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Prof. Dr. Jose A. Sobrino
University of Valencia

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0 Remote Sensing
0 urban heat island (UHI)
0 land surface temperature
0 atmospheric correction
0 Evapotranspiration

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land surface temperature
Remote Sensing
Evapotranspiration
atmospheric correction
Land surface emissivity
urban heat island (UHI)
Cal/val

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Short Biography

José Antonio Sobrino, Ph.D. in Physics (1989). He is currently Full Professor in Earth Physics and coordinator of the Global Change Unit head (UCG) group, http://www.uv.es/ucg. President of the Spanish Association of Remote Sensing. Member of the Mission Advisory Group of the Land Surface Temperature Monitoring (LSTM) of the European Space Agency (ESA). Member of the Scientific Group of the Indian-French TRISHNA mission. He was member of Earth Science Advisory Committee (ESAC) of ESA and Coordinator of the Land Product Validation/Land Surface Temperature and Emissivity Committee on Earth Observations Satellites (CEOS). He is President of Recent Advances in Quantitative Remote Sensing symposiums and Coordinator for the National Earth Observation Network (RNOT). His research interest include techniques and applications of thermal remote sensing, such as land and sea surface temperature and emissivity, thermal inertia, urban heat island, evapotranspiration, cal/val activities, etc. Author of ~250 publications, ~ 300 international conference papers, 30 book chapters, h-index of 58, 20 Theses directed and PI of more than 60 research projects. Guest Editor of 10 special issues in International Journals. Award Carta Poblament of the City of Torrent 2011: Science, Technology and Environment. In 2019, he was the recipient of the King Jaime I Award in Spain, the highest recognition for environmental protection in Spain. Visit https://www.uv.es/sobrino/ for more information

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Journal article
Published: 29 May 2021 in Remote Sensing
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The aim of this research is to explore the analysis of methods allowing a synergetic use of information exchange between Earth Observation (EO) data and growth models in order to provide high spatial and temporal resolution actual evapotranspiration predictions. An assimilation method based on the Ensemble Kalman Filter algorithm allows for combining Sentinel-2 data with a new version of Simple Algorithm For Yield (SAFY_swb) that considers the effect of the water balance on yield and estimates the daily trend of evapotranspiration (ET). Our study is relevant in the context of demonstrating the effectiveness and necessity of satellite missions such as Land Surface Temperature Monitoring (LSTM), to provide high spatial and temporal resolution data for agriculture. The proposed method addresses the problem both from a spatial point of view, providing maps of the areas of interest of the main biophysical quantities of vegetation (LAI, biomass, yield and actual Evapotranspiration), and from a temporal point of view, providing a simulation on a daily basis of the aforementioned variables. The assimilation efficiency was initially evaluated with a synthetic, large and heterogeneous dataset, reaching values of 70% even for high measurement errors of the assimilated variable. Subsequently, the method was tested in a case study in central Italy, allowing estimates of the daily Actual Evapotranspiration with a relative RMSE of 18%. The novelty of this research is in proposing a solution that partially solves the main problems related to the synergistic use of EO data with crop growth models, such as the difficult calibration of initial parameters, the lack of frequent high-resolution data or the high computational cost of data assimilation methods. It opens the way to future developments, such as the use of simultaneous assimilation of multiple variables, to deeper investigations using more specific datasets and exploiting the advanced tools.

ACS Style

Paolo Silvestro; Raffaele Casa; Jan Hanuš; Benjamin Koetz; Uwe Rascher; Dirk Schuettemeyer; Bastian Siegmann; Drazen Skokovic; José Sobrino; Marin Tudoroiu. Synergistic Use of Multispectral Data and Crop Growth Modelling for Spatial and Temporal Evapotranspiration Estimations. Remote Sensing 2021, 13, 2138 .

AMA Style

Paolo Silvestro, Raffaele Casa, Jan Hanuš, Benjamin Koetz, Uwe Rascher, Dirk Schuettemeyer, Bastian Siegmann, Drazen Skokovic, José Sobrino, Marin Tudoroiu. Synergistic Use of Multispectral Data and Crop Growth Modelling for Spatial and Temporal Evapotranspiration Estimations. Remote Sensing. 2021; 13 (11):2138.

Chicago/Turabian Style

Paolo Silvestro; Raffaele Casa; Jan Hanuš; Benjamin Koetz; Uwe Rascher; Dirk Schuettemeyer; Bastian Siegmann; Drazen Skokovic; José Sobrino; Marin Tudoroiu. 2021. "Synergistic Use of Multispectral Data and Crop Growth Modelling for Spatial and Temporal Evapotranspiration Estimations." Remote Sensing 13, no. 11: 2138.

Original research article
Published: 04 May 2021 in Frontiers in Remote Sensing
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Although numerous instruments are continuously observing our planet, the general public has a low access to these data, either being provided with a delay, or needing some scientific background to exploit them. Our objective here is to bridge this gap, by using the data we receive from our MSG-SEVIRI (Meteosat Second Generation—Spinning Enhanced Visible InfraRed Imager) station, by processing them to retrieve accurate characteristics of the surface, and uploading them within 5 min of image completion on a dedicated webpage, updated with every new data. For half our planet, we provide in real time a specifically designed quickook and surface temperature every 15 min, with an accuracy similar to the one achieved in previous works. We also provide every 15 min the hotspots detected using a validated approach. Once a day, we provide vegetation greenness [Normalized Difference Vegetation Index (NDVI)] for the 12:00 UTC acquisition, corresponding to the best illumination case, as well as anomalies for NDVI and surface temperature. These parameters are provided freely as full resolution images, readily understandable for the end-user, in three different languages (English, Spanish and French): https://www.uv.es/iplsat. These data, along with short time lapse videos, can be used for educational, scientific and communication purposes.

ACS Style

José A. Sobrino; Yves Julien. Near Real-Time Processing Chain for MSG SEVIRI Data for Free and Immediate Earth Monitoring Capabilities. Frontiers in Remote Sensing 2021, 2, 1 .

