scholarly journals SENTINEL 2 AND LANDSAT-8 BANDS SENSITIVITY ANALYSIS FOR MAPPING OF ALKALINE SOIL IN NORTHERN DRY ZONE OF KARNATAKA, INDIA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 307-313 ◽  
Author(s):  
S. Meti ◽  
P. D. Lakshmi ◽  
M. S. Nagaraja ◽  
V. Shreepad ◽  
Keyword(s):  
High Resolution ◽  
Soil Ph ◽  
Satellite Data ◽  
Soil Salinity ◽  
Degradation Process ◽  
Landsat 8 ◽  
Alkaline Soil ◽  
Policy Makers ◽  
Dry Zone ◽  
Sentinel 2

<p><strong>Abstract.</strong> Soil salinization is most common land degradation process occurring in deep vertisol of northern dry zone of Karnataka, India. Accurate and high resolution spatial information on salinization can assist policy makers to better target areas for interventions to avoid aggravation of soil degradation process. Digital soil mapping using satellite data has been identified as a potential means of obtaining soil information. This paper focuses on exploring possibility of using new generation medium resolution Landsat-8 and Sentinel-2 satellite data to map alkaline soils of Ramthal irrigation project area in north Karnataka. Surface soil salinity parameters of zone 20 were correlated with reflectance values of different band and band combination and traditional salinity indices and result has indicated that SWIR bands of both satellite showed significant negative correlation with soil pH, EC (r&amp;thinsp;=&amp;thinsp;&amp;minus;0.39 to &amp;minus;0.45) whereas visible and NIR bands did not show significant relation. However rationing of SWIR bands with visible blue band has significantly improved the correlation with soil pH and EC (r&amp;thinsp;=&amp;thinsp;+0.60 to +0.70). Traditional salinity index based on visible bands failed to show significant correlation with soil parameters. It is interesting to note that SWIR bands alone did not show significant correlation with soil sodicity parameters like exchangeable Na, SAR, RSC but band rationing with blue bands has significantly improved the correlation (r&amp;thinsp;=&amp;thinsp;0.45). High resolution soil salinity map was prepared using simple linear regression model and using this map will serve as base map for the policy makers.</p>

scholarly journals Assessment and inventory of forest parameters using medium-high resolution satellite data

2021 ◽  
Author(s):  
Ειρήνη Χρυσάφη
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Satellite Data ◽  
Earth Observation ◽  
Landsat 8 ◽  
Spectral Instrument ◽  
Stacked Generalization ◽  
Sentinel 2

