PEOPLEPaulina Bartkowiak

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Paulina Bartkowiak

Senior Researcher
Institute for Earth Observation

T +39 0471 055 275

paulina.bartkowiak@eurac.edu

https://orcid.org/0000-0002-1172-5942

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About

About

Remote sensing of vegetation
Energy-water land surface modeling
Time-series explanatory data analysis
Data-driven modeling

Short bio

Paulina Bartkowiak is a postdoc in the Biosphere and Hydrosphere Research Group at the Institute for Earth Observation, EURAC Research. She is interested in evapotranspiration (ET) modeling using thermal and optical remote sensing to monitor and understand vegetation conditions and dynamics. She also focuses on solving some of the challenges of ET retrieval in the highly complex Alpine region. She has contributed to several projects including 4DMed-Hydrology, Caleidoscope, and SENWISE with a main focus on estimating plant water stress in particular in forests and grasslands using an energy balance approach.

Links

ORCID: https://orcid.org/0000-0002-1172-5942

ResearchGate: https://www.researchgate.net/profile/Paulina-Bartkowiak

LinkedIn: https://www.linkedin.com/in/paulina-bartkowiak

Scientific Publications

Journal article2024

A Copernicus-based evapotranspiration dataset at 100 m spatial resolution over four Mediterranean basins

Bartkowiak P, Ventura B, Jacob A, Castelli M

Bartolomeo Ventura, Alexander Jacob, Mariapina Castelli, Paulina Bartkowiak

Projekt: 4DMED-Hydrology

Evapotranspiration (ET) is responsible for regulating the hydrological cycle, with a relevant impact on air humidity and precipitation that is particularly important in the context of acute drought events in recent years. With the intensification of rainfall deficits and extreme heat events, the Mediterranean region requires regular monitoring to enhance water resource management. Even though remote sensing provides spatially continuous information for estimating ET on large scales, existing global products with spatial resolutions ≥ 0.5 km are insufficient for capturing spatial detail at a local level. In the framework of ESA's 4DMED-Hydrology project, we generate an ET dataset at both high spatial and high temporal resolutions using the Priestley–Taylor Two-Source Energy Balance (TSEB-PT) model driven by Copernicus satellite data. We build an automatic workflow to generate a 100 m ET product by combining data from Sentinel-2 (S2) MultiSpectral Instrument (MSI) and Sentinel-3 (S3) land surface temperature (LST) with ERA5 climate reanalysis derived within the period 2017–2021 over four Mediterranean basins in Italy, Spain, France, and Tunisia (Po, Ebro, Hérault, and Medjerda). First, original S2 data are pre-processed before deriving 100 m inputs for the ET estimation. Next, biophysical variables, like leaf area index and fractional vegetation cover, are generated, and then they are temporally composited within a 10 d window according to S3 acquisitions. Consequently, decadal S2 mosaics are used to derive the remaining TSEB-PT inputs. In parallel, we sharpen 1 km S3 by exploiting the dependency between coarse-resolution LST and 100 m S2 reflectances using a decision tree algorithm. Afterwards, climate forcings are utilized to model energy fluxes and then for daily ET retrieval. The daily ET composites demonstrate reasonable TSEB-PT estimates. Based on the validation results against eight eddy covariance (EC) towers between 2017 and 2021, the model predicts 100 m ET with an average RMSE of 1.38 mm d−1 and a Pearson coefficient equal to 0.60. Regardless of some constraints mostly related to the high complexity of EC sites, TSEB-PT can effectively estimate 100 m ET, which opens up new opportunities for monitoring the hydrological cycle on a regional scale. The full dataset is freely available at https://doi.org/10.48784/b90a02d6-5d13-4acd-b11c-99a0d381ca9a, https://doi.org/10.48784/fb631817-189f-4b57-af6a-38cef217bad3, https://doi.org/10.48784/70cd192c-0d46-4811-ad1d-51a09734a2e9, and https://doi.org/10.48784/7abdbd94-ddfe-48df-ab09-341ad2f52e47 for the Ebro, Hérault, Medjerda, and Po catchments, respectively (Bartkowiak et al., 2023a–d).

