Fast bowtie effect elimination for MODIS L1B data

Ren, Ruizhi; Guo, Shuxu; Gu, Lingjia

doi:10.1016/s1005-8885(09)60435-0

Cited by 10 publications

(7 citation statements)

References 7 publications

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“…This bowtie effect also appeared for MODIS products and almost all of the cross-track-scanning imaging radiometers [74][75][76]. Many quick and efficient algorithms have been developed to remove these effects on single data [67,77,78], but the impact of the bowtie effect on the time series classification still exists. Although the error can be easily removed by combining the two classes through visual interpretation, it still causes difficulty for automatic batch processing.…”

Section: Discussionmentioning

confidence: 99%

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Crop Mapping Using PROBA-V Time Series Data at the Yucheng and Hongxing Farm in China

Zhang

Zheng

et al. 2016

Remote Sensing

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PROBA-V is a new global vegetation monitoring satellite launched in the second quarter of 2013 that provides data with a 100 m to 1 km spatial resolution and a daily to 10-day temporal resolution in the visible and near infrared (VNIR) bands. A major mission of the PROBA-V satellite is global agriculture monitoring, in which the accuracy of crop mapping plays a key role. In countries such as China, crop fields are typically small, in assorted shapes and with various management approaches, which deem traditional methods of crop identification ineffective, and accuracy is highly dependent on image resolution and acquisition time. The five-day temporal and 100 m spatial resolution PROBA-V data make it possible to automatically identify crops using time series phenological information. This paper takes advantage of the improved spatial and temporal resolution of the PROBA-V data, to map crops at the Yucheng site in Shandong Province and the Hongxing farm in Heilongjiang province of China. First, the Swets filter algorithm was employed to eliminate noisy pixels and fill in data gaps on time series data during the growing season. Then, the crops are classified based on the Iterative Self-Organizing Data Analysis Technique (ISODATA) clustering, the maximum likelihood method (MLC) and similarity analysis. The mapping results were validated using field-collected crop type polygons and high resolution crop maps based on GaoFen-1 satellite (GF-1) data in 16 m resolution. Our study showed that, for the Yucheng site, the cropping system is simple, mainly dominated by winter wheat-maize rotation. The overall accuracy of crop identification was 73.39% which was slightly better than the result derived from MODIS data. For the Hongxing farm, the cropping system is more complex (i.e., more than three types of crops were planted). The overall accuracy of the crop mapping by PROBA-V was 73.29% which was significantly higher than the MODIS product (46.81%). This study demonstrates that time series PROBA-V data can serve as a useful source for reliable crop identification and area estimation. The high revisiting frequency and global coverage of the PROBA-V data show good potential for future global crop mapping and agricultural monitoring.

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Section: Discussionmentioning

confidence: 99%

“…Yucheng is in a scan-to-scan overlapping observation area of PROBA-V and MODIS satellites. The bowtie effect between two strips on the overlap led to an apparent stripping effect on the classification results [67].…”

Section: Crop Mappingmentioning

confidence: 99%

Crop Mapping Using PROBA-V Time Series Data at the Yucheng and Hongxing Farm in China

Zhang

Zheng

et al. 2016

Remote Sensing

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“…Using the swath MOD10_L2 is complicated by the so-called "bowtie effect" of the MODIS instrument, which causes an overlap of the satellite field of view, producing a data repetition. This effect increases with the distance from nadir and can be especially dramatic at the edge of the image (G omez-Landesa et al, 2004;Ren et al, 2010). Several studies therefore recommend using the MOD10A1 daily or the MOD10A2 eight-day data products for multi-temporal evaluation of changes in snow-covered area (Hall and Riggs, 2007;Parajka et al, 2012;Gascoin et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

Extracting Snow Cover Time Series Data from Open Access Web Mapping Tile Services

Kadlec

Miller

Ames

2016

J American Water Resour Assoc

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The probability of the presence of snow cover at a given location over time is a critical input to hydrologic simulation models in snowpack‐driven watersheds. While a number of open access web mapping tile services exist for viewing images of current and historical snow cover over large regions, no equally accessible tools exist for extracting numerical time series data of snow cover probability defined at particular point locations. This article presents the design, development, and testing of a new open source script and web application for snow cover probability time series extraction from map images. The script is deployed as a web app using the Tethys framework making it accessible to novice users through a user interface. A WaterML web‐API gives access to third‐party applications for automation and embedding in modeling tools. The full design of the script is presented such that it can serve as a model for similar or extended tools that may be developed by others. A set of use case experiments is presented demonstrating the full functionality of the script and its limitations, and an example application for ground validation of the Moderate Resolution Imaging Spectroradiometer snow cover dataset is discussed.

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“…Moreover, the more near to image edge, the more it can be seen. In the procedure of bowtie effect elimination, we use the previous proposed algorithm [13]. It firstly detects the rough positions of overlapping data.…”

Section: Modis Data Preprocessingmentioning

confidence: 99%

Automatic Cloud Detection and Removal Algorithm for MODIS Remote Sensing Imagery

Gu¹,

Ren²,

Zhang³

2011

JSW

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Cloud is one of the most common interferers in Moderate Resolution Imaging Spectrum-radiometer (MODIS) remote sensing imagery. Because of cloud interference, much important and useful information covered by cloud cannot be recovered well. How to detect and remove cloud from MODIS imagery is an important issue for wide application of remote sensing data. In general, cloud can be roughly divided into two types, namely, thin cloud and thick cloud. In order to effectively detect and eliminate cloud, an automatic algorithm of cloud detection and removal is proposed in this paper. Firstly, several necessary preprocessing works need to be done for MODIS L1B data, including geometric precision correction, bowtie effect elimination and stripe noise removal. Furthermore, through analyzing the cloud spectral characters derived from the thirty-six bands of MODIS data, it can be found the spectral reflections of ground and cloud are different in various MODIS bands. Hence, cloud and ground can be respectively identified based on the analysis of multispectral characters derived from MODIS imagery. Cloud removal processing mainly aims at cloud region rather than whole image, which can improve processing efficiency. As for thin cloud and thick cloud regions, the corresponding cloud removal algorithms are proposed in this paper. Experimental results demonstrate that the proposed algorithms can effectively detect and remove cloud from MODIS imagery, which can meet the demands of post-processing of remote sensing imagery.

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Fast bowtie effect elimination for MODIS L1B data

Cited by 10 publications

References 7 publications

Crop Mapping Using PROBA-V Time Series Data at the Yucheng and Hongxing Farm in China

Crop Mapping Using PROBA-V Time Series Data at the Yucheng and Hongxing Farm in China

Extracting Snow Cover Time Series Data from Open Access Web Mapping Tile Services

Automatic Cloud Detection and Removal Algorithm for MODIS Remote Sensing Imagery

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