Τα μεσογειακά δάση χαρακτηρίζονται από υψηλή χωροχρονική ετερογένεια και αποτελούν ένα από σημαντικότερα σημεία της βιοποικιλότητας στον πλανήτη. Η σημαντική αξία τους και το ευρύ φάσμα των οικοσυστημικών υπηρεσιών που παρέχουν, αναγνωρίζεται ευρέως από επιστήμονες, διεθνείς συμβάσεις και οργανισμούς. Ωστόσο, η ευπάθεια τους σε ανθρώπινες και φυσικές απειλές έχει ως αποτέλεσμα την διατάραξη τους. Συνεπώς, σχέδια βιώσιμης διαχείρισης και αειφορικής ανάπτυξης καθίστανται ως επιτακτική ανάγκη. Οι πρακτικές παρακολούθησης και απογραφής δασών απαιτούν την αξιόπιστη εκτίμηση δασικών παραμέτρων, όπως η κυκλική επιφάνεια, ο αριθμός δέντρων ανά μονάδα επιφάνειας και ξυλώδες όγκου. Η ετερογένεια των μεσογειακών δασών και η δύσκολη πρόσβασής τους, καθιστά την επιστήμη της τηλεπισκόπησης ως εξαιρετικά χρήσιμο μέσο για την αξιολόγηση των δασικών πόρων. Η τεχνολογία της τηλεπισκόπησης και τα ανοιχτά δεδομένα τηλεπισκόπησης παρέχουν μεγάλες δυνατότητες στον τομέα της δασολογίας και στην δασική απογραφή. Επιπλέον, η ταχεία πρόοδος στους αλγόριθμους τεχνητής νοημοσύνης διευκολύνει την ανάλυση ευρέος φάσματος δεδομένων. Σε αυτό το πλαίσιο, ο συνδυασμός αυτών των ισχυρών εργαλείων (δεδομένα τηλεπισκόπησης και προσεγγίσεις μηχανικής μάθησης) συνιστά μια πολλά υποσχόμενη, αλλά και ερευνητική πρόκληση, για την εκτίμηση δασικών παραμέτρων. Στη παρούσα διατριβή, εξετάζονται διάφορες προσεγγίσεις για την βελτιστοποίηση της εκτίμησης δασικών παραμέτρων με την χρήση δορυφορικών εικόνων και τεχνικών μηχανικής μάθησης.Η δομή της παρούσας διατριβής αποτελείται από τρία μέρη. Το πρώτο μέρος αποτελείται από τέσσερα κεφάλαια. Αρχικά, στο Κεφάλαιο 1, γίνεται μια εισαγωγή στην αξία των μεσογειακών δασών, στις υπηρεσίες που παρέχουν και στις απειλές που αντιμετωπίζουν. Το Κεφάλαιο 2 τονίζει την ανάγκη αειφορικής διαχείρισης των δασών και κατ 'επέκταση της απογραφής και αξιόπιστης εκτίμησης δασικών παραμέτρων. Στο Κεφάλαιο 3, παρουσιάζονται εν συντομία πηγές δεδομένων τηλεπισκόπησης και η συμβολή τους σε δασικές εφαρμογές και ιδιαίτερα στην εκτίμηση δασικών παραμέτρων, σε περιοχές της Μεσογείου. Το κεφάλαιο 4, αποτελεί μια εισαγωγή στους αλγόριθμους τεχνητής νοημοσύνης και μηχανικής μάθησης και πώς αυτές οι προσεγγίσεις εφαρμόζονται στον τομέα της τηλεπισκόπησης και της δασολογίας. Τέλος παρουσιάζονται τα ερευνητικά ερωτήματα και τα αντικείμενα της παρούσας διατριβής. Το δεύτερο μέρος αποτελείται από τέσσερα άρθρα, εκ των οποίων, το πρώτο (Κεφάλαιο 7) έχει δημοσιευτεί στο περιοδικό Remote Sensing of Environment (2017) και αφορά την εκτίμηση δασικών παραμέτρων χρησιμοποιώντας δια-εποχιακές εικόνες Landsat 8 Operational Land Imager. Το δεύτερο άρθρο (Κεφάλαιο 8) έχει δημοσιευτεί στο Remote Sensing Letters (2017) και αφορά τις σχέσεις μεταξύ ξυλώδες όγκου και εικόνων Sentinel-2 Multi Spectral Instrument. Το τρίτο άρθρο (Κεφάλαιο 9) έχει δημοσιευτεί στο περιοδικό International Journal of Applied Earth Observation and Geoinformation (2019) και αφορά την αξιολόγηση των δορυφορικών δεδομένων Sentinel-2 Multi Spectral Instrument για την εκτίμηση του ξυλώδες όγκου. Το τελευταίο άρθρο (Κεφάλαιο 10) που προορίζεται προς δημοσίευση, αποτελεί μια προκαταρκτική μελέτη για την εκτίμηση του ξυλώδες όγκου σε ένα μεσογειακό δασικό οικοσύστημα, με μία μετά-μαθησιακή προσέγγιση και την ανάπτυξη ενός μοντέλου συσσωρευμένης γενίκευσης (stacked generalization). Τέλος, στο τρίτο μέρος της παρούσας διατριβής παρουσιάζονται συνοπτικά οι απαντήσεις των ερωτημάτων που τέθηκαν στην παρούσα διατριβή και τα προβλήματα - περιορισμοί που αντιμετωπίστηκαν. Επίσης, προτείνονται δυνατότητες και προοπτικές εξέλιξης της παρούσας έρευνας, που θα μπορούσε να αποτελέσουν αντικείμενο για μελλοντική έρευνα.

scholarly journals STAIR 2.0: A Generic and Automatic Algorithm to Fuse Modis, Landsat, and Sentinel-2 to Generate 10 m, Daily, and Cloud-/Gap-Free Surface Reflectance Product

