Retrieval of vegetation height in rice fields using polarimetric SAR interferometry with TanDEM-X data

This work presents for the first time a demonstration with satellite data of polarimetric SAR interferometry (PolInSAR) applied to the retrieval of vegetation height in rice fields. Three series of dual-pol interferometric SAR data acquired with large baselines (2–3 km) by the TanDEM-X system during its science phase (April–September 2015) are exploited. A novel inversion algorithm especially suited for rice fields cultivated in flooded soil is proposed and evaluated. The validation is carried out over three test sites located in geographically different areas: Sevilla (SW Spain), Valencia (E Spain), and Ipsala (W Turkey), in which different rice types are present. Results are obtained during the whole growth cycle and demonstrate that PolInSAR is useful to produce accurate height estimates (RMSE 10–20 cm) when plants are tall enough (taller than 25–40 cm), without relying on external reference information.This work has been supported by the Spanish Ministry of Economy and Competitiveness (MINECO) and EU FEDER under project TIN2014-55413-C2-2-P. The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement 606983, and the Land-SAF (the EUMETSAT Network of Satellite Application Facilities) project. The in-situ measurements in the Ipsala site were conducted with the funding of The Scientific and Technological Research Council of Turkey (TUBITAK, Project No.: 113Y446)

Deep learning for agricultural land use classification from Sentinel-2

[ES] En el campo de la teledetección se ha producido recientemente un incremento del uso de técnicas de aprendizaje profundo (deep learning). Estos algoritmos se utilizan con éxito principalmente en la estimación de parámetros y en la clasificación de imágenes. Sin embargo, se han realizado pocos esfuerzos encaminados a su comprensión, lo que lleva a ejecutarlos como si fueran “cajas negras”. Este trabajo pretende evaluar el rendimiento y acercarnos al entendimiento de un algoritmo de aprendizaje profundo, basado en una red recurrente bidireccional de memoria corta a largo plazo (2-BiLSTM), a través de un ejemplo de clasificación de usos de suelo agrícola de la Comunidad Valenciana dentro del marco de trabajo de la política agraria común (PAC) a partir de series temporales de imágenes Sentinel-2. En concreto, se ha comparado con otros algoritmos como los árboles de decisión (DT), los k-vecinos más cercanos (k-NN), redes neuronales (NN), máquinas de soporte vectorial (SVM) y bosques aleatorios (RF) para evaluar su precisión. Se comprueba que su precisión (98,6% de acierto global) es superior a la del resto en todos los casos. Por otra parte, se ha indagado cómo actúa el clasificador en función del tiempo y de los predictores utilizados. Este análisis pone de manifiesto que, sobre el área de estudio, la información espectral y espacial derivada de las bandas del rojo e infrarrojo cercano, y las imágenes correspondientes a las fechas del período de verano, son la fuente de información más relevante utilizada por la red en la clasificación. Estos resultados abren la puerta a nuevos estudios en el ámbito de la explicabilidad de los resultados proporcionados por los algoritmos de aprendizaje profundo en aplicaciones de teledetección.[EN] The use of deep learning techniques for remote sensing applications has recently increased. These algorithms have proven to be successful in estimation of parameters and classification of images. However, little effort has been made to make them understandable, leading to their implementation as “black boxes”. This work aims to evaluate the performance and clarify the operation of a deep learning algorithm, based on a bi-directional recurrent network of long short-term memory (2-BiLSTM). The land use classification in the Valencian Community based on Sentinel-2 image time series in the framework of the common agricultural policy (CAP) is used as an example. It is verified that the accuracy of the deep learning techniques is superior (98.6 % overall success) to that other algorithms such as decision trees (DT), k-nearest neighbors (k-NN), neural networks (NN), support vector machines (SVM) and random forests (RF). The performance of the classifier has been studied as a function of time and of the predictors used. It is concluded that, in the study area, the most relevant information used by the network in the classification are the images corresponding to summer and the spectral and spatial information derived from the red and near infrared bands. 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Capability assessment of the SEVIRI/MSG GPP product for the detection of areas affected by water stress