AMA Style

José A. Sobrino, Yves Julien. Near Real-Time Processing Chain for MSG SEVIRI Data for Free and Immediate Earth Monitoring Capabilities. Frontiers in Remote Sensing. 2021; 2 ():1.

Chicago/Turabian Style

José A. Sobrino; Yves Julien. 2021. "Near Real-Time Processing Chain for MSG SEVIRI Data for Free and Immediate Earth Monitoring Capabilities." Frontiers in Remote Sensing 2, no. : 1.

Journal article
Published: 02 March 2021 in Remote Sensing
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National Oceanic and Atmospheric Administration–Advanced Very High Resolution Radiometer (NOAA-AVHRR) data provides the possibility to build the longest Land Surface Temperature (LST) dataset to date, starting in 1981 up to the present. However, due to the orbital drift of the NOAA platforms, no LST dataset is available before 2000 and the arrival of newer platforms. Although numerous methods have been developed to correct this orbital drift effect on the LST, a lack of validation has prevented their application. This is the gap we bridge here by using the 15 min temporal resolution of Meteosat Second Generation–Spinning Enhanced Visible and Infra-Red Imager (MSG-SEVIRI) data to simulate drifted and reference LST time series. We then use these time series to validate an orbital drift correction method based on solar zenith angle (SZA) anomalies that we presented in a previous work (C1), as well as two variations of this approach (C0 and C2). Our results show that the C0 method performs better than the two others, although its overall bias absolute value ranges up to 1 K, while standard deviation values remain around 3 K. This is verified for most land covers, for all NOAA platforms, and these statistics remain mostly stable with noise on SZA time series (from 0° to ±10°). With this study, we show that orbital drift correction methods can be thoroughly validated and that such validation should aim toward bias absolute values below 0.1 K and standard deviation values around 1.4 K at coarse spatial resolution. To validate other orbital drift correction approaches, the drifted and reference time series used in this work are freely available for download from the first author’s webpage. This will be the first step toward the building of an orbital-drift-corrected long-term LST dataset.

ACS Style

Yves Julien; José Sobrino. NOAA-AVHRR Orbital Drift Correction: Validating Methods Using MSG-SEVIRI Data as a Benchmark Dataset. Remote Sensing 2021, 13, 925 .

AMA Style

Yves Julien, José Sobrino. NOAA-AVHRR Orbital Drift Correction: Validating Methods Using MSG-SEVIRI Data as a Benchmark Dataset. Remote Sensing. 2021; 13 (5):925.

Chicago/Turabian Style

Yves Julien; José Sobrino. 2021. "NOAA-AVHRR Orbital Drift Correction: Validating Methods Using MSG-SEVIRI Data as a Benchmark Dataset." Remote Sensing 13, no. 5: 925.

Journal article
Published: 13 December 2020 in Remote Sensing
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The feasibility of combining remotely sensed land surface temperature data (LST) and an energy–water balance model for improving evapotranspiration estimates over time distributed in space in the Capitanata irrigation consortium is analysed. The energy–water balance FEST-EWB model (flash flood event-based spatially distributed rainfall–runoff transformation—energy–water balance model) computes continuously in time and is distributed in space soil moisture (SM) and evapotranspiration (ET) fluxes solving for a land surface temperature that closes the energy–water balance equations. The comparison between modelled and observed LST was used to calibrate the model soil parametres with a newly developed pixel to pixel calibration procedure. The effects of the calibration procedure were analysed against ground measures of soil moisture and evapotranspiration. The FEST-EWB model was run at 30 m of spatial resolution for the period between 2013 and 2018. Absolute errors of 2.5 °C were obtained for LST estimates against satellite data; while RMSE around 0.06 and 40 Wm−2 are found for ground measured soil moisture and latent heat flux, respectively.

ACS Style

Chiara Corbari; Drazen Jovanovic; Luigi Nardella; Josè Sobrino; Marco Mancini. Evapotranspiration Estimates at High Spatial and Temporal Resolutions from an Energy-Water Balance Model and Satellite Data in the Capitanata Irrigation Consortium. Remote Sensing 2020, 12, 4083 .

AMA Style

Chiara Corbari, Drazen Jovanovic, Luigi Nardella, Josè Sobrino, Marco Mancini. Evapotranspiration Estimates at High Spatial and Temporal Resolutions from an Energy-Water Balance Model and Satellite Data in the Capitanata Irrigation Consortium. Remote Sensing. 2020; 12 (24):4083.

Chicago/Turabian Style

Chiara Corbari; Drazen Jovanovic; Luigi Nardella; Josè Sobrino; Marco Mancini. 2020. "Evapotranspiration Estimates at High Spatial and Temporal Resolutions from an Energy-Water Balance Model and Satellite Data in the Capitanata Irrigation Consortium." Remote Sensing 12, no. 24: 4083.

Journal article
Published: 13 October 2020 in Remote Sensing
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Urban heat islands (UHIs) can present significant risks to human health. Santiago, Chile has around 7 million residents, concentrated in an average density of 480 people/km2. During the last few summer seasons, the highest extreme maximum temperatures in over 100 years have been recorded. Given the projections in temperature increase for this metropolitan region over the next 50 years, the Santiago UHI could have an important impact on the health and stress of the general population. We studied the presence and spatial variability of UHIs in Santiago during the summer seasons from 2005 to 2017 using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery and data from nine meteorological stations. Simple regression models, geographic weighted regression (GWR) models and geostatistical interpolations were used to find nocturnal thermal differences in UHIs of up to 9 °C, as well as increases in the magnitude and extension of the daytime heat island from summer 2014 to 2017. Understanding the behavior of the UHI of Santiago, Chile, is important for urban planners and local decision makers. Additionally, understanding the spatial pattern of the UHI could improve knowledge about how urban areas experience and could mitigate climate change.