2020 ◽  
Vol 12 (19) ◽  
pp. 3209
Author(s):  
Yunan Luo ◽  
Kaiyu Guan ◽  
Jian Peng ◽  
Sibo Wang ◽  
Yizhi Huang
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Satellite Data ◽  
Temporal Frequency ◽  
Fusion Product ◽  
High Temporal Resolution ◽  
Surface Reflectance ◽  
Land Surfaces ◽  
Fine Resolution ◽  
Sentinel 2

Remote sensing datasets with both high spatial and high temporal resolution are critical for monitoring and modeling the dynamics of land surfaces. However, no current satellite sensor could simultaneously achieve both high spatial resolution and high revisiting frequency. Therefore, the integration of different sources of satellite data to produce a fusion product has become a popular solution to address this challenge. Many methods have been proposed to generate synthetic images with rich spatial details and high temporal frequency by combining two types of satellite datasets—usually frequent coarse-resolution images (e.g., MODIS) and sparse fine-resolution images (e.g., Landsat). In this paper, we introduce STAIR 2.0, a new fusion method that extends the previous STAIR fusion framework, to fuse three types of satellite datasets, including MODIS, Landsat, and Sentinel-2. In STAIR 2.0, input images are first processed to impute missing-value pixels that are due to clouds or sensor mechanical issues using a gap-filling algorithm. The multiple refined time series are then integrated stepwisely, from coarse- to fine- and high-resolution, ultimately providing a synthetic daily, high-resolution surface reflectance observations. We applied STAIR 2.0 to generate a 10-m, daily, cloud-/gap-free time series that covers the 2017 growing season of Saunders County, Nebraska. Moreover, the framework is generic and can be extended to integrate more types of satellite data sources, further improving the quality of the fusion product.

scholarly journals High-Resolution Sea Surface Temperature and Salinity in the Global Ocean from Raw Satellite Data

2019 ◽  
Vol 11 (19) ◽  
pp. 2191 ◽  
Author(s):  
Encarni Medina-Lopez ◽  
Leonardo Ureña-Fuentes
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Satellite Data ◽  
Google Earth ◽  
Sea Surface ◽  
Global Ocean ◽  
The Neural Network ◽  
Wide Range ◽  
Sentinel 2

The aim of this work is to obtain high-resolution values of sea surface salinity (SSS) and temperature (SST) in the global ocean by using raw satellite data (i.e., without any band data pre-processing or atmospheric correction). Sentinel-2 Level 1-C Top of Atmosphere (TOA) reflectance data is used to obtain accurate SSS and SST information. A deep neural network is built to link the band information with in situ data from different buoys, vessels, drifters, and other platforms around the world. The neural network used in this paper includes shortcuts, providing an improved performance compared with the equivalent feed-forward architecture. The in situ information used as input for the network has been obtained from the Copernicus Marine In situ Service. Sentinel-2 platform-centred band data has been processed using Google Earth Engine in areas of 100 m × 100 m. Accurate salinity values are estimated for the first time independently of temperature. Salinity results rely only on direct satellite observations, although it presented a clear dependency on temperature ranges. Results show the neural network has good interpolation and extrapolation capabilities. Test results present correlation coefficients of 82 % and 84 % for salinity and temperature, respectively. The most common error for both SST and SSS is 0.4 ∘ C and 0 . 4 PSU. The sensitivity analysis shows that outliers are present in areas where the number of observations is very low. The network is finally applied over a complete Sentinel-2 tile, presenting sensible patterns for river-sea interaction, as well as seasonal variations. The methodology presented here is relevant for detailed coastal and oceanographic applications, reducing the time for data pre-processing, and it is applicable to a wide range of satellites, as the information is directly obtained from TOA data.

Experiments on Machine Learning Techniques for Soil Classification Using Sentinel-2 Products

2020 ◽  
Author(s):  
Victor Bacu ◽  
Teodor Stefanut ◽  
Dorian Gorgan
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Satellite Data ◽  
Field Measurements ◽  
Soil Classification ◽  
Machine Learning Techniques ◽  
Classification Models ◽  
Spatial Features ◽  
Learning Techniques ◽  
Sentinel 2

