Ana B. Ruescas

Thermal remote sensing in the framework of the SEN2FLEX project: field measurements, airborne data and applications

A description of thermal radiometric field measurements carried out in the framework of the European project SENtinel‐2 and Fluorescence Experiment (SEN2FLEX) is presented. The field campaign was developed in the region of Barrax (Spain) during June and July 2005. The purpose of the thermal measurements was to retrieve biogeophysical parameters such as land surface emissivity (LSE) and temperature (LST) to validate airborne‐based methodologies and to characterize different surfaces. Thermal measurements were carried out using two multiband field radiometers and several broadband field radiometers, pointing at different targets. High‐resolution images acquired with the Airborne Hyperspectral Scanner (AHS) sensor were used to retrieve LST and LSE, applying the Temperature and Emissivity Separation (TES) algorithm as well as single‐channel (SC) and two‐channel (TC) methods. To this purpose, 10 AHS thermal infrared (TIR) bands (8–13 µm) were considered. LST and LSE estimations derived from AHS data were used to obtain heat fluxes and evapotranspiration (ET) as an application of thermal remote sensing in the context of agriculture and water management. To this end, an energy balance equation was solved using the evaporative fraction concept involved in the Simplified Surface Energy Balance Index (S‐SEBI) model. The test of the different algorithms and methods against ground‐based measurements showed root mean square errors (RMSE) lower than 1.8 K for temperature and lower than 1.1 mm/day for daily ET.

Temporal analysis of normalized difference vegetation index (NDVI) and land surface temperature (LST) parameters to detect changes in the Iberian land cover between 1981 and 2001

In past decades, the Iberian Peninsula has been shown to have suffered vegetation changes such as desertification and reforestation. Normalized difference vegetation index (NDVI) and land surface temperature (LST) parameters, estimated from data acquired by the Advanced Very High Resolution Radiometer (AVHRR) sensor onboard the National Oceanic and Atmospheric Administration (NOAA) satellite series, are particularly adapted to assess these changes. This work presents an application of the yearly land-cover dynamics (YLCD) methodology to analyse the behaviour of the vegetation, which consists of a combined multitemporal study of the NDVI and LST parameters on a yearly basis. Throughout the 1981–2001 period, trend analysis of the YLCD parameters emphasizes the areas that have endured the greatest changes in their vegetation. This result is corroborated by results from previous studies.

An Optical Classification Tool for Global Lake Waters

Shallow and deep lakes receive and recycle organic and inorganic substances from within the confines of these lakes, their watershed and beyond. Hence, a large range in absorption and scattering and extreme differences in optical variability can be found between and within global lakes. This poses a challenge for atmospheric correction and bio-optical algorithms applied to optical remote sensing for water quality monitoring applications. To optimize these applications for the wide variety of lake optical conditions, we adapted a spectral classification scheme based on the concept of optical water types. The optical water types were defined through a cluster analysis of in situ hyperspectral remote sensing reflectance spectra collected by partners and advisors of the European Union 7th Framework Programme (FP7) Global Lakes Sentinel Services (GLaSS) project. The method has been integrated in the Envisat-BEAM software and the Sentinel Application Platform (SNAP) and generates maps of water types from image data. Two variations of water type classification are provided: one based on area-normalized spectral reflectance focusing on spectral shape (6CN, six-class normalized) and one that retains magnitude with no modification to the reflectance signal (6C). This resulted in a protocol, or processing scheme, that can also be applied or adapted for Sentinel-3 Ocean and Land Colour Imager (OLCI) datasets. We apply both treatments to MERIS imagery of a variety of European lakes to demonstrate its applicability. The studied target lakes cover a range of biophysical types, from shallow turbid to deep and clear, as well as eutrophic and dark absorbing waters, rich in colored dissolved organic matter (CDOM). In shallow, high-reflecting Dutch and Estonian lakes with high sediment load, 6C performed better, while in deep, low-reflecting clear Italian and Swedish lakes, 6CN performed better. The 6CN classification of in situ data is promising for very dark, high CDOM, absorbing lakes, but we show that our atmospheric correction of the imagery was insufficient to corroborate this. We anticipate that the application of the protocol to other lakes with unknown in-water characterization, but with comparable biophysical properties will suggest similar atmospheric correction (AC) and in-water retrieval algorithms for global lakes.

Examining the Effects of Dust Aerosols on Satellite Sea Surface Temperatures in the Mediterranean Sea Using the Medspiration Matchup Database

AbstractDust aerosol plumes from the Sahara cover the Mediterranean Sea regularly during the summer months (June–August) and occasionally during other seasons. Dust can absorb infrared longwave radiation, thus causing a drop in sea surface temperature (SST) retrievals from satellite. To quantify the magnitude of this absorption and to understand the sources of the biases that might be introduced when trying to validate SST algorithms with in situ bulk temperatures, the effects of the dust absorption are studied using the Medspiration Match-up Database. This database provides in situ and satellite SSTs derived from the Advanced Very High Resolution Radiometer (AVHRR) and the Advanced Along-Track Scanning Radiometer (AATSR), and the difference between skin and bulk measurements is calculated in order to obtain errors or residuals, which are classified by ranges and compared to an aerosol optical thickness product derived from the sensors. The behavior of the residuals is studied and there is clear correspon...

