This page has only limited features, please log in for full access.

Unclaimed
Gorka Mendiguren
Department of Conservation and Natural Sciences, National Museum of Denmark, 2800 Lyngby, Denmark

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 19 June 2021 in Remote Sensing
Reads 0
Downloads 0

Spatial patterns in long-term average evapotranspiration (ET) represent a unique source of information for evaluating the spatial pattern performance of distributed hydrological models on a river basin to continental scale. This kind of model evaluation is getting increased attention, acknowledging the shortcomings of traditional aggregated or timeseries-based evaluations. A variety of satellite remote sensing (RS)-based ET estimates exist, covering a range of methods and resolutions. There is, therefore, a need to evaluate these estimates, not only in terms of temporal performance and similarity, but also in terms of long-term spatial patterns. The current study evaluates four RS-ET estimates at moderate resolution with respect to spatial patterns in comparison to two alternative continental-scale gridded ET estimates (water-balance ET and Budyko). To increase comparability, an empirical correction factor between clear sky and all-weather ET, based on eddy covariance data, is derived, which could be suitable for simple corrections of clear sky estimates. Three RS-ET estimates (MODIS16, TSEB and PT-JPL) and the Budyko method generally display similar spatial patterns both across the European domain (mean SPAEF = 0.41, range 0.25–0.61) and within river basins (mean SPAEF range 0.19–0.38), although the pattern similarity within river basins varies significantly across basins. In contrast, the WB-ET and PML_V2 produced very different spatial patterns. The similarity between different methods ranging over different combinations of water, energy, vegetation and land surface temperature constraints suggests that robust spatial patterns of ET can be achieved by combining several methods.

ACS Style

Simon Stisen; Mohsen Soltani; Gorka Mendiguren; Henrik Langkilde; Monica Garcia; Julian Koch. Spatial Patterns in Actual Evapotranspiration Climatologies for Europe. Remote Sensing 2021, 13, 2410 .

AMA Style

Simon Stisen, Mohsen Soltani, Gorka Mendiguren, Henrik Langkilde, Monica Garcia, Julian Koch. Spatial Patterns in Actual Evapotranspiration Climatologies for Europe. Remote Sensing. 2021; 13 (12):2410.

Chicago/Turabian Style

Simon Stisen; Mohsen Soltani; Gorka Mendiguren; Henrik Langkilde; Monica Garcia; Julian Koch. 2021. "Spatial Patterns in Actual Evapotranspiration Climatologies for Europe." Remote Sensing 13, no. 12: 2410.

Journal article
Published: 04 September 2018 in Water
Reads 0
Downloads 0

Hydrologic models are conventionally constrained and evaluated using point measurements of streamflow, which represent an aggregated catchment measure. As a consequence of this single objective focus, model parametrization and model parameter sensitivity typically do not reflect other aspects of catchment behavior. Specifically for distributed models, the spatial pattern aspect is often overlooked. Our paper examines the utility of multiple performance measures in a spatial sensitivity analysis framework to determine the key parameters governing the spatial variability of predicted actual evapotranspiration (AET). The Latin hypercube one-at-a-time (LHS-OAT) sampling strategy with multiple initial parameter sets was applied using the mesoscale hydrologic model (mHM) and a total of 17 model parameters were identified as sensitive. The results indicate different parameter sensitivities for different performance measures focusing on temporal hydrograph dynamics and spatial variability of actual evapotranspiration. While spatial patterns were found to be sensitive to vegetation parameters, streamflow dynamics were sensitive to pedo-transfer function (PTF) parameters. Above all, our results show that behavioral model definitions based only on streamflow metrics in the generalized likelihood uncertainty estimation (GLUE) type methods require reformulation by incorporating spatial patterns into the definition of threshold values to reveal robust hydrologic behavior in the analysis.

ACS Style

Mehmet Cüneyd Demirel; Julian Koch; Gorka Mendiguren; Simon Stisen. Spatial Pattern Oriented Multicriteria Sensitivity Analysis of a Distributed Hydrologic Model. Water 2018, 10, 1188 .

AMA Style

Mehmet Cüneyd Demirel, Julian Koch, Gorka Mendiguren, Simon Stisen. Spatial Pattern Oriented Multicriteria Sensitivity Analysis of a Distributed Hydrologic Model. Water. 2018; 10 (9):1188.

