Land surface emissivity is a crucial parameter for obtaining the land surface temperature and estimating the land surface energy budget from remote sensing data. The current emissivity products always have a coarser spatial resolution than the products from the visible and near‐infrared data. This study focused on the generation of an emissivity product at a spatial resolution of 30 m using a new global land cover product called Finer‐Resolution Observation and Monitoring of Global Land Cover and Landsat images. Summer‐average emissivity products in four narrowbands (Landsat 5/Thematic Mapper Band 6, Landsat 7/Enhanced Thematic Mapper Plus Band 6, and Landsat 8 Thermal Infrared Sensor bands 1 and 2) and two broadbands (3–14 μm and 8–13.5 μm) were produced in China. Results illustrated that the narrowband emissivities ranged from 0.95 to 0.99, whereas the broadband emissivities ranged from 0.93 to 0.99 in the study area. Intercomparisons in different places showed that the new emissivity was close to Advanced Spaceborne Thermal Emission and Reflection Radiometer emissivity with a difference of about 0.015 for narrowband emissivity and about 0.02 for broadband emissivity on a regional scale. For application purposes, the emissivities were released in the Worldwide Reference System 2 and geographic coordinate systems with several spatial resolutions resampled from its original scale of 30 m.

Atmospheric water vapor (wv) is required for the accurate retrieval of the land surface temperature from remote sensing data and other applications. This work aims to estimate wv from Landsat 8 Thermal InfraRed Sensor (TIRS) images using a new modified split-window covariance-variance ratio (MSWCVR) method on the basis of the brightness temperatures of two thermal infrared bands. Results show that the MSWCVR method can theoretically retrieve wv with an accuracy better than 0.3g/cm(2) for dry atmosphere (wv<2g/cm(2)) conditions and better than 0.5g/cm(2) for wet atmosphere conditions. The method was applied at different locations with dry and moist atmospheres and was validated at 42 ground sites using AERONET (Aerosol Robotic Network) ground-measured data and MODIS (Moderate Resolution Imaging Spectroradiometer) products. The results show that the retrieved wv from the TIRS data is highly correlated with the wv of AERONET and MODIS but is generally larger. This difference was probably attributed to the uncertainty of radiometric calibration and stray light coming outside from field of view of TIRS instrument in the current images. Consequently, the data quality and radiometric calibration of the TIRS data should be improved in the future.

The urbanization process changed the urban ecological land and consequently affected the quality of urban residents' environment, and it was very important to obtain urban ecological land cover information. In this paper, an object-oriented method was proposed to extract urban ecological land cover from the multiple-channel images acquired by Chinese Gaofen-1 (GF-1) satellite. Taking Beijing City as an example, five ecological land covers, including water, vegetation, road, building land and bare land, were classified using new classification rules based on the spectral, geometry and texture information in the GF-1 image. The result showed that the urban land covers were accurately identified and its validation accuracy was up to 90%.

This paper developed a practical split-window (SW) algorithm to estimate land surface temperature (LST) from Thermal Infrared Sensor (TIRS) aboard Landsat 8. The coefficients of the SW algorithm were determined based on atmospheric water vapor sub-ranges, which were obtained through a modified split-window covariance–variance ratio method. The channel emissivities were acquired from newly released global land cover products at 30 m and from a fraction of the vegetation cover calculated from visible and near-infrared images aboard Landsat 8. Simulation results showed that the new algorithm can obtain LST with an accuracy of better than 1.0 K. The model consistency to the noise of the brightness temperature, emissivity and water vapor was conducted, which indicated the robustness of the new algorithm in LST retrieval. Furthermore, based on comparisons, the new algorithm performed better than the existing algorithms in retrieving LST from TIRS data. Finally, the SW algorithm was proven to be reliable through application in different regions. To further confirm the credibility of the SW algorithm, the LST will be validated in the future.

On the basis of the thermal infrared radiative transfer theory, this paper addressed the retrieval of Land Surface Temperature (LST) from Landsat 8-the latest satellite in the Landsat Data Continuity Mission (LDCM) project in two thermal infrared channels, using the Generalized Split-Window (GSW) algorithm. Meanwhile, a linear bidirectional reflectance distribution function (BRDF) models were used to estimate the emissivity according to different surface classification. A series of ranging of typical surface emissivity and the atmospheric water vapor content (WV) were used into an accurate atmospheric radiative transfer model MODTRAN 4.3 to derive the coefficients in the algorithm. The simulation result showed the LST estimated by the algorithm with the Root Mean Square Error (RMSE) is 1.26K for the all ranges of the atmospheric WV and the results could be better in lower atmospheric WV condition.

This study performed an on-orbit evaluation of noise level for the Operational Land Imager (OLI) onboard Landsat 8 using early images over ground homogeneous sites. The signal-to-noise ratios (SNR) were higher than 160 of OLI nine bands at typical radiance level, while the noise equivalent radiance difference (NE∆L) and the noise equivalent reflectance difference (NE∆ρ) were respectively lower than 0.8 W/m(2)/µm/sr and 0.002. Compared to pre-launch predictions, the on-orbit low noise and high SNR almost satisfied requirements for OLI bands, and can provide a prior knowledge for uncertainty analysis of OLI images in monitoring land surface, oceanic, and atmospheric status.

