Jordi Cristóbal

Revision of the Single-Channel Algorithm for Land Surface Temperature Retrieval From Landsat Thermal-Infrared Data

This paper presents a revision, an update, and an extension of the generalized single-channel (SC) algorithm developed by Jimenez-Munoz and Sobrino (2003), which was particularized to the thermal-infrared (TIR) channel (band 6) located in the Landsat-5 Thematic Mapper (TM) sensor. The SC algorithm relies on the concept of atmospheric functions (AFs) which are dependent on atmospheric transmissivity and upwelling and downwelling atmospheric radiances. These AFs are fitted versus the atmospheric water vapor content for operational purposes. In this paper, we present updated fits using MODTRAN 4 radiative transfer code, and we also extend the application of the SC algorithm to the TIR channel of the TM sensor onboard the Landsat-4 platform and the enhanced TM plus sensor onboard the Landsat-7 platform. Five different atmospheric sounding databases have been considered to create simulated data used for retrieving AFs and to test the algorithm. The test from independent simulated data provided root mean square error (rmse) values below 1 K in most cases when atmospheric water vapor content is lower than 2 g middotcm-2. For values higher than 3 g middotcm-2, errors are not acceptable, as what occurs with other SC algorithms. Results were also tested using a land surface temperature map obtained from one Landsat-5 image acquired over an agricultural area using inversion of the radiative transfer equation and the atmospheric profile measured in situ at the sensor overpass time. The comparison with this ldquoground-truthrdquo map provided an rmse of 1.5 K.

Land Surface Temperature Retrieval Methods From Landsat-8 Thermal Infrared Sensor Data

The importance of land surface temperature (LST) retrieved from high to medium spatial resolution remote sensing data for many environmental studies, particularly the applications related to water resources management over agricultural sites, was a key factor for the final decision of including a thermal infrared (TIR) instrument on board the Landsat Data Continuity Mission or Landsat-8. This new TIR sensor (TIRS) includes two TIR bands in the atmospheric window between 10 and 12 μm, thus allowing the application of split-window (SW) algorithms in addition to single-channel (SC) algorithms or direct inversions of the radiative transfer equation used in previous sensors on board the Landsat platforms, with only one TIR band. In this letter, we propose SC and SW algorithms to be applied to Landsat-8 TIRS data for LST retrieval. Algorithms were tested with simulated data obtained from forward simulations using atmospheric profile databases and emissivity spectra extracted from spectral libraries. Results show mean errors typically below 1.5 K for both SC and SW algorithms, with slightly better results for the SW algorithm than for the SC algorithm with increasing atmospheric water vapor contents.

Modeling air temperature through a combination of remote sensing and GIS data

Air temperature is involved in many environmental processes such as actual and potential evapotranspiration, net radiation and species distribution. Ground meteorological stations provide important local data of air temperature, but a continuous surface for large and heterogeneous areas is also needed. In this paper we present a hybrid methodology between Remote Sensing and Geographical Information Systems to retrieve daily instantaneous, mean, maximum and minimum air temperatures (2002–2004) as well as monthly and annual mean, maximum and minimum air temperatures (2000-2005) on a regional scale (Catalonia, northeast of the Iberian Peninsula) by means of multiple regression analysis and spatial interpolation techniques. To perform multiple regression analysis we have used geographical and multiresolution remotely sensed variables as predictors. The geographical variables we have included are altitude, latitude, continentality and solar radiation. As remote sensing predictors, we have selected those variables that are most closely related with air temperature such as albedo, land surface temperature (LST) and NDVI obtained from Landsat-5 (TM), Landsat-7 (ETM+), NOAA (AVHRR) and TERRA (MODIS) satellites. The best air temperature models are obtained when remote sensing variables are combined with geographical variables: averaged R2 = 0.60 and averaged root mean square error (RMSE) = 1.75C for daily temperatures, and averaged R2 = 0.86 and averaged RMSE = 1.00C for monthly and annual temperatures. The results also show that combined models appear in a higher frequency than only geographical or only remote sensing models (87%, 11% and 2% respectively) and that LST and NDVI are the most powerful remote sensing predictors in air temperature modeling.