DOI Quelle

DOI: 10.5194/essd-16-4709-2024

Conference proceedings article2024

Reconstruction of satellite-based evapotranspiration under all-sky conditions in the Alps

Bartkowiak P, Maines E, Crespi A, Castelli M

Mariapina Castelli, Alice Crespi, Paulina Bartkowiak, Elena Maines

Projekt: Kalibrierung erdbeobachtungsbasierter Auswirkungen von Trockenheit auf Bergwälder durch Überwachung von Kohlenstoffbindung und Transpiration: von der Reaktion einzelner Bäume bis zur Extrapolation auf regionale Ebene

Evapotranspiration (ET) driven by thermal infrared remote sensing provides spatiotemporally distributed information on water availability at large scale. However, cloudy-sky conditions result in gaps in satellite-based ET, especially in complex landscapes such as the Alps. In this regard, we investigate different reconstruction approaches to fill missing ET at six alpine flux sites in Italy and Switzerland. The gapfilling is performed by three main steps: establishing a relationship between clear-sky actual ET and different estimates of reference quantities, interpolation of these dependencies to overcast conditions, and predicting ET by applying interpolated factors to reference quantities. The performance of the reconstruction strongly depends on landcover, achieving the best results over grasslands (RMSE = 0.38 mm day-1) and less performant outcomes over forest (RMSE = 0.70 mm day-1) when reference quantities from Penman-Monteith and Priestley-Taylor formulations are applied. The findings of this work give next-future perspectives for all-sky ET retrieval using remote sensing.

DOI

DOI: 10.1109/IGARSS53475.2024.10641718

Database2023

Daily evaporation product - Ebro basin (Version v1) [Data set]

Bartkowiak P, Ventura B, Jacob A, Castelli M

Bartolomeo Ventura, Alexander Jacob, Mariapina Castelli, Paulina Bartkowiak

Projekt: 4DMED-Hydrology

Evaporation (E) is the estimated flux of water evaporated from the land surface, including vegetation and bare soil. The product is generated from Two-source Energy Balance (TSEB) model forced by ESA Copernicus Sentinel-2A/B MSI and Sentinel-3A/B SLSTR LST imagery together with ECMWF ERA5 reanalysis data. Evaporation maps are available at 100-m spatial resolution for the period 2017-2021 over the Ebro basin in Spain. The spatial extent corresponds to Sentinel-2 tiling grids overlapping with the study area. The E layer contains one single band with evaporation values per day [mm/day] corresponding to Sentinel-3 acquisition day. Invalid pixels, mainly due to cloud contamination and lack of input data for TSEB, are filled with NaN values. Evaporation outputs are generated in Cloud Optimized GeoTIFF (COG) format with metadata included in the file attributes. Datasets are available for each month (Jan-Dec) of the year separately in the form of stacked daily E observations.

DOI Quelle

DOI: 10.48784/b90a02d6-5d13-4acd-b11c-99a0d381ca9a

Database2023

Daily evaporation product - Herault basin (Version v1) [Data set]

Bartkowiak P, Ventura B, Jacob A, Castelli, M

Bartolomeo Ventura, Alexander Jacob, Mariapina Castelli, Paulina Bartkowiak

Projekt: 4DMED-Hydrology

Evaporation (E) is the estimated flux of water evaporated from land surface, including vegetation and bare soil. The product is generated from Two-source Energy Balance (TSEB) model forced by ESA Copernicus Sentinel-2A/B MSI and Sentinel-3A/B SLSTR LST imagery together with ECMWF ERA5 reanalysis data. Evaporation maps are available at 100-m spatial resolution for the period 2017-2021 over Hérault basin in France. The spatial extent corresponds to Sentinel-2 tiling grids overlapping with the study area. The E layer contains one single band with evaporation values per day [mm/day] corresponding to Sentinel-3 acquisition day. Invalid pixels, mainly due to cloud contamination and lack of the input data for TSEB, are filled with NaN values. Evaporation outputs are generated in Cloud Optimized GeoTIFF (COG) format with metadata included in the file attributes. Datasets are available for each month (Jan-Dec) of the year separately in the form of stacked daily E observations.

