MODIS 1 km LST product and contained more detailed spatial textures.

Retrieval of Land Surface Temperature over the Heihe River Basin Using HJ-1B Thermal Infrared Data

This special issue has its origins in the Third International Symposium on Recent Advances in Quantitative Remote Sensing (RAQRS) held in Torrent, Spain, from 27 September to 1 October 2010. However, by this I do not mean that we are simply reprinting articles from that meeting. Around 280 remote-sensing specialists from 31 countries participated in the symposium, with more than 260 contributions presented in both plenary and poster sessions according to the scientific topics selected. A selection of 37 articles from the meeting have been updated and expanded, subjected to this journal’s rigorous peer review system, and edited by Prof. José A. Sobrino, the Chairman of the Symposium, and they constitute this special issue of the International Journal of Remote Sensing. In the early days of remote sensing, most work involved visual interpretation of images. But now we have moved on. Much of the work on images these days does not involve visual interpretation but involves digital image processing, while many remote-sensing systems now in use do not involve image data at all. This symposium addressed the scientific advances in quantitative remote sensing and their connection with real applications. Its main goal was to assess the most recent developments in both theory and applications in the field of remote sensing, as well as to provide a forum for researchers in this subject to exchange points of view and report their latest results. The articles selected to appear in this special issue cover a very wide range of quantitative aspects of the use of remote sensing. The dominant areas include land surface temperature (Freitas et al. 2013; Göttsche, Olesen, and Bork-Unkelbach 2013; Li et al. 2013; Ouyang et al. 2013; Qian, Li, and Nerry 2013; Raissouni et al. 2013; Smith et al. 2013; Sobrino et al. 2013; Wang, Tang, and Li 2013), soil moisture and evapotranspiration (Corbari et al. 2013; Dente et al. 2013; Recondo et al. 2013; Sepulcre-Cantó et al. 2013; Song et al. 2013; Tang et al. 2013; Zhao and Li 2013), and various other land applications (Abbott, Gillespie, and Kahle 2013; Amici et al. 2013; Arenas-Castro et al. 2013; Calle, Salvador, and González-Alonso 2013; Cierniewski et al. 2013; Danilina et al. 2013; Lagouarde et al. 2013; Le Hegarat-Mascle and Ottle 2013; Wang et al. 2013; Wu, Tang, and Li 2013). Other topics attracted some interest on atmospheric and meteorological studies (Boussetta et al. 2013; Bravo-Aranda et al. 2013; Campo et al. 2013; Castellanos et al. 2013; Pérez-Ramírez et al. 2013; Segura et al. 2013). Finally, there are three articles on various marine applications of remote sensing (Despini and Teggi 2013; Font et al. 2013; Zapata-Ramírez et al. 2013) and a couple of articles on calibration and the integration of data from different sensors (Gao et al. 2013; Villa et al. 2013). The RAQRS symposium was sponsored and organized by the Global Change Unit of the University of Valencia, Spain. Other sponsors included the City Council of Torrent, the European Space Agency (ESA), the National Aeronautics and Space Administration (NASA), the Ministerio de Ciencia e Innovación (MICINN), and the Consellería de Empresa, Universidad y Ciencia de la Generalitat Valenciana.

Third International Symposium on Recent Advances in Quantitative Remote Sensing

Land surface temperature LST is one of the key state variables for many applications. This article aims to apply our previously developed LST retrieval method to infrared atmospheric sounding interferometer IASI and atmospheric infrared sounder AIRS data. On the basis of the opposite characteristics of the atmospheric spectral absorption and surface spectral emissivity, a ‘downwelling radiance residual index’ DRRI has been recalled and improved to obtain LST and emissivity. To construct an efficient DRRI, an automatic channel selection procedure has been proposed, and 11 groups of channels have been selected within the range 800–1000 cm−1. The DRRI has been tested with IASI and AIRS data. For the IASI data, the radiosonde data have been used to correct for atmospheric effects and to retrieve LST, while the atmospheric profiles retrieved from AIRS data have been used to perform the atmospheric corrections and subsequently to estimate LST from AIRS data. The differences between IASI-and Moderate Resolution Imaging Spectroradiometer MODIS-derived LSTs are no more than 2 K, while the differences between AIRS-and MODIS-derived LSTs are less than 5 K. Even though an exceptionally problematic value occurred –12.89 K, the overall differences between AIRS-estimated LST and the AIRS L2 LST product are no more than 5 K. Although the IASI-derived LST is more accurate than the AIRS-derived one, the convenient retrieval of AIRS atmospheric profile made this method more applicable. Limitations and uncertainties in retrieving LST using the DRRI method are also discussed.

Guoting Kang

Papers

Preliminary applications of a land surface temperature retrieval method to IASI and AIRS data