PACO: Python-Based Atmospheric Correction

Reyes, R. de los; Langheinrich, Maximilian; Schwind, Peter; Richter, Rudolf; Pflug, Bringfried; Bachmann, Martin; Müller, Rupert; Carmona, E.; Zekoll, Viktoria; Reinartz, Peter

doi:10.3390/s20051428

Cited by 23 publications

(8 citation statements)

References 39 publications

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“…The red edge (bands 5, 6, 7), near infrared (band 8A), and shortwave infrared bands (bands 11 and 12), available in 20 m spatial resolution, were resampled to 10 m using the nearest neighbor algorithm. The atmospheric correction algorithm "Python-Based Atmospheric COrrection" (PACO) [68] was applied to obtain Level-2A Bottom-Of-Atmosphere (BOA) reflectance from the Level-1C data. Given only low altitudinal differences within the cropland regions of Germany, a digital elevation model was not used in the atmospheric correction process.…”

Section: Satellite Data and Pre-processingmentioning

confidence: 99%

Mapping Crop Types of Germany by Combining Temporal Statistical Metrics of Sentinel-1 and Sentinel-2 Time Series with LPIS Data

et al. 2022

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Nationwide and consistent information on agricultural land use forms an important basis for sustainable land management maintaining food security, (agro)biodiversity, and soil fertility, especially as German agriculture has shown high vulnerability to climate change. Sentinel-1 and Sentinel-2 satellite data of the Copernicus program offer time series with temporal, spatial, radiometric, and spectral characteristics that have great potential for mapping and monitoring agricultural crops. This paper presents an approach which synergistically uses these multispectral and Synthetic Aperture Radar (SAR) time series for the classification of 17 crop classes at 10 m spatial resolution for Germany in the year 2018. Input data for the Random Forest (RF) classification are monthly statistics of Sentinel-1 and Sentinel-2 time series. This approach reduces the amount of input data and pre-processing steps while retaining phenological information, which is crucial for crop type discrimination. For training and validation, Land Parcel Identification System (LPIS) data were available covering 15 of the 16 German Federal States. An overall map accuracy of 75.5% was achieved, with class-specific F1-scores above 80% for winter wheat, maize, sugar beet, and rapeseed. By combining optical and SAR data, overall accuracies could be increased by 6% and 9%, respectively, compared to single sensor approaches. While no increase in overall accuracy could be achieved by stratifying the classification in natural landscape regions, the class-wise accuracies for all but the cereal classes could be improved, on average, by 7%. In comparison to census data, the crop areas could be approximated well with, on average, only 1% of deviation in class-specific acreages. Using this streamlined approach, similar accuracies for the most widespread crop types as well as for smaller permanent crop classes were reached as in other Germany-wide crop type studies, indicating its potential for repeated nationwide crop type mapping.

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Section: Satellite Data and Pre-processingmentioning

confidence: 99%

Mapping Crop Types of Germany by Combining Temporal Statistical Metrics of Sentinel-1 and Sentinel-2 Time Series with LPIS Data

et al. 2022

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“…Within the DESIS Ground Segment, the PACO software (de los Reyes et al, 2020) is implemented as L2A processor. The algorithms implemented in PACO are inherited from AT-COR (Richter, 1998).…”

Section: Desis L2a Processor and Productsmentioning

confidence: 99%

“…• Pre-classification mask (QL QU ALIT Y −2): pixel mask of coded 8-bit integer flag, following the criteria and the flagging criteria detailed in (Alonso et al, 2019) (de los Reyes et al, 2020.…”

Section: L2a Productsmentioning

confidence: 99%

“…In order to calculate a surface reflectance mean over the highest amount of pixels possible, the minimum ROI of interest to consider should include at least 5x5 pixels (150 x 150 m). In order to compare with previous studies on multi-spectral sensors like Sentinel-2 (de los Reyes et al, 2020) and to minimize Bi-directional reflection (BRD) effects, illumination conditions of sun zenith angle < 30 • and off-nadir tilt angle <10 • are only considered. The accuracy, the precision and the uncertainty are calculated in this study only for scenes over Gobabeb site (see Figure 3, where at east 7 scenes are available under the before mentioned illumination conditions.…”

Section: Validation Of Bottom-of-atmosphere Products: Surface Reflect...mentioning

confidence: 99%

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The Desis L2a Processor and Validation of L2a Products Using Aeronet and Radcalnet Data

Reyes

Alonso

Bachmann

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The hyperspectral instrument ”DLR Earth Sensing Imaging Spectrometer” (DESIS) is a VNIR sensor on-board of the International Space Station (ISS) and operational since October 2019. DESIS acquires images of Earth on user request with a swath of about 30 km width and 235 bands with a Full Width at Half Maximum (FWHM) of 3.5 nm in the spectral range between 400 to 1000 nm.In this article we will present the basis of the atmospheric correction by PACO software, implemented inside the DESIS Ground Segment as L2A processor. The resulting L2A products will be validated against independent in-situ measurements.The aerosol optical thickness and water vapor will be compared with the Aerosol Robotic Network (AERONET) measurements and the surface reflectance will be validated with the Radiometric Calibration Network (RadCalNet) data.

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“…The L2A water algorithm is based on the Module Inversion Program (MIP) [36], and is not further described within this paper. The EnMAP Level 2A land processor is based on PACO [37] (Python-Based Atmospheric Correction, see also [1] for its implementation as DESIS L2A processor). PACO is a descendant of the well-known ATCOR [38,39].…”

Section: Level 2a Processing and Related Metadatamentioning

confidence: 99%

Analysis-Ready Data from Hyperspectral Sensors—The Design of the EnMAP CARD4L-SR Data Product

et al. 2021

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Today, the ground segments of the Landsat and Sentinel missions provide a wealth of well-calibrated, characterized datasets which are already orthorectified and corrected for atmospheric effects. Initiatives such as the CEOS Analysis Ready Data (ARD) propose and ensure guidelines and requirements so that such datasets can readily be used, and interoperability within and between missions is a given. With the increasing availability of data from operational and research-oriented spaceborne hyperspectral sensors such as EnMAP, DESIS and PRISMA, and in preparation for the upcoming global mapping missions CHIME and SBG, the provision of analysis ready hyperspectral data will also be of increasing interest. Within this article, the design of the EnMAP Level 2A Land product is illustrated, highlighting the necessary processing steps for CEOS Analysis Ready Data for Land (CARD4L) compliant data products. This includes an overview of the design of the metadata, quality layers and archiving workflows, the necessary processing chain (system correction, orthorectification and atmospheric correction), as well as the resulting challenges of this procedure. Thanks to this operational approach, the end user will be provided with ARD products including rich metadata and quality information, which can readily be integrated in analysis workflows, and combined with data from other sensors.

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PACO: Python-Based Atmospheric Correction

Cited by 23 publications

References 39 publications

Mapping Crop Types of Germany by Combining Temporal Statistical Metrics of Sentinel-1 and Sentinel-2 Time Series with LPIS Data

Mapping Crop Types of Germany by Combining Temporal Statistical Metrics of Sentinel-1 and Sentinel-2 Time Series with LPIS Data

The Desis L2a Processor and Validation of L2a Products Using Aeronet and Radcalnet Data

Analysis-Ready Data from Hyperspectral Sensors—The Design of the EnMAP CARD4L-SR Data Product

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