&lt;p&gt;Agricultural management relies on good, comprehensive and reliable information on the environment and, in particular, the characteristics of the soil. The soil composition, humidity and temperature can fluctuate over time, leading to migration of plant crops, changes in the schedule of agricultural work, and the treatment of soil by chemicals. Various techniques are used to monitor soil conditions and agricultural activities but most of them are based on field measurements. Satellite data opens up a wide range of solutions based on higher resolution images (i.e. spatial, spectral and temporal resolution). Due to this high resolution, satellite data requires powerful computing resources and complex algorithms. The need for up-to-date and high-resolution soil maps and direct access to this information in a versatile and convenient manner is essential for pedology and agriculture experts, farmers and soil monitoring organizations.&lt;/p&gt;&lt;p&gt;Unfortunately, the satellite image processing and interpretation are very particular to each area, time and season, and must be calibrated by the real field measurements that are collected periodically. In order to obtain a fairly good accuracy of soil classification at a very high resolution, without using interpolation methods of an insufficient number of measurements, the prediction based on artificial intelligence techniques could be used. The use of machine learning techniques is still largely unexplored, and one of the major challenges is the scalability of the soil classification models toward three main directions: (a) adding new spatial features (i.e. satellite wavelength bands, geospatial parameters, spatial features); (b) scaling from local to global geographical areas; (c) temporal complementarity (i.e. build up the soil description by samples of satellite data acquired along the time, on spring, on summer, in another year, etc.).&lt;/p&gt;&lt;p&gt;The presentation analysis some experiments and highlights the main issues on developing a soil classification model based on Sentinel-2 satellite data, machine learning techniques and high-performance computing infrastructures. The experiments concern mainly on the features and temporal scalability of the soil classification models. The research is carried out using the HORUS platform [1] and the HorusApp application [2], [3], which allows experts to scale the computation over cloud infrastructure.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;References:&lt;/p&gt;&lt;p&gt;[1] Gorgan D., Rusu T., Bacu V., Stefanut T., Nandra N., &amp;#8220;Soil Classification Techniques in Transylvania Area Based on Satellite Data&amp;#8221;. World Soils 2019 Conference, 2 - 3 July 2019, ESA-ESRIN, Frascati, Italy (2019).&lt;/p&gt;&lt;p&gt;[2] Bacu V., Stefanut T., Gorgan D., &amp;#8220;Building soil classification maps using HorusApp and Sentinel-2 Products&amp;#8221;, Proceedings of the Intelligent Computer Communication and Processing Conference &amp;#8211; ICCP, in IEEE press (2019).&lt;/p&gt;&lt;p&gt;[3] Bacu V., Stefanut T., Nandra N., Rusu T., Gorgan D., &amp;#8220;Soil classification based on Sentinel-2 Products using HorusApp application&amp;#8221;, Geophysical Research Abstracts, Vol. 21, EGU2019-15746, 2019, EGU General Assembly (2019).&lt;/p&gt;

An empirical cross sensor model for high-resolution multispectral imagery

2020 ◽  
Author(s):  
Manivasagam Vellalapalayam Subramanian ◽  
Gregoriy Kaplan ◽  
Offer Rozenstein
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Transformation Model ◽  
Sensor Data ◽  
Absolute Difference ◽  
Landsat 8 ◽  
Vegetation Monitoring ◽  
Spectral Angle Mapper ◽  
Statistical Measures ◽  
Sentinel 2

&lt;p&gt;The availability of public-domain high-resolution satellite imagery such as Sentinel-2 and Landsat-8 has increased earth observation (EO) studies across the globe. Empirically combining different EO sensor data into a single dataset increases the temporal coverage, which is useful for land-cover monitoring. In this study, a transformation model was developed for Sentinel-2 and Vegetation and Environmental New micro Spacecraft (VEN&amp;#956;S) imagery over Israel. Both sensors offer high spatio-temporal resolution imagery, i.e., VEN&amp;#956;S has a 10m spatial resolution with a two-day revisit period, and Sentinel-2 has a 10-20 m spatial resolution with a five-day revisit period. Near-simultaneously acquired imagery was employed for the transformation model development. The model coefficients were derived for the overlapping spectral regions of both sensors. Further, the transformation model performance was tested using various statistical measures, namely, orthogonal distance regression (ODR), spectral angle mapper (SAM), and mean absolute difference (MAD). The validation results highlighted that MAD values were reduced between Sentinel-2 and transformed VEN&amp;#956;S reflectance. Similarly, the ODR slope values became closer to one, and the overall spectral similarity increased as demonstrated by a decrease in SAM values. This transformation function creates a unified reflectance dataset in the form of a dense time-series of observation, especially useful for vegetation monitoring.&lt;/p&gt;

scholarly journals High Resolution Sentinel-2 Images for Improved Bathymetric Mapping of Coastal and Lake Environments

