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

n/

Evapotranspiration Changes over the European Alps: Consistency of Trends and Their Drivers between the MOD16 and SSEBop Algorithms

In the Alps, understanding how climate change is affecting evapotranspiration (ET) is relevant due to possible implications on water availability for large lowland areas of Europe. Here, changes in ET were studied based on 20 years of MODIS data. MOD16 and operational Simplified Surface Energy Balance (SSEBop) products were compared with eddy-covariance data and analyzed for trend detection. The two products showed a similar relationship with ground observations, with RMSE between 0.69 and 2 mm day−1, and a correlation coefficient between 0.6 and 0.83. A regression with the potential drivers of ET showed that, for climate variables, ground data were coherent with MOD16 at grassland sites, where r2 was 0.12 for potential ET, 0.17 for precipitation, and 0.57 for air temperature, whereas ground data agreed with SSEBop at forest sites, with an r2 of 0.46 for precipitation, no correlation with temperature, and negative correlation with potential ET. Interestingly, ground-based correlation corresponded to SSEBop for leaf area index (LAI), while it matched with MOD16 for land surface temperature (LST). Through the trend analysis, both MOD16 and SSEBop revealed positive trends in the south-west, and negative trends in the south and north-east. Moreover, in summer, positive trends prevailed at high elevations for grasslands and forests, while negative trends dominated at low elevations for croplands and grasslands. However, the Alpine area share with positive ET trends was 16.6% for MOD16 and 3.9% for SSEBop, while the share with negative trends was 1.2% for MOD16 and 15.3% for SSEBop. A regression between trends in ET and in climate variables, LST, and LAI indicated consistency, especially between ET, temperature, and LAI increase, but low correlation. Overall, the discrepancies in the trends, and the fact that none of the two products outperformed the other when compared to ground data, suggest that, in the Alps, SSEBop and MOD16 might not be accurate enough to be a robust basis to study ET changes

Assessing solar radiation components over the alpine region Advanced modeling techniques for environmental and technological applications.

This thesis examines various methods for estimating the spatial distribution of solar radiation, and in particular its diffuse and direct components in mountainous regions. The study area is the Province of Bolzano (Italy). The motivation behind this work is that radiation components are an essential input for a series of applications, such as modeling various natural processes, assessing the effect of atmospheric pollutants on Earth's climate, and planning technological applications converting solar energy into electric power. The main mechanisms that should be considered when estimating solar radiation are: absorption and scattering by clouds and aerosols, and shading, reflections and sky obstructions by terrain. Ground-based measurements capture all these effects, but are unevenly distributed and poorly available in the Italian Alps. Consequently they are inadequate for assessing spatially distributed incoming radiation through interpolation. Furthermore conventional weather stations generally do not measure radiation components. As an alternative, decomposition methods can be applied for splitting global irradiance into the direct and diffuse components. In this study a logistic function was developed from the data measured at three alpine sites in Italy and Switzerland. The validation of this model gave MAB = 51 Wm^-2, and MBD = -17 Wm^-2 for the hourly averages of diffuse radiation. In addition, artificial intelligence methods, such as artificial neural networks (ANN), can be applied for reproducing the functional relationship between radiation components and meteorological and geometrical factors. Here a multilayer perceptron ANN model was implemented which derives diffuse irradiance from global irradiance and other predictors. Results show good accuracy (MAB in [32,43] Wm^-2, and MBD in [-7,-25] Wm^-2) suggesting that ANN are an interesting tool for decomposing solar radiation into direct and diffuse, and they can reach low error and high generality. On the other hand, radiative transfer models (RTM) can describe accurately the effect of aerosols and clouds. Indeed in this study the RTM libRadtran was exploited for calculating vertical profiles of direct aerosol radiative forcing, atmospheric absorption and heating rate from measurements of black carbon, aerosol number size distribution and chemical composition. This allowed to model the effect of aerosols on radiation and climate. However, despite their flexibility in including as much information as available on the atmosphere, RTM are computationally expensive, thus their operational application requires optimization strategies. Algorithms based on satellite data can overcome these limitations. They exploit RTM-based look up tables for modeling clear-sky radiation, and derive the radiative effect of clouds from remote observations of reflected radiation. However results strongly depend on the spatial resolution of satellite data and on the accuracy of the external input. In this thesis the algorithm HelioMont, developed by MeteoSwiss, was validated at three alpine locations. This algorithm exploits high temporal resolution METEOSAT satellite data (1 km at nadir). Results indicate that the algorithm is able to provide monthly climatologies of both global irradiance and its components over complex terrain with an error of 10 Wm^-2. However the estimation of the diffuse and direct components of irradiance on daily and hourly time scale is associated with an error exceeding 50 Wm^-2, especially under clear-sky conditions. This problem is attributable to the low spatial and temporal resolution of aerosol distribution in the atmosphere used in the clear-sky scheme. To quantify the potential improvement, daily averages of accurate aerosol and water vapor data were exploited at the AERONET stations of Bolzano and Davos. Clear-sky radiation was simulated by the RTM libRadtran, and low values of bias were found between RTM simulations and ground measurements. This confirmed that HelioMont performance would benefit from more accurate local-scale aerosol boundary conditions. In summary, the analysis of different methods demonstrates that algorithms based on geostationary satellite data are a suitable tool for reproducing both the temporal and the spatial variability of surface radiation at regional scale. However better performances are achievable with a more detailed characterization of the local-scale clear-sky atmospheric conditions. In contrast, for plot scale applications, either the logistic function or ANN can be used for retrieving solar radiation components