DOI Quelle

DOI: 10.48784/FB631817-189F-4B57-AF6A-38CEF217BAD3

Database2023

Daily evaporation product - Medjerda basin (Version v1) [Data set]

Bartkowiak P, Ventura B, Jacob A, Castelli M

Bartolomeo Ventura, Alexander Jacob, Mariapina Castelli, Paulina Bartkowiak

Projekt: 4DMED-Hydrology

Evaporation (E) is the estimated flux of water evaporated from land surface, including vegetation and bare soil. The product is generated from Two-source Energy Balance (TSEB) model forced by ESA Copernicus Sentinel-2A/B MSI and Sentinel-3A/B SLSTR LST imagery together with ECMWF ERA5 reanalysis data. Evaporation maps are available at 100-m spatial resolution for the period 2017-2021 over Medjerda basin in Tunisia. The spatial extent corresponds to Sentinel-2 tiling grids overlapping with the study area. The E layer contains one single band with evaporation values per day [mm/day] corresponding to Sentinel-3 acquisition day. Invalid pixels, mainly due to cloud contamination and lack of the input data for TSEB, are filled with NaN values. Evaporation outputs are generated in Cloud Optimized GeoTIFF (COG) format with metadata included in the file attributes. Datasets are available for each month (Jan-Dec) of the year separately in the form of stacked daily E observations.

DOI Quelle

DOI: 10.48784/70cd192c-0d46-4811-ad1d-51a09734a2e9

Database2023

Daily evaporation product - Po basin (Version v1) [Data set]

Bartkowiak P, Ventura B, Jacob A, Castelli M

Bartolomeo Ventura, Alexander Jacob, Mariapina Castelli, Paulina Bartkowiak

Projekt: 4DMED-Hydrology

Evaporation (E) is the estimated flux of water evaporated from land surface, including vegetation and bare soil. The product is generated from Two-source Energy Balance (TSEB) model forced by ESA Copernicus Sentinel-2A/B MSI and Sentinel-3A/B SLSTR LST imagery together with ECMWF ERA5 reanalysis data. Evaporation maps are available at 100-m spatial resolution for the period 2017-2021 over Po basin in Italy. The spatial extent corresponds to Sentinel-2 tiling grids overlapping with the study area. The E layer contains one single band with evaporation values per day [mm/day] corresponding to Sentinel-3 acquisition day. Invalid pixels, mainly due to cloud contamination and lack of the input data for TSEB, are filled with NaN values. Evaporation outputs are generated in Cloud Optimized GeoTIFF (COG) format with metadata included in the file attributes. Datasets are available for each month (Jan-Dec) of the year separately in the form of stacked daily E observations.

DOI Quelle

DOI: 10.48784/7ABDBD94-DDFE-48DF-AB09-341AD2F52E47

Conference proceedings article2022

Evaluation of remotely-sensed evapotranspiration datasets at different spatial and temporal scales at forest and grassland sites in Italy

De Santis D, D'Amato C, Bartkowiak P, Azimi S, Castelli M, Rigon R, Massari C

Mariapina Castelli, Paulina Bartkowiak

Remote sensing measurements offer great potential to quantify evapotranspiration (ET) more accurately. This work assesses several remotely-sensed ET datasets at high, medium and coarse scale through a comparison with eddy covariance (EC) measurements. In particular, daily retrievals obtained from the Sen-ET model and the ECOSTRESS ET product, both having resolutions of tens of meters, were considered. In addition, 3-km daily ET estimates derived from SEVIRI were analyzed, together with 8-day MODIS ET composites at 500-m spatial resolution. The remotely-sensed actual ET retrievals at medium and coarse scale were compared with EC observations at five forest and grassland sites over different climate zones in Italy. Additionally, for one of these sites (Monte Bondone), the high-resolution ET estimates within the EC footprint were also evaluated. Sen-ET retrievals demonstrated a remarkable ability to describe EC latent heat fluxes (e.g., Pearson correlation coefficient, R, equal to 0.83, and unbiased root-mean-square-difference, ubRMSD, equal to 0.85 mm/day), and therefore great potential for producing high-resolution ET estimates, while ECOSTRESS data were less performant (e.g., R = 0.46, and $\boldsymbol{ubRMSD}=1.65\boldsymbol{mm}/\text{day})$ . Despite the spatial differences between EC footprint size and the medium along with coarse scale of remotely-sensed datasets, the latter both showed a good agreement with the measured EC fluxes. The SEVIRI ET, in particular, proved to be significantly suitable for the ET description also at a local scale (at daily time step, average R = 0.84, and average $\boldsymbol{u b R M S D=\mathbf0. 8 3}$ mm/day over the study sites), despite a tendency to underestimate in-situ measurements (negative biases ranging between 0.2 and 0.85 mm/day were observed). This study contributes to the topic of evaluating specific remotely-sensed ET datasets under different conditions. The obtained results generally confirm the value of satellite-derived retrievals for ET estimate and monitoring, as well as their potential to improve ET modeling.