2019 ◽  
Author(s):  
Ali P. Yunus ◽  
Dou Jie ◽  
Xuan Song ◽  
Ram Avtar
Keyword(s):  
High Resolution ◽  
Landsat 8 ◽  
Mobile Bay ◽  
Landsat 8 Oli ◽  
Transport Pathways ◽  
Satellite Technology ◽  
Bathymetric Data ◽  
Crucial Component ◽  
Bathymetric Mapping ◽  
Sentinel 2

Bathymetry of nearshore coastal environments and lakes are constantly reworking because of the change in the patterns of energy dispersal and related sediment transport pathways. Therefore, updated and accurate bathymetric models are a crucial component in providing basic information for scientific, managerial, and geographical studies. Recent advances in satellite technology have revolutionized the acquisition of bathymetric profiles, offering new vistas in mapping. This contribution analysed the suitability of high resolution Sentinel-2 images for bathymetric mapping of coastal and lake environments. The bathymetric algorithm for satellite imageries was developed based on the available high resolution bathymetric data for Mobile Bay, Tampa Bay and Lake Huron regions obtained from National Oceanic and Atmospheric Administration (NOAA) National Geophysical Data Center (NGDC). The results demonstrate that the satellite derived bathymetry is efficient for retrieving depths up to 10 m for coastal regions and up to 30 m for lake environment. The root mean square error (RMSE) varies between 1.99 m and 2.80 m for the three regions. A comparison of Sentinel-2 derived bathymetry is also carried with the Landsat 8 OLI derived bathymetry. The results suggest Sentinel-2 images are capable of producing much accurate bathymetric maps than those from the Landsat 8 OLI images. Our work demonstrated that the freely available Sentinel-2 imagery proved to be a reliable method for acquiring updated high resolution bathymetric information for large areas in short span of time.

The CMEMS High Resolution Coastal Service

2021 ◽  
Author(s):  
Dimitry Van der Zande ◽  
Kerstin Stelzer ◽  
Martin Böttcher ◽  
João Felipe Cardoso dos Santos ◽  
Carole Lebreton ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Landsat 8 ◽  
Backscatter Coefficient ◽  
Ocean Colour ◽  
New Production ◽  
In Situ Data ◽  
Full Picture ◽  
Spm Concentration ◽  
Sentinel 2

&lt;p&gt;High-quality satellite-based ocean colour products can provide valuable support and insights in management and monitoring of coastal ecosystems. Today&amp;#8217;s availability of Earth Observation (EO) data is unprecedented including traditional medium resolution ocean colour systems (e.g. SeaWiFS, MODIS-AQUA, MERIS, Sentinel-3/OLCI), high resolution land sensors (e.g. Sentinel-2/MSI, Landsat-8/OLI, Pleiades) and geostationary satellites (e.g. SEVIRI). Each of these sensors offers specific advantages in terms of spatial, temporal or radiometric characteristics.&lt;/p&gt;&lt;p&gt;As a new production unit, the high resolution coastal service will be integrated in CMEMS. It offers 12 different products which are covered within the Ocean Colour Thematic Assembly Centre (OCTAC). The products can be categorized in two groups: 1) near real time (NRT) and Multi-Year near real time (MYNRT). The products are generated the coastal waters (20km stripe for the coastline) for all European Seas and are provided in 100m spatial resolution. All products are based on Sentinel-2 MSI data. The primary OCTAC variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral Remote Sensing Reflectance (RRS). This, together with the Particulate Backscatter Coefficient (BBP), constitute the category of the optics products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm. The transparency products include turbidity (TUR) and Suspended Particulate Matter (SPM) concentration. They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions. The geophysical product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging. The NRT products are generally provided withing 24 hours after end of the acquisition day, while monthly averaged products are provided few days after end of the respective month. A third group of products are daily gap-filled products which are provided once in a quarter. Validation of the variables has been performed by match-up analysis with in situ data as well as by comparison of the high resolution products with the well established Low Resolution CMEMS Ocean Colour products. The products will be introduced in the CMEMS service by May 2021. We will present the products themselves as well as the validation results for the different variables. The known limitations will be reported in order to provide a full picture of the new service.&lt;/p&gt;

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