Surface Energy Balance Model Research Articles

Evaluation of the Thermal Performance of Two Passive Facade System Solutions for Sustainable Development

Sustainable development is essential for the future of the planet. Using passive elements, like ventilated facades based on insulation and air chambers, or living walls, which are solutions based on nature, is a powerful strategy for cities to improve their thermal environment, reduce energy consumption, and mitigate the effects of climate change. This approach allows for the quantification of the influence of passive surfaces on energy fluxes compared to bare surfaces. In addition, it delves into understanding how the incorporation of vegetation on building facades alters surface energy fluxes, involving a combination of physical and biochemical processes. This comprehensive investigation seeks to harness the potential of passive and natural solutions to address the pressing challenges of urban sustainability and climate resilience. This research uses a surface energy balance model to analyze the thermal performance of two facades using experimental data from a PASLINK test cell. This study uses the grey box RC model, which links continuous-time ordinary differential equations with discrete measurement data points. This model provides insight into the complex interplay among factors that influence the thermal behavior of building facades, with the goal of comprehensively understanding how ventilated and green facades affect the dynamics of energy flow compared to conventional facades. The initial thermal resistance of the bare facade was 0.75 (°C m2)/W. The introduction of a ventilated facade significantly increased this thermal resistance to 2.47 (°C m2)/W due to the insulating capacity of the air chamber and its insulating layer (1.70 (°C m2)/W). Regarding the modular living wall, it obtained a thermal resistance value of 1.22 (°C m2)/W (this vegetated facade does not have an insulating layer). In this context, the modular living wall proved to be effective in reducing convective energy by 68% compared with the non-green facade. It is crucial to highlight that evapotranspiration was the primary mechanism for energy dissipation in the green facade. The experiments conclusively show that both the modular living wall and open-ventilated facade significantly reduce solar heat loads compared with non-passive bare wall facades, demonstrating their effectiveness in enhancing thermal performance and minimizing heat absorption.

Toward a real validation of passive microwave snowmelt detection algorithms over the Antarctic Ice sheet

Antarctic Ice Sheet (AIS) surface snowmelt can profoundly impact climatological and hydrological processes, as well as energy and mass balances. Passive microwave remote sensing has proven highly effective in detecting snowmelt, and the accuracy of the algorithms is typically assessed using air temperature (Tair). Nonetheless, the occurrence of snowmelt cannot be solely determined by Tair being above the melting point, it can only provide evaluation of the algorithms rather than a real validation. This study is the first to conduct a reliable validation of six commonly used algorithms by using the melt rate data from a surface energy balance model, including the cross-polarized gradient ratio (XPGR) algorithm, the diurnal amplitude variation (DAV) algorithm, the Microwave Emission Model of Layered Snowpacks based (MEMLS-based) algorithm, the constant threshold algorithm (CTA), the Ashcraft and Long Algorithm (ALA), and the M + 30 K (M30 hereafter) algorithm. Results suggest that the M30 algorithm and MEMLS-based algorithm have overall better performances, with high overall accuracies (with a value of 95.81 % and 95.56 %) and high kappa coefficient (with a value of 0.69 and 0.68). Though Tair is not reliable for evaluating the algorithms, a threshold of Tair = − 0.8 ℃ can serve as an alternative criterion in the absence of melt rate data. Additionally, two algorithms with the highest accuracy were selected to investigate interannual snowmelt trends in the Antarctic and the sub-regions. The results show a significant increase in melt extent and density in the Wilkes and Adelie sub-region during the period 1979–2020.

A Remote Sensing Approach for Assessing Daily Cumulative Evapotranspiration Integral in Wheat Genotype Screening for Drought Adaptation.

This study considers critical aspects of water management and crop productivity in wheat cultivation, specifically examining the daily cumulative actual evapotranspiration (ETa). Traditionally, ETa surface energy balance models have provided estimates at discrete time points, lacking a holistic integrated approach. Field trials were conducted with 22 distinct wheat varieties, grown under both irrigated and rainfed conditions over a two-year span. Leaf area index prediction was enhanced through a robust multiple regression model, incorporating data acquired from an unmanned aerial vehicle using an RGB sensor, and resulting in a predictive model with an R2 value of 0.85. For estimation of the daily cumulative ETa integral, an integrated approach involving remote sensing and energy balance models was adopted. An examination of the relationships between crop yield and evapotranspiration (ETa), while considering factors like year, irrigation methods, and wheat cultivars, unveiled a pronounced positive asymptotic pattern. This suggests the presence of a threshold beyond which additional water application does not significantly enhance crop yield. However, a genetic analysis of the 22 wheat varieties showed no correlation between ETa and yield. This implies opportunities for selecting resource-efficient wheat varieties while minimizing water use. Significantly, substantial disparities in water productivity among the tested wheat varieties indicate the possibility of intentionally choosing lines that can optimize grain production while minimizing water usage within breeding programs. The results of this research lay the foundation for the development of resource-efficient agricultural practices and the cultivation of crop varieties finely attuned to water-scarce regions.

