Recovering missing pixels for Landsat ETM+ SLC-off imagery using multi-temporal regression analysis and a regularization method

Zeng, Chao; Shen, Huanfeng; Zhang, Liangpei

doi:10.1016/j.rse.2012.12.012

Cited by 249 publications

(127 citation statements)

References 26 publications

Supporting

Mentioning

126

Contrasting

Unclassified

Order By: Relevance

“…To further extend the value of the Landsat 7 ETM + sensor for forest change applications, various gap-filling algorithms have been developed and evaluated (Wulder et al 2008b;Chen et al 2011;Zeng et al 2013). Monitoring capabilities from Landsat sensors will be improved by the Sentinel-2 constellation, which will offer a similar multispectral sensor (MSI, Table 2) but with a 290 km swath and a higher revisit rate of five days under the same viewing conditions (Drusch et al 2012).…”

Section: What Sensors Are Available?mentioning

confidence: 99%

Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective

et al. 2016

View full text Add to dashboard Cite

show abstract

Section: What Sensors Are Available?mentioning

confidence: 99%

Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Considering that an oversized selection window will not only consume computing time, but also cause the loss of geospatial relevance, a maximum window size was set. The initial window size and maximum window size were set as 7 and 99, respectively, and the minimum sample points, N, was empirically set to 10 based on the condition of AOD coverage, according to our previous study in WLR [29].…”

Section: Methodsmentioning

confidence: 99%

“…The surface reflectance effects are included by using NDVI specific fitting curves in the AOD retrieval process implemented by the DB enhanced algorithm [17]. Synthetically considering these three factors, in this study, the NDVI-based weighted linear regression (NWLR) recovery method was proposed based on our previous weighted linear regression (WLR) methods [29]. The NWLR method deliberates the geographical weight, AOD similarity, and NDVI similarity, in order to recover AOD by considering the characteristics of the AOD retrieval algorithm rather than by mathematical merging.…”

Section: Introductionmentioning

confidence: 99%

Improving Spatial Coverage for Aqua MODIS AOD using NDVI-Based Multi-Temporal Regression Analysis

et al. 2017

Self Cite

View full text Add to dashboard Cite

Abstract:The Moderate Resolution Imaging Spectroradiometer (MODIS) provides widespread Aerosol Optical Depth (AOD) datasets for climatological and environmental health research. Since MODIS AOD clearly lacks coverage in orbit-scanning gaps and cloud obscuration, some applications will benefit from data recovery using multi-temporal AOD. Aimed at qualitatively describing the relationship between multi-temporal AOD, AOD loadings and Normalized Difference Vegetation Index (NDVI) have been considered based on the mechanism of satellite AOD retrieval. Accordingly, the NDVI-based Weighted Linear Regression (NWLR) has been proposed to recover AOD by synthetically weighing AOD similarity, spatial proximity, and NDVI similarity. To evaluate the performance of AOD recovery, simulated experiments applying gap and window masks were conducted in South Asia and Beijing, respectively. The evaluation results demonstrated that the linear regression R 2 achieved 0.8 and the absolute relative errors remained steady. Further validation was conducted between the recovered and actual AODs using 56 Aerosol Robotic Network (AERONET) sites in East and South Asia from 2013 to 2015, which demonstrated that over 41% of recovered AODs fell within the expected error (EE) envelope. Additional validation conducted in South Asia and Beijing showed that recovery by NWLR did not expand satellite-derived AOD errors, and the accuracy of recovered AOD was consistent with the accuracy of the original Aqua MODIS Deep Blue (DB) AOD. The recovery results illustrated that AOD coverage was improved in most regions, especially in North China, Mongolia, and South Asia, which could provide better support in aerosol spatio-temporal analysis and aerosol data assimilation.

show abstract

“…With respect to spectral resolution, Landsat MSS has four bands, Landsat TM has seven bands and ETM+ has eight bands. However, the malfunction of the Scan Line Corrector (SLC) on the ETM+ sensor makes the application of ETM+ images limited [27,28]. The latest version of the Landsat images, the Landsat OLI, has 11 bands (Table 1).…”

Section: Developments Of Landsat Datamentioning

confidence: 99%

Developments in Landsat Land Cover Classification Methods: A Review

2017

View full text Add to dashboard Cite

Land cover classification of Landsat images is one of the most important applications developed from Earth observation satellites. The last four decades were marked by different developments in land cover classification methods of Landsat images. This paper reviews the developments in land cover classification methods for Landsat images from the 1970s to date and highlights key ways to optimize analysis of Landsat images in order to attain the desired results. This review suggests that the development of land cover classification methods grew alongside the launches of a new series of Landsat sensors and advancements in computer science. Most classification methods were initially developed in the 1970s and 1980s; however, many advancements in specific classifiers and algorithms have occurred in the last decade. The first methods of land cover classification to be applied to Landsat images were visual analyses in the early 1970s, followed by unsupervised and supervised pixel-based classification methods using maximum likelihood, K-means and Iterative Self-Organizing Data Analysis Technique (ISODAT) classifiers. After 1980, other methods such as sub-pixel, knowledge-based, contextual-based, object-based image analysis (OBIA) and hybrid approaches became common in land cover classification. Attaining the best classification results with Landsat images demands particular attention to the specifications of each classification method such as selecting the right training samples, choosing the appropriate segmentation scale for OBIA, pre-processing calibration, choosing the right classifier and using suitable Landsat images. All these classification methods applied on Landsat images have strengths and limitations. Most studies have reported the superior performance of OBIA on different landscapes such as agricultural areas, forests, urban settlements and wetlands; however, OBIA has challenges such as selecting the optimal segmentation scale, which can result in over or under segmentation, and the low spatial resolution of Landsat images. Other classification methods have the potential to produce accurate classification results when appropriate procedures are followed. More research is needed on the application of hybrid classifiers as they are considered more complex methods for land cover classification.

show abstract

Recovering missing pixels for Landsat ETM+ SLC-off imagery using multi-temporal regression analysis and a regularization method

Cited by 249 publications

References 26 publications

Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective

Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective

Improving Spatial Coverage for Aqua MODIS AOD using NDVI-Based Multi-Temporal Regression Analysis

Developments in Landsat Land Cover Classification Methods: A Review

Contact Info

Product

Resources

About