Prediction of topsoil organic carbon using airborne and satellite hyperspectral imagery

The Airborne Hyperspectral Scanner (AHS) and the Hyperion satellite hyperspectral sensors were evaluated for their ability to predict topsoil organic carbon (C) in burned mountain areas of northwestern Spain slightly covered by heather vegetation. Predictive models that estimated total organic C (TOC) and oxidizable organic C (OC) content were calibrated using two datasets: a ground observation dataset with 39 topsoil samples collected in the field (for models built using AHS data), and a dataset with 200 TOC/OC observations predicted by AHS (for models built using Hyperion data). For both datasets, the prediction was performed by stepwise multiple linear regression (SMLR) using reflectances and spectral indices (SI) obtained from the images, and by the widely-used partial least squares regression (PLSR) method. SMLR provided a performance comparable to or even better than PLSR, while using a lower number of channels. SMLR models for the AHS were based on a maximum of eight indices, and showed a coefficient of determination in the leave-one-out cross-validation R2 = 0.60–0.62, while models for the Hyperion sensor showed R2 = 0.49–0.61, using a maximum of 20 indices. Although slightly worse models were obtained for the Hyperion sensor, which was attributed to its lower signal-to-noise ratio (SNR), the prediction of TOC/OC was consistent across both sensors. The relevant wavelengths for TOC/OC predictions were the red region of the spectrum (600–700 nm), and the short wave infrared region between ~2000–2250 nm. The use of SMLR and spectral indices based on reference channels at ~1000 nm was suitable to quantify topsoil C, and provided an alternative to the more complex PLSR method

Preliminary assessment of VIS-NIR-SWIR spectroscopy with a portable instrument for the detection of Staphylococcus aureus biofilms on surfaces

6 p.Bacterial biofilms constitute a major source of sanitary problems and economic losses in the food industry. Indeed, biofilm removal may require intense mechanical cleaning procedures or very high concentrations of disinfectants or both, which can be damaging to the environment and human health. This study assessed the efficacy of a technique based on spectroscopy in the visible, near-infrared, and short-wavelength infrared range for the quick detection of biofilms formed on polystyrene by the pathogenic bacterium Staphylococcus aureus. To do that, biofilms corresponding to three S. aureus strains, which differed in biofilm-forming ability and composition of the extracellular matrix, were allowed to develop for 5 or 24 h, representing an active formation stage and mature biofilms, respectively. Spectral analysis of the samples, corresponding to three biological replicates of each condition, was then performed by using a portable device. The results of these experiments showed that partial least squares discriminant analysis of the spectral profile could discriminate between surfaces containing attached bacterial biomass and non-inoculated ones. In this model, the two first principal components accounted for 39 and 19% of the variance and the estimated error rate stabilized after four components. Cross-validation accuracy of this assessment was 100%. This work lays the foundation for subsequent development of a spectroscopy-based protocol that allows biofilm detection on food industrial surfacesS

Empirical models for estimating air temperature using MODIS Land Surface Temperature (and Spatiotemporal Variables) in the Hurd Peninsula of Livingston Island, Antarctica, between 2000 and 2016