ACS Style

Daniel Montaner-Fernández; Luis Morales-Salinas; José Rodriguez; Luz Cárdenas-Jirón; Alfredo Huete; Guillermo Fuentes-Jaque; Waldo Pérez-Martínez; Julián Cabezas. Spatio-Temporal Variation of the Urban Heat Island in Santiago, Chile during Summers 2005–2017. Remote Sensing 2020, 12, 3345 .

AMA Style

Daniel Montaner-Fernández, Luis Morales-Salinas, José Rodriguez, Luz Cárdenas-Jirón, Alfredo Huete, Guillermo Fuentes-Jaque, Waldo Pérez-Martínez, Julián Cabezas. Spatio-Temporal Variation of the Urban Heat Island in Santiago, Chile during Summers 2005–2017. Remote Sensing. 2020; 12 (20):3345.

Chicago/Turabian Style

Daniel Montaner-Fernández; Luis Morales-Salinas; José Rodriguez; Luz Cárdenas-Jirón; Alfredo Huete; Guillermo Fuentes-Jaque; Waldo Pérez-Martínez; Julián Cabezas. 2020. "Spatio-Temporal Variation of the Urban Heat Island in Santiago, Chile during Summers 2005–2017." Remote Sensing 12, no. 20: 3345.

Journal article
Published: 11 October 2020 in International Journal of Applied Earth Observation and Geoinformation
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A methodology to estimate the extent of areas affected by forest fires, as well as the burn severity levels using Sentinel 2 images (10 and 20 m) is proposed and applied to the fires occurred in October 2017 in Spain and Portugal. An extension larger than 250,000 ha and 4 burn severity levels (low, moderate, high and very high) have been obtained. The comparison with the European Forest Fire Information System (EFFIS), which uses MODIS images (250 m), shows that the methodology improves the area estimate by 10 % in commission area. In terms of burn severity levels, the Separability index (SI) and the Kappa statistic (k) show a high correlation between Sentinel-2 and EFFIS (SI values higher than one in all cases and k higher than 0.69, respectively).

ACS Style

Rafael Llorens; José Antonio Sobrino; Cristina Fernández; José M. Fernández-Alonso; José Antonio Vega. A methodology to estimate forest fires burned areas and burn severity degrees using Sentinel-2 data. Application to the October 2017 fires in the Iberian Peninsula. International Journal of Applied Earth Observation and Geoinformation 2020, 95, 102243 .

AMA Style

Rafael Llorens, José Antonio Sobrino, Cristina Fernández, José M. Fernández-Alonso, José Antonio Vega. A methodology to estimate forest fires burned areas and burn severity degrees using Sentinel-2 data. Application to the October 2017 fires in the Iberian Peninsula. International Journal of Applied Earth Observation and Geoinformation. 2020; 95 ():102243.

Chicago/Turabian Style

Rafael Llorens; José Antonio Sobrino; Cristina Fernández; José M. Fernández-Alonso; José Antonio Vega. 2020. "A methodology to estimate forest fires burned areas and burn severity degrees using Sentinel-2 data. Application to the October 2017 fires in the Iberian Peninsula." International Journal of Applied Earth Observation and Geoinformation 95, no. : 102243.

Journal article
Published: 05 October 2020 in Atmosphere
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Evapotranspiration (ET) is one of the least understood components of the hydrological cycle. Its applications are varied, from agricultural, ecological and hydrological monitoring, to control of the evolution of climate change. The goal of this work was to analyze the influence that uncertainties in the estimate of land surface temperature (Ts) can cause on ET estimates by S-SEBI model in the Pampa biome area. Also, the specificities of native grassland of Pampa biome related to energy balance were analyzed. The results indicate that the daily evapotranspiration is higher when the pixel Ts is lower, which also shows the influence of land use on the variability of ET. The results demonstrated that the S-SEBI is less dependent on Ts estimation than other models reported in the literature, such as the SEBS, which not exceed 0.5 mm/day in grasslands. The evapotranspiration variability between forest and grassland were lower than expected, demonstrating that the Pampa biome have in Rio Grande do Sul the same importance that forests regarding to the processes of the hydrological cycle, since it covers 63% of the State.

ACS Style

Nájila Rocha; Pâmela Käfer; Drazen Skokovic; Gustavo Veeck; Lucas Diaz; Eduardo Kaiser; Cibelle Carvalho; Rafael Cruz; José Sobrino; Débora Roberti; Silvia Rolim. The Influence of Land Surface Temperature in Evapotranspiration Estimated by the S-SEBI Model. Atmosphere 2020, 11, 1059 .

AMA Style

Nájila Rocha, Pâmela Käfer, Drazen Skokovic, Gustavo Veeck, Lucas Diaz, Eduardo Kaiser, Cibelle Carvalho, Rafael Cruz, José Sobrino, Débora Roberti, Silvia Rolim. The Influence of Land Surface Temperature in Evapotranspiration Estimated by the S-SEBI Model. Atmosphere. 2020; 11 (10):1059.

Chicago/Turabian Style

Nájila Rocha; Pâmela Käfer; Drazen Skokovic; Gustavo Veeck; Lucas Diaz; Eduardo Kaiser; Cibelle Carvalho; Rafael Cruz; José Sobrino; Débora Roberti; Silvia Rolim. 2020. "The Influence of Land Surface Temperature in Evapotranspiration Estimated by the S-SEBI Model." Atmosphere 11, no. 10: 1059.

