Penghai Wu

A spatial and temporal reflectance fusion model considering sensor observation differences

This article proposes a spatial–temporal expansion method for remote-sensing reflectance by blending observations from sensors with different spatial and temporal characteristics. Compared with the methods used in the past, the main characteristic of the proposed method is consideration of sensor observation differences between different cover types when calculating the weight function of the fusion model. The necessity of the temporal difference factor commonly used in spatial–temporal fusion is also analysed in this article. The method was tested and quantitatively assessed under different landscape situations. The results indicate that the proposed fusion method improves the prediction accuracy of fine-resolution reflectance.

Reconstructing MODIS LST Based on Multitemporal Classification and Robust Regression

The Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) product can offer accurate LST with high temporal and spatial resolution, but the quality is often degraded by cloud. To improve the usability of the MODIS LST, this letter proposes a reconstruction method based on multitemporal data. First, a multitemporal classification is employed to distinguish the different land surface types. The invalid LST values can then be predicted using a robust regression with the multitemporal information from the other LSTs. Finally, postprocessing is proposed to eliminate outliers. Simulated and actual experiments show that the method can accurately reconstruct the missing values.

Land-surface temperature retrieval at high spatial and temporal resolutions based on multi-sensor fusion

Land-surface temperature (LST) is of great significance for the estimation of radiation and energy budgets associated with land-surface processes. However, the available satellite LST products have either low spatial resolution or low temporal resolution, which constrains their potential applications. This paper proposes a spatiotemporal fusion method for retrieving LST at high spatial and temporal resolutions. One important characteristic of the proposed method is the consideration of the sensor observation differences between different land-cover types. The other main contribution is that the spatial correlations between different pixels are effectively considered by the use of a variation-based model. The method was tested and assessed quantitatively using the different sensors of Landsat TM/ETM+, moderate resolution imaging spectroradiometer and the geostationary operational environmental satellite imager. The validation results indicate that the proposed multisensor fusion method is accurate to about 2.5 K.

A Spatial and Temporal Nonlocal Filter-Based Data Fusion Method

The tradeoff in remote sensing instruments that balances the spatial resolution and temporal frequency limits our capacity to monitor spatial and temporal dynamics effectively. The spatiotemporal data fusion technique is considered as a cost-effective way to obtain remote sensing data with both high spatial resolution and high temporal frequency, by blending observations from multiple sensors with different advantages or characteristics. In this paper, we develop the spatial and temporal nonlocal filter-based fusion model (STNLFFM) to enhance the prediction capacity and accuracy, especially for complex changed landscapes. The STNLFFM method provides a new transformation relationship between the fine-resolution reflectance images acquired from the same sensor at different dates with the help of coarse-resolution reflectance data, and makes full use of the high degree of spatiotemporal redundancy in the remote sensing image sequence to produce the final prediction. The proposed method was tested over both the Coleambally Irrigation Area study site and the Lower Gwydir Catchment study site. The results show that the proposed method can provide a more accurate and robust prediction, especially for heterogeneous landscapes and temporally dynamic areas.

A Review on Recent Developments in Fully Polarimetric SAR Image Despeckling

The use of synthetic aperture radar (SAR) technology with quad-polarization data requires efficient polarimetric SAR (PolSAR) speckle filtering algorithms. During the last three decades, many effective methods have been developed to reduce the speckle in PolSAR images, and recent studies have generally shown a trend developing from local single-point filtering to nonlocal patch-based or globally collaborative filtering. The main goals of this paper are to make a comprehensive review of the existing PolSAR despeckling algorithms and highlight the recent development trends. In the experimental part, the filtering results obtained with both simulated and real PolSAR images are deployed to compare the performance of some of the state-of-the-art despeckling algorithms, which shows that all of the selected filters have their individual strengths and weaknesses.

Restoring Aqua MODIS band 6 by other spectral bands using compressed sensing theory

The Aqua satellite launched in May 2002 is a key one in the Earth Observation System (EOS) mission of NASA. In the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Aqua, 15 of all the 20 detectors in the band 6 are out of working. Therewith missing data exists. It is very significant to find the way to reconstruct its incomplete data efficiently. This paper proposes an approach to retrieve the missing data by the newborn Compressed Sensing (CS) theory, which can account for not only the large-area inpainting, but also the spatial autocorrelation property and the complementarity relationship among spectral bands. The experimental results validate that the proposed algorithm performs well compared with the state-of-art methods.

Spatiotemporal analysis of water area annual variations using a Landsat time series: a case study of nine plateau lakes in Yunnan province, China

ABSTRACT Lakes are sensitive to both climate change and human activities, and therefore serve as an excellent indicator of environmental change. Based on a time series of Landsat images over the last 16 years, this article attempts to provide a first picture of the annual variations in area of nine plateau lakes in Yunnan province, China. The modified normalized difference water index (MNDWI) and object-based image analysis (OBIA) are used to extract the waterbodies. Compared with the visual interpretation (VI) of the lakes, the precision of the combined method is greater than 99.7%. A spatiotemporal analysis is also carried out for the lakes. The results show that the water areas of most of the plateau lakes have been stable over the last 16 years, although some years have shown significant changes. However, it should be noted that Lake Qilu and Lake Yilong shrunk significantly after 2011. Moreover, the orientation of the shrinkage is different. Limited evidence suggests that the differences in the area change of the nine plateau lakes are caused by both climate change and human activities.

Relationships analysis of land surface temperature with vegetation indicators and impervious surface fraction by fusing multi-temporal and multi-sensor remotely sensed data

It is known that vegetation, and impervious surface are very important factors to affect the LST distribution in surface urban heat island (SUHI) analysis. However, the trade-off between temporal resolution and spatial resolution and/or the influence of cloud covering, make it difficult to obtain fine-scale spatial-temporal relationship analysis. To relieve these difficulties, this study employs multi-temporal and multi-sensor fusion methods for summer spatial-temporal relationships of Land surface temperature (LST) with normalized difference vegetation index (NDVI), vegetation fraction (VF) and impervious surface fraction (ISF) analysis on Wuhan city of China. Here, the correlation analysis was extended from two-dimensional to three-dimensional by using the continuous fused data (from 1988 to 2013). Our analysis indicates there is a strong negative relationship between LST and NDVI as well as VF, whereas the relationship between LST and ISF is obvious positive correlation. In addition, we also find that all these relationships are spatial-temporal steady. This result suggest that increasing impervious surface area means enhance LST, whereas increasing vegetation means weaken LST in summer, especially in the “warm edge” area. We believe the use of continuous long-term data weakened the interference of data quality and improve the reliability.