The use of Pseudoinvariant Areas (PIA) makes it possible to carry out a reasonably robust and automatic radiometric correction for long time series of remote sensing imagery, as shown in previous studies for large data sets of Landsat MSS, TM, and ETM+ imagery. In addition, they can be employed to obtain more coherence among remote sensing data from different sensors. The present work validates the use of PIA for the radiometric correction of pairs of images acquired almost simultaneously (Landsat-7 (ETM+) or Landsat-8 (OLI) and Sentinel-2A (MSI)). Four pairs of images from a region in SW Spain, corresponding to four different dates, together with field spectroradiometry measurements collected at the time of satellite overpass were used to evaluate a PIA-based radiometric correction. The results show a high coherence between sensors (r² = 0.964) and excellent correlations to in-situ data for the MiraMon implementation (r² > 0.9). Other methodological alternatives, ATCOR3 (ETM+, OLI, MSI), SAC-QGIS (ETM+, OLI, MSI), 6S-LEDAPS (ETM+), 6S-LaSRC (OLI), and Sen2Cor-SNAP (MSI), were also evaluated. Almost all of them, except for SAC-QGIS, provided similar results to the proposed PIA-based approach. Moreover, as the PIA-based approach can be applied to almost any image (even to images lacking of extra atmospheric information), it can also be used to solve the robust integration of data from new platforms, such as Landsat-8 or Sentinel-2, to enrich global data acquired since 1972 in the Landsat program. It thus contributes to the program’s continuity, a goal of great interest for the environmental, scientific, and technical community.

Culturally dependent human social behaviours involving artificial light usage can potentially affect light pollution patterns and thereby impact the night‐time ecology in populated areas, although to date this has not been examined globally. By analysing continuous (monthly), highly resolved, spatially explicit data on global night lights (Visible and Infrared Imaging Radiometer Suite–Day/Night Band‐VIIRS‐DNB; 2014–2019) with circular statistical techniques, we evaluated whether macro‐cultural activities involving social aggregations and the use of artificial lights shape annual lighting patterns globally. Scheduled routines associated with cultural‐specific festivities appear to be important drivers of observed seasonal patterns in urban night‐time lights. For instance, the display of Christmas lights between Christmas and Epiphany Day celebrations (December–January) coincides with the annual peak in urban night‐time light intensity in Christian countries. Analogously, night celebrations during the Holy Month of Ramadam (from May to July) or the month‐long period of Karthika Masam (from October to November) fits with annual night light peaks in Muslim and Hindu countries. Annual peaks of urban light intensity in China and Vietnam also match with Chinese and Vietnamese (Tê't) New Year celebrations (January–February). In contrast, predominantly Buddhist countries, which do not have such prominent and prolonged celebrations involving artificial lights, show a relatively uniform distribution of night light peaks throughout the annual cycle. Social behaviour and sociocultural contexts help explain how people modify the global nightscape and contribute to light pollution globally. Understanding the cultural contexts responsible for peaks in artificial light usage is an important first step if humans are to mitigate any deleterious effects associated with global increases in night‐time light pollution. Read the free Plain Language Summary for this article on the Journal blog.

Doñana National Park is located in the southwest of the Iberian Peninsula, where water scarcity is recurrent, together with a high heterogeneity in species and ecosystems. Monitoring carbon assimilation is essential to improve knowledge of global change in natural vegetation cover. In this work, a light use efficiency (LUE) model was applied to estimate gross primary production (GPP) in two ecosystems of Doñana, xeric shrub (drought resistant) and seasonal marsh (with grasslands dependent on water hydroperiod) and validated with in situ data from eddy covariance (EC) towers installed in both ecosystems. The model was applied in two ways: (1) using the fraction of absorbed photosynthetically active radiation (FAPAR) from Sentinel-2 and meteorological data from reanalysis (ERA5), and (2) using Sentinel-2 FAPAR, reanalysis solar radiation (ERA5) and the Sentinel-2 land surface water index (LSWI). In both cases and for both ecosystems, the error values are acceptable (below 1 gC/m²) and in both ecosystems the model using the LSWI gave better results (R² of 0.8 in marshes and 0.51 in xeric shrubs). The results also show a greater influence of the water status of the system than of the meteorological variables in this area.