Journal article
Published: 25 June 2020 in Remote Sensing
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Retrieval of land surface temperature (LST) from satellite data allows to estimate the surface urban heat island (SUHI) as the difference between the LST obtained in the urban area and the LST of its surroundings. However, this definition depends on the selection of the urban and surroundings references, which translates into greater difficulty in comparing SUHI values in different urban agglomerations across the world. In order to avoid this problem, a methodology is proposed that allows reliable quantification of the SUHI. The urban reference is obtained from the European Space Agency Climate Change Initiative Land Cover and three surroundings references are considered; that is, the urban adjacent (Su), the future adjacent (Sf), and the peri-urban (Sp), which are obtained from mathematical expressions that depend exclusively on the urban area. In addition, two formulations of SUHI are considered: SUHIMAX and SUHIMEAN, which evaluate the maximum and average SUHI of the urban area for each of the three surrounding references. As the urban population growth phenomenon is a world-scale problem, this methodology has been applied to 71 urban agglomerations around the world using LST data obtained from the sea and land surface temperature radiometer (SLSTR) on board Sentinel-3A. The results show average values of SUHIMEAN of (1.8 ± 0.9) °C, (2.6 ± 1.3) °C, and (3.1 ± 1.7) °C for Su, Sf, and Sp, respectively, and an average difference between SUHIMAX and SUHIMEAN of (3.1 ± 1.1) °C. To complete the study, two additional indices have been considered: the Urban Thermal Field Variation Index (UFTVI) and the Discomfort Index (DI), which proved to be essential for understanding the SUHI phenomenon and its consequences on the quality of life of the inhabitants.

ACS Style

José Antonio Sobrino; Itziar Irakulis. A Methodology for Comparing the Surface Urban Heat Island in Selected Urban Agglomerations Around the World from Sentinel-3 SLSTR Data. Remote Sensing 2020, 12, 2052 .

AMA Style

José Antonio Sobrino, Itziar Irakulis. A Methodology for Comparing the Surface Urban Heat Island in Selected Urban Agglomerations Around the World from Sentinel-3 SLSTR Data. Remote Sensing. 2020; 12 (12):2052.

Chicago/Turabian Style

José Antonio Sobrino; Itziar Irakulis. 2020. "A Methodology for Comparing the Surface Urban Heat Island in Selected Urban Agglomerations Around the World from Sentinel-3 SLSTR Data." Remote Sensing 12, no. 12: 2052.

Letter
Published: 24 June 2020 in Remote Sensing
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This is an update of Sobrino et al.’s paper, published in January 2020, which extends the calculation of the Earth’s surface temperature to the period 2003–2019 and uses the new version 2019.0 for the sea surface temperature product MODIS, which is available from 15 January 2020 and replaces version 2014.0. The land surface temperature was estimated from the MCD11C1 product for the same period. The results corroborate the temperature anomalies retrieved from climate models and improve the comparison with global annual air temperatures estimated by the NOAA’s National Climatic Data Center (NOAA-NCDC), with a correlation coefficient of 0.96. In addition, a trend of 0.021 ± 0.001 °C/year increase was found for the Earth’s surface temperature in this 17-year period.

ACS Style

José Sobrino; Susana García-Monteiro; Yves Julien. Surface Temperature of the Planet Earth from Satellite Data over the Period 2003–2019. Remote Sensing 2020, 12, 2036 .

AMA Style

José Sobrino, Susana García-Monteiro, Yves Julien. Surface Temperature of the Planet Earth from Satellite Data over the Period 2003–2019. Remote Sensing. 2020; 12 (12):2036.

Chicago/Turabian Style

José Sobrino; Susana García-Monteiro; Yves Julien. 2020. "Surface Temperature of the Planet Earth from Satellite Data over the Period 2003–2019." Remote Sensing 12, no. 12: 2036.

Letter
Published: 08 January 2020 in Remote Sensing
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The Intergovernmental Panel on Climate Change regular scientific assessments of global warming is based on measurements of air temperature from weather stations, buoys or ships. More specifically, air temperature annual means are estimated from their integration into climate models, with some areas (Africa, Antarctica, seas) being clearly underrepresented. Present satellites allow estimation of surface temperature for a full coverage of our planet with a sub-daily revisit frequency and kilometric resolution. In this work, a simple methodology is developed that allows estimating the surface temperature of Planet Earth with MODIS Terra and Aqua land and sea surface temperature products, as if the whole planet was reduced to a single pixel. The results, through a completely independent methodology, corroborate the temperature anomalies retrieved from climate models and show a linear warming trend of 0.018 ± 0.007 °C/yr.

ACS Style

José Antonio Sobrino; Yves Julien; Susana García-Monteiro. Surface Temperature of the Planet Earth from Satellite Data. Remote Sensing 2020, 12, 218 .

AMA Style

José Antonio Sobrino, Yves Julien, Susana García-Monteiro. Surface Temperature of the Planet Earth from Satellite Data. Remote Sensing. 2020; 12 (2):218.

Chicago/Turabian Style

José Antonio Sobrino; Yves Julien; Susana García-Monteiro. 2020. "Surface Temperature of the Planet Earth from Satellite Data." Remote Sensing 12, no. 2: 218.

Journal article
Published: 18 June 2019 in Remote Sensing
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Urban Heat Islands (UHIs) at the surface and canopy levels are major issues in urban planification and development. For this reason, the comprehension and quantification of the influence that the different land-uses/land-covers have on UHIs is of particular importance. In order to perform a detailed thermal characterisation of the city, measures covering the whole scenario (city and surroundings) and with a recurrent revisit are needed. In addition, a resolution of tens of meters is needed to characterise the urban heterogeneities. Spaceborne remote sensing meets the first and the second requirements but the Land Surface Temperature (LST) resolutions remain too rough compared to the urban object scale. Thermal unmixing techniques have been developed in recent years, allowing LST images during day at the desired scales. However, while LST gives information of surface urban heat islands (SUHIs), canopy UHIs and SUHIs are more correlated during the night, hence the development of thermal unmixing methods for night LSTs is necessary. This article proposes to adapt four empirical unmixing methods of the literature, Disaggregation of radiometric surface Temperature (DisTrad), High-resolution Urban Thermal Sharpener (HUTS), Area-To-Point Regression Kriging (ATPRK), and Adaptive Area-To-Point Regression Kriging (AATPRK), to unmix night LSTs. These methods are based on given relationships between LST and reflective indices, and on invariance hypotheses of these relationships across resolutions. Then, a comparative study of the performances of the different techniques is carried out on TRISHNA synthesized images of Madrid. Since TRISHNA is a mission in preparation, the synthesis of the images has been done according to the planned specification of the satellite and from initial Aircraft Hyperspectral Scanner (AHS) data of the city obtained during the DESIREX 2008 capaign. Thus, the coarse initial resolution is 60 m and the finer post-unmixing one is 20 m. In this article, we show that: (1) AATPRK is the most performant unmixing technique when applied on night LST, with the other three techniques being undesirable for night applications at TRISHNA resolutions. This can be explained by the local application of AATPRK. (2) ATPRK and DisTrad do not improve significantly the LST image resolution. (3) HUTS, which depends on albedo measures, misestimates the LST, leading to the worst temperature unmixing. (4) The two main factors explaining the obtained performances are the local/global application of the method and the reflective indices used in the LST-index relationship.