Chicago/Turabian Style

Mehmet Cüneyd Demirel; Julian Koch; Gorka Mendiguren; Simon Stisen. 2018. "Spatial Pattern Oriented Multicriteria Sensitivity Analysis of a Distributed Hydrologic Model." Water 10, no. 9: 1188.

Journal article
Published: 01 March 2018 in Remote Sensing of Environment
Reads 0
Downloads 0

The overarching objective of this study was to produce a disaggregated SMOS Soil Moisture (SM) product using land surface parameters from a geostationary satellite in a region covering a diverse range of ecosystem types. SEVIRI data at 15 min temporal resolution were used to derive the Temperature and Vegetation Dryness Index (TVDI) that served as SM proxy within the disaggregation process. West Africa (3°N 26°W; 28°N 26°E) was selected as a case study as it presents both an important North-South climate gradient and a diverse range of ecosystem types. The main challenge was to set up a methodology applicable over a large area that overcomes the constraints of SMOS (low spatial resolution) and TVDI (requires similar atmospheric forcing and triangular shape formed when plotting morning rise temperature versus fraction of vegetation cover) in order to produce a 0.05° resolution disaggregated SMOS SM product at the sub-continental scale. Consistent cloud cover appeared as one of the main constraints for deriving TVDI, especially during the rainy season and in the southern parts of the region and a large adjustment window (105 × 105 SEVIRI pixels) was therefore deemed necessary. Both the original and the disaggregated SMOS SM products described well the seasonal dynamics observed at six locations of in situ observations. However, there was an overestimation in both products for sites in the humid southern regions; most likely caused by the presence of forest. Both TVDI and the associated disaggregated SM product were found to be highly sensitive to algorithm input parameters; especially for conditions of high fraction of vegetation cover. Additionally, seasonal dynamics in TVDI did not follow the seasonal patterns of SM. Still, its spatial heterogeneity was found to be a good proxy for disaggregating SMOS SM data; main river networks and spatial patterns of SM extremes (i.e. droughts and floods) not seen in the original SMOS SM product were revealed in the disaggregated SM product for a test case of July–September 2012. The disaggregation methodology thereby successfully increased the spatial resolution of SMOS SM, with potential application for local drought/flood monitoring of importance for the livelihood of the population of West Africa.

ACS Style

T. Tagesson; Stephanie Horion; Héctor Nieto; V. Zaldo Fornies; Gorka Mendiguren Gonzalez; Claire Bulgin; D. Ghent; R. Fensholt. Disaggregation of SMOS soil moisture over West Africa using the Temperature and Vegetation Dryness Index based on SEVIRI land surface parameters. Remote Sensing of Environment 2018, 206, 424 -441.

AMA Style

T. Tagesson, Stephanie Horion, Héctor Nieto, V. Zaldo Fornies, Gorka Mendiguren Gonzalez, Claire Bulgin, D. Ghent, R. Fensholt. Disaggregation of SMOS soil moisture over West Africa using the Temperature and Vegetation Dryness Index based on SEVIRI land surface parameters. Remote Sensing of Environment. 2018; 206 ():424-441.

Chicago/Turabian Style

T. Tagesson; Stephanie Horion; Héctor Nieto; V. Zaldo Fornies; Gorka Mendiguren Gonzalez; Claire Bulgin; D. Ghent; R. Fensholt. 2018. "Disaggregation of SMOS soil moisture over West Africa using the Temperature and Vegetation Dryness Index based on SEVIRI land surface parameters." Remote Sensing of Environment 206, no. : 424-441.