Mapping sub-pixel burnt percentage using AVHRR data. Application to the Alcalaten area in Spain

The purpose of this work is to estimate at sub-pixel scale the percentage of burnt land using the Advanced Very High Resolution Radiometer (AVHRR) through a simple approach. This methodology is based on multi-temporal spectral mixture analysis (MSMA), which uses a normalized difference vegetation index (NDVI) and a land-surface temperature (LST) image as input bands. The area of study is located in the Alcalaten region in Castellon (Spain), a typical semi-arid Mediterranean region. The results have shown an extension of approximately 55 km2 affected by fire, which is only 5% lower than the statistic reports provided by the Environmental Ministry of Spain. Finally, we include a map of the area showing the percentage of estimated burnt area per pixel and its associated uncertainties. The map was validated through supervised classification of an Airborne Hyperspectral Sensor (AHS) image taken on 27 September 2007. Results have a high accuracy, with a mean error of 6.5%.

Machine Learning Regression Approaches for Colored Dissolved Organic Matter (CDOM) Retrieval with S2-MSI and S3-OLCI Simulated Data

The colored dissolved organic matter (CDOM) variable is the standard measure of humic substance in waters optics. CDOM is optically characterized by its spectral absorption coefficient, a C D O M at at reference wavelength (e.g., ≈ 440 nm). Retrieval of CDOM is traditionally done using bio-optical models. As an alternative, this paper presents a comparison of five machine learning methods applied to Sentinel-2 and Sentinel-3 simulated reflectance ( R r s ) data for the retrieval of CDOM: regularized linear regression (RLR), random forest regression (RFR), kernel ridge regression (KRR), Gaussian process regression (GPR) and support vector machines (SVR). Two different datasets of radiative transfer simulations are used for the development and training of the machine learning regression approaches. Statistics comparison with well-established polynomial regression algorithms shows optimistic results for all models and band combinations, highlighting the good performance of the methods, especially the GPR approach, when all bands are used as input. Application to an atmospheric corrected OLCI image using the reflectance derived form the alternative neural network (Case 2 Regional) is also shown. Python scripts and notebooks are provided to interested users.

Fluorescence estimation in the framework of the CEFLES2 campaign

Chlorophyll fluorescence (ChF) is a relevant indicator of the actual plant physiological status. In this article different methods to measure ChF from remote sensing are evaluated: the Fraunhofer Line Discrimination (FLD), the Fluorescence Radiative Method (FRM) and the improved Fraunhofer Line Discrimination (iFLD). The three methods have been applied to data acquired in the framework of the CarboEurope, FLEX and Sentinel-2 (CEFLES2) campaign in Les Landes, France in September 2007. Comparing with in situ measurements, the results indicate that the methods that provide the best results are the FLD and the iFLD with root mean square errors (RMSEs) of 0.4 and 0.5 mW m−2 sr−1 nm−1, respectively, while the FRM provides an error of 0.8 mW m−2 sr−1 nm−1.

Retrieval of coloured dissolved organic matter with machine learning methods

The coloured dissolved organic matter (CDOM) concentration is the standard measure of humic substance in natural waters. CDOM measurements by remote sensing is calculated using the absorption coefficient (a) at a certain wavelength (e.g. ≈ 440nm). This paper presents a comparison of four machine learning methods for the retrieval of CDOM from remote sensing signals: regularized linear regression (RLR), random forest (RF), kernel ridge regression (KRR) and Gaussian process regression (GPR). Results are compared with the established polynomial regression algorithms. RLR is revealed as the simplest and most efficient method, followed closely by its nonlinear counterpart KRR.

The Land Surface Temperature Synergistic Processor in BEAM: A Prototype towards Sentinel-3

Land Surface Temperature (LST) is one of the key parameters in the physics of land-surface processes on regional and global scales, combining the results of all surface-atmosphere interactions and energy fluxes between the surface and the atmosphere. With the advent of the European Space Agency (ESA) Sentinel 3 (S3) satellite, accurate LST retrieval methodologies are being developed by exploiting the synergy between the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Radiometer (SLSTR). In this paper we explain the implementation in the Basic ENVISAT Toolbox for (A)ATSR and MERIS (BEAM) and the use of one LST algorithm developed in the framework of the Synergistic Use of The Sentinel Missions For Estimating And Monitoring Land Surface Temperature (SEN4LST) project. The LST algorithm is based on the split-window technique with an explicit dependence on the surface emissivity. Performance of the methodology is assessed by using MEdium Resolution Imaging Spectrometer/Advanced Along-Track Scanning Radiometer (MERIS/AATSR) pairs, instruments with similar characteristics than OLCI/ SLSTR, respectively. The LST retrievals were properly validated against in situ data measured along one year (2011) in three test sites, and inter-compared to the standard AATSR level-2 product with satisfactory results. The algorithm is implemented in BEAM using as a basis the MERIS/AATSR Synergy Toolbox. Specific details about the processor validation can be found in the validation report of the SEN4LST project.

Ocean colour and land remote sensing training using beam

Brockmann Consult (BC) is a private company dedicated to environmental management and data processing. BC offers value added products and thematic information derived from remote sensing data and other scientific consultancy. Training is part of our service portfolio and includes three separate areas of application, characterized by the type of trainee: 1) Regular training courses for students with an academic background; 2) Pro training courses addressing the development of BEAM or doing some scientific research using our tools; 3) Application training courses for clients that would like to get in-depth knowledge and training on usage of the value added products that are delivered by the company as part of the service.