In this article, we present empirical models for estimating daily mean air temperature (Ta) in the Hurd Peninsula of Livingston Island (Antarctica) using Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) data and spatiotemporal variables. The models were obtained and validated using the daily mean Ta from three Spanish in situ meteorological stations (AEMET stations), Juan Carlos I (JCI), Johnsons Glacier (JG), and Hurd Glacier (HG), and three stations in our team’s monitoring sites, Incinerador (INC), Reina Sofía (SOF), and Collado Ramos (CR), as well as daytime and nighttime Terra-MODIS LST and Aqua-MODIS LST data between 2000 and 2016. Two types of multiple linear regression (MLR) models were obtained: models for each individual station (for JCI, INC, SOF, and CR—not for JG and HG due to a lack of data) and global models using all stations. In the study period, the JCI and INC stations were relocated, so we analyzed the data from both locations separately (JCI1 and JCI2; INC1 and INC2). In general, the best individual Ta models were obtained using daytime Terra LST data, the best results for CR being followed by JCI2, SOF, and INC2 (R2 = 0.5–0.7 and RSE = 2 °C). Model cross validation (CV) yielded results similar to those of the models (for the daytime Terra LST data: R2CV = 0.4–0.6, RMSECV = 2.5–2.7 °C, and bias = −0.1 to 0.1 °C). The best global Ta model was also obtained using daytime Terra LST data (R2 = 0.6 and RSE = 2 °C; in its validation: R2CV = 0.5, RMSECV = 3, and bias = −0.03), along with the significant (p < 0.05) variables: linear time (t) and two time harmonics (sine-cosine), distance to the coast (d), slope (s), curvature (c), and hour of LST observation (H). Ta and LST data were carefully corrected and filtered, respectively, prior to its analysis and comparison. The analysis of the Ta time series revealed different cooling/warming trends in the locations, indicating a complex climatic variability at a spatial scale in the Hurd Peninsula. The variation of Ta in each station was obtained by the Locally Weighted Regression (LOESS) method. LST data that was not “good quality” usually underestimated Ta and were filtered, which drastically reduced the LST data (<5% of the studied days). Despite the shortage of “good” MODIS LST data in these cold environments, all months were represented in the final dataset, demonstrating that the MODIS LST data, through the models obtained in this article, are useful for estimating long-term trends in Ta and generating mean Ta maps at a global level (1 km2 spatial resolution) in the Hurd Peninsula of Livingston Island

Comparison of MODIS-derived land surface temperatures with in situ temperatures measured in the Hurd Peninsula, Livingston Island, Antarctica: first results

Ponencia presentada en: XVII Congreso de la Asociación Española de Teledetección celebrado en Murcia, del 3 al 7 de octubre de 2017.[ES]En el marco del proyecto PERMASNOW estamos comparando los datos térmicos MODIS (LST, 1-km) con los obtenidos in situ (Ta y Ts) en las estaciones meteorológicas de la AEMET y las propias del proyecto en la península Hurd, isla Livingston (Antártida). Este trabajo muestra los primeros resultados obtenidos para las estaciones de la AEMET: Juan Carlos I (JCI), Glaciar Johnson (GJ) y Glaciar Hurd (GH) en el periodo marzo-2000/julio-2002 y febrero-2016. Se comparan los datos diarios LST con los valores medios diarios de Ta y Ts. Se observa una alta correlación lineal entre Ta y Ts, restringiéndose entonces la comparación a Ta. Se confirma también la tendencia lineal ligeramente decreciente de Ta en el periodo estudiado. Aunque la nubosidad limita la disponibilidad de datos LST, sin embargo, el mayor problema proviene de la calidad de los datos LST, observando que los que no son de “good quality” generalmente subestiman mucho LST y no son fiables. El producto MODIS-albedo diario (500-m) nos ayuda a mejorar el filtrado de datos de “other quality” y “cloud”, además de clasificar la cubierta en tierra (con/sin nieve) o agua (nieve/hielo fundido). El filtrado reduce a un 3-8% los días con datos disponibles en JCI y GJ y elimina todos en GH. Un ajuste lineal simple no explica bien la relación LST (tierra/agua)-Ta (R2=0,1-0,4), recurriendo a regresiones lineales múltiples para tener en cuenta las variaciones anuales/estacionales en esta relación. Así R2 sube a 0,3-0,6, siendo mejor en JCI (R2=0,6 y RSE~2°C). Se concluye que los datos LST-MODIS sirven para estimar tendencias a largo plazo en Ta a nivel global en la isla Livingston. Mejorar la calidad de los datos LST en este tipo de ambientes fríos es esencial.[EN]In the framework of the PERMASNOW project, we are comparing the MODIS thermal data (LST, 1-km) with those obtained in situ (Ta and Ts) at the AEMET meteorological stations and the project’s stations in the Hurd Peninsula, Livingston Island (Antarctica). This article shows the first results obtained at the AEMET stations: Juan Carlos I (JCI), Glacier Johnson (GJ) and Glacier Hurd (GH) in the period of March-2000/July-2002 and February-2016. The daily LST data are compared with the daily mean values of Ta and Ts. A high linear correlation between Ta and Ts is observed, and thus, the comparison being restricted to Ta. The slightly decreasing linear trend of Ta in the studied period is also confirmed. Although the cloudiness limits the availability of LST data, however, the main problem proceeds from the quality of the LST data, observing that those with no “good quality” usually underestimate LST and are not reliable. The daily MODIS albedo product (500-m) helps us to improve the filtering of data with “other quality” and “cloud”, besides of classifying the cover in land (with/without snow) or water (melting snow/ice). The filtering reduces to 3-8% the days with available data at JCI and GJ, and eliminates all of them in GH. A simple linear fit does not explain well the relationship LST (land/water)-Ta (R2=0.1-0.4), appealing to multiple linear regressions to take into account the annual/seasonal variations in this relationship. So, R2 goes up to 0.3-0.6, being better at JCI (R2=0.6 and RSE~2°C). It is concluded that the MODIS-LST data are useful for estimating long-term trends in Ta at a global level in the Livingston Island. Improving the quality of the LST data in this type of cold environments is essential.Este trabajo ha sido financiado por el Ministerio de Economía y Competitividad (MINECO), a través del proyecto PERMASNOW (CTM2014-52021-R)