DOI

DOI: 10.1109/MetroAgriFor55389.2022.9964755

Journal article2022

Land Surface Temperature Reconstruction Under Long-Term Cloudy-Sky Conditions at 250 m Spatial Resolution: Case Study of Vinschgau/Venosta Valley in the European Alps

Bartkowiak P, Castelli M, Crespi A, Niedrist G, Zanotelli D, Colombo R, Notarnicola C

Georg Niedrist, Alice Crespi, Claudia Notarnicola, Mariapina Castelli, Paulina Bartkowiak

—In this article, we present a new concept for predicting satellite-derived land surface temperature (LST) under cloudy skies over vegetated areas in the Alps. Although many different reconstruction methods have been developed, they require rarely available inputs, or they restore missing pixels from clear-sky observations with low spatial resolution (1–5 km), which makes them unreliable in heterogenous ecosystems. Given these limitations, we propose a station-based procedure to predict cloud-covered grids from 1-km Terra MODIS LST at 250 m spatial resolution. First, we explored correlations between ground-measured LST and air temperature in conjunction with other geo-biophysical variables under cloudy-sky conditions derived from ESRA clear-sky radiation model. Considering a high site dependency driven by different landcovers, in-situ data were aggregated into three groups (forest, permanent crops, grassland) and then, models were established. Next, the regressions were applied to 250-m gridded predictors to estimate cloud-covered LST pixels for six Terra MODIS LST images in 2014. While for permanent crops and forest group linear modelling was the most efficient, neural networks achieved the best performance for grasslands. The reconstructions showed reasonable LST distribution considering landscape heterogeneity of the region. The results were validated against timeseries of ground-measured LST in 2014. The models achieved reliable performance with an average R2 of 0.84 and root-mean-square error of 2.12 °C. Despite some limitations, mainly due to diversified character of cloudy-sky conditions and high heterogeneity of gridded predictors, the method can effectively reconstruct overcast MODIS data at subpixel level, which shows great potential for producing cloud-free LSTs in complex ecosystems.

DOI Quelle

DOI: 10.1109/JSTARS.2022.3147356

Conference proceedings article2022

Two-source energy balance modeling of evapotranspiration with thermal remote sensing at different spatial resolutions: a case study of the European Alps

Bartkowiak P, Castelli M, Colombo R, Notarnicola C

Claudia Notarnicola, Mariapina Castelli, Paulina Bartkowiak

Many satellite-derived evapotranspiration (ET) estimates rely on coarse resolution (CR) land surface temperature (LST) from 1-km thermal infrared bands offered by NASA and ESA instruments, like Terra MODIS and Sentinel-3 SLSTR. This affects prediction performance of ET, especially in complex regions, such as the Alps. Since most two-source energy balance (TSEB) models assume no thermal variability within a pixel, a major challenge in ET modelling is related to cell grid heterogeneity. Given this limitation, we investigate the potential of kernel-driven downscaling to obtain sub-kilometer LST products based on fine resolution (FR) sensors, i.e., Sentinel-2 MSI and MODIS VNIR, for estimating TSEB-based ET over South Tyrol, in the South-Eastern Alps. To this aim, we exploit relationships between CR LST and FR predictors using trees-based algorithms. Due to reduced capabilities of univariate models in complex ecosystems, multi-source predictors are considered, including multispectral reflectances, spectral indices, solar radiation, and topography. The performance of the TSEB model driven by disaggregated outputs is evaluated against original 1-km LST and ground-based fluxes from two eddy covariance towers. In general, turbulent fluxes forced with downscaled LST resulted in RMSE of 86 Wm-2 and mean bias of 55 Wm-2, which translated to 8% and 15% decrease in the respective estimates when compared to TSEB results with 1-km LST. Despite some limitations, mainly related to small-scale changes in landcover and topography that control LSTs and consequently affect TSEB-based ET estimates, the enhanced land surface temperature has potential for providing energy fluxes at finer spatial resolution in heterogenous ecosystems.