ACS Style

Carlos Granero-Belinchon; Aurelie Michel; Jean-Pierre Lagouarde; Jose A. Sobrino; Xavier Briottet. Night Thermal Unmixing for the Study of Microscale Surface Urban Heat Islands with TRISHNA-Like Data. Remote Sensing 2019, 11, 1449 .

AMA Style

Carlos Granero-Belinchon, Aurelie Michel, Jean-Pierre Lagouarde, Jose A. Sobrino, Xavier Briottet. Night Thermal Unmixing for the Study of Microscale Surface Urban Heat Islands with TRISHNA-Like Data. Remote Sensing. 2019; 11 (12):1449.

Chicago/Turabian Style

Carlos Granero-Belinchon; Aurelie Michel; Jean-Pierre Lagouarde; Jose A. Sobrino; Xavier Briottet. 2019. "Night Thermal Unmixing for the Study of Microscale Surface Urban Heat Islands with TRISHNA-Like Data." Remote Sensing 11, no. 12: 1449.

Journal article
Published: 27 May 2019 in Remote Sensing
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This work is linked to the future Indian–French high spatio-temporal TRISHNA (Thermal infraRed Imaging Satellite for High-resolution natural resource Assessment) mission, which includes shortwave and thermal infrared bands, and is devoted amongst other things to the monitoring of urban heat island events. In this article, the performance of seven empirical thermal unmixing techniques applied on simulated TRISHNA satellite images of an urban scenario is studied across spatial resolutions. For this purpose, Top Of Atmosphere (TOA) images in the shortwave and Thermal InfraRed (TIR) ranges are constructed at different resolutions (20 m, 40 m, 60 m, 80 m, and 100 m) and according to TRISHNA specifications (spectral bands and sensor properties). These images are synthesized by correcting and undersampling DESIREX 2008 Airborne Hyperspectral Scanner (AHS) images of Madrid at 4 m resolution. This allows to compare the Land Surface Temperature (LST) retrieval of several unmixing techniques applied on different resolution images, as well as to characterize the evolution of the performance of each technique across resolutions. The seven unmixing techniques are: Disaggregation of radiometric surface Temperature (DisTrad), Thermal imagery sHARPening (TsHARP), Area-To-Point Regression Kriging (ATPRK), Adaptive Area-To-Point Regression Kriging (AATPRK), Urban Thermal Sharpener (HUTS), Multiple Linear Regressions (MLR), and two combinations of ground classification (index-based classification and K-means classification) with DisTrad. Studying these unmixing techniques across resolutions also allows to validate the scale invariance hypotheses on which the techniques hinge. Each thermal unmixing technique has been tested with several shortwave indices, in order to choose the best one. It is shown that (i) ATPRK outperforms the other compared techniques when characterizing the LST of Madrid, (ii) the unmixing performance of any technique is degraded when the coarse spatial resolution increases, (iii) the used shortwave index does not strongly influence the unmixing performance, and (iv) even if the scale-invariant hypotheses behind these techniques remain empirical, this does not affect the unmixing performances within this range of resolutions.

ACS Style

Carlos Granero-Belinchon; Aurelie Michel; Jean-Pierre Lagouarde; Jose A. Sobrino; Xavier Briottet. Multi-Resolution Study of Thermal Unmixing Techniques over Madrid Urban Area: Case Study of TRISHNA Mission. Remote Sensing 2019, 11, 1251 .

AMA Style

Carlos Granero-Belinchon, Aurelie Michel, Jean-Pierre Lagouarde, Jose A. Sobrino, Xavier Briottet. Multi-Resolution Study of Thermal Unmixing Techniques over Madrid Urban Area: Case Study of TRISHNA Mission. Remote Sensing. 2019; 11 (10):1251.

Chicago/Turabian Style

Carlos Granero-Belinchon; Aurelie Michel; Jean-Pierre Lagouarde; Jose A. Sobrino; Xavier Briottet. 2019. "Multi-Resolution Study of Thermal Unmixing Techniques over Madrid Urban Area: Case Study of TRISHNA Mission." Remote Sensing 11, no. 10: 1251.

Journal article
Published: 26 May 2019 in Forests
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Forest fires in Galicia have become a serious environmental problem over the years. This is especially the case in the Pontevedra region, where in October 2017 large fires (>500 hectares) burned more than 15,000 Ha. In addition to the area burned being of relevance, it is also very important to know quickly and accurately the different severity degrees that soil has suffered in order to carry out an optimal restoration campaign. In this sense, the use of remote sensing with the Sentinel-2 and Landsat-8 satellites becomes a very useful resource due to the variations that appear in soil after a forest fire (changes in soil cover are noticeably appreciated with spectral information). To calculate these variations, the spectral indices NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) are used, both before and after the fire and their differences (dNBR and dNDVI, respectively). In addition, as a reference for a correct discrimination between severity degrees, severity data measured in situ after the fire are used to classified at 5 levels of severity and 6 levels of severity. Therefore, this study aims to establish a methodology, which relates remote-sensing data (spectral indices) and severity degrees measured in situ. The R2 statistic and the pixel classification accuracy results show the existing synergy of the Sentinel-2 dNBR index with the 5 severity degrees classification (R2 = 0.74 and 81% of global accuracy) and, for this case, the good applicability of remote sensing in the forest fire field.