Assessment of Despiking Methods for Turbulence Data in Micrometeorology

AbstractThe computation of turbulent fluxes of heat, momentum, and greenhouse gases requires measurements taken at high sampling frequencies. An important step in this process involves the detection and removal of sudden, short-lived variations that do not represent physical processes and that contaminate the data (i.e., spikes). The objective of this study is to assess the performance of several noteworthy despiking methodologies in order to provide a benchmark assessment and to provide a recommendation that is most applicable to high-frequency micrometeorological data in terms of efficiency and simplicity. The performance of a statistical time window–based algorithm widely used in micrometeorology is compared to three other methodologies (phase space, wavelet based, and median filter). These algorithms are first applied to a synthetic signal (a clean reference version and then one with spikes) in order to assess general performance. Afterward, testing is done on a time series of actual CO2 concentration...

Temperature regimes and turbulent heat fluxes across a heterogeneous canopy in an Alaskan boreal forest

We evaluate local differences in thermal regimes and turbulent heat fluxes across the heterogeneous canopy of a black spruce boreal forest on discontinuous permafrost in interior Alaska. The data were taken during an intensive observing period in the summer of 2013 from two micrometeorological towers 600 m apart in a central section of boreal forest, one in a denser canopy (DC) and the other in a sparser canopy, but under approximately similar atmospheric boundary layer (ABL) flow conditions. Results suggest that on average 34% of the half-hourly periods in a day are nonstationary, primarily during night and during ABL transitions. Also, thermal regimes differ between the two towers; specifically between midnight and 0500 Alaska Standard Time (AKST) it is about 3°C warmer at DC. On average, the sensible heat flux at DC was greater. For midday periods, the difference between those fluxes exceeded 30% of the measured flux and over 30 W m−2 in magnitude more than 60% of the time. These differences are due to higher mechanical mixing as a result of the increased density of roughness elements at DC. Finally, the vertical distribution of turbulent heat fluxes verifies a maximum atop the canopy crown (2.6 h) when compared with the subcanopy (0.6 h) and above canopy (5.1 h), where h is the mean canopy height. We argue that these spatial and vertical variations of sensible heat fluxes result from the complex scale aggregation of energy fluxes over a heterogeneous canopy.

An improved methodology to map Snow Cover by means of Landsat and MODIS imagery

In this article we propose a methodology to determine snow cover by means of Landsat-7 ETM+ and Landsat-5 TM images, as well as an improvement in daily Snow Cover TERRA- MODIS product (MOD10A1), between 2002 and 2005. Both methodologies are based on a NDSI threshold > 0.4. In the Landsat case, and although this threshold also selects water bodies, we have obtained optimal results using a mask of water bodies and generating a pre-boundary snow mask around the snow cover. Moreover, an important improvement in snow cover mapping in shadow cast areas by means of a hybrid classification has been obtained. Using these results as ground truth we have verified MODIS Snow Cover product using coincident dates. In the MODIS product, we have noted important commission errors in water bodies, forest covers and orographic shades because of the NDVI-NDSI filter applied to this product. In order to improve MODIS snow cover determination using MODIS images, we propose a hybrid methodology based on experience with Landsat images, which provide greater spatial resolution.

Improving Air Temperature Modelization by Means of Remote Sensing Variables

In this article we present a hybrid methodology between Remote Sensing and Geographical Information Systems to retrieve instantaneous, mean, maximum and minimum air temperatures for daily, monthly and annual periods between 2000 and 2005 on a regional scale (Catalonia, North-West Spain) by means of multiple regression analysis and spatial interpolation techniques. Best air temperature models are obtained when remote sensing variables are combined with geographical variables: averaged test R2=0.67 and averaged RMS error=1.22degC for daily temperatures and averaged test R2=0.90 and averaged RMS error =0.84degC for monthly and annual temperatures.

Hotspot detection and spatial distribution of methane emissions from landfills by a surface probe method.

