Cloud Mask Intercomparison eXercise (CMIX): An evaluation of cloud masking algorithms for Landsat 8 and Sentinel-2

Cloud cover is a major limiting factor in exploiting time-series data acquired by optical spaceborne remote sensing sensors. Multiple methods have been developed to address the problem of cloud detection in satellite imagery and a number of cloud masking algorithms have been developed for optical sensors but very few studies have carried out quantitative intercomparison of state-of-the-art methods in this domain. This paper summarizes results of the first Cloud Masking Intercomparison eXercise (CMIX) conducted within the Committee Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV). CEOS is the forum for space agency coordination and cooperation on Earth observations, with activities organized under working groups. CMIX, as one such activity, is an international collaborative effort aimed at intercomparing cloud detection algorithms for moderate-spatial resolution (10–30 m) spaceborne optical sensors. The focus of CMIX is on open and free imagery acquired by the Landsat 8 (NASA/USGS) and Sentinel-2 (ESA) missions. Ten algorithms developed by nine teams from fourteen different organizations representing universities, research centers and industry, as well as space agencies (CNES, ESA, DLR, and NASA), are evaluated within the CMIX. Those algorithms vary in their approach and concepts utilized which were based on various spectral properties, spatial and temporal features, as well as machine learning methods. Algorithm outputs are evaluated against existing reference cloud mask datasets. Those datasets vary in sampling methods, geographical distribution, sample unit (points, polygons, full image labels), and generation approaches (experts, machine learning, sky images). Overall, the performance of algorithms varied depending on the reference dataset, which can be attributed to differences in how the reference datasets were produced. The algorithms were in good agreement for thick cloud detection, which were opaque and had lower uncertainties in their identification, in contrast to thin/semi-transparent clouds detection. Not only did CMIX allow identification of strengths and weaknesses of existing algorithms and potential areas of improvements, but also the problems associated with the existing reference datasets. The paper concludes with recommendations on generating new reference datasets, metrics, and an analysis framework to be further exploited and additional input datasets to be considered by future CMIX activities

Integrated models for remotely sensed data analysis towards the detection and monitoring of urban land cover changes