ACS Style

Jose Antonio Sobrino; Rafael Llorens; Cristina Fernández; José M. Fernández-Alonso; José Antonio Vega. Relationship between Soil Burn Severity in Forest Fires Measured In Situ and through Spectral Indices of Remote Detection. Forests 2019, 10, 457 .

AMA Style

Jose Antonio Sobrino, Rafael Llorens, Cristina Fernández, José M. Fernández-Alonso, José Antonio Vega. Relationship between Soil Burn Severity in Forest Fires Measured In Situ and through Spectral Indices of Remote Detection. Forests. 2019; 10 (5):457.

Chicago/Turabian Style

Jose Antonio Sobrino; Rafael Llorens; Cristina Fernández; José M. Fernández-Alonso; José Antonio Vega. 2019. "Relationship between Soil Burn Severity in Forest Fires Measured In Situ and through Spectral Indices of Remote Detection." Forests 10, no. 5: 457.

Review
Published: 29 December 2018 in Remote Sensing
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The surface urban heat island (SUHI), which represents the difference of land surface temperature (LST) in urban relativity to neighboring non-urban surfaces, is usually measured using satellite LST data. Over the last few decades, advancements of remote sensing along with spatial science have considerably increased the number and quality of SUHI studies that form the major body of the urban heat island (UHI) literature. This paper provides a systematic review of satellite-based SUHI studies, from their origin in 1972 to the present. We find an exponentially increasing trend of SUHI research since 2005, with clear preferences for geographic areas, time of day, seasons, research foci, and platforms/sensors. The most frequently studied region and time period of research are China and summer daytime, respectively. Nearly two-thirds of the studies focus on the SUHI/LST variability at a local scale. The Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM+)/Thermal Infrared Sensor (TIRS) and Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) are the two most commonly-used satellite sensors and account for about 78% of the total publications. We systematically reviewed the main satellite/sensors, methods, key findings, and challenges of the SUHI research. Previous studies confirm that the large spatial (local to global scales) and temporal (diurnal, seasonal, and inter-annual) variations of SUHI are contributed by a variety of factors such as impervious surface area, vegetation cover, landscape structure, albedo, and climate. However, applications of SUHI research are largely impeded by a series of data and methodological limitations. Lastly, we propose key potential directions and opportunities for future efforts. Besides improving the quality and quantity of LST data, more attention should be focused on understudied regions/cities, methods to examine SUHI intensity, inter-annual variability and long-term trends of SUHI, scaling issues of SUHI, the relationship between surface and subsurface UHIs, and the integration of remote sensing with field observations and numeric modeling.

ACS Style

Decheng Zhou; Jingfeng Xiao; Stefania Bonafoni; Christian Berger; Kaveh Deilami; Yuyu Zhou; Steve Frolking; Rui Yao; Zhi Qiao; José A. Sobrino. Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives. Remote Sensing 2018, 11, 48 .

AMA Style

Decheng Zhou, Jingfeng Xiao, Stefania Bonafoni, Christian Berger, Kaveh Deilami, Yuyu Zhou, Steve Frolking, Rui Yao, Zhi Qiao, José A. Sobrino. Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives. Remote Sensing. 2018; 11 (1):48.

Chicago/Turabian Style

Decheng Zhou; Jingfeng Xiao; Stefania Bonafoni; Christian Berger; Kaveh Deilami; Yuyu Zhou; Steve Frolking; Rui Yao; Zhi Qiao; José A. Sobrino. 2018. "Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives." Remote Sensing 11, no. 1: 48.

Journal article
Published: 27 November 2018 in International Journal of Applied Earth Observation and Geoinformation
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NDVI (Normalized Difference Vegetation Index) time series usually suffer from remaining cloud presence, even after data pre-processing. To address this issue, numerous gap-filling (or reconstruction) techniques have been developed in the literature, although their comparison has mainly been local to regional, with only two global studies to date, and has led to sometimes contradictory results. This study builds on these different comparisons, by testing different parameterizations for five NDVI temporal profile reconstruction techniques, namely HANTS (Harmonic Analysis of Time Series), IDR (iterative Interpolation for Data Reconstruction), Savitzky-Golay, Asymmetric Gaussian and Double Logistic, and then comparing them as generally parameterized, and then with the best of the tested parameterizations. These comparisons show that the HANTS reconstruction technique provides lower errors in cloud prone areas, while the IDR method works best with shorter cloud covers. However, the remaining errors in cloud prone areas are still high, and there is room for new reconstruction techniques. Although these results are only applicable to the range of the tested parameterizations, these latter have been chosen within widely used configurations, and should provide interested users with a better understanding of the different methods and the best parameterization for their needs, as well as an estimate of the expected error in the reconstruction of NDVI time series.

ACS Style

Yves Julien; José A. Sobrino. Optimizing and comparing gap-filling techniques using simulated NDVI time series from remotely sensed global data. International Journal of Applied Earth Observation and Geoinformation 2018, 76, 93 -111.

AMA Style

Yves Julien, José A. Sobrino. Optimizing and comparing gap-filling techniques using simulated NDVI time series from remotely sensed global data. International Journal of Applied Earth Observation and Geoinformation. 2018; 76 ():93-111.

Chicago/Turabian Style

Yves Julien; José A. Sobrino. 2018. "Optimizing and comparing gap-filling techniques using simulated NDVI time series from remotely sensed global data." International Journal of Applied Earth Observation and Geoinformation 76, no. : 93-111.