Variability of the air temperature and its comparison with MODIS Land Surface Temperature in the Hurd Peninsula of the Livingston Island, Antarctica, between 2000 and 2016

Ponencia presentada en: XVIII Congreso de la Asociación Española de Teledetección celebrado en Valladolid del 24 al 27 septiembre 2019.[ES]En los ambientes polares, tan vulnerables al cambio climático, el estudio de la temperatura es imprescindible. Por ello, y en el marco del proyecto PERMASNOW, en este trabajo hemos estudiado en detalle la variación de la temperatura del aire (Ta) en la península Hurd de la isla Livingston (Antártida) y hemos tratado de estimarla a partir de los datos de temperatura de la superficie terrestre (LST, por sus siglas en inglés) del sensor MODIS entre los años 2000 y 2016. Utilizamos datos de Ta media diaria obtenidos en seis puntos, tres estaciones meteorológicas de AEMET y tres estaciones del proyecto PERMASNOW: Juan Carlos I (JCI), Glaciar Johnson (GJ), Glaciar Hurd (GH), Incinerador (INC), Reina Sofía (RS) y Collado Ramos (CR), respectivamente. Los datos MODIS LST son los diurnos y nocturnos de los satélites Terra y Aqua. La tendencia en Ta se ha analizado mediante la regresión localmente ponderada (LOESS, por sus siglas en inglés) y la relación Ta -LST con regresiones lineales múltiples. Concluimos que Ta en el área de estudio varía: se observa en la estación JCI, más cercana a la costa, una tendencia al enfriamiento con valores entre –2,3 y –3,0°C década–1. En cambio, las estaciones más alejadas de la costa y de mayor altitud muestran una tendencia al calentamiento (entre +0,2 y +0,8°C década-1). Los mejores modelos de estimación de Ta a partir de LST y variables temporales se obtienen con los datos diurnos de Terra (R2 = 0,5-0,7; RSE = 2°C), exceptuando GJ, donde la variable LST no es significativa. Con la validación cruzada (CV) se aprecian también, excepto en GJ, mejores resultados con los datos diurnos de Terra (R2 CV = 0,5-0,6; RMSECV = 2,5-2,6°C). Finalmente, concluimos que los datos MODIS LST son útiles para estimar tendencias de Ta a largo plazo en el área de estudio.[EN]In polar zones, where satellite data are very useful due to the limited in situ data, it is therefore essential to study the air temperature behaviour. In the framework of the PERMASNOW project we estimated the air temperature (Ta) in the Hurd Peninsula of Livingston Island (Antarctica) from the land surface temperature (LST) data of the MODIS between 2000 and 2016. We worked with Ta data obtained at six stations (3 AEMET meteorological stations and 3 PERMASNOW project stations: Juan Carlos I (JCI), Johnson Glacier (JG), Hurd Glacier (HG), Incinerator (INC), Reina Sofia (RS) and Collado Ramos (CR), respectively. In addition, we analyzed daytime and nighttime LST data from the Terra and Aqua satellites. Locally weighted regression (LOESS) and multiple linear regressions were used for statistical analysis. We conclude that the Ta in the study area varies: a cooling trend with values between –2.3 and –3.0°C decade-1 is observed in JCI, which is closer to the coast. On the other hand, the stations farther from the coast and at higher altitudes show a warming trend (between +0.2 and +0.8°C decade-1). The best Ta models are obtained with Terra daytime data (R2 = 0.5-0.7 and RSE = 2°C), except JG, where the LST variable is not significant. With cross validation (CV), better results are also seen, except in JG, with the daytime Terra data (R2 CV = 0.5-0.6, RMSECV = 2.5-2.6°C). In summary, MODIS LST data are useful for estimating long-term Ta trends in the study area.Esta investigación fue posible gracias a la financiación de la Agencia Estatal de Investigación a través del proyecto PERMASNOW [CTM2014-52021-R], la ayuda de la Universidad de Oviedo al Grupo de Investigación RSApps en 2018 [PAPI-18-GR-2016-0005] y las ayudas obtenidas por A.C.-P. (“Severo Ochoa” del Gobierno del Principado de Asturias [BP17-151] y “Ayuda Predoctoral” de la Universidad de Oviedo)

