The methods and automatic technology aimed at imagery georeferencing, cloud screening, atmospheric and radiometric correction of KMSS-M satellite data

Kolbudaev, P.A.; Плотников, Д.Е.; Loupian, Е.А.; Proshin, А.А.; Matveev, Alexey S.

doi:10.1051/e3sconf/202133301006

Cited by 2 publications

(7 citation statements)

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“…A detailed description of components of KMSS data processing workflow was given in several studies [6,8,31,33]. In this section, we focus on key elements of the workflow and their performance.…”

Section: Kmss Data Processingmentioning

confidence: 99%

“…Unlike KMSS-M, KMSS-2 features a classical nadir-viewing geometry (Figure 1). KMSS data are advantageous for operational monitoring, change detection, and vegetation state assessment [6,7]. The Meteor-M\KMSS satellite system is characterized by a demanded combination of spatial resolution and revisit rate, providing that Russia is completely covered with observations every 3-5 days.…”

Section: Introductionmentioning

confidence: 99%

“…Automated workflows for KMSS data processing are aimed at imagery georeferencing, clouds, and shadows detection, as well as atmospheric correction to provide seasonal and multiyear time series of surface reflectance and vegetation indices over northern Eurasia, including the grain belt of Russia [8]. KMSS Level-2 data prepared with KMSS data are advantageous for operational monitoring, change detection, and vegetation state assessment [6,7]. The Meteor-M\KMSS satellite system is characterized by a demanded combination of spatial resolution and revisit rate, providing that Russia is completely covered with observations every 3-5 days.…”

Section: Introductionmentioning

confidence: 99%

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Accuracy Assessment of Atmospheric Correction of KMSS-2 Meteor-M #2.2 Data over Northern Eurasia

Plotnikov,

Kolbudaev,

Matveev

et al. 2023

Remote Sensing

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Atmospheric correction of satellite remote sensing data is a prerequisite for a large variety of applications, including time series analysis and quantitative assessment of the Earth’s vegetation cover. It was earlier reported that an atmospherically corrected KMSS-M (Meteor-M #2) dataset was produced for Russia and neighboring countries. The methodology adopted for atmospheric correction was based on localized histogram matching of target KMSS-M and MODIS reference gap-free and date-matching imagery. In this paper, we further advanced the methodology and quantitatively assessed Level-2 surface reflectance analysis-ready datasets, operatively produced for KMSS-2 instruments over continental scales. Quantitative assessment was based on accuracy, precision, and uncertainty (APU) metrics produced for red and near-infrared bands of the KMSS-2 instrument based on a reference derived from a MODIS MOD09 reconstructed surface reflectance. We compared error distributions at 5%, 20%, and 50% levels of cloudiness and indicated that the cloudiness factor has little impact on the robustness of the atmospheric correction regardless of the band. Finally, the spatial and temporal gradients of accuracy metrics were investigated over northern Eurasia and across different seasons. It was found that for the vast majority of observations, accuracy falls within the −0.010–0.035 range, while precision and uncertainty were below 0.06 for any band. With the successful launch of the most recent Meteor-M #2.3 with a new KMSS-2 instrument onboard, the efficiency and interoperability of the constellation are expected to increase.

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Section: Kmss Data Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Accuracy Assessment of Atmospheric Correction of KMSS-2 Meteor-M #2.2 Data over Northern Eurasia

Plotnikov,

Kolbudaev,

Matveev

et al. 2023

Remote Sensing

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“…Универсальность и технологическое удобство разработанного метода заключается в том, что корректируемое изоб-ражение может не быть радиометрически калибровано, а используемое при допривязке эталонное изображение может иметь кратно более низкое пространственное разрешение. Выбор метрики корреляции для уточнения привязки обусловлен также и её универсальностью при организации полного цикла подготовки данных КМСС-М, включающего, помимо допривязки, маскирование облачности и теней, докалибровку, атмосферную коррекцию и восстановление пропусков во временных сериях изображений -этапы, на которых также используется рассчитанное в процессе допривязки максимальное значение корреляции, что более подробно описано в работах [19,23].…”

Section: методологияunclassified

“…Разработанный метод географической привязки был включен в технологию потоковой обработки данных КМСС-М, развернутую в ИКИ РАН [23]. Технология предусматривает переход на тайловую (гранульную) структуру пространственной организации данных КМСС-М в широтно-долготной проекции, введение фиксированного стандарта номенклатуры имен продуктов для повышения удобства пространственно-временного анализа, распространения продуктов и их производных.…”

Section: технология автоматической географической привязки изображени...unclassified

An automatic method for subpixel registration of KMSS-M imagery based on coarse-resolution actualized reference

Plotnikov¹,

Kolbudaev²,

Loupian³

2022

Computer Optics

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The paper describes a method for automatic subpixel-accurate geographic referencing of imagery acquired by KMSS-M with 60 m spatial resolution, based on leveraging a coarse, reconstructed, cloud-free and daily updated MODIS surface reflectance reference image. The method is based on maximizing Pearson's correlation value when determining an optimal local displacement of the distorted image fragment by comparing with the reference image. To assess the effectiveness of the method when used over continental-scale and heterogeneous areas, three experiments were carried out providing quantitative estimates of imagery registration errors: an experiment with model datasets, an experiment to estimate the absolute registration error of MODIS reference imagery, and an experiment to estimate the registration error of geocorrected KMSS-M data. Experimental evaluation of the method based on model datasets of decameter-resolution Sentinel-2 (MSI) imagery demonstrated its robustness when used over a variety of environmental conditions over a one year-long observation period. The average georeferencing error of MODIS coarse-resolution reference was shown to be less than 20 meters in Red and Near-infrared bands. Corrected KMSS-M imagery evaluation over the Russian Grain Belt within 2020 has shown, on average, the subpixel referencing accuracy both in Red and Near-infrared bands, while the average absolute georeferencing error of the original uncorrected KMSS-M imagery was shown to be about 3 kilometers. Subpixel registration accuracy of KMSS-M imagery, corrected with MODIS-based coarse-resolution reference, opens new prospects for using multi-temporal analysis of this multispectral surface reflectance data in a variety of scientific and practical applications associated with vegetation cover satellite monitoring. The technological flexibility of the method ensures its applicability to data from other satellite systems for Earth optical remote sensing.

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The methods and automatic technology aimed at imagery georeferencing, cloud screening, atmospheric and radiometric correction of KMSS-M satellite data

Cited by 2 publications

References 4 publications

Accuracy Assessment of Atmospheric Correction of KMSS-2 Meteor-M #2.2 Data over Northern Eurasia

Accuracy Assessment of Atmospheric Correction of KMSS-2 Meteor-M #2.2 Data over Northern Eurasia

An automatic method for subpixel registration of KMSS-M imagery based on coarse-resolution actualized reference

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