[ES] Se presenta el nuevo producto de producción primaria bruta (GPP) de EUMETSAT derivado a partir de datos del satélite geoestacionario SEVIRI/MSG (MGPP LSA-411) y se evalúa su potencial para detectar zonas afectadas por estrés hídrico (hot spots). El producto GPP se basa en la aproximación de Monteith, que modela la GPP de la vegetación como el producto de la radiación fotosintéticamente activa (PAR) incidente, la fracción de PAR absorbida (fAPAR) y un factor de eficiencia de uso de la radiación (ε). El potencial del producto MGPP para detectar hot spots se evalúa, utilizando un periodo corto de tres años, a escala local y regional, comparando con datos in situ derivados de medidas en torres eddy covariance (EC) y con datos GPP derivados de satélite (producto de 8 días MOD17A2H.v6 a 500 m y producto de 10 días GDMP a 1 km). Los resultados preliminares sobre el uso del producto MGPP en la evaluación de la respuesta del ecosistema a posibles eventos de déficit de agua ponen de manifiesto que este producto, calculado íntegramente a partir de datos MSG (EUMETSAT), ofrece una alternativa prometedora para detectar y caracterizar zonas afectadas por sequía a través de la incorporación de un coeficiente de estrés hídrico.[EN] This study aims to introduce a completely new and recently launched 10-day GPP product based on data from the geostationary MSG satellite (MGPP LSA-411) and to assess its capability to detect areas affected by water stress (hot spots). The GPP product is based on Monteith’s concept, which models GPP as the product of the incoming photosynthetically active radiation (PAR), the fractional absorption of that flux (fAPAR) and a lightuse efficiency factor (ε). Preliminary results on the use of the MGPP product in the assessment of ecosystem response to rainfall deficit events are presented in this work for a short period of three years. The robustness of this product is evaluated at both site and regional scales across the MSG disk using eddy covariance (EC) GPP measurements and Earth Observing (EO)-based GPP products, respectively. The EO-based products belong to the 8-day MOD17A2H v6 at 500 m and the 10-day GDMP at 1 km. The results reveal the MGPP product, derived entirely from MSG (EUMETSAT) products, as an efficient alternative to detect and characterize areas under water scarcity by means of a coefficient of water stress.Trabajo financiado por los proyectos LSA SAF (EUMETSAT) y ESCENARIOS (CGL2012–35831). Agradecemos a los responsables de las torres EC la cesión de los datos de GPP.Martínez, B.; Sánchez-Ruiz, S.; Campos-Taberner, M.; García-Haro, FJ.; Gilabert, MA. (2020). 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Bulletin of the American Meteorological Society, 69(3), 278-295. https://doi.org/10.1175/1520- 0477(1988)069%3C0278:APTRMM%3E2.0.CO;2Vicente-Serrano, S.M., Azorín-Molina, C., Peña- Gallardo, M., Tomas-Burguera, M., Domínguez- Castro, F., et al. 2015. A high-resolution spatial assessment of the impacts of drought variability on vegetation activity in Spain from 1981 to 2015. Natural Hazards and Earth System Sciences, 19, 1189- 1213. https://doi.org/10.5194/nhess-19-1189-2019Zhang, Y., Yu, G., Yang, J., Wimberly, M.C., Zhang, X., Tao, J., et al. 2014. Climate driven global changes in carbon use efficiency. Global Ecology and Biogeography, 23, 144-155. https://doi.org/10.1111/geb.12086Zhao, M., Running, S.W., Heinsch, F.A., Nemani, R.R. 2011. MODIS derived terrestrial primary production. Land Remote Sensing and Global Environmental Change. Springer, New York, pp. 635-660. https://doi.org/10.1007/978-1-4419-6749-7_2

Evaluation of the LSA-SAF gross primary production product derived from SEVIRI/MSG data (MGPP)

