Modelo de balanço de energia para estimar a evapotranspiração real de dados satélites e meteorológicos

Evapotranspiration (ET) is the process whereby water present in the soil is transferred to the atmosphere as vapor. ET is one of the most important fluxes in the hydrological cycle, with estimates of more than 60% of precipitation returning to the atmosphere through ET. Different methods based on weather information have been used to estimate reference ET (ET0), but they provide regional nature estimates, since ET0 expresses only the evaporating power of the atmosphere. The ET that actually takes place from a given plant cover is known as real ET (ETR) and its estimation is usually more complex since it requires information about the current state of the vegetation. Satellite information is an attractive tool to obtain data on vegetation and soil moisture, which can be complemented with meteorological information. This paper proposes and evaluates an energy balance model to calculate ETR using data from satellite imagery and meteorological stations. The model is based on SEBAL (surface energy balance algorithm for land), which was modified for automatically selecting/classifying pixels by thresholds. The generated model was tested in two typical wheat/soy bean farming areas of Argentina. The results showed an appropriate segregation of the dominant soil cover types and a high concordance of obtained data with those present in the literature.La evapotranspiración (ET) es el proceso mediante el cual el agua presente en el suelo se transfiere a la atmósfera en forma de vapor. ET es uno de los flujos más importantes en el ciclo hidrológico, con estimaciones de más del 60% de las precipitaciones que regresan a la atmósfera a través de ET. Se han utilizado diferentes métodos basados en información meteorológica para estimar ET de referencia (ET0), pero proporcionan estimaciones de la naturaleza regional, ya que ET0 expresa solo el poder de evaporación de la atmósfera. La ET que realmente tiene lugar a partir de una cobertura vegetal dada se conoce como ET real (ETR) y su estimación suele ser más compleja ya que requiere información sobre el estado actual de la vegetación. La información satelital es una herramienta atractiva para obtener datos sobre la vegetación y la humedad del suelo, que se puede complementar con información meteorológica. Este documento propone y evalúa un modelo de balance de energía para calcular ETR utilizando datos de imágenes satelitales y estaciones meteorológicas. El modelo se basa en SEBAL (algoritmo de balance de energía superficial para tierra) que fue modificado, en el presente trabajo, para seleccionar/clasificar automáticamente píxeles por umbrales. El modelo generado se probó en dos zonas típicas del cultivo de trigo y soja de Argentina. Los resultados mostraron una segregación apropiada de los tipos de cobertura del suelo dominante y una alta concordancia con los datos obtenidos con aquellos presentes en la literatura.A evapotranspiração (ET) é o processo pelo qual a água presente no solo é transferida para a atmosfera como vapor. ET é um dos fluxos mais importantes no ciclo hidrológico, com estimativas de mais de 60% de precipitação retornando à atmosfera através de ET. Diferentes métodos baseados em informações meteorológicas têm sido usados para estimar a referência ET (ET0), mas fornecem estimativas de natureza regional, uma vez que ET0 expressa apenas o poder de evaporação da atmosfera. O ET que realmente ocorre a partir de uma determinada cobertura vegetal é conhecido como ET real (ETR) e sua estimativa é geralmente mais complexa, uma vez que requer informações sobre o estado atual da vegetação. A informação de satélite é uma ferramenta atrativa para obter dados sobre a umidade da vegetação e do solo, que pode ser complementada com informações meteorológicas. Este artigo propõe e avalia um modelo de balanço de energia para calcular o ETR usando dados de imagens de satélite e estações meteorológicas. O modelo é baseado no SEBAL (algoritmo de equilíbrio de energia superficial para terra), que foi modificado para selecionar/classificar automaticamente pixels por limiares. O modelo gerado foi testado em duas áreas de cultivo de trigo e soja da Argentina. Os resultados mostraram uma adequada segregação dos tipos de cobertura do solo dominante e uma alta concordância com os dados obtidos com os presentes na literatura.Fil: Gavilán, Sebastian. Provincia de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Universidad Autónoma de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción; ArgentinaFil: Pastore, Juan Ignacio. Universidad Nacional de Mar del Plata; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Quignard, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Autónoma de Entre Rí­os. Facultad de Ciencia y Tecnología. Centro Regional de Geomática; ArgentinaFil: Marasco, Nestor Damián. Universidad Tecnológica Nacional; ArgentinaFil: Aceñolaza, Pablo Gilberto. Provincia de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Universidad Autónoma de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción; Argentin

Metodología operativa para la obtención de datos históricos de precipitación a partir de la misión satelital Tropical Rainfall Measuring Mission. Validación de resultados con datos de pluviómetros