La aplicación PhenoApp ha sido desarrollada en el marco de los proyectos eLTER Plus y SUMHAL, como una herramienta dirigida a científicos y gestores de los sitios integrados en la red eLTER, con la cual puede realizarse un seguimiento de la fenología a largo plazo de diferentes cubiertas vegetales. La aplicación proporciona un mapa dinámico, que permite la selección de cualquier sitio de la red y consultar las métricas fenológicas de cada píxel o grupo de píxeles generadas con la serie de imágenes Sentinel 2 usando las librerías de Python Ndvi2Gif y PhenoPY. La aplicación integra también los productos de fenología de MODIS (MCD12Q2.006) y de Copernicus Sentinel 2 High Resolution Vegetation Phenology Product (HR-VPP). Además, la aplicación incorpora un formulario que permite al usuario proporcionar los datos de fenología obtenidos in situ (mediante observación directa o fenocámaras), que se usarán para realizar una validación de los distintos productos obtenidos vía satélite. A modo de ejemplo, se muestra la validación efectuada en uno de los sitios de la red eLTER ubicado en el Espacio Natural de Doñana (END), usando como datos in situ los proporcionados por la red de fenocámaras instaladas en la Reserva Biológica de Doñana a partir de 2016, dentro del marco de la Infraestructura Científica y Técnica Singular de Doñana (ICTS-Doñana). Un análisis de validación preliminar pone de manifiesto la necesidad de considerar las discrepancias entre los distintos productos y métodos de acuerdo con la variabilidad fenológica inherente a cada ecosistema.

El Parque Nacional de Doñana, situado en el suroeste de la Península Ibérica, se enfrenta a unos desafíos ambientales sustanciales, con eventos extremos de sequía cada vez más frecuentes y un papel clave como hotspotde biodiversidad. La comprensión de la dinámica del carbono es esencial para avanzar en el conocimiento sobre los efectos del cambio climático en las cubiertas naturales y en los servicios ecosistémicos que proveen. En este estudio, se ha aplicado un modelo de eficiencia en el uso de la luz (LUE) para estimar la producción primaria bruta (PPB) en dos tipos de ecosistemas representativos de Doñana: el matorral xerófilo, caracterizado precisamente por su resistencia a la sequía estival, y la marisma estacional, que sufre ciclos de inundación y desecación y alberga pastizales y vegetación acuática dependientes del hidroperiodo. La validación del modelo se realizó con datos in situ medidos a través de torres de flujos de energía y carbono (Eddy Covariance, EC) instaladas en ambos ecosistemas. En la aplicación del modelo LUE se han usado únicamente datos procedentes de teledetección y de reanálisis: i) Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) de Sentinel-2, ii) datos de reanálisis de radiación solar (ERA5-Land) y iii) el índice multiespectral Land Surface Water Index (LSWI) para las imágenes de Sentinel-2. Esta metodología ha demostrado ser muy adecuada, con valores de error cuadrático medio (RMSE) inferiores a 0,50 g C/m2 y coeficientes de determinación (R2) de hasta 0,82 en la marisma y 0,67 en el matorral xerófilo. Una vez validado, este método basado en el modelo LUE y con datos de teledetección y reanálisis permitió efectuar el seguimiento de la dinámica de carbono en Doñana, contribuyendo así a una mejor comprensión de la respuesta de estos sistemas naturales frente a los eventos extremos en el periodo analizado.

In this paper we assess spatial and temporal correlation of different available Earth Observation (EO) products provided through downstream services with in-situ measurements for validation purposes. We selected three relevant ecosystem indicators such as Land Surface Phenology, Land Surface Temperature and Surface Water, being widely available as standard remote sensing products using different methods and satellite sensors. The study seeks to contribute to develop a global framework to address the urgent need for coordinated cal/val activities of EO products. As a pilot, we made use of systematic data collected at Doñana LTSER Platform, one of the long-term measuring sites from the European Research Infrastructure eLTER. In order to enhance EO use we developed the GeeLTERMap tool and PyVPP python package to enable the retrieval of the selected EO products for any site. Phenology metrics estimated by in situ phenocams were used to assess Copernicus High Resolution Vegetation Phenology and Productivity (VPP), MODIS MCD12Q2 and PhenoPy metrics. In situ measurements by calibrated broadband Infrared Radiometers were used to validate LST retrieved from MODIS, Landsat and Sentinel-3 images. Finally, field sampling and in situ automatic camera photos were used to evaluate different multispectral indices and simple bands to map water occurrence using Sentinel-2 images. Results for phenology validation show HR-VPP as the most accurate EO product with an average Root Mean Square Error (RMSE) of 56 days for all metrics across different land covers. For LST, Landsat showed lower RMSE LST values than Sentinel-3 SLSTR and MODIS MOD11A1 products. Finally, Sentinel-2 MSI Band 12 was the most accurate band to delineate water bodies of Doñana shallow marshes. The developed tools and protocols for validation using long-term data from in situ sampling and measurements will be one of the services provided by eLTER Research Infrastructure.