Using a crop water stress index to determine water use efficiency in a raspberry crop in the Mediterranean Central Chile

AbstractWater availability is projected to decrease in Mediterranean Central Chile, necessitating sustainable production strategies based on improved irrigation management. This study focuses on estimating water use efficiency (WUE) in raspberry crops at two validation sites using remote sensing and soil irrigation data. By employing the crop water stress index (CWSI), we demonstrate the potential of this tool in enhancing irrigation management and establishing sustainable production practices. The results demonstrate the successful estimation of actual evapotranspiration (ETa) using the Operational Simplified Surface Energy Balance (SSEBop) model (coefficient of determination [R2] = 0.92; root mean square error [RMSE] = 0.97 mm day−1), while the CWSI indicated high stress levels after 5 days of irrigation. Moreover, validation at two sites reveals significant differences in applied irrigation, with sites A and B receiving 17,097 and 3760 m3 ha−1, respectively, while the average water demand is close to 5300 m3 ha−1. These variations result in discrepancies in WUE, with values of 0.79 and 3.64 kg m−3. By integrating remote sensing indices and soil data, this study proposes that maintaining an 85% ETa rate during noncritical periods can enhance WUE. This work demonstrates the potential use of a water stress index to monitor crops in the Chilean central zone for efficient water resource use under future scarcity scenarios.

Modeling of surface energy balance for Icelandic glaciers using remote-sensing albedo

Abstract. During the melt season, absorbed solar energy, modulated at the surface by albedo, is one of the main governing factors controlling surface melt variability for glaciers in Iceland. An energy balance model was applied with the possibility of utilizing spatiotemporal Moderate Resolution Imaging Spectroradiometer (MODIS) satellite-derived daily surface albedo driven by high-resolution climate forcing data to reconstruct the surface energy balance (SEB) for all Icelandic glaciers for the period 2000–2021. The SEB was reconstructed from April through September for 2000–2021 at a daily time step with a 500 m spatial resolution. Validation was performed using observations from various glaciers spanning distinct locations and elevations with good visual and statistical agreement. The results show that spatiotemporal patterns for the melt season have high annual and interannual variability for Icelandic glaciers. The variability was influenced by high climate variability, deposition of light-absorbing particles (LAPs) from volcanic eruptions and dust hotspots in pro-glacial areas close to the glaciers. Impacts of LAPs can lead to significant melt enhancement due to lowering of albedo and increased short-wave radiative energy forced at the surface. Large impacts on the SEB were observed for years with high LAP deposits, such as the volcanic eruption years of 2004, 2010 and 2011 and the sand- and dust-rich year of 2019. The impacts of volcanic eruptions and other LAP events were estimated using historical mean albedo under the same climatology forcing to provide estimations of melt energy enhancements. The impact of LAPs was often significant even though the glaciers were far away from the eruption location. On average, the melt enhancements due to LAPs were ∼27 % in 2010, ∼16 % in 2011 and ∼14 % in 2019 for Vatnajökull, Hofsjökull and Langjökull.

Investigation of potential geothermal well locations in Nigeria using remote sensing and field measurement