Journal article
Published: 20 February 2018 in Hydrology and Earth System Sciences
Reads 0
Downloads 0

Satellite-based earth observations offer great opportunities to improve spatial model predictions by means of spatial-pattern-oriented model evaluations. In this study, observed spatial patterns of actual evapotranspiration (AET) are utilised for spatial model calibration tailored to target the pattern performance of the model. The proposed calibration framework combines temporally aggregated observed spatial patterns with a new spatial performance metric and a flexible spatial parameterisation scheme. The mesoscale hydrologic model (mHM) is used to simulate streamflow and AET and has been selected due to its soil parameter distribution approach based on pedo-transfer functions and the build in multi-scale parameter regionalisation. In addition two new spatial parameter distribution options have been incorporated in the model in order to increase the flexibility of root fraction coefficient and potential evapotranspiration correction parameterisations, based on soil type and vegetation density. These parameterisations are utilised as they are most relevant for simulated AET patterns from the hydrologic model. Due to the fundamental challenges encountered when evaluating spatial pattern performance using standard metrics, we developed a simple but highly discriminative spatial metric, i.e. one comprised of three easily interpretable components measuring co-location, variation and distribution of the spatial data. The study shows that with flexible spatial model parameterisation used in combination with the appropriate objective functions, the simulated spatial patterns of actual evapotranspiration become substantially more similar to the satellite-based estimates. Overall 26 parameters are identified for calibration through a sequential screening approach based on a combination of streamflow and spatial pattern metrics. The robustness of the calibrations is tested using an ensemble of nine calibrations based on different seed numbers using the shuffled complex evolution optimiser. The calibration results reveal a limited trade-off between streamflow dynamics and spatial patterns illustrating the benefit of combining separate observation types and objective functions. At the same time, the simulated spatial patterns of AET significantly improved when an objective function based on observed AET patterns and a novel spatial performance metric compared to traditional streamflow-only calibration were included. Since the overall water balance is usually a crucial goal in hydrologic modelling, spatial-pattern-oriented optimisation should always be accompanied by traditional discharge measurements. In such a multi-objective framework, the current study promotes the use of a novel bias-insensitive spatial pattern metric, which exploits the key information contained in the observed patterns while allowing the water balance to be informed by discharge observations.

ACS Style

Mehmet C. Demirel; Juliane Mai; Gorka Mendiguren; Julian Koch; Luis Samaniego; Simon Stisen. Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model. Hydrology and Earth System Sciences 2018, 22, 1299 -1315.

AMA Style

Mehmet C. Demirel, Juliane Mai, Gorka Mendiguren, Julian Koch, Luis Samaniego, Simon Stisen. Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model. Hydrology and Earth System Sciences. 2018; 22 (2):1299-1315.

Chicago/Turabian Style

Mehmet C. Demirel; Juliane Mai; Gorka Mendiguren; Julian Koch; Luis Samaniego; Simon Stisen. 2018. "Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model." Hydrology and Earth System Sciences 22, no. 2: 1299-1315.

Journal article
Published: 30 November 2017 in Hydrology and Earth System Sciences
Reads 0
Downloads 0

Distributed hydrological models are traditionally evaluated against discharge stations, emphasizing the temporal and neglecting the spatial component of a model. The present study widens the traditional paradigm by highlighting spatial patterns of evapotranspiration (ET), a key variable at the land–atmosphere interface, obtained from two different approaches at the national scale of Denmark. The first approach is based on a national water resources model (DK-model), using the MIKE-SHE model code, and the second approach utilizes a two-source energy balance model (TSEB) driven mainly by satellite remote sensing data. Ideally, the hydrological model simulation and remote-sensing-based approach should present similar spatial patterns and driving mechanisms of ET. However, the spatial comparison showed that the differences are significant and indicate insufficient spatial pattern performance of the hydrological model.The differences in spatial patterns can partly be explained by the fact that the hydrological model is configured to run in six domains that are calibrated independently from each other, as it is often the case for large-scale multi-basin calibrations. Furthermore, the model incorporates predefined temporal dynamics of leaf area index (LAI), root depth (RD) and crop coefficient (Kc) for each land cover type. This zonal approach of model parameterization ignores the spatiotemporal complexity of the natural system. To overcome this limitation, this study features a modified version of the DK-model in which LAI, RD and Kc are empirically derived using remote sensing data and detailed soil property maps in order to generate a higher degree of spatiotemporal variability and spatial consistency between the six domains. The effects of these changes are analyzed by using empirical orthogonal function (EOF) analysis to evaluate spatial patterns. The EOF analysis shows that including remote-sensing-derived LAI, RD and Kc in the distributed hydrological model adds spatial features found in the spatial pattern of remote-sensing-based ET.

