A Supplementary Module to Improve Accuracy of the Quality Assessment Band in Landsat Cloud Images

Cloud contamination is a serious obstacle for the application of Landsat data. To popularize the applications of Landsat data, each Landsat image includes the corresponding Quality Assessment (QA) band, in which cloud and cloud shadow pixels have been flagged. However, previous studies suggested that Landsat QA band still needs to be modified to fulfill the requirement of Landsat data applications. In this study, we developed a Supplementary Module to improve the original QA band (called QA_SM). On one hand, QA_SM extracts spectral and geometrical features in the target Landsat cloud image from the original QA band. On the other, QA_SM incorporates the temporal change characteristics of clouds and cloud shadows between the target and reference images. We tested the new method at four local sites with different land covers and the Landsat-8 cloud cover validation dataset (“L8_Biome”). The experimental results show that QA_SM performs better than the original QA band and the multi-temporal method ATSA (Automatic Time-Series Analyses). QA_SM decreases omission errors of clouds and shadows in the original QA band effectively but meanwhile does not increase commission errors. Besides, the better performance of QA_SM is less affected by the selections of reference images because QA_SM considers the temporal change of land surface reflectance that is not caused by cloud contamination. By further designing a quantitative assessment experiment, we found that the QA band generated by QA_SM improves cloud-removal performance on Landsat cloud images, suggesting the benefits of the new method to advance the applications of Landsat data.

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Improving the Quality Assessment band in Landsat cloud images for the application of cloud removal

10.5194/egusphere-egu21-14320 ◽

2021 ◽

Author(s):

Boli Yang ◽

Yan Feng ◽

Ruyin Cao

Keyword(s):

Quality Assessment ◽

Land Surface ◽

Missing Values ◽

Similarity Index ◽

Visual Assessment ◽

New Method ◽

Landsat Data ◽

Cloud Detection ◽

Cloud Removal ◽

Ultimate Objective

Cloud contamination is a serious obstacle for the application of Landsat data. Thick clouds can completely block land surface information and lead to missing values. The reconstruction of missing values in a Landsat cloud image&#160;requires the cloud and cloud shadow mask.&#160;In this study, we raised the issue that the quality of the quality assessment (QA) band in current Landsat products cannot meet the requirement of thick-cloud removal. To address this issue, we developed a new method (called Auto-PCP) to preprocess the original QA band, with the ultimate objective to improve the performance of cloud removal on Landsat cloud images. We tested the new method at four test sites and compared cloud-removed images generated by using three different QA bands, including the original QA band, the modified QA band by a dilation of two pixels around cloud and cloud shadow edges, and the QA band processed by Auto-PCP (&#8220;QA_Auto-PCP&#8221;). Experimental results, from both&#160;actual and simulated Landsat cloud images, show that&#160;QA_Auto-PCP achieved the best visual assessment for the cloud-removed images, and had the smallest RMSE values and the largest Structure SIMilarity index (SSIM) values. The improvement for the performance of cloud removal by QA_Auto-PCP is because the new method&#160;substantially decreases omission errors of clouds and shadows in the original QA band, but meanwhile does not increase commission errors. Moreover,&#160;Auto-PCP is easy to implement and uses the same data as cloud removal without additional image collections. We expect that&#160;Auto-PCP can further popularize cloud removal and advance the application of Landsat data. &#160;&#160;&#160;&#160;&#160;Keywords: Cloud detection, Cloud shadows, Cloud simulation, Cloud removal, MODTRAN

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Landsat-8 and Sentinel-2 burned area mapping - A combined sensor multi-temporal change detection approach

Remote Sensing of Environment ◽

10.1016/j.rse.2019.111254 ◽

2019 ◽

Vol 231 ◽

pp. 111254 ◽

Cited By ~ 31

Author(s):

David P. Roy ◽

Haiyan Huang ◽

Luigi Boschetti ◽

Louis Giglio ◽

Lin Yan ◽

...