The objective of this study is to describe a completely new 10-day gross primary production (GPP) product (MGPP LSA-411) based on data from the geostationary SEVIRI/MSG satellite within the LSA SAF (Land Surface Analysis SAF) as part of the SAF (Satellite Application Facility) network of EUMETSAT. The methodology relies on the Monteith approach. It considers that GPP is proportional to the absorbed photosynthetically active radiation APAR and the proportionality factor is known as the light use efficiency ε. A parameterization of this factor is proposed as the product of a εmax, corresponding to the canopy functioning under optimal conditions, and a coefficient quantifying the reduction of photosynthesis as a consequence of water stress. A three years data record (2015–2017) was used in an assessment against site-level eddy covariance (EC) tower GPP estimates and against other Earth Observation (EO) based GPP products. The site-level comparison indicated that the MGPP product performed better than the other EO based GPP products with 48% of the observations being below the optimal accuracy (absolute error < 1.0 g m−2 day−1) and 75% of these data being below the user requirement threshold (absolute error < 3.0 g m−2 day−1). The largest discrepancies between the MGPP product and the other GPP products were found for forests whereas small differences were observed for the other land cover types. The integration of this GPP product with the ensemble of LSA-SAF MSG products is conducive to meet user needs for a better understanding of ecosystem processes and for improved understanding of anthropogenic impact on ecosystem services.The objective of this study is to describe a completely new 10-day gross primary production (GPP) product (MGPP LSA-411) based on data from the geostationary SEVIRI/MSG satellite within the LSA SAF (Land Surface Analysis SAF) as part of the SAF (Satellite Application Facility) network of EUMETSAT. The methodology relies on the Monteith approach. It considers that GPP is proportional to the absorbed photosynthetically active radiation APAR and the proportionality factor is known as the light use efficiency epsilon. A parameterization of this factor is proposed as the product of a epsilon(max), corresponding to the canopy functioning under optimal conditions, and a coefficient quantifying the reduction of photosynthesis as a consequence of water stress. A three years data record (2015-2017) was used in an assessment against site-level eddy covariance (EC) tower GPP estimates and against other Earth Observation (EO) based GPP products. The site-level comparison indicated that the MGPP product performed better than the other EO based GPP products with 48% of the observations being below the optimal accuracy (absolute error <1.0 g m(-2) day(-1)) and 75% of these data being below the user requirement threshold (absolute error <3.0 g m(-2) day(-1)). The largest discrepancies between the MGPP product and the other GPP products were found for forests whereas small differences were observed for the other land cover types. The integration of this GPP product with the ensemble of LSA-SAF MSG products is conducive to meet user needs for a better understanding of ecosystem processes and for improved understanding of anthropogenic impact on ecosystem services.Peer reviewe

On-line tools to improve the presentation skills of scientific results

[EN] In experimental sciences and engineering it is essential to communicate and present the results effectively. The authors have participated in several educational innovation projects since 2016, aimed at developing of materials to improve the communication skills of scientific results. An exhaustive and updated compilation of the international rules that constitute the basis for the writted and oral scientific presentations was carried out. The good teaching practices in these fields were also identified. The results of those previous projects have shown the need to incorporate web questionnaires and other interactive content into the educational program. These are adapted to the demands of the students and provide a training feeback. In this contribution, the new materials that are being developed within the innovation project UV-SFPIE_PID19-1096780, funded by the University of Valencia, are presented. They are devoted to facilitate the acquisition of communication skills of scientific results. In particular, these tools combine ICT self-learning environments with traditional classroom teaching (blended learning). The project methodology includes educational data mining aimed at identifying the most effective materials and activities to achieve its objectives. The aim of these mixed learning tools is to facilitate the acquisition by the students of the necessary skills of oral and written communication, improve their presentation skills and, consequently, also their employability as university graduates.This work has been supported by the University of Valencia through project SFPIE_PID19-1096780.Campos-Taberner, M.; Gilabert, M.; Manzanares, J.; Mafé, S.; Cervera, J.; García-Haro, F.; Martínez, B.... (2020). On-line tools to improve the presentation skills of scientific results. IATED. 4907-4910. https://doi.org/10.21125/inted.2020.1342S4907491

Vegetation vulnerability to drought in Spain

Revista oficial de la Asociación Española de Teledetección[EN] Frequency of climatic extremes like long duration droughts has increased in Spain over the last century.The use of remote sensing observations for monitoring and detecting drought is justified on the basis that vegetation vigor is closely related to moisture condition. We derive satellite estimates of bio-physical variables such as fractional vegetation cover (FVC) from MODIS/EOS and SEVIRI/MSG time series. The study evaluates the strength of temporal relationships between precipitation and vegetation condition at time-lag and cumulative rainfall intervals. From this analysis, it was observed that the climatic disturbances affected both the growing season and the total amount of vegetation. However, the impact of climate variability on the vegetation dynamics has shown to be highly dependent on the regional climate, vegetation community and growth stages. In general, they were more significant in arid and semiarid areas, since water availability most strongly limits vegetation growth in these environments.[EN] Los extremos climáticos se han incrementado en España a los largo del último siglo; por ello, su análisis se ha convertido en una línea prioritaria de conocimiento con objeto fundamental de diseñar planes para la gestión y mitigación de sus efectos. Los datos de satélite permiten analizar las variaciones en la actividad de la vegetación a varias escalas temporales y su respuesta a la variabilidad climática. En este trabajo se pone de manifiesto la vulnera-bilidad de la vegetación en España ante condiciones ambientales extremas a través de las correlaciones entre índices meteorológicos de sequía (SPI) y variables biofísicas extraídas de datos MODIS/EOS y SEVIRI/MSG. Las anomalías en la vegetación, como indicadores de las condiciones de humedad de la misma, pueden ayudar a cuantificar y gestionar episodios meteorológicos extremos y hacer un seguimiento de la misma. Las mayores correlaciones se han obtenido en las regiones áridas y semiáridas y durante los meses de máxima actividad de la vegetación, generalmente entre mayo y junio.Este trabajo se enmarca en los proyectos DULCINEA (CGL2005–04202), RESET CLIMATE (CGL2012–35831), LSA SAF (EUMETSAT) y ERMES (FP7-SPACE-2013, Contract 606983).García-Haro, F.; Campos-Taberner, M.; Sabater, N.; Belda, F.; Moreno, A.; Gilabert, M.; Martínez, B.... (2014). Vulnerabilidad de la vegetación a la sequía en España. Revista de Teledetección. (42):29-38. https://doi.org/10.4995/raet.2014.2283SWORD29384