ACS Style

Gorka Mendiguren; Julian Koch; Simon Stisen. Spatial pattern evaluation of a calibrated national hydrological model – a remote-sensing-based diagnostic approach. Hydrology and Earth System Sciences 2017, 21, 5987 -6005.

AMA Style

Gorka Mendiguren, Julian Koch, Simon Stisen. Spatial pattern evaluation of a calibrated national hydrological model – a remote-sensing-based diagnostic approach. Hydrology and Earth System Sciences. 2017; 21 (12):5987-6005.

Chicago/Turabian Style

Gorka Mendiguren; Julian Koch; Simon Stisen. 2017. "Spatial pattern evaluation of a calibrated national hydrological model – a remote-sensing-based diagnostic approach." Hydrology and Earth System Sciences 21, no. 12: 5987-6005.

Preprint content
Published: 09 October 2017
Reads 0
Downloads 0

Satellite based earth observations offer great opportunities to improve spatial model predictions by means of spatial pattern oriented model evaluations. In this study, observed spatial patterns of actual evapotranspiration (AET) are utilized for spatial model calibration tailored to target the pattern performance of the model. The proposed calibration framework combines temporally aggregated observed spatial patterns with a new spatial performance metric and a flexible spatial parameterisation scheme. The mesoscale Hydrologic Model (mHM) is used to simulate streamflow and AET and has been selected due to its soil parameter distribution approach based on pedotransfer functions and the build in multiscale parameter regionalization. In addition two new domain specific spatial parameter distribution options have been incorporated in the model in order to increase the flexibility of root fraction coefficient and potential evapotranspiration correction parameterisations, based on soil type and vegetation density. These parametrisations are utilized as they are most relevant for simulated AET patterns from the hydrologic model. Due to the fundamental challenges encountered when evaluating spatial pattern performance using standard metrics, we developed a simple but highly discriminative spatial metric i.e. comprised of three easily interpretable components measuring co-location, variation and distribution of the spatial data. The study shows that with flexible spatial model parameterisation used in combination with the appropriate objective functions, the simulated spatial patterns of actual evapotranspiration become substantially more similar to the satellite based estimates. Overall 26 parameters are identified for calibration through a sequential screening approach based on a combination of streamflow and spatial pattern metrics. The robustness of the calibrations is tested using an ensemble of nine calibrations based on different seed numbers using the shuffled complex evolution optimizer. The calibration results reveal a limited trade-offs between streamflow dynamics and spatial patterns illustrating the benefit of combining separate observation types and objective functions. At the same time, the simulated spatial patterns of AET significantly improved when including an objective function based on observed AET patterns and a novel spatial performance metric compared to traditional streamflow only calibration. Since the overall water balance is usually a crucial goal in the hydrologic modelling, spatial pattern oriented optimization should always be accompanied by traditional discharge measurements. In such a multi-objective framework, the current study promotes the use of a novel bias-insensitive spatial pattern metric, which exploits the key information contained in the observed patterns while allowing the water balance to be informed by discharge observations.

ACS Style

Mehmet C. Demirel; Juliane Mai; Gorka Mendiguren; Julian Koch; Luis Samaniego; Simon Stisen. Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model. 2017, 2017, 1 -22.

AMA Style

Mehmet C. Demirel, Juliane Mai, Gorka Mendiguren, Julian Koch, Luis Samaniego, Simon Stisen. Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model. . 2017; 2017 ():1-22.

Chicago/Turabian Style

Mehmet C. Demirel; Juliane Mai; Gorka Mendiguren; Julian Koch; Luis Samaniego; Simon Stisen. 2017. "Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model." 2017, no. : 1-22.

Preprint content
Published: 06 July 2017
Reads 0
Downloads 0
ACS Style

Gorka Mendiguren González. Response to reviewer 2. 2017, 1 .

AMA Style

Gorka Mendiguren González. Response to reviewer 2. . 2017; ():1.

Chicago/Turabian Style

Gorka Mendiguren González. 2017. "Response to reviewer 2." , no. : 1.

Preprint content
Published: 06 July 2017
Reads 0
Downloads 0
ACS Style

Gorka Mendiguren González. Response to reviewer 1. 2017, 1 .

AMA Style

Gorka Mendiguren González. Response to reviewer 1. . 2017; ():1.

