Yves Julien

Global land surface phenology trends from GIMMS database

A double logistic function has been used to describe global inventory mapping and monitoring studies (GIMMS) normalized difference vegetation index (NDVI) yearly evolution for the 1981 to 2003 period, in order to estimate land surface phenology parameter. A principal component analysis on the resulting time series indicates that the first components explain 36, 53 and 37% of the variance for the start, end and length of growing season, respectively, and shows generally good spatial homogeneity. Mann–Kendall trend tests have been carried out, and trends were estimated by linear regression. Maps of these trends show a global advance in spring dates of 0.38 days per year, a global delay in autumn dates of 0.45 days per year and a global increase of 0.8 days per year in the growing seasons validated by comparison with previous works. Correlations between retrieved phenological parameters and climate indices generally showed a good spatial coherence.

Extended source pyramid wave-front sensor for the human eye.

We describe a new wave-front sensor based on the previously proposed pyramid sensor. This new sensor uses an extended source instead of a point-like source avoiding in this manner the oscillation of the pyramid. After an introductory background the sensor functioning is described. Among other possible optical testing uses, we apply the sensor to measure the wave-front aberration of the human eye. An experimental system built to test this specific application is described. Results obtained both in an articficial eye and in a real eye are presented. A discussion about the sensor characteristics, the experimental results and future work prospects is also included.

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.

Global trends in NDVI-derived parameters obtained from GIMMS data

The Normalized Difference Vegetation Index (NDVI) has been proven to be useful to assess vegetation changes around the world, in spite of limitations such as sensitivity to cloud or snow contamination. In order to map vegetation changes at global scale, this study uses NDVI time series provided by the GIMMS (Global Inventory Modeling and Mapping Studies) group, which were fitted annually to a double logistic function. This fitting procedure allowed for retrieval of NDVI-derived parameters which were tested for trends using Mann–Kendall statistics. These trends were validated by comparison at 73 ground control points documented as change hotspots. The obtained trends for NDVI-derived parameters provide information to the remote sensing community on the regions more susceptible to having suffered changes, to complement other traditional methods such as national inventories and field work. Additionally, the validation procedure can be applied to any results obtained from a global dataset.

Emissivity mapping over urban areas using a classification-based approach: Application to the Dual-use European Security IR Experiment (DESIREX)

Abstract In this work a methodology to provide an emissivity map of an urban area is presented. The methodology is applied to the city of Madrid (Spain) using data provided by the Airborne Hyperspectral Scanner (AHS) in 2008. From the data a classification map with twelve different urban materials was created. Each material was then characterized by a different emissivity, whose values were obtained from the application of the TES algorithm to in situ measurements and values extracted from the ASTER spectral library. This new emissivity map could be used as a basis for determining the temperature of the city and to understand the urban heat island effect in terms of spatial distribution and size.

Evaluation of the surface urban heat island effect in the city of Madrid by thermal remote sensing

The surface urban heat island (SUHI) effect is defined as the increased surface temperatures in urban areas in contrast to cooler surrounding rural areas. In this article, the evaluation of the SUHI effect in the city of Madrid (Spain) from thermal infrared (TIR) remote-sensing data is presented. The data were obtained from the framework of the Dual-use European Security IR Experiment (DESIREX) campaign that was carried out during June and July 2008 in Madrid. The campaign combined the collection of airborne hyperspectral and in situ measurements. Thirty spectral and spatial high-resolution images were acquired with the Airborne Hyperspectral Scanner (AHS) sensor in a 11, 21, and 4 h UTC scheme. The imagery was used to retrieve the SUHI effect by applying the temperature and emissivity separation (TES) algorithm. The results show a nocturnal SUHI effect with a highest value of 5 K. This maximum value agrees within 1 K with the highest value of the urban heat island (UHI) observed using air temperature data (AT). During the daytime, this situation is reversed and the city becomes a negative heat island.

NOAA-AVHRR Orbital Drift Correction From Solar Zenithal Angle Data

This paper presents a new method for NOAAs (National Ocean and Atmospheric Administration) orbital drift correction. This method is pixel-based, and in opposition with most methods previously developed, does not need explicit knowledge of land cover. This method is applied to AVHRR (Advanced Very High Resolution Radiometer) channel information, and relies only on the additional knowledge of solar zenithal angle (SZA) and acquisition date information. In a first step, anomalies in SZA and channel time series are retrieved, and screened out for anomalous values. Then, the part of the parameter anomaly which is explained by SZA anomaly is removed from the data, to estimate new parameter anomalies, and this iteratively until the influence of SZA anomalies is totally removed from the parameter data. This correction has been applied to bimonthly AVHRR data provided by the GIMMS group (Global Inventory Modeling and Mapping Studies), covering Africa from November 2000 to December 2006. NDVI and LST (land surface temperature) have been estimated from raw and corrected data, and averaged over homogeneous vegetation classes. Differences between raw and corrected averaged parameters show an improvement in the quality of the data. In order to validate this method, a whole week (10 to 17 July 2004) of METEOSAT SEVIRI (Spinning Enhanced Visible and InfraRed Imager) data have been used, from which LST have been estimated using a similar method to the one used to retrieve LST from AVHRR data. The comparison between both platforms at the same time of acquisition shows good concordance.

Phenology Estimation From Meteosat Second Generation Data

Many studies have focused on land surface phenology, for example as a means to characterize both water and carbon cycles for climate model inputs. However, the Spinning Enhanced Visible Infra-Red Imager (SEVIRI) sensor onboard Meteosat Second Generation (MSG) geostationary satellite has never been used for this goal. Here, five years of MSG-SEVIRI data have been processed to retrieve Normalized Difference Vegetation Index (NDVI) daily time series. Due to existing gaps as well as atmospheric and cloud contamination in the time series, an algorithm based on the iterative Interpolation for Data Reconstruction (IDR) has been developed and applied to SEVIRI NDVI time series, from which phenological parameters have been retrieved. The modified IDR (M-IDR) algorithm shows results of a similar quality to the original method, while dealing more efficiently with increased temporal resolution. The retrieved phenological phases were then analyzed and compared with an independent MODIS (Moderate resolution Imaging Spectrometer) dataset. Comparison of SEVIRI and MODIS-derived phenology with a pan-European ground phenology record shows a high accuracy of the SEVIRI-retrieved green-up and brown-down dates (within days) for most of the selected European validation sites, while differences with MODIS product are higher although this can be explained by differences in methodology. This confirms the potential of MSG data for phenological studies, with the advantage of a quicker availability of the data.

Trend Analysis of Global MODIS-Terra Vegetation Indices and Land Surface Temperature Between 2000 and 2011

Previous works have shown that the combination of vegetation indices with land surface temperature (LST) improves the analysis of vegetation changes. Here, global MODIS-Terra monthly data from 2000 to 2011 were downloaded and organized into LST, NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) time series. These time series were then corrected from cloud and atmospheric residual contamination through the IDR (iterative Interpolation for Data Reconstruction) method. Then, statistics were retrieved from both corrected time series, and the YLCD (Yearly Land Cover Dynamics) approach has been applied to data sources (NDVI-LST and EVI-LST) to analyze changes in the vegetation. Finally, trends were retrieved and their statistical significance was assessed through the Mann-Kendall statistical framework. Global statistics show that both data sets lead to similar trends, as is the case for the spatial distribution of observed trends. These trends confirm previous results as well as prediction of climate warming consequences, such as a marked increase in boreal temperatures.