Downscaling rice yield simulation at sub-field scale using remotely sensed LAI data

Crop modeling and remote sensing are key tools to gain deeper understanding on cropping system dynamics and, ultimately, to increase the sustainability of agricultural productions. This study presents a system to estimate rice yields at sub-field scale based on the integration of a biophysical model and remotely sensed products. Leaf area index (LAI) data derived from decametric optical imageries (i.e., Landsat-8, Landsat-7 and Sentinel\u20132A) were assimilated into the WARM rice model via automatic recalibration of crop parameters at a fine spatial resolution (30 m 7 30 m), targeting the lowest error between simulated and remotely sensed LAI. The performance of the system was evaluated by comparing simulated yield using default and recalibrated parameters at sub-field scale with yield maps generated by a GPS-equipped harvester. The training dataset included 40 paddy fields in Northern Italy, which were sampled during three cropping seasons, from 2014 to 2016. The assimilation of remotely sensed LAI into model parameters increased the accuracy of the system: MAE and RRMSE were 0.66 t ha-1 [CI: 0.54 t ha-1\u20130.78 t ha-1] and 13.8% [CI: 11.7%\u201315.7%], respectively, whereas they were 0.82 t ha-1 [CI: 0.68 t ha-1\u20130.96 t ha-1) and 15.7% [CI: 14.1%,\u201317.4%] without assimilation. Moreover, the system allowed to properly reproduce the within-field yield variability, thus laying the basis for possible applications in precision agriculture advisory services

Exploring Ecosystem Functioning in Spain with Gross and Net Primary Production Time Series

The main objective of this study is to analyze the spatial and temporal variability of gross and net primary production (GPP and NPP) in Peninsular Spain across 15 years (2004&ndash;2018) and determine the relationship of those carbon fluxes with precipitation and air temperature. A time series study of daily GPP, NPP, mean air temperature, and monthly standardized precipitation index (SPI) at 1 km spatial resolution is conducted to analyze the ecosystem status and adaptation to changing environmental conditions. Spatial variability is analyzed for vegetation and specific forest types. Temporal dynamics are examined from a multiresolution analysis based on the wavelet transform (MRA-WT). The Mann&ndash;Kendall nonparametric test and the Theil&ndash;Sen slope are applied to quantify the magnitude and direction of trends (increasing or decreasing) within the time series. The use of MRA-WT to extract the annual component from daily series increased the number of statistically significant pixels. At pixel level, larger significant GPP and NPP negative changes (p-value &lt; 0.1) are observed, especially in southeastern Spain, eastern Mediterranean coastland, and central Spain. At annual temporal scale, forests and irrigated crops are estimated to have twice the GPP of rainfed crops, shrublands, grasslands, and sparse vegetation. Within forest types, deciduous broadleaved trees exhibited the greatest annual NPP, followed by evergreen broadleaved and evergreen needle-leaved tree species. Carbon fluxes trends were correlated with precipitation. The temporal analysis based on daily TS demonstrated an increase of accuracy in the trend estimates since more significant pixels were obtained as compared to annual resolution studies (72% as to only 17%)

Intercomparison of instruments for measuring leaf area index over rice

Leaf area index (LAI) is a key biophysical parameter used to determine foliage cover and crop growth in environmental studies in order to assess crop yield. LAI estimates can be classified as direct or indirect methods. Direct methods are destructive, time consuming, and difficult to apply over large fields. Indirect methods are non-destructive and cost-effective due to its portability, accuracy and repeatability. In this study, we compare indirect LAI estimates acquired from two classical instruments such as LAI-2000 and digital cameras for hemispherical photography, with LAI estimates acquired with a smart app (PocketLAI) installed on a mobile smartphone. In this work it is shown that LAI estimates obtained with the classical instruments and with a smartphone are well correlated. Consequently, results presented in this work allow considering PocketLAI as a powerful alternative to the classical instruments for LAI monitoring during field campaigns