The current remotely sensed data triggered the development of novel applications enabling the monitoring of urban areas. Nowadays digital image processing is focusing on the development of algorithms for the effective mapping of the dynamic urban environment. This tendency is reinforced by the research interest of the National Mapping and Cadastral Agencies in most European countries towards the implementation of strategies for the updating of 2D topographic databases. The proposed multitemporal image analysis combines the advantages of both pixel- and object-based image analysis. Change detection algorithms are applied on the simplified images for the detection of altered image pixels. Object-based image analysis is subsequently introduced for the efficient handling of changes by implementing a knowledge-based classification scheme. The developed algorithm is designed to provide a solution even when only optical satellite imagery is available. A second alternative change detection approach is established, diverging from the previous in a way that it exploits vector information of an existing geodatabase along with imagery data. A knowledge-base model is designed for the extraction of the objects of interest with the support of geodatabase information. The quantitative and qualitative evaluation of the proposed methodologies is performed with the help of ancillary ground truth data. The quantitative evaluation is accomplished by employing the standard performance evaluation measures of detection completeness, correctness and overall quality, which have been widely applied on building identification studies. The experimental results and the performed quantitative evaluation demonstrate the potential of the developed knowledge-based classification frameworks. The complexity of urban features dictates the combination of spectral, geometric and topological information under an efficient object-oriented analysis.Τα σύγχρονα τηλεπισκοπικά δεδομένα αποτέλεσαν το έναυσμα για την ανάπτυξη νέων εφαρμογών καθιστώντας εφικτή την παρακολούθηση του αστικού περιβάλλοντος. Στις μέρες μας υπάρχει μία τάση διαρκούς εξέλιξης των τεχνικών ψηφιακής ανάλυσης για την αποτελεσματική παρακολούθηση της δυναμικής του αστικού χώρου. Η τάση αυτή ενισχύεται από το ερευνητικό ενδιαφέρον της πλειοψηφίας των εθνικών οργανισμών χαρτογράφησης και κτηματολογίου σε ευρωπαϊκό επίπεδο για μεθοδολογίες εντοπισμού μεταβολών και ενημέρωσης των χαρτογραφικών βάσεων. Στην παρούσα διατριβή μελετάται η δυνατότητα βελτίωσης των τεχνικών εντοπισμού μεταβολών σε αστικές περιοχές είτε με την ανάλυση διαχρονικών εικόνων, είτε με το συνδυασμό υφιστάμενης ψηφιακής χαρτογραφικής βάσης και πολύ υψηλής ανάλυσης τηλεπισκοπικών δεδομένων. Αρχικά προτείνεται μία μεθοδολογία ανάλυσης διαχρονικών δεδομένων η οποία συνδυάζει τα πλεονεκτήματα των αλγορίθμων ανάλυσης εικόνας με βάση τη ψηφίδα και των αλγορίθμων με βάση τα αντικείμενα. Η προσέγγιση αυτή παρουσιάζει ιδιαίτερο ενδιαφέρον δεδομένου ότι στηρίζεται αποκλειστικά στην πληροφορία των τηλεπισκοπικών δεδομένων για τη διαχείριση πολύπλοκων αστικών δομών. Η δεύτερη προσέγγιση εντοπισμού μεταβολών χρησιμοποιεί μία υφιστάμενη ψηφιακή χαρτογραφική βάση δεδομένων. Η γνώση που εμπεριέχεται στη χαρτογραφική βάση αξιοποιείται με τον καθορισμό κανόνων, οι οποίοι ενσωματώνονται στη διαδικασία ανάλυσης της εικόνας. Η ακρίβεια των προτεινόμενων συστημάτων εντοπισμού μεταβολών προκύπτει μέσω της σύγκρισης των αποτελεσμάτων με πολύγωνα επίγειας αλήθειας τα οποία αναφέρονται στα νέα κτήρια των περιοχών μελέτης. Η εκτίμηση της αξιοπιστίας των αποτελεσμάτων των δύο μεθοδολογιών αποδεικνύει την υπεροχή της αντικειμενοστραφούς ανάλυσης εικόνας στη διαχείριση του αστικού χώρου και συγκεκριμένα στην αναγνώριση των νέων κτηρίων

Decision Support on Monitoring and Disaster Management in Agriculture with Copernicus Sentinel Applications

The successful implementation of the European Commission’s Common Agricultural Policy (CAP) and the insurance coverage in case of a natural disaster requires precise and regular mapping of crop types and detailed delineation of the disasters’ effects by frequent and accurate controls. Free and open access policy to Copernicus Sentinel data offers a big volume of data to the users on a consistent and complete basis. Today, the Sentinels are involved in an increasing number of agriculture applications, but their effective exploitation is still being investigated and the development of efficient tools, aligned to the user’s needs, is yet to be realised. To this end, the DiAS (Disaster and Agriculture Sentinel Applications) project proposes methods for decision support in agriculture using Sentinel data for crop type mapping, as well as mapping of the extend of fire and flood effects in agricultural areas. The DiAS Decision Support System (DSS) is designed in consultation with potential users in participatory approach and aims to provide a prototype tool, which provides assistance to the responsible paying agencies and insurance organizations to make decisions on farmers’ subsidies and compensations. The DiAS DSS prototype and its functionalities are presented in this paper and its use is demonstrated through example applications for two test sites in Greece. The DiAS DSS demonstrates the necessity for the development of similar tools, as this emerges from the user’s requirements, and wishes to stimulate and inspire further research and development

Monitoring water quality parameters of lake Koronia by means of long time-series multispectral satellite images