Variabilidad ultravioleta del nucleo de la galaxia Seyfert 1 Fairall 9

Centro de Informacion y Documentacion Cientifica (CINDOC). C/Joaquin Costa, 22. 28002 Madrid. SPAIN / CINDOC - Centro de Informaciòn y Documentaciòn CientìficaSIGLEESSpai

Variabilidad ultravioleta del núcleo de la galaxia Seyfert 1 Fairall 9

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Ciencias, Departamento de Física Teórica. Fecha de lectura: 25-03-1994

Prediction of Topsoil Organic Carbon Using Airborne and Satellite Hyperspectral Imagery

The Airborne Hyperspectral Scanner (AHS) and the Hyperion satellite hyperspectral sensors were evaluated for their ability to predict topsoil organic carbon (C) in burned mountain areas of northwestern Spain slightly covered by heather vegetation. Predictive models that estimated total organic C (TOC) and oxidizable organic C (OC) content were calibrated using two datasets: a ground observation dataset with 39 topsoil samples collected in the field (for models built using AHS data), and a dataset with 200 TOC/OC observations predicted by AHS (for models built using Hyperion data). For both datasets, the prediction was performed by stepwise multiple linear regression (SMLR) using reflectances and spectral indices (SI) obtained from the images, and by the widely-used partial least squares regression (PLSR) method. SMLR provided a performance comparable to or even better than PLSR, while using a lower number of channels. SMLR models for the AHS were based on a maximum of eight indices, and showed a coefficient of determination in the leave-one-out cross-validation R2 = 0.60–0.62, while models for the Hyperion sensor showed R2 = 0.49–0.61, using a maximum of 20 indices. Although slightly worse models were obtained for the Hyperion sensor, which was attributed to its lower signal-to-noise ratio (SNR), the prediction of TOC/OC was consistent across both sensors. The relevant wavelengths for TOC/OC predictions were the red region of the spectrum (600–700 nm), and the short wave infrared region between ~2000–2250 nm. The use of SMLR and spectral indices based on reference channels at ~1000 nm was suitable to quantify topsoil C, and provided an alternative to the more complex PLSR method