A surface probe method previously developed was used to detect hotspots and to determine spatial variation of methane (CH4) emissions from three landfills located in Mexico, with an intermediate or a final cover, as well as with or without a landfill gas collection system. The method was effective in the three landfills and allowed mapping of CH4 emissions with a resolution of 24-64 measurements per hectare, as well as the detection and quantification of hotspots, with a moderate experimental effort. In the three selected landfills, CH4 emissions were quantified to 10, 72, and 575gm(-2)d(-1). Two straightforward parameters describing the spatial distribution of CH4 emissions were also developed. The first parameter provides the percentage of area responsible for a given percentage of total emissions, while the second parameter assigns a numerical value to flux homogeneity. Together, the emissions map and the spatial distribution parameters offer an appropriate tool to landfill operators willing to begin recovering CH4 emissions or to improve the effectiveness of an existing recovery system. This method may therefore help to reduce the greenhouse gas footprint of landfills, which are still the primary option for waste management in developing countries.

Efecto del cerclaje escleral en la cirugía vítreo-retiniana sobre la morfología y biomecánica de la córnea

OBJECTIVE To investigate the effects of encircling scleral buckle (SB) on corneal biomechanical properties of the cornea and its morphological parameters. METHODS We prospectively examined twelve eyes diagnosed with vitreous haemorrhage undergoing pars plana vitrectomy (PPV), and fifteen eyes undergoing combined PPV and scleral buckle (PPV/SB) for repair of rhegmatogenous retinal detachment (RRD). Corneal biomechanical properties, including corneal hysteresis (CH) and corneal resistance factor (CRF), were measured with an Ocular Response Analyser (ORA) before and 1-month postoperatively. The ORA also determined the values of intraocular pressure (IOPg) and corneal compensated IOP (IOPcc). Finally, four morphological parameters of the cornea were measured with the Orbscan II topographer (Orbtek, Inc.): mean corneal power, thinnest corneal point (μm), and anterior chamber depth (ACD). RESULTS CH decreased significantly from 10.2+/-1.7mmHg to 7.6+/-1.1mmHg after PPV/SB (p=0.003), but not after PPV alone (9.8+/-3.2mmHg vs 11.6+/-2.7mmHg, P=.465). CRF did not change significantly after surgery in both groups. IOPg and IOPcc increased significantly in the PPV/SB group (P=.019 and P=.010, respectively) but not in PPV group (P=.715 and P=.273, respectively). Unlike the PPV group, values were significantly higher than IOPg values before (P=.001) and after surgery (P=.003) in the PPV/SB group IOPcc. Neither the PPV/SB group nor the PPV group showed any significant changes in the corneal morphological parameters after surgery (P>.05). CONCLUSIONS SB surgery leads to a change in the corneal biomechanical properties without altering corneal morphological parameters. It may cause an underestimation error in IOP measurement. PPV may be a less invasive surgical approach for the repair of noncomplex RRD than PPV/SB.

An Improved Single-Channel Method to Retrieve Land Surface Temperature from the Landsat-8 Thermal Band

Land surface temperature (LST) is one of the sources of input data for modeling land surface processes. The Landsat satellite series is the only operational mission with more than 30 years of archived thermal infrared imagery from which we can retrieve LST. Unfortunately, stray light artifacts were observed in Landsat-8 TIRS data, mostly affecting Band 11, currently making the split-window technique impractical for retrieving surface temperature without requiring atmospheric data. In this study, a single-channel methodology to retrieve surface temperature from Landsat TM and ETM+ was improved to retrieve LST from Landsat-8 TIRS Band 10 using near-surface air temperature (Ta) and integrated atmospheric column water vapor (w) as input data. This improved methodology was parameterized and successfully evaluated with simulated data from a global and robust radiosonde database and validated with in situ data from four flux tower sites under different types of vegetation and snow cover in 44 Landsat-8 scenes. Evaluation results using simulated data showed that the inclusion of Ta together with w within a single-channel scheme improves LST retrieval, yielding lower errors and less bias than models based only on w. The new proposed LST retrieval model, developed with both w and Ta, yielded overall errors on the order of 1 K and a bias of −0.5 K validated against in situ data, providing a better performance than other models parameterized using w and Ta or only w models that yielded higher error and bias.