In this study, a comprehensive 30-year (1984-2016) water quality parameter database for Lake Koronia - one of the most important Ramsar wetlands of Greece - was compiled from Landsat imagery. The reliability of the data was evaluated by comparing water Quality Element (QE) values computed from Landsat data against in situ data. Water quality algorithms developed from previous studies, specifically for the determination of Water Temperature and pH, were applied to Landsat images. In addition, Water Depth, as along with the distribution of floating vegetation and cyanobacterial blooms, were mapped. The performed comprehensive analysis posed certain questions regarding the applicability of single empirical models across multi-temporal, multi-sensor datasets, towards the accurate prediction of key water quality indicators for shallow inland systems. Overall, this assessment demonstrates that despite some limitations, satellite imagery can provide an accurate means of obtaining comprehensive spatial and temporal coverage of key water quality characteristics.17618

Monitoring water quality parameters of Lake Koronia by means of long time-series multispectral satellite images

In this study, a comprehensive 30-year (1984–2016) water quality parameter database for Lake Koronia – one of the most important Ramsar wetlands of Greece – was compiled from Landsat imagery. The reliability of the data was evaluated by comparing water Quality Element (QE) values computed from Landsat data against in situ data. Water quality algorithms developed from previous studies, specifically for the determination of Water Temperature and pH, were applied to Landsat images. In addition, Water Depth, as along with the distribution of floating vegetation and cyanobacterial blooms, were mapped. The performed comprehensive analysis posed certain questions regarding the applicability of single empirical models across multi-temporal, multi-sensor datasets, towards the accurate prediction of key water quality indicators for shallow inland systems. Overall, this assessment demonstrates that despite some limitations, satellite imagery can provide an accurate means of obtaining comprehensive spatial and temporal coverage of key water quality characteristics

A Spectral Unmixing Model for the Integration of Multi-Sensor Imagery: A Tool to Generate Consistent Time Series Data

The Sentinel missions have been designed to support the operational services of the Copernicus program, ensuring long-term availability of data for a wide range of spectral, spatial and temporal resolutions. In particular, Sentinel-2 (S-2) data with improved high spatial resolution and higher revisit frequency (five days with the pair of satellites in operation) will play a fundamental role in recording land cover types and monitoring land cover changes at regular intervals. Nevertheless, cloud coverage usually hinders the time series availability and consequently the continuous land surface monitoring. In an attempt to alleviate this limitation, the synergistic use of instruments with different features is investigated, aiming at the future synergy of the S-2 MultiSpectral Instrument (MSI) and Sentinel-3 (S-3) Ocean and Land Colour Instrument (OLCI). To that end, an unmixing model is proposed with the intention of integrating the benefits of the two Sentinel missions, when both in orbit, in one composite image. The main goal is to fill the data gaps in the S-2 record, based on the more frequent information of the S-3 time series. The proposed fusion model has been applied on MODIS (MOD09GA L2G) and SPOT4 (Take 5) data and the experimental results have demonstrated that the approach has high potential. However, the different acquisition characteristics of the sensors, i.e. illumination and viewing geometry, should be taken into consideration and bidirectional effects correction has to be performed in order to reduce noise in the reflectance time series

Atmospheric Correction Inter-Comparison Exercise

The Atmospheric Correction Inter-comparison eXercise (ACIX) is an international initiative with the aim to analyse the Surface Reflectance (SR) products of various state-of-the-art atmospheric correction (AC) processors. The Aerosol Optical Thickness (AOT) and Water Vapour (WV) are also examined in ACIX as additional outputs of AC processing. In this paper, the general ACIX framework is discussed; special mention is made of the motivation to initiate the experiment, the inter-comparison protocol, and the principal results. ACIX is free and open and every developer was welcome to participate. Eventually, 12 participants applied their approaches to various Landsat-8 and Sentinel-2 image datasets acquired over sites around the world. The current results diverge depending on the sensors, products, and sites, indicating their strengths and weaknesses. Indeed, this first implementation of processor inter-comparison was proven to be a good lesson for the developers to learn the advantages and limitations of their approaches. Various algorithm improvements are expected, if not already implemented, and the enhanced performances are yet to be assessed in future ACIX experiments