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

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

Agricultural Drought Detection with MODIS Based Vegetation Health Indices in Southeast Germany

Droughts during the growing season are projected to increase in frequency and severity in Central Europe in the future. Thus, area-wide monitoring of agricultural drought in this region is becoming more and more important. In this context, it is essential to know where and when vegetation growth is primarily water-limited and whether remote sensing-based drought indices can detect agricultural drought in these areas. To answer these questions, we conducted a correlation analysis between the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) within the growing season from 2001 to 2020 in Bavaria (Germany) and investigated the relationship with land cover and altitude. In the second step, we applied the drought indices Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Vegetation Health Index (VHI) to primarily water-limited areas and evaluated them with soil moisture and agricultural yield anomalies. We found that, especially in the summer months (July and August), on agricultural land and grassland and below 800 m, NDVI and LST are negatively correlated and thus, water is the primary limiting factor for vegetation growth here. Within these areas and periods, the TCI and VHI correlate strongly with soil moisture and agricultural yield anomalies, suggesting that both indices have the potential to detect agricultural drought in Bavaria

Monitoring daily evapotranspiration in the Alps exploiting Sentinel-2 and meteorological data

The lack of satellite data with high spatial resolution (<100 m) and temporal frequency (<daily) has long limited the modelling of spatially explicit evapotranspiration (ET) for supporting the management of agricultural and natural resources, especially in regions with heterogeneous land-cover and topography. The recent availability of the Sentinel family of satellites of the European Space Agency (ESA) opens new possibilities for exploiting and improving existent modelling chains. The objective of this work is to implement a process to estimate actual ET of mountain natural and agricultural ecosystems, taking advantage of the high spatial resolution of Sentinel-2. Given the absence of high resolution climate model data in the area of interest, that is located in the North-eastern Italian Alps, we rely on ground meteorological data, and combine them with Sentinel-2 and Meteosat Second Generation (MSG) satellite data, in the framework of a simplified water balance model. Firstly, we calculate daily potential ET (ETo) by the Penman-Monteith (PM) equation driven by sub-hourly meteorological data (air temperature, wind speed, air humidity) collected at the stations of the Province of Bolzano, and net radiation derived from MSG SEVIRI shortwave and longwave radiation, emissivity and albedo. We test and compare two approaches to estimate spatially distributed ETo by geostatistical interpolation methods, with the aid of a high-Resolution digital elevation model: i) interpolating and downscaling meteorological parameters and then calculating ETo; ii) calculating ETo at stations with a full set of parameters, and then interpolating ETo. Secondly, we compute a water stress coefficient based on the ratio between cumulated ETo and precipitation, which represents the short-term water scarcity effect on transpiration and evaporation. Thirdly, we calculate actual ET by the classical FAO dual Kc approach, modified to be applied both over managed and unmanaged vegetated land. We separate transpiring and evaporating surfaces by an estimate of the fractional green vegetation cover derived from Sentinel-2 NDVI data, assumed constant during the five days revisit time of Sentinel-2. Finally, to assess the model, we compare ET with latent heat measured at eddy covariance Towers located in areas belonging to different land-use classes. The operational implementation of this procedure can be exploited in agriculture to forecast water demand and plan irrigation, and in the management of natural resources, to design strategies to face climate changes and extremes

From Monitoring to Understanding: Towards a Digital Twin for Hydrological Drought Prediction

Gives an introduction on how to go from monitoring to understanding in the context of drought events in alpine environments. It further shows how such a system can be built integrating machine learning models with physical based models and technologies like openEO for describing workflows that can realize a digital twin for seasonal forecasting of drough events in the context of an early warning system

Monitoring of Alpine grasslands with multitemporal optical and radar remote sensing

The European Alps are providing important ecosystem resources such as water, food, or timber. Satellite remote sensing can act as an important data source for the observation of many parameters related to these resources, offering spatially continuous, high-resolution measurements for large areas. This talk will concentrate on the possibilities for the monitoring of Alpine grasslands with optical and radar remote sensing, which is related to several projects at the Institute of Earth Observation at Eurac Research. The aims are twofold: 1) to get a better understanding of the multitemporal remote sensing signals in relation to grassland and soil conditions in mountain areas; 2) to explore the capability to distinguish different types of grassland using multitemporal radar Images, in combination with optical images and in-situ data. The Copernicus satellites Sentinel-1 and Sentinel-2 act as the main source of information. Sentinel-1 carries a so-called Synthetic Aperture Radar sensor, which is an active sensor operating in the microwave region of the electromagnetic spectrum (C-Band). It consists of a constellation of two satellites, which produce an image of the same area every six days. In its main operational mode, Sentinel-1 generates images with an original resolution of 5 by 20 m. Due to the characteristics of an active sensor and the used wavelength, images can be acquired independent of the sun and the atmospheric conditions (i.e. clouds). The Multispectral Instrument (MSI) aboard Sentinel-2 is measuring the Earth’s reflected radiance in 13 spectral bands from the visible light to short-wave-infrared, with spatial resolutions between 10 and 60 m. The constellation of two satellites provides an image over the same area every 5 days. Studying the Sentinel-1 data, we can show that the multi-temporal SAR signal shows good potential for the monitoring of different types of crop and grasslands and the soil conditions (especially soil moisture). Optical data are required to clearly distinguish between grassland and other types of crop. Despite the limitations of SAR in mountainous Areas, due to the independence of the weather conditions it is possible to clearly detect the phenological Dynamics, as well as the time of harvest of grasslands. Overall, the combination of optical and radar imagery allows to overcome some of the limitations of the two individual sensing methods to produce more reliable observations