Chicago/Turabian Style

Gorka Mendiguren González. 2017. "Response to reviewer 1." , no. : 1.

Preprint content
Published: 25 April 2017
Reads 0
Downloads 0

Distributed hydrological models are traditionally evaluated against discharge stations, emphasizing the temporal and neglecting the spatial component of a model. The present study widens the traditional paradigm by highlighting spatial patterns of evapotranspiration (ET), a key variable at the land-atmosphere interface, obtained from two different approaches at the national scale of Denmark. The first approach is based on a national water resources model (DK-model), using the MIKE-SHE model code, and the second approach utilizes a two source energy balance model (TSEB) driven mainly by satellite remote sensing data. The main hypothesis of the study is that while both approaches are essentially estimates, the spatial patterns of the remote sensing based approach are explicitly driven by observed land surface temperature and therefore represent the most direct spatial pattern information of ET; enabling its use for distributed hydrological model evaluation. Ideally the hydrological model simulation and remote sensing based approach should present similar spatial patterns and driving mechanism of ET. However, the spatial comparison showed that the differences are significant and indicating insufficient spatial pattern performance of the hydrological model. The differences in spatial patterns can partly be explained by the fact that the hydrological model is configured to run in 6 domains that are calibrated independently from each other, as it is often the case for large scale multi-basin calibrations. Furthermore, the model incorporates predefined temporal dynamics of Leaf Area Index (LAI), root depth (RD) and Crop coefficient (Kc) for each land cover type. This zonal approach of model parametrization ignores the spatio-temporal complexity of the natural system. To overcome this limitation, the study features a modified version of the DK-Model in which LAI, RD, and KC are empirically derived using remote sensing data and detailed soil property maps in order to generate a higher degree of spatio-temporal variability and spatial consistency between the 6 domains. The effects of these changes are analyzed by using the empirical orthogonal functions (EOF) analysis to evaluate spatial patterns. The EOF-analysis shows that including remote sensing derived LAI, RD and KC in the distributed hydrological model adds spatial features found in the spatial pattern of remote sensing based ET.

ACS Style

Gorka Mendiguren; Julian Koch; Simon Stisen. Spatial pattern evaluation of a calibrated national hydrological model – a remote sensing based diagnostic approach. 2017, 2017, 1 -28.

AMA Style

Gorka Mendiguren, Julian Koch, Simon Stisen. Spatial pattern evaluation of a calibrated national hydrological model – a remote sensing based diagnostic approach. . 2017; 2017 ():1-28.

Chicago/Turabian Style

Gorka Mendiguren; Julian Koch; Simon Stisen. 2017. "Spatial pattern evaluation of a calibrated national hydrological model – a remote sensing based diagnostic approach." 2017, no. : 1-28.

Journal article
Published: 29 September 2015 in Biogeosciences
Reads 0
Downloads 0

This study evaluates three different metrics of water content of an herbaceous cover in a Mediterranean wooded grassland (dehesa) ecosystem. Fuel moisture content (FMC), equivalent water thickness (EWT) and canopy water content (CWC) were estimated from proximal sensing and MODIS satellite imagery. Dry matter (Dm) and leaf area index (LAI) connect the three metrics and were also analyzed. Metrics were derived from field sampling of grass cover within a 500 m MODIS pixel. Hand-held hyperspectral measurements and MODIS images were simultaneously acquired and predictive empirical models were parametrized. Two methods of estimating FMC and CWC using different field protocols were tested in order to evaluate the consistency of the metrics and the relationships with the predictive empirical models. In addition, radiative transfer models (RTM) were used to produce estimates of CWC and FMC, which were compared with the empirical ones. Results revealed that, for all metrics spatial variability was significantly lower than temporal. Thus we concluded that experimental design should prioritize sampling frequency rather than sample size. Dm variability was high which demonstrates that a constant annual Dm value should not be used to predict EWT from FMC as other previous studies did. Relative root mean square error (RRMSE) evaluated the performance of nine spectral indices to compute each variable. Visible Atmospherically Resistant Index (VARI) provided the lowest explicative power in all cases. For proximal sensing, Global Environment Monitoring Index (GEMI) showed higher statistical relationships both for FMC (RRMSE = 34.5 %) and EWT (RRMSE = 27.43 %) while Normalized Difference Infrared Index (NDII) and Global Vegetation Monitoring Index (GVMI) for CWC (RRMSE = 30.27 % and 31.58 % respectively). When MODIS data were used, results showed an increase in R2 and Enhanced Vegetation Index (EVI) as the best predictor for FMC (RRMSE = 33.81 %) and CWC (RRMSE = 27.56 %) and GEMI for EWT (RRMSE = 24.6 %). Differences in the viewing geometry of the platforms can explain these differences as the portion of vegetation observed by MODIS is larger than when using proximal sensing including the spectral response from scattered trees and its shadows. CWC was better predicted than the other two water content metrics, probably because CWC depends on LAI, that shows a notable seasonal variation in this ecosystem. Strong statistical relationship was found between empirical models using indices sensible to chlorophyll activity (NDVI or EVI which are not directly related to water content) due to the close relationship between LAI, water content and chlorophyll activity in grassland cover, which is not true for other types of vegetation such as forest or shrubs. The empirical methods tested outperformed FMC and CWC products based on radiative transfer model inversion.