Keyword(s):

Change Detection ◽

Temporal Change ◽

Burned Area ◽

Landsat 8 ◽

Detection Approach ◽

Burned Area Mapping ◽

Multi Temporal ◽

Combined Sensor ◽

Sentinel 2

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RELATIVE RADIOMETRIC NORMALIZATION USING SEVERAL AUTOMATICALLY CHOSEN REFERENCE IMAGES FOR MULTI-SENSOR, MULTI-TEMPORAL SERIES

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-2-2020-845-2020 ◽

2020 ◽

Vol V-2-2020 ◽

pp. 845-852

Author(s):

C. Hessel ◽

R. Grompone von Gioi ◽

J. M. Morel ◽

G. Facciolo ◽

P. Arias ◽

...

Keyword(s):

Time Series ◽

Atmospheric Correction ◽

Linear Interpolation ◽

Landsat 8 ◽

Quality Metric ◽

Multi Temporal ◽

Reference Images ◽

Random Sample Consensus ◽

Affine Correction ◽

Sentinel 2

Abstract. We propose a method for the relative radiometric normalization of long, multi-sensor image time series. This allows to increase the revisit time under comparable conditions. Although the relative radiometric normalization is a well-studied problem in the remote sensing community, the availability of an increasing number of images gives rise to new problems. For example, given long series spanning several years, finding features that are maintained through the whole period of time becomes arduous. Instead, we propose in this paper to use automatically detected reference images chosen by maximization of a quality metric. For each image, two affine correction models are robustly estimated using random sample consensus, using the two closest reference images; the final correction is obtained by linear interpolation. For each pair of source and reference images, pseudo-invariant features are obtained using a similarity measure invariant to radiometric changes. A final tone-mapping step outputs the images in the standard 8-bits range. This method is illustrated by the fusion of time series of Sentinel-2 at correction levels 1C, 2A, and Landsat-8 images. By using only the atmospherically corrected Sentinel-2 L2A images as anchors, the full output series inherits this atmospheric correction.

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Multi-Temporal Landsat Data Automatic Cloud Removal Using Poisson Blending

IEEE Access ◽

10.1109/access.2020.2979291 ◽

2020 ◽

Vol 8 ◽

pp. 46151-46161 ◽

Cited By ~ 1

Author(s):

Changmiao Hu ◽

Lian-Zhi Huo ◽

Zheng Zhang ◽

Ping Tang

Keyword(s):

Landsat Data ◽

Multi Temporal ◽

Cloud Removal

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PEMANFAATAN CITRA PENGINDERAN JAUH MULTI-TEMPORAL UNTUK DETEKSI URBAN HEAT ISLAND (UHI) TERHADAP PERUBAHAN PENGGUNAAN LAHAN DI KABUPATEN BULELENG

MAJALAH ILMIAH GLOBE ◽

10.24895/mig.2020.22-2.1046 ◽

2020 ◽

Vol 22 (2) ◽

pp. 71

Author(s):

A Sediyo Adi Nugraha ◽

Dewa Made Atmaja

Keyword(s):

Maximum Likelihood ◽

Urban Heat Island ◽

Land Surface Temperature ◽

Land Surface ◽

Landsat 8 ◽

Heat Island ◽

Landsat 8 Oli ◽

Landsat 7 ◽

Multi Temporal ◽

Urban Heat

Fenomena Urban Heat Island (UHI) sering dipengaruhi oleh kepadatan penduduk dan perubahan penggunaan lahan. Perubahan tesebut memiliki hubungan dengan peningkatan suhu permukaan (Land Surface Temperature/LST) sebagai awal terjadinya UHI. Deteksi perubahan penggunaan lahan dan suhu permukaan dilakukan dari tahun 2000, 2010, dan 2018 pada daerah Kabupaten Buleleng dan berfokus pada Kecamatan Buleleng karena memiliki perubahan lahan terbangun lebih cepat dibandingkan kecamatan lain. Tujuannya untuk mengetahuii bagaimana fenomena UHI itu terjadi akibat dari perubahan penggunaan lahan. Selain itu, seberapa besar peningkatan suhu permukaan selama 18 tahun khususnya di Kecamatan Buleleng dengan mengetahui kondisi ditribusi dan intensitas UHI. Metode yang digunakan dalam deteksi UHI menggunakan citra penginderaan jauh multi-temporal yaitu citra Landsat 7 ETM+ dan citra Landsat 8 OLI/TIRS (The Operational Land Imager and the Thermal Infrared Scanner) sebagai data primer. Pengolahan data akan berfokus pada ekstraksi suhu permukaan dengan metode Split-Windows Algorithm Sobrino (SWA-S) untuk Landsat 8 dan metode Brightness Temperature Emissivity Correction untuk Landsat 7, kemudian Maximum Likelihood sebagai metode penggunaan lahan. Hasil pengolahan menunjukkan bahwa penggunaan metode yang berbeda memberikan dampak terhadap fenomena UHI. Perbedaan suhu selama 18 tahun sebesar sebesar ±5°C hal itu dipengaruhi dari kondisi awan dan bayangan. Perubahan penggunaan lahan dari tahun 2000 hingga 2018 terdapat peningkatan lahan terbangun di Kecamatan Buleleng dan peningkatan suhu permukan sebesar 2°-7°C dari lahan terbangun. Fenomena UHI untuk distribusi dan instensitas UHI terjadi di daerah pusat perkotaan dan kenaikan intensitas UHI sebesar 1.75°C. kesimpulannya bahwa perubahan lahan terbangun memberikan dampak kenaikan suhu permukaan dan menyebabkan fenomena UHI.