Precipitation information is critical for understanding the hydrological equilibrium on a global scale. Rain, with other conditions, represents a factor of interest for agricultural production. Therefore, the need of carrying out studies that make possible to understand spatial and temporal variability of rain becomes evident. This paper presents a methodology that allows the automatic downloading of time series of precipitation data from the Tropical Rainfall Measurement Mission (TRMM) from the Google Earth Engine (GEE) platform and validate it with a series of meteorological data. The system was developed under the GEE platform for downloading the TRMM data. As a case of study, the Arroyo Las Conchas basin in the Entre Ríos province, in Argentina was established. To validate the results, a set of data was generated with rainfall information in 16 year period, by measuring the rain gauges for the area of influence at the meteorological station in the Instituto Nacional de Tecnología Agropecuaria (INTA) Station at Oro Verde, Paraná, Entre Ríos, Argentina. The results through the evaluation process show a close relationship between both sources of information. The proposed methodology will allow generating sets of historical rainfall data to study the hydrological regime of the Las Conchas Stream basin.La información de precipitación es crítica para la comprensión del equilibrio hidrológico a escala global. La lluvia, junto con otras variables ambientales tales como evapotranspiración, temperatura, humedad relativa, entre otras, representa un factor de interés para la producción agrícola. Debido a esto, surge la necesidad de llevar adelante estudios que posibiliten comprender mejor la variabilidad espacial y temporal de las mismas. En este trabajo se presenta una metodología que permite automatizar la descarga de series temporales de datos de precipitación de la Misión de Medición de la Lluvia Tropical (Tropical Rainfall Measuring Mission (TRMM)) desde la plataforma Google Earth Engine (GEE) y validar los datos obtenidos con una serie de datos históricos de una estación meteorológica. Con este fin se desarrolló un sistema bajo la plataforma GEE para la generación y descarga de datos TRMM. Como caso de estudio se fijó la cuenca del Arroyo Las Conchas de la Provincia de Entre Ríos, Argentina. Para la validación de los resultados, se generó un set de datos con la información de precipitaciones desde el 1 de enero del 2000 al 31 de diciembre del 2015, medida por pluviómetros, para el área de influencia de la estación meteorológica de la Estación Experimental Agropecuaria del INTA de Oro Verde, departamento de Paraná, provincia de Entre Ríos, Argentina. Los resultados obtenidos mediante el proceso de evaluación muestran que existe una estrecha relación entre ambas fuentes de información. La metodología propuesta permitirá generar sets de datos históricos de precipitación para estudiar el régimen hídrico en regiones de difícil acceso o en cuencas extensas y poco pobladas.Fil: Gavilán, Sebastian. Provincia de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Universidad Autónoma de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción; ArgentinaFil: Pastore, Juan Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica; ArgentinaFil: Uranga, Javier Nicolas. Comision Nacional de Actividades Espaciales. Instituto de Altos Estudios Espaciales "Mario Gulich"; ArgentinaFil: Ferral, Anabella. Comision Nacional de Actividades Espaciales. Instituto de Altos Estudios Espaciales "Mario Gulich"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Lighezzolo, Andrés. Comision Nacional de Actividades Espaciales. Instituto de Altos Estudios Espaciales "Mario Gulich"; ArgentinaFil: Aceñolaza, Pablo Gilberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentin

EUNIS Habitat Classification: Expert system, characteristic species combinations and distribution maps of European habitats

Aim: The EUNIS Habitat Classification is a widely used reference framework for European habitat types (habitats), but it lacks formal definitions of individual habitats that would enable their unequivocal identification. Our goal was to develop a tool for assigning vegetation‐plot records to the habitats of the EUNIS system, use it to classify a European vegetation‐plot database, and compile statistically‐derived characteristic species combinations and distribution maps for these habitats. Location: Europe. Methods: We developed the classification expert system EUNIS‐ESy, which contains definitions of individual EUNIS habitats based on their species composition and geographic location. Each habitat was formally defined as a formula in a computer language combining algebraic and set‐theoretic concepts with formal logical operators. We applied this expert system to classify 1,261,373 vegetation plots from the European Vegetation Archive (EVA) and other databases. Then we determined diagnostic, constant and dominant species for each habitat by calculating species‐to‐habitat fidelity and constancy (occurrence frequency) in the classified data set. Finally, we mapped the plot locations for each habitat. Results: Formal definitions were developed for 199 habitats at Level 3 of the EUNIS hierarchy, including 25 coastal, 18 wetland, 55 grassland, 43 shrubland, 46 forest and 12 man‐made habitats. The expert system classified 1,125,121 vegetation plots to these habitat groups and 73,188 to other habitats, while 63,064 plots remained unclassified or were classified to more than one habitat. Data on each habitat were summarized in factsheets containing habitat description, distribution map, corresponding syntaxa and characteristic species combination. Conclusions: EUNIS habitats were characterized for the first time in terms of their species composition and distribution, based on a classification of a European database of vegetation plots using the newly developed electronic expert system EUNIS‐ESy. The data provided and the expert system have considerable potential for future use in European nature conservation planning, monitoring and assessment