Journal article
Published: 08 October 2018 in Philosophical Transactions of the Royal Society B: Biological Sciences
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The recent 2015–2016 El Niño (EN) event was considered as strong as the EN in 1997–1998. Given such magnitude, it was expected to result in extreme warming and moisture anomalies in tropical areas. Here we characterize the spatial patterns of temperature anomalies and drought over tropical forests, including tropical South America (Amazonia), Africa and Asia/Indonesia during the 2015–2016 EN event. These spatial patterns of warming and drought are compared with those observed in previous strong EN events (1982–1983 and 1997–1998) and other moderate to strong EN events (e.g. 2004–2005 and 2009–2010). The link between the spatial patterns of drought and sea surface temperature anomalies in the central and eastern Pacific is also explored. We show that indeed the EN2015–2016 led to unprecedented warming compared to the other EN events over Amazonia, Africa and Indonesia, as a consequence of the background global warming trend. Anomalous accumulated extreme drought area over Amazonia was found during EN2015–2016, but this value may be closer to extreme drought area extents in the other two EN events in 1982–1983 and 1997–1998. Over Africa, datasets disagree, and it is difficult to conclude which EN event led to the highest accumulated extreme drought area. Our results show that the highest values of accumulated drought area over Africa were obtained in 2015–2016 and 1997–1998, with a long-term drying trend not observed over the other tropical regions. Over Indonesia, all datasets suggest that EN 1982–1983 and EN 1997–1998 (or even the drought of 2005) led to a higher extreme drought area than EN2015–2016. Uncertainties in precipitation datasets hinder consistent estimates of drought severity over tropical regions, and improved reanalysis products and station records are required.This article is part of a discussion meeting issue ‘The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications’.

ACS Style

Juan C. Jimenez; Jonathan Barichivich; Cristian Mattar; Ken Takahashi; Andres Santamaría - Artigas; Jose A. Sobrino; Yadvinder Malhi. Spatio-temporal patterns of thermal anomalies and drought over tropical forests driven by recent extreme climatic anomalies. Philosophical Transactions of the Royal Society B: Biological Sciences 2018, 373, 20170300 .

AMA Style

Juan C. Jimenez, Jonathan Barichivich, Cristian Mattar, Ken Takahashi, Andres Santamaría - Artigas, Jose A. Sobrino, Yadvinder Malhi. Spatio-temporal patterns of thermal anomalies and drought over tropical forests driven by recent extreme climatic anomalies. Philosophical Transactions of the Royal Society B: Biological Sciences. 2018; 373 (1760):20170300.

Chicago/Turabian Style

Juan C. Jimenez; Jonathan Barichivich; Cristian Mattar; Ken Takahashi; Andres Santamaría - Artigas; Jose A. Sobrino; Yadvinder Malhi. 2018. "Spatio-temporal patterns of thermal anomalies and drought over tropical forests driven by recent extreme climatic anomalies." Philosophical Transactions of the Royal Society B: Biological Sciences 373, no. 1760: 20170300.

Conference paper
Published: 01 July 2018 in IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
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Evolution in the Copernicus Space Component (CSC) is foreseen in the mid-2020s to meet priority Copernicus user needs not addressed by the existing infrastructure, and/or to reinforce services by monitoring capability in the thematic domains of CO 2 , polar, and agriculture/forestry. This evolution will be synergetic with the enhanced continuity of services for the next generation of CSC. The “High Spatio-Temporal Resolution Land Surface Temperature Monitoring (LSTM) Mission”, identified as one of the CSC Expansion High Priority Candidate Missions (HPCM), currently undergoes an ESA preparatory phase (phase A/B1) study to establish mission feasibility. The LSTM mission shall provide enhanced measurements of land surface temperature with a focus responding to user requirements related to agricultural monitoring.

ACS Style

Benjamin Koetz; Wim Bastiaanssen; Michael Berger; Pierre Defourney; Umberto Del Bello; Matthias Drusch; Mark Drinkwater; Ricardo Duca; Valerie Fernandez; Darren Ghent; Radoslaw Guzinski; Jippe Hoogeveen; Simon Hook; Jean-Pierre Lagouarde; Guido Lemoine; Ilias Manolis; Philippe Martimort; Jeff Masek; Michel Massart; Claudia Notarnicola; Jose A. Sobrino; Thomas Udelhoven. High Spatio- Temporal Resolution Land Surface Temperature Mission - a Copernicus Candidate Mission in Support of Agricultural Monitoring. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium 2018, 8160 -8162.

AMA Style

Benjamin Koetz, Wim Bastiaanssen, Michael Berger, Pierre Defourney, Umberto Del Bello, Matthias Drusch, Mark Drinkwater, Ricardo Duca, Valerie Fernandez, Darren Ghent, Radoslaw Guzinski, Jippe Hoogeveen, Simon Hook, Jean-Pierre Lagouarde, Guido Lemoine, Ilias Manolis, Philippe Martimort, Jeff Masek, Michel Massart, Claudia Notarnicola, Jose A. Sobrino, Thomas Udelhoven. High Spatio- Temporal Resolution Land Surface Temperature Mission - a Copernicus Candidate Mission in Support of Agricultural Monitoring. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. 2018; ():8160-8162.

Chicago/Turabian Style

Benjamin Koetz; Wim Bastiaanssen; Michael Berger; Pierre Defourney; Umberto Del Bello; Matthias Drusch; Mark Drinkwater; Ricardo Duca; Valerie Fernandez; Darren Ghent; Radoslaw Guzinski; Jippe Hoogeveen; Simon Hook; Jean-Pierre Lagouarde; Guido Lemoine; Ilias Manolis; Philippe Martimort; Jeff Masek; Michel Massart; Claudia Notarnicola; Jose A. Sobrino; Thomas Udelhoven. 2018. "High Spatio- Temporal Resolution Land Surface Temperature Mission - a Copernicus Candidate Mission in Support of Agricultural Monitoring." IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium , no. : 8160-8162.

Conference paper
Published: 01 July 2018 in IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
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Landsat 8 (L8) satellite was launched on February 11, 2013 with two thermal bands located in the atmospheric window between 10-12 μm. Continuous monitoring of the Thermal Infrared Sensor (TIRS) onboard of L8 was performed over two Spanish test sites - Barrax and Doñana - in order to contribute to the quality of TIRS data. In this work, a Vicarious Calibration (VC) of the TIRS bands was performed between years 2013-2016 in order to assess the new Stray Light (SL) data correction. The results of VC show us that band 10 and 11 provide accurate results (bias near to zero, and precision around 0.8 K) which is an improvement - especially for band 11 - in comparison to preprocessed SL data. This little better performance has direct influence on Split Window (SW) algorithm, lowering its standard deviation into 0.3 K, as is shown in this work.