ABSTRACT Geothermal energy is a renewable energy that has not been utilised as solar, wind, and biomass energy because of the cost of exploration, cost of implementation, and possible environmental concerns such as enhancing the chances of earthquakes, and tremors.The remote sensing technique and Surface Energy Balance model was proposed to determine the absorbed terrestrial radiation and other salient parameters. Forty years remote sensing dataset (1980–2019), was obtained from the MERRA-2 for three hundred and two (302) locations across Nigeria. The acquired thermal parameters were processed using known models. The heat catchment with the highest magnitude of 330.244 W/m2 are found in Nembe, Okirika, Yenagoa (Niger-Delta basin); 334.198 W/m2 in Ozubulu, Oguta, Obolo (Anambra basin); 332.877 W/m2 in Okitipupa, Atijere (Dahomey basin); 318.021 W/m2 in Eban, Mokwa (Bida basin); 318.682 W/m2 in Kumaganum, Gashua, Geidam (Chad basin); 321.050 W/m2 in Kuchi, Gwandi, Gwadabawa (Sokoto basin); 289.247 W/m2 in Gombe, Potiskum, Kafarati (Upper Benue basin); 312.964 W/m2 in Lokoja, Otukpa (Mid-Benue basin); and 338.962 W/m2 Malumfashi, Kano (Basement Complex). Likely geothermal wells were listed within the research. It is recommended that more ground measurements should be carried out in the basement complexes and sedimentary basins of Nigeria.

Statistically parameterizing and evaluating a positive degree-day model to estimate surface melt in Antarctica from 1979 to 2022

Abstract. Surface melting is one of the primary drivers of ice shelf collapse in Antarctica and is expected to increase in the future as the global climate continues to warm because there is a statistically significant positive relationship between air temperature and melting. Enhanced surface melt will impact the mass balance of the Antarctic Ice Sheet (AIS) and, through dynamic feedbacks, induce changes in global mean sea level (GMSL). However, the current understanding of surface melt in Antarctica remains limited in terms of the uncertainties in quantifying surface melt and understanding the driving processes of surface melt in past, present and future contexts. Here, we construct a novel grid-cell-level spatially distributed positive degree-day (PDD) model, forced with 2 m air temperature reanalysis data and spatially parameterized by minimizing the error with respect to satellite estimates and surface energy balance (SEB) model outputs on each computing cell over the period 1979 to 2022. We evaluate the PDD model by performing a goodness-of-fit test and cross-validation. We assess the accuracy of our parameterization method, based on the performance of the PDD model when considering all computing cells as a whole, independently of the time window chosen for parameterization. We conduct a sensitivity experiment by adding ±10 % to the training data (satellite estimates and SEB model outputs) used for PDD parameterization and a sensitivity experiment by adding constant temperature perturbations (+1, +2, +3, +4 and +5 ∘C) to the 2 m air temperature field to force the PDD model. We find that the PDD melt extent and amounts change analogously to the variations in the training data with steady statistically significant correlations and that the PDD melt amounts increase nonlinearly with the temperature perturbations, demonstrating the consistency of our parameterization and the applicability of the PDD model to warmer climate scenarios. Within the limitations discussed, we suggest that an appropriately parameterized PDD model can be a valuable tool for exploring Antarctic surface melt beyond the satellite era.

Estimation of actual evapotranspiration using the simplified-surface energy balance index model on an irrigated agricultural farm

Evapotranspiration (ET) plays a crucial role in the energy and water balance of agricultural ecosystems and is a vital component of the hydrological cycle. Efficient irrigation water management relies on accurate spatiotemporal coverage of crop ET across a farm. Thanks to the availability of multi-temporal high-resolution satellite datasets and remote sensing-based surface energy balance models, near-real-time estimation of ET is now possible. This study utilized Landsat 8/9 data to estimate ET using the simplified surface energy balance index (S-SEBI) model, which was then compared to eddy covariance measurements over a semi-arid agricultural farm in New Delhi, India during the post-monsoon periods of 2021-22 and 2022-23. The S-SEBI model predicted daily ET from Landsat 8/9 data with an average correlation coefficient and RMSE of 0.89 and 0.79 mm/day, respectively. The spatiotemporal map was also used to evaluate the model's performance, and it could accurately differentiate between ET over dryland crops and well-irrigated wheat fields on the farm. Despite underestimating ET (0.51 mm/day) during the initial growing season (Nov-Dec) and overestimating it (0.73 mm/day) during mid-season (Feb-Mar), the S-SEBI model can still be an operational tool for mapping ET with high accuracy and sufficient variation across pixels, making it an ideal option for incorporating into irrigation scheduling.