<scp>ReSurveyEurope</scp>: A database of resurveyed vegetation plots in Europe

AbstractAimsWe introduce ReSurveyEurope — a new data source of resurveyed vegetation plots in Europe, compiled by a collaborative network of vegetation scientists. We describe the scope of this initiative, provide an overview of currently available data, governance, data contribution rules, and accessibility. In addition, we outline further steps, including potential research questions.ResultsReSurveyEurope includes resurveyed vegetation plots from all habitats. Version 1.0 of ReSurveyEurope contains 283,135 observations (i.e., individual surveys of each plot) from 79,190 plots sampled in 449 independent resurvey projects. Of these, 62,139 (78%) are permanent plots, that is, marked in situ, or located with GPS, which allow for high spatial accuracy in resurvey. The remaining 17,051 (22%) plots are from studies in which plots from the initial survey could not be exactly relocated. Four data sets, which together account for 28,470 (36%) plots, provide only presence/absence information on plant species, while the remaining 50,720 (64%) plots contain abundance information (e.g., percentage cover or cover–abundance classes such as variants of the Braun‐Blanquet scale). The oldest plots were sampled in 1911 in the Swiss Alps, while most plots were sampled between 1950 and 2020.ConclusionsReSurveyEurope is a new resource to address a wide range of research questions on fine‐scale changes in European vegetation. The initiative is devoted to an inclusive and transparent governance and data usage approach, based on slightly adapted rules of the well‐established European Vegetation Archive (EVA). ReSurveyEurope data are ready for use, and proposals for analyses of the data set can be submitted at any time to the coordinators. Still, further data contributions are highly welcome.</jats:sec

Maize (Zea Mays L.) Yield Estimation Using High Spatial and Temporal Resolution Sentinel-2 Remote Sensing Data

Maize (Zea mays L.) is one of the world’s most important annual cereal crops and its yield can be estimated for a wide variety of purposes. The objective of this work is to evaluate in which stage of crop the best fit between remote sensing data and real yield occurs to predict yield in corn seed crops. For this, polynomial regression models were used between spectral indices of vegetationand real yield in 10 days time’s windows covering the critical period for generation of performance. Subsequently, the predictive capacity of the best goodness of fit model was evaluated by comparing estimates with those made using a conventional field estimation method. This experiment was carried out in production fields located in Tandil and Loberia district inside ofthe Argentine Pampas Region in southeast of Buenos Aires province in summer (from january to march) of 2020. We found the highest level of adjustment between vegetal index and real yield (R2 = 0.91) in the time window of 110 to 120 days after sowing (DAS) corresponding to the end ofthe critical period. Then, the predictive performance was evaluated, satellite model shows an underestimation of 53 kg/ha (0.72% relative error) while the conventional method underestimated by 955 kg/ha (13% relative error). A close relationship between remote sensing data and grain yield at the end of the critical period of maize can be evidenced, and this information can be used to predict yield early in the southeast of Buenos Aires province. Using the methodology here developed it is recommended to analyze- time series of satellite vegetal index in maize crops in other regions and climates to make more robust the yield prediction system.Fil: Gavilán, Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica; ArgentinaFil: Aceñolaza, Pablo Gilberto. Provincia de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Universidad Autónoma de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción; ArgentinaFil: Pastore, Juan Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica; Argentin