The radiometric performance of remotely-sensed images is important for the applications of such data in monitoring land surface, ocean and atmospheric status. One requirement placed on the Thermal Infrared Sensor (TIRS) onboard Landsat 8 was that the noise-equivalent change in temperature (NEΔT) should be ≤0.4 K at 300 K for its two thermal infrared bands. In order to optimize the use of TIRS data, this study investigated the on-orbit NEΔT of the TIRS two bands from a scene-based method using clear-sky images over uniform ground surfaces, including lake, deep ocean, snow, desert and Gobi, as well as dense vegetation. Results showed that the NEΔTs of the two bands were 0.051 and 0.06 K at 300 K, which exceeded the design specification by an order of magnitude. The effect of NEΔT on the land surface temperature (LST) retrieval using a split window algorithm was discussed, and the estimated NEΔT could contribute only 3.5% to the final LST error in theory, whereas the required NEΔT could contribute up to 26.4%. Low NEΔT could improve the application of TIRS images. However, efforts are needed in the future to remove the effects of unwanted stray light that appears in the current TIRS images.

This objective of this paper is to estimate atmospheric water vapor (wv) from the latest Landsat 8 Thermal InfRared Sensor (TIRS) image by using a new modified split-window covariance-variance ratio (MSWCVR) method. Model analysis showed that the MSWCVR method can theoretically retrieve wv with an accuracy better than 0.45 g/cm 2 for most atmospheric moisture conditions. The MSWCVR was evaluated by using AERONET ground-measured data and cross-compared with MODIS products in 2013 at forty two ground sites, and results presented that the retrieved wv from TIRS data was highly correlated with but generally larger (about 1.0 g/cm 2 ) than two others. The reasons for this uncertainty were mainly ascribed to data systematic noise and radiative calibration error. Future work must pay more attention to the data quality and radiative calibration of Landsat 8 TIRS data.

Remote sensing monitoring and inversion research of vegetation water content is one of the most important developments of quantitative remote sensing theory and application. The common features of spectral reflectance from vegetation foliage upon leaf dehydration are decreasing water absorption in the near-infrared (NIR) and short-wave-infrared (SWIR). We studied that leaf water indexes in the NIR and SWIR were the most suitable for the assessment of leaf water content and developed a new model TWI (triangle vegetation water index) to estimate both canopy and single leaf water content. From the results of validation perspective, this model has better estimation accuracy at both canopy and single leaf water content comparing to WI, PWI, and NDWI. Validation was established on the basis of PROSPECT+SAIL estimation and ground measured data. So the model had good reliability and great potential to monitor vegetation water content.

Simulation of remote sensing images plays a significant role in performance analysis of sensors and quality assessment of remote sensing image. This paper presents a method to simulate remote sensing image bands using high-resolution data and spectral libraries. High spatial resolution imagery is firstly classified into several land surface types using a supervised classification method. Secondly, hyperspectral data is employed to extract endmembers after a chain of procedures (i.e. radiometric calibration, remove of bad bands, noise reduction, geometric and atmospheric correction). The image-extracted spectra together with the spectral profiles selected from existing spectral libraries are then used to build a new spectral library. After that, the spectra in the new library are mapped to the previous land use classification image according to the land surface type and this step yields an image with both high-spectral and high-spatial resolution, which is used to generate image bands with lower resolution after pixel mixing and spectral normalization. This method is based on the linear spectral mixture model and spectral response functions and has clear physical interpretation. Finally, the proposed method has been applied to simulate the Landsat TM 5 image bands using QuickBird data and Hyperion data and the simulated image bands are compared with the original image bands. The results show that this method can effectively simulate image bands with a good accuracy.

The objective of the study is to utilize the local spatial statistics in multi-source remote sensing to analyze and extract surface anomalies in coal mining areas. We illustrated the equations and characteristics of three local spatial statistics, and then calculated the textual bands of them. In contrast with the selected optimal bands, the local spatial analysis improved the classification accuracy from 93% up to 98% based on Supporting Vector Machine (SVM) Classification. In addition, a few Ground Truth Region of Interests (ROIs) were also derived in the multi-spectral image. By means of the hyper-spectral remotely sensed image covering the ROIs, we directly identified six different surface objects or anomalies and inferred that a clustering of minerals and sandy soil with dense vegetation was a developing coalfield, which should be verified in the ground survey.

Mixing spectrum is one of remote sensing theory and applicationresearch emphases.Mixingspectrum provides theoretical support for remote sensing quantitative inversion. With the processof research, all kinds of mixed pixel models were continuously developed.On the basis of previous researches, this study proposes a new radiative transfer modelto simulatespectrum mixed by soil vegetation binary structure.Compared withverification based on actual ground measured data, the model has a better accuracy than the linear model.

Soil moisture not only is an important parameter in precision agriculture, but is the main parameter in crop condition monitoring. We developed the method of soil moisture monitoring and evaluation with the new remote sensing platform, modified the existing indexes of PDI and MPDI, established inversion model and invert the soil moisture.