Search Results (5)

Quantifying and mapping evapotranspiration (ET) from land surfaces is crucial in the context of climate change. For decades, remote sensing data have been utilized to estimate ET, leading to the development of numerous algorithms. However, their application is still non-trivial, mainly due to practical constraints. This paper introduces geeSSEBI, a Google Earth Engine implementation of the S-SEBI (Simplified Surface Energy Balance Index) model, for deriving ET from Landsat data and ERA5-land radiation. The source code and a graphical user interface implemented as a Google Earth Engine application are provided. The model ran on 871 images, and the estimates were evaluated against multiyear data of four eddy covariance towers belonging to the ICOS network, representative of both forests and agricultural landscapes. The model showed an RMSE of approximately 1 mm/day, and a significant correlation with the observed values, at all the sites. A procedure to upscale the data to monthly is proposed and tested as well, and its accuracy evaluated. Overall, the model showed acceptable accuracy, while performing better on forest ecosystems than on agricultural ones, especially at daily and monthly timescales. This implementation is particularly valuable for mapping evapotranspiration in data-scarce environments by utilizing Landsat archives and ERA5-land radiation estimates. Full article

Lithium mining has become a controversial issue in the transition to green technologies due to the intervention in natural basins that impact the native flora and fauna in these environments. Large resources of this element are concentrated in Andean salt flats in South America, where extraction is much easier than in other geological configurations. The Pozuelos highland salt flat, located in northern Argentina (Salta’s Province), was chosen for this study due to the presence of different evaporitic crusts and its proven economic potential in lithium-rich brines. A comprehensive analysis of a 5.5-year-long time series of its microwave backscatter with Synthetic Aperture Radar (SAR) images yielded significant insights into the dynamics of their crusts. During a field campaign conducted near the acquisition of three SAR images (Sentinel-1, ALOS-2/PALSAR-2, and SAOCOM-1), field measurements were collected for computational modeling of the SAR response. The temporal backscattering coefficients for the crusts in the salt flat are directly linked to rainfall events, where changes in surface roughness, soil moisture, and water table depth represent the most critical variables. Field parameters were employed to model the backscattering response of the salt flat using the Small Slope Approximation (SSA) model. Salt concentration of the subsurface brine and the water table depth over the slightly to moderately roughed crusts were quantitatively derived from Bayesian inference of the ALOS-2/PALSAR-2 and SAOCOM-1 SAR backscattering coefficient data. The results demonstrated the potential for subsurface estimation with L-band dual-polarization images, constrained to crusts compatible with the feasibility range of the layered model. Full article

Land surface temperature (LST) is one of the sources of input data for modeling land surface processes. The Landsat satellite series is the only operational mission with more than 30 years of archived thermal infrared imagery from which we can retrieve LST. Unfortunately, stray light artifacts were observed in Landsat-8 TIRS data, mostly affecting Band 11, currently making the split-window technique impractical for retrieving surface temperature without requiring atmospheric data. In this study, a single-channel methodology to retrieve surface temperature from Landsat TM and ETM+ was improved to retrieve LST from Landsat-8 TIRS Band 10 using near-surface air temperature (T_a) and integrated atmospheric column water vapor (w) as input data. This improved methodology was parameterized and successfully evaluated with simulated data from a global and robust radiosonde database and validated with in situ data from four flux tower sites under different types of vegetation and snow cover in 44 Landsat-8 scenes. Evaluation results using simulated data showed that the inclusion of T_a together with w within a single-channel scheme improves LST retrieval, yielding lower errors and less bias than models based only on w. The new proposed LST retrieval model, developed with both w and T_a, yielded overall errors on the order of 1 K and a bias of −0.5 K validated against in situ data, providing a better performance than other models parameterized using w and T_a or only w models that yielded higher error and bias. Full article

Extreme heat poses significant risks to the world’s growing urban population, and the heat stress to human health is likely to escalate with the anthropogenically increased temperatures projected by climate models. Thus, the additional heat from the urban heat island (UHI) effect needs to be quantified, including the spatial pattern. This study focuses on the city of Valencia (Spain), investigating the intensity and spatial pattern of UHI during three consecutive hot summer days accompanying a heat record. For the analysis, long-term in situ measurements and remote sensing data were combined. The UHI effect was evaluated using two approaches: (a) based on air temperature (AT) time-series from two meteorological stations and (b) using land surface temperature (LST) images from MODIS products by NASA with 1 km resolution. The strongest nighttime UHI estimated from AT was 2.3 °C, while the most intense surface UHI calculated as the difference between the LST of urban and rural regions (defined by NDVI) was 2.6 °C—both measured during the night after the record hot day. To assess the human thermal comfort in the city the Discomfort Index was applied. With the increasing number of tropical nights, the mitigation of nighttime UHI is a pressing issue that should be taken into consideration in climate-resilient urban planning. Full article

The Global Change Unit (GCU) at the University of Valencia has been involved in several calibration/validation (cal/val) activities carried out in dedicated field campaigns organized by ESA and other organisms. However, permanent stations are required in order to ensure a long-term and continuous calibration of on-orbit sensors. In the framework of the CEOS-Spain project, the GCU has managed the set-up and launch of experimental sites in Spain for the calibration of thermal infrared sensors and the validation of Land Surface Temperature (LST) products derived from those data. Currently, three sites have been identified and equipped: the agricultural area of Barrax (39.05 N, 2.1 W), the marshland area in the National Park of Doñana (36.99 N, 6.44 W), and the semi-arid area of the National Park of Cabo de Gata (36.83 N, 2.25 W). This work presents the performance of the permanent stations installed over the different test areas, as well as the cal/val results obtained for a number of Earth Observation sensors: SEVIRI, MODIS, and TIRS/Landsat-8. Full article