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An Efficient Approach to Remove Thick Cloud in VNIR Bands of Multi-Temporal Remote Sensing Images

Remote Sensing ◽

10.3390/rs11111284 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1284 ◽

Cited By ~ 3

Author(s):

Wenhui Du ◽

Zhihao Qin ◽

Jinlong Fan ◽

Maofang Gao ◽

Fei Wang ◽

...

Keyword(s):

Remote Sensing ◽

Cloud Cover ◽

Agricultural Drought ◽

Target Image ◽

Remote Sensing Images ◽

Efficient Approach ◽

Multi Temporal ◽

Reference Images ◽

Surface Patterns ◽

Cloud Removal

Cloud-free remote sensing images are required for many applications, such as land cover classification, land surface temperature retrieval and agricultural-drought monitoring. Cloud cover in remote sensing images can be pervasive, dynamic and often unavoidable. Current techniques of cloud removal for the VNIR (visible and near-infrared) bands still encounters the problem of pixel values estimated for the cloudy area incomparable and inconsistent with the cloud-free region in the target image. In this paper, we proposed an efficient approach to remove thick clouds and their shadows in VNIR bands using multi-temporal images with good maintenance of DN (digital number) value consistency. We constructed the spectral similarity between the target image and reference one for DN value estimation of the cloudy pixels. The information reconstruction was done with 10 neighboring cloud-free pair-pixels with the highest similarity over a small window centering the cloudy pixel between target and reference images. Four Landsat5 TM images around Nanjing city of Jiangsu Province in Eastern China were used to validate the approach over four representative surface patterns (mountain, plain, water and city) for diverse sizes of cloud cover. Comparison with the conventional approaches indicates high accuracy of the approach in cloud removal for the VNIR bands. The approach was applied to the Landsat8 OLI (Operational Land Imager) image on 29 April 2016 in Nanjing area using two reference images. Very good consistency was achieved in the resulted images, which confirms that the proposed approach could be served as an alternative for cloud removal in the VNIR bands using multi-temporal images.

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Analysis on Thermal Islands Effectors in Ramadi City, Iraq Using Multi-temporal Landsat Images

Al-Adab Journal ◽

10.31973/aj.v1i135.983 ◽

2020 ◽

Vol 1 (135) ◽

pp. 67-78

Author(s):

Ismael Abbas Hurat

Keyword(s):

Surface Temperature ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Landsat 8 ◽

Landsat Images ◽

Water Index ◽

Normalized Difference Water Index ◽

Landsat 7 ◽

Multi Temporal

This paper analyzes the effects of urban density, vegetation cover, and water body on thermal islands measured by land surface temperature in Al Anbar province, Iraq using multi-temporal Landsat images. Images from Landsat 7 ETM and Landsat 8 OLI for the years 2000, 2014, and 2018 were collected, pre-processed, and anal yzed. The results suggested that the strongest correlation was found between the Normalized Difference Built-up Index (NDBI) and the surface temperature. The correlation between the Normalized Difference Vegetation Index (NDVI) and the surface temperature was slightly weaker compared to that of NDBI. However, the weakest correlation was found between the Normalized Difference Water Index (NDWI) and the temperature. The results obtained in this research may help the decision makers to take actions to reduce the effects of thermal islands by looking at the details in the produced maps and the analyzed values of these spectral indices.

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