Seasonal and long-term dynamics in forest microclimate effects: global pattern and mechanism

Although the biophysical effects of afforestation or deforestation on local climate are recognized, the biophysical consequences of seasonal and long-term dynamics in forests on understory microclimate, which creates microrefugia for forest organisms under global warming, remain less well understood. To fill this research gap, we combined a three-layered (i.e., canopy, forest air space and understory soil) land surface energy balance model and Intrinsic Biophysical Mechanism Model and quantify seasonal (warm minus cool seasons) and long-term changes (later minus former periods) in the biophysical effects of forest dynamics on understory air temperature (ΔTa) and soil surface temperature (ΔTs). We found that high latitudes forests show strongest negative seasonal variations in both ΔTa and ΔTs, followed by moderate latitudes forests. In contrast, low latitudes forests exhibit positive seasonal variations in ΔTa and weak negative seasonal variations in ΔTs. For the long-term variations, ΔTs increases systematically at all three latitudes. However, the situation differs greatly for ΔTs, with a weak increase at low and moderate latitudes, but a slight decrease at high latitudes. Overall, changes in sensible and latent heat fluxes induced by forest dynamics (such as leaf area index), by altering the aerodynamic resistances of canopy and soil surface layers, are the main factors driving changes in forest microclimate effects. In addition, this study also develops an aerodynamic resistance coefficient {f}_{{rm{r}}}^{1} to combine the air temperature effects and surface soil temperature effects and proposes an indicator – ΔTSu, that is, Delta {T}_{{rm{Su}}}=Delta {T}_{{rm{s}}}+(frac{1}{{f}_{{rm{r}}}^{1}}-1)Delta {T}_{{rm{a}}}, as a possible benchmark for evaluating the total biophysical effects of forests on temperatures.

Everest South Col Glacier did not thin during the period 1984–2017

Abstract. The South Col Glacier is a small body of ice and snow (approx. 0.2 km2) located at the very high elevation of 8000 m a.s.l. (above sea level) on the southern ridge of Mt. Everest. A recent study by Potocki et al. (2022) proposed that South Col Glacier is rapidly losing mass. This is in contradiction to our comparison of two digital elevation models derived from aerial photographs taken in December 1984 and a stereo Pléiades satellite acquisition from March 2017, from which we estimate a mean elevation change of 0.01 ± 0.05 m a−1. To reconcile these results, we investigate some aspects of the surface energy and mass balance of South Col Glacier. From satellite images and a simple model of snow compaction and erosion, we show that wind erosion has a major impact on the surface mass balance due to the strong seasonality in precipitation and wind and that it cannot be neglected. Additionally, we show that the melt amount predicted by a surface energy and mass balance model is very sensitive to the model structure and implementation. Contrary to previous findings, melt is likely not a dominant ablation process on this glacier, which remains mostly snow-covered during the monsoon.

Evapotranspiration estimation using Surface Energy Balance Model and medium resolution satellite data: An operational approach for continuous monitoring

Monitoring spatial and temporal trends of water use is of utmost importance to ensure water and food security in river basins that are challenged by water scarcity and climate change induced abnormal weather patterns. To quantify water consumption by the agriculture sector, continuous monitoring is required over different spatial scales ranging from field (< 1 ha) to basin. The demand driven requirement of covering large areas yet providing spatially distributed information makes the use of in-situ measurement devices unfeasible. Earth observation satellites and remote sensing techniques offer an effective alternative in estimating the consumptive use of water (Actual EvapoTranspiration (ETa) fluxes) by using periodic observations from the visible and infrared spectral region. Optical satellite data, however, is often hindered by noises due to cloud cover, cloud shadow, aerosols and other satellite related issues such as Scan Line Corrector (SLC) failure in Landsat 7 breaking the continuity of temporal observations. These gaps have to be statistically filled in order to compute aggregated seasonal and annual estimates of ETa. In this paper, we introduce an approach to develop a gap-filled multi-year monthly ETa maps at medium spatial resolution of 30 m. The method includes two major steps: (i) estimation of ETa using the python based implementation of surface energy balance model called PySEBAL and (ii) temporal interpolation using Locally Weighted Regression (LWR) model followed by spline based spatial interpolation to fill the gaps over time and space. The approach is applied to a large endorheic Lake Urmia Basin (LUB) basin with a surface area of ~ 52,970 km2 in Iran for the years 2013–2015 using Landsat 7 and 8 satellite data. The results show that the implemented gap filling approach could reconstruct the monthly ETa dynamics over different agriculture land use types, while retaining the high spatial variability. A comparison with a similar dataset from FAO WaPOR reported a very high correlation with R2 of 0.93. The study demonstrates the applicability of this approach to a larger basin which is extendible and reproducible to other geographical areas.