DOI Quelle

DOI: 10.1117/12.2646185

Journal article2019

Downscaling Land Surface Temperature from MODIS Dataset with Random Forest Approach over Alpine Vegetated Areas

Bartkowiak P, Castelli M, Notarnicola C

Claudia Notarnicola, Mariapina Castelli, Paulina Bartkowiak

In this study, we evaluated three different downscaling approaches to enhance spatial
resolution of thermal imagery over Alpine vegetated areas. Due to the topographical and land-cover
complexity and to the sparse distribution of meteorological stations in the region, the remotely-sensed
land surface temperature (LST) at regional scale is of major area of interest for environmental
applications. Even though the Moderate Resolution Imaging Spectroradiometer (MODIS) LST fills
the gap regarding high temporal resolution and length of the time-series, its spatial resolution is not
adequate for mountainous areas. Given this limitation, random forest algorithm for downscaling
LST to 250 m spatial resolution was evaluated. This study exploits daily MODIS LST with a
spatial resolution of 1 km to obtain sub-pixel information at 250 m spatial resolution. The nonlinear
relationship between coarse resolution MODIS LST (CR) and fine resolution (FR) explanatory variables
was performed by building three different models including: (i) all pixels (BM), (ii) only pixels with
more than 90% of vegetation content (EM1) and (iii) only pixels with 75% threshold of homogeneity
for vegetated land-cover classes (EM2). We considered normalized difference vegetation index (NDVI)
and digital elevation model (DEM) as predictors. The performances of the thermal downscaling
methods were evaluated by the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE)
between the downscaled dataset and Landsat LST. Validation indicated that the error values for
vegetation fraction (EM1, EM2) were smaller than for basic modelling (BM). BM model determined
averaged RMSE of 2.3 K and MAE of 1.8 K. Enhanced methods (EM1 and EM2) gave slightly better
results yielding 2.2 K and 1.7 K for RMSE and MAE, respectively. In contrast to the EMs, BM showed
a reduction of 22% and 18% of RMSE and MAE respectively with regard to Landsat and the original
MODIS LST. Despite some limitations, mainly due to cloud contamination effect and coarse resolution
pixel heterogeneity, random forest downscaling exhibits a large potential for producing improved
LST maps.

DOI Quelle

DOI: 10.3390/rs11111319

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Journal article2024

A Copernicus-based evapotranspiration dataset at 100 m spatial resolution over four Mediterranean basins

Bartkowiak P, Ventura B, Jacob A, Castelli M

Bartolomeo Ventura, Alexander Jacob, Mariapina Castelli, Paulina Bartkowiak

Projekt: 4DMED-Hydrology

DOI Quelle

DOI: 10.5194/essd-16-4709-2024

Paulina Bartkowiak

About

Scientific Publications

A Copernicus-based evapotranspiration dataset at 100 m spatial resolution over four Mediterranean basins

Reconstruction of satellite-based evapotranspiration under all-sky conditions in the Alps

Daily evaporation product - Ebro basin (Version v1) [Data set]

Daily evaporation product - Herault basin (Version v1) [Data set]

Daily evaporation product - Medjerda basin (Version v1) [Data set]

Daily evaporation product - Po basin (Version v1) [Data set]

Evaluation of remotely-sensed evapotranspiration datasets at different spatial and temporal scales at forest and grassland sites in Italy

Land Surface Temperature Reconstruction Under Long-Term Cloudy-Sky Conditions at 250 m Spatial Resolution: Case Study of Vinschgau/Venosta Valley in the European Alps

Two-source energy balance modeling of evapotranspiration with thermal remote sensing at different spatial resolutions: a case study of the European Alps

Downscaling Land Surface Temperature from MODIS Dataset with Random Forest Approach over Alpine Vegetated Areas

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Externe Inhalte

Scientific Publications

A Copernicus-based evapotranspiration dataset at 100 m spatial resolution over four Mediterranean basins

Reconstruction of satellite-based evapotranspiration under all-sky conditions in the Alps

Daily evaporation product - Ebro basin (Version v1) [Data set]

Daily evaporation product - Herault basin (Version v1) [Data set]

Daily evaporation product - Medjerda basin (Version v1) [Data set]

Daily evaporation product - Po basin (Version v1) [Data set]

Evaluation of remotely-sensed evapotranspiration datasets at different spatial and temporal scales at forest and grassland sites in Italy

Land Surface Temperature Reconstruction Under Long-Term Cloudy-Sky Conditions at 250 m Spatial Resolution: Case Study of Vinschgau/Venosta Valley in the European Alps

Two-source energy balance modeling of evapotranspiration with thermal remote sensing at different spatial resolutions: a case study of the European Alps

Downscaling Land Surface Temperature from MODIS Dataset with Random Forest Approach over Alpine Vegetated Areas