ACS Style

G. Mendiguren; M. Pilar Martín; H. Nieto; J. Pacheco-Labrador; S. Jurdao. Seasonal variation in grass water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site. Biogeosciences 2015, 12, 5523 -5535.

AMA Style

G. Mendiguren, M. Pilar Martín, H. Nieto, J. Pacheco-Labrador, S. Jurdao. Seasonal variation in grass water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site. Biogeosciences. 2015; 12 (18):5523-5535.

Chicago/Turabian Style

G. Mendiguren; M. Pilar Martín; H. Nieto; J. Pacheco-Labrador; S. Jurdao. 2015. "Seasonal variation in grass water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site." Biogeosciences 12, no. 18: 5523-5535.

Preprint content
Published: 13 April 2015 in Biogeosciences Discussions
Reads 0
Downloads 0

This study evaluates three different metrics of vegetation water content estimated from proximal sensing and MODIS satellite imagery: Fuel Moisture Content (FMC), Equivalent Water Thickness (EWT) and Canopy Water Content (CWC). Dry matter (Dm) and Leaf area Index (LAI) were also analyzed in order to connect FMC with EWT and EWT with CWC, respectively. This research took place in a Fluxnet site located in Mediterranean wooded grassland (dehesa) ecosystem in Las Majadas del Tietar (Spain). Results indicated that FMC and EWT showed lower spatial variation than CWC. The spatial variation within the MODIS pixel was not as critical as its temporal trend, so to capture better the variability, fewer plots should be sampled but more times. Due to the high seasonal Dm variability, a constant annual value would not work to predict EWT from FMC. Relative root mean square error (RRMSE) evaluated the performance of nine spectral indices to compute each variable. VARI provided the worst results in all cases. For proximal sensing, GEMI worked best for both FMC (RRMSE = 34.5%) and EWT (RRMSE = 27.43%) while NDII and GVMI performed best for CWC (RRMSE =30.27% and 31.58% respectively). For MODIS data, results were a bit better with EVI as the best predictor for FMC (RRMSE = 33.81%) and CWC (RRMSE = 27.56%) and GEMI for EWT (RRMSE = 24.6%). To explain these differences, proximal sensing measures only grasslands at nadir view angle, but MODIS includes also trees, their shades, and other artifacts at up to 20° view angle. CWC was better predicted than the other two water content variables, probably because CWC depends on LAI, which is highly correlated to the spectral indices. Finally, these empirical methods outperformed FMC and CWC products based on radiative transfer model inversion.

ACS Style

G. Mendiguren; M. P. Martín; H. Nieto; J. Pacheco-Labrador; S. Jurdao. Seasonal variation in vegetation water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site. Biogeosciences Discussions 2015, 1 .

AMA Style

G. Mendiguren, M. P. Martín, H. Nieto, J. Pacheco-Labrador, S. Jurdao. Seasonal variation in vegetation water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site. Biogeosciences Discussions. 2015; ():1.

Chicago/Turabian Style

G. Mendiguren; M. P. Martín; H. Nieto; J. Pacheco-Labrador; S. Jurdao. 2015. "Seasonal variation in vegetation water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site." Biogeosciences Discussions , no. : 1.