Assimilation of Sentinel-2 Biophysical Variables into a Digital Twin for the Automated Irrigation Scheduling of a Vineyard

Decision support systems (DSS) are needed to carry out precision irrigation. Key issues in this regard include how to deal with spatial variability and the adoption of deficit irrigation strategies at the field scale. A software application originally designed for water balance-based automated irrigation scheduling locally fine-tuned through the use of sensors has been further developed with the emerging paradigm of both digital twins and the Internet of Things (IoT). The aim of this research is to demonstrate the feasibility of automatically scheduling the irrigation of a commercial vineyard when adopting regulated deficit irrigation (RDI) strategies and assimilating in near real time the fraction of absorbed photosynthetically active radiation (fAPAR) obtained from Sentinel-2 imagery. In addition, simulations of crop evapotranspiration obtained by the digital twin were compared with remote sensing estimates using surface energy balance models and Copernicus-based inputs. Results showed that regression between instantaneous fAPAR and in situ measurements of the fraction of intercepted photosynthetically active radiation (fIPAR) had a coefficient of determination (R2) ranging from 0.61 to 0.91, and a root mean square deviation (RMSD) of 0.10. The conversion of fAPAR to a daily time step was dependent on row orientation. A site-specific automated irrigation scheduling was successfully adopted and an adaptive response allowed spontaneous adjustments in order to stress vines to a certain level at specific growing stages. Simulations of the soil water balance components performed well. The regression between digital twin simulations and remote sensing-estimated actual (two-source energy balance Priestley–Taylor modeling approach, TSEB-PTS2+S3) and potential (Penman–Monteith approach) evapotranspiration showed RMSD values of 0.98 mm/day and 1.14 mm/day, respectively.

Analytical framework based on thermodynamics to estimate spatially distributed surface energy fluxes from remotely sensed radiations

The Limited surface observations of turbulent heat fluxes result in incomplete knowledge about the surface energy balance that drives the climate system. The highly parameterized surface energy balance models suffer from significant uncertainties. Remote sensing information of incoming and outgoing radiation fluxes are important input variables for turbulent heat flux models, though analytical solutions of surface energy budget from these variables are yet to be derived. Here, we developed a novel, purely physics-based analytical method grounded on the thermodynamic principle of maximum power. The approach derives the total turbulent heat flux only from the four inputs of incoming and outgoing longwave and shortwave radiations at the land surface, which are available from remotely sensed satellite data. The proposed approach does not use any parameterization, unlike the existing surface energy balance models, and hence does not suffer from uncertainty due to the same. We validated our methodology with 102 eddy covariance observation stations around the globe with different land use land covers from FLUXNET2015 and available urban datasets. Based on monthly averages of the total turbulent flux estimates for the eddy covariance sites, we observed root-mean-square error (RMSE) of 23.2 ± 10.9 Wm−2, a mean bias error (MBE) of 11.9 ± 13.1 Wm−2 and R2 value of 0.86 ± 0.15. Using the satellite observations of radiation fluxes from CERES at a spatial resolution of 10, we obtained the global flux field of total turbulent flux (QJ) and land surface heat storage (ΔQs) fluxes. On validation of QJ with FLUXNET sites for six grids for different land use land cover, we found RMSE of 20.1 ± 7 Wm−2, MBE of 14.4 ± 9 Wm−2 and R2 value of 0.96 ± 0.02. Further, from the evaporative stress factor of GLEAM, which is based on microwave remotely sensed vegetation optical depth and root zone soil moisture, we have obtained spatially distributed global estimates of Sensible(H) and latent heat (LE). In addition, our analytical estimates address the distribution of residual energy associated with the surface energy balance closure problem driven by the land use land cover. The theoretical estimates of all surface energy balance components from remote sensing based observations will improve our understanding of surface warming for different land use land covers across the globe.

