About: Earth observation is a research topic. Over the lifetime, 3828 publications have been published within this topic receiving 44085 citations. The topic is also known as: EO.
Papers published on a yearly basis
TL;DR: In this paper, the first evaluation of the MODIS surface reflectance product accuracy, in comparison with other data products and in the context of MODIS instrument performance since launch, is presented.
TL;DR: The further exploitation of hyperspectral imaging on a global basis awaits the launch of a high performance imaging spectrometer and more researchers with sufficient resources to take advantage of the vast information content inherent in the data.
TL;DR: Current techniques of multi-source remote sensing data fusion are reviewed and their future trends and challenges are discussed through the concept of hierarchical classification, i.e., pixel/data level, feature level and decision level.
Abstract: With the fast development of remote sensor technologies, e.g. the appearance of Very High Resolution (VHR) optical sensors, SAR, LiDAR, etc., mounted on either airborne or spaceborne platforms, multi-source remote sensing data fusion techniques are emerging due to the demand for new methods and algorithms. The general fusion techniques have been well developed and applied in various fields ranging from satellite earth observation to computer vision, medical image processing, defence security and so on. Despite the fast development, the techniques remain challenging for multi-source data fusion within varying spatial and temporal resolutions. This article reviews current techniques of multi-source remote sensing data fusion and discusses their future trends and challenges through the concept of hierarchical classification, i.e., pixel/data level, feature level and decision level. This article concentrates on discussing optical panchromatic and multi-spectral data fusing methods. So far, the pixel level fus...
TL;DR: The potential of DL in environmental remote sensing, including land cover mapping, environmental parameter retrieval, data fusion and downscaling, and information reconstruction and prediction, will be analyzed and a typical network structure will be introduced.
TL;DR: For the investigated sites and scenes, results show that the LST inverted from the radiative transfer equation-based method using band 10 has the highest accuracy with RMSE lower than 1 K, while the SW algorithm has moderate accuracy and the SC method has the lowest accuracy.
Abstract: Accurate inversion of land surface geo/biophysical variables from remote sensing data for earth observation applications is an essential and challenging topic for the global change research. Land surface temperature (LST) is one of the key parameters in the physics of earth surface processes from local to global scales. The importance of LST is being increasingly recognized and there is a strong interest in developing methodologies to measure LST from the space. Landsat 8 Thermal Infrared Sensor (TIRS) is the newest thermal infrared sensor for the Landsat project, providing two adjacent thermal bands, which has a great benefit for the LST inversion. In this paper, we compared three different approaches for LST inversion from TIRS, including the radiative transfer equation-based method, the split-window algorithm and the single channel method. Four selected energy balance monitoring sites from the Surface Radiation Budget Network (SURFRAD) were used for validation, combining with the MODIS 8 day emissivity product. For the investigated sites and scenes, results show that the LST inverted from the radiative transfer equation-based method using band 10 has the highest accuracy with RMSE lower than 1 K, while the SW algorithm has moderate accuracy and the SC method has the lowest accuracy.
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