A comparative framework for broad-scale plot-based vegetation classification

Contains fulltext : 149053.pdf (publisher's version ) (Closed access

Benchmarking plant diversity of Palaearctic grasslands and other open habitats

Aims Understanding fine-grain diversity patterns across large spatial extents is fundamental for macroecological research and biodiversity conservation. Using the GrassPlot database, we provide benchmarks of fine-grain richness values of Palaearctic open habitats for vascular plants, bryophytes, lichens and complete vegetation (i.e., the sum of the former three groups). Location Palaearctic biogeographic realm. Methods We used 126,524 plots of eight standard grain sizes from the GrassPlot database: 0.0001, 0.001, 0.01, 0.1, 1, 10, 100 and 1,000 m2 and calculated the mean richness and standard deviations, as well as maximum, minimum, median, and first and third quartiles for each combination of grain size, taxonomic group, biome, region, vegetation type and phytosociological class. Results Patterns of plant diversity in vegetation types and biomes differ across grain sizes and taxonomic groups. Overall, secondary (mostly semi-natural) grasslands and natural grasslands are the richest vegetation type. The open-access file ”GrassPlot Diversity Benchmarks” and the web tool “GrassPlot Diversity Explorer” are now available online (https://edgg.org/databases/GrasslandDiversityExplorer) and provide more insights into species richness patterns in the Palaearctic open habitats. Conclusions The GrassPlot Diversity Benchmarks provide high-quality data on species richness in open habitat types across the Palaearctic. These benchmark data can be used in vegetation ecology, macroecology, biodiversity conservation and data quality checking. While the amount of data in the underlying GrassPlot database and their spatial coverage are smaller than in other extensive vegetation-plot databases, species recordings in GrassPlot are on average more complete, making it a valuable complementary data source in macroecology

Benchmarking plant diversity of Palaearctic grasslands and other open habitats

Aims: Understanding fine-grain diversity patterns across large spatial extents is fundamental for macroecological research and biodiversity conservation. Using the GrassPlot database, we provide benchmarks of fine-grain richness values of Palaearctic open habitats for vascular plants, bryophytes, lichens and complete vegetation (i.e., the sum of the former three groups). Location: Palaearctic biogeographic realm. Methods: We used 126,524 plots of eight standard grain sizes from the GrassPlot database: 0.0001, 0.001, 0.01, 0.1, 1, 10, 100 and 1,000 m(2) and calculated the mean richness and standard deviations, as well as maximum, minimum, median, and first and third quartiles for each combination of grain size, taxonomic group, biome, region, vegetation type and phytosociological class. Results: Patterns of plant diversity in vegetation types and biomes differ across grain sizes and taxonomic groups. Overall, secondary (mostly semi-natural) grasslands and natural grasslands are the richest vegetation type. The open-access file "GrassPlot Diversity Benchmarks" and the web tool "GrassPlot Diversity Explorer" are now available online () and provide more insights into species richness patterns in the Palaearctic open habitats. Conclusions: The GrassPlot Diversity Benchmarks provide high-quality data on species richness in open habitat types across the Palaearctic. These benchmark data can be used in vegetation ecology, macroecology, biodiversity conservation and data quality checking. While the amount of data in the underlying GrassPlot database and their spatial coverage are smaller than in other extensive vegetation-plot databases, species recordings in GrassPlot are on average more complete, making it a valuable complementary data source in macroecology

Effects of climate and atmospheric nitrogen deposition on early to mid-term stage litter decomposition across biomes

International audienceLitter decomposition is a key process for carbon and nutrient cycling in terrestrial ecosystems and is mainly controlled by environmental conditions, substrate quantity and quality as well as microbial community abundance and composition. In particular, the effects of climate and atmospheric nitrogen (N) deposition on litter decomposition and its temporal dynamics are of significant importance, since their effects might change over the course of the decomposition process. Within the TeaComposition initiative, we incubated Green and Rooibos teas at 524 sites across nine biomes. We assessed how macroclimate and atmospheric inorganic N deposition under current and predicted scenarios (RCP 2.6, RCP 8.5) might affect litter mass loss measured after 3 and 12 months. Our study shows that the early to mid-term mass loss at the global scale was affected predominantly by litter quality (explaining 73% and 62% of the total variance after 3 and 12 months, respectively) followed by climate and N deposition. The effects of climate were not litter-specific and became increasingly significant as decomposition progressed, with MAP explaining 2% and MAT 4% of the variation after 12 months of incubation. The effect of N deposition was litter-specific, and significant only for 12-month decomposition of Rooibos tea at the global scale. However, in the temperate biome where atmospheric N deposition rates are relatively high, the 12-month mass loss of Green and Rooibos teas decreased significantly with increasing N deposition, explaining 9.5% and 1.1% of the variance, respectively. The expected changes in macroclimate and N deposition at the global scale by the end of this century are estimated to increase the 12-month mass loss of easily decomposable litter by 1.1– 3.5% and of the more stable substrates by 3.8–10.6%, relative to current mass loss. In contrast, expected changes in atmospheric N deposition will decrease the mid-term mass loss of high-quality litter by 1.4–2.2% and that of low-quality litter by 0.9–1.5% in the temperate biome. Our results suggest that projected increases in N deposition may have the capacity to dampen the climate-driven increases in litter decomposition depending on the biome and decomposition stage of substrate