ACS Style

D. Skokovic; Jose A. Sobrino; J. C. Jimenez; G. Soria; Y. Julien; José Gomis-Cebolla; S. Garcia-Monteiro. Vicarious Calibration of Landsat-8 Thermal Data Collections and its Influence on Split-Window Algorithm Validation. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium 2018, 4312 -4315.

AMA Style

D. Skokovic, Jose A. Sobrino, J. C. Jimenez, G. Soria, Y. Julien, José Gomis-Cebolla, S. Garcia-Monteiro. Vicarious Calibration of Landsat-8 Thermal Data Collections and its Influence on Split-Window Algorithm Validation. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. 2018; ():4312-4315.

Chicago/Turabian Style

D. Skokovic; Jose A. Sobrino; J. C. Jimenez; G. Soria; Y. Julien; José Gomis-Cebolla; S. Garcia-Monteiro. 2018. "Vicarious Calibration of Landsat-8 Thermal Data Collections and its Influence on Split-Window Algorithm Validation." IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium , no. : 4312-4315.

Journal article
Published: 29 June 2018 in Revista de Teledetección
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Assessment of rural fire severity is fundamental to evaluate fire damages and to analyze recovery processes in a low-cost and efficient way. Burnt areas covering shrubs and grasslands were estimated in more than 30,000 km2 in Argentina from December 2016 to January 2017. The study area presented in this work is located in the South of the Buenos Aires province, and it covers a semiarid area with the presence of xerophilous shrubs and grasslands. This is one of the most abundant ecosystem in Central and Southern Argentina. Field campaigns were carried out over the area affected by the fire in order to georreference the burnt plots and characterized the fire severity in 5 levels. The objective of this work is to analyze the feasibility of new satellites Sentinel-2 for fire studies, as well as provide a comparison to Landsat-8 derived results, because this mission has been one of the most used in it. Pre-fire and postfire Sentinel-2 and Landsat-8 imagery were used to analyze different band combinations to compute a Normalized Difference Spectral Index (NDSI), as well as the difference of this index before and after the fire (dNDSI). Results show a significant correlation (R2 =0.72 and estimation error of 0.77) between dNDSI derived from Sentinel-2 and the severity levels obtained in the field campaign using bands 8a and 12 (NIR and SWIR), the same bands as used in the Normalized Burn Ratio (NBR). Moreover, results derived from Sentinel-2 are better than results derived from Landsat-8 (R2 =0.63 and estimation error of 0.92). Furthermore, it is observed that the correlation is improved when Sentinel-2 bands 6 and 5 (located in the Red-Edge region) are considered (R2 =0.74 and estimation error of 0.76). An inverse correlation has been observed between the recovery of vegetation four months after the fire and the fire severity level.

ACS Style

J. Delegido; A. Pezzola; A. Casella; C. Winschel; E. P. Urrego; J. C. Jiménez; J. A. Sobrino; G. Soria; J. Moreno. Estimación del grado de severidad de incendios en el sur de la provincia de Buenos Aires, Argentina, usando Sentinel-2 y su comparación con Landsat-8. Revista de Teledetección 2018, 47 -60.

AMA Style

J. Delegido, A. Pezzola, A. Casella, C. Winschel, E. P. Urrego, J. C. Jiménez, J. A. Sobrino, G. Soria, J. Moreno. Estimación del grado de severidad de incendios en el sur de la provincia de Buenos Aires, Argentina, usando Sentinel-2 y su comparación con Landsat-8. Revista de Teledetección. 2018; (51):47-60.

Chicago/Turabian Style

J. Delegido; A. Pezzola; A. Casella; C. Winschel; E. P. Urrego; J. C. Jiménez; J. A. Sobrino; G. Soria; J. Moreno. 2018. "Estimación del grado de severidad de incendios en el sur de la provincia de Buenos Aires, Argentina, usando Sentinel-2 y su comparación con Landsat-8." Revista de Teledetección , no. 51: 47-60.

Journal article
Published: 29 June 2018 in Revista de Teledetección
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This paper introduces the Time Series Simulation for Benchmarking of Reconstruction Techniques (TISSBERT) dataset, intended to provide a benchmark for the validation and comparison of time series reconstruction methods. Such methods are routinely used to estimate vegetation characteristics from optical remotely sensed data, where the presence of clouds decreases the usefulness of the data. As for their validation, these methods have been compared with previously published ones, although with different approaches, which sometimes lead to contradictory results. We designed the TISSBERT dataset to be generic so that it could simulate realistic reference and cloud-contaminated time series at global scale. To that end, we estimated both cloud-free and cloud-contaminated Normalized Difference Vegetation Index (NDVI) statistics for randomly selected control points and each day of the year from the Long Term Data Record Version 4 (LTDR-V4) dataset by assuming different statistical distributions. The best approach was then applied to the whole dataset, and validity of the results were estimated through the Kolmogorov-Smirnov statistic. The dataset elaboration is described thoroughly along with how to use it. The advantages and drawbacks of this dataset are then discussed, which emphasize the realistic simulation of the cloud-contaminated and reference time series. This dataset can be obtained from the authors upon demand. It will be used in a next paper to compare widely used NDVI time series reconstruction methods.

ACS Style

Y. Julien; J. A. Sobrino. TISSBERT: A benchmark for the validation and comparison of NDVI time series reconstruction methods. Revista de Teledetección 2018, 19 -31.

AMA Style

Y. Julien, J. A. Sobrino. TISSBERT: A benchmark for the validation and comparison of NDVI time series reconstruction methods. Revista de Teledetección. 2018; (51):19-31.

Chicago/Turabian Style

Y. Julien; J. A. Sobrino. 2018. "TISSBERT: A benchmark for the validation and comparison of NDVI time series reconstruction methods." Revista de Teledetección , no. 51: 19-31.