Characteristics of anthropogenic heat with different modeling ideas and its driving effect on urban heat islands in seven typical Chinese cities

Anthropogenic heat (AH), an essential urban heat source, is often overlooked or simplified in research on the multiple temporal and spatial driving mechanisms of the urban heat island (UHI), and case studies investigating the impacts of different AH connotations are scarce. This study estimated the AH in seven typical Chinese cities based on a remote sensing surface energy balance model (AHseb) and an energy consumption inventory-machine learning model (AHinv). The intensity of the surface UHI was extracted using land surface temperatures, and then the linear mixed-effects model and geographic detectors were used to analyze the driving effect of AH on the UHI. Despite the similar shapes of the spatial profile curves, the AH derived from the two models differed in both temporal and spatial characteristics, which was more typical in winter and in urban centers, and AHinv had a more notable central spread feature than AHseb. The AH driving effects on UHI were notably influenced by spatial and temporal heterogeneity, particularly in regions with distinct background climates. However, after controlling for the random effects of the background climate, AH still exhibited a considerable enhancing effect on the UHI. AHseb outperformed AHinv in terms of linear positive correlation and interpretation rate for UHI. Meanwhile, interactions with other potential factors enhanced AH driving effects. Consequently, UHI mitigation must be tailored to the local context by integrating multiple drivers, and for the heating effects of AH, it is necessary to develop specific mitigation measures by limiting the conversion of AHinv to AHseb in addition to reducing the heat production. The findings offer guidance for analyzing and optimizing urban thermal climates with a focus on AH or energy consumption control.

Determining grapevine water use under different sustainable agronomic practices using METRIC-UAV surface energy balance model

Determining grapevine water use under different sustainable agronomic practices using METRIC-UAV surface energy balance model

Subsurface Porewater Flow Accelerates Talik Development Under the Alaska Highway, Yukon: A Prelude to Road Collapse and Final Permafrost Thaw?

AbstractThe presence of taliks (perennially unfrozen zones in permafrost areas) adversely affects the thermal stability of infrastructure in cold regions, including roads. The role of heat advection on talik development and feedback on permafrost degradation has not been quantified methodically in this context. We incorporate a surface energy balance model into a coupled groundwater flow and energy transport numerical model (SUTRA‐ice). The model, calibrated with long‐term observations (1997–2018 on the Alaska Highway), is used to investigate and quantify the role of heat advection on talik initiation and development under a road embankment. Over the 25‐year simulation period, the new model is driven by reconstructed meteorological data and has a good agreement with near surface soil temperatures. The model successfully reproduces the increasing depth to the permafrost table (mean absolute error &lt;0.2 m), and talik development. The results demonstrate that heat advection provides an additional energy source that expedites the rate of permafrost thaw and roughly doubles the rate of permafrost table deepening, compared to purely conductive thawing. Talik initially formed and grew over time under the combined effect of water flow, snow insulation, road construction and climate warming. Talik formation creates a new thermal state under the road embankment, resulting in acceleration of underlying permafrost degradation, due to the positive feedback of heat accumulation created by trapped unfrozen water. In a changing climate, mobile water flow will play a more important role in permafrost thaw and talik development under road embankments, and is likely to significantly increase maintenance costs and reduce the long‐term stability of the infrastructure.

Insights from the first winter weather observations near Mount Everest's summit

Insights from the first winter weather observations near Mount Everest's summit

High-resolution debris-cover mapping using UAV-derived thermal imagery: limits and opportunities

Abstract. Debris-covered glaciers are widespread in high mountain ranges on earth. However, the dynamic evolution of debris-covered glacier surfaces is not well understood, in part due to difficulties in mapping debris-cover thickness in high spatiotemporal resolution. In this study, we present land surface temperatures (LSTs) of supraglacial debris cover and their diurnal variability measured from an unpiloted aerial vehicle (UAV) at a high (15 cm) spatial resolution. We test two common approaches to derive debris-thickness maps by (1) solving a surface energy balance model (SEBM) in conjunction with meteorological reanalysis data and (2) least squares regression of a rational curve using debris-thickness field measurements. In addition, we take advantage of the measured diurnal temperature cycle and estimate the rate of change of heat storage within the debris cover. Both approaches resulted in debris-thickness estimates with an RMSE of 6 to 8 cm between observed and modeled debris thicknesses, depending on the time of the day. Although the rational curve approach requires in situ field measurements, the approach is less sensitive to uncertainties in LST measurements compared to the SEBM approach. However, the requirement of debris-thickness measurements can be an inhibiting factor that supports the SEB approach. Because LST varies throughout the day, the success of a rational function to express the relationship between LST and debris thickness also varies predictably with the time of day. During the period when the debris cover is warming, LST is heavily influenced by the aspect of the terrain. As a result, clear-sky morning flights that do not consider the aspect effects can be problematic. Our sensitivity analysis of various parameters in the SEBM highlights the relevance of the effective thermal conductivity when LST is high. The residual and variable bias of UAV-derived LSTs during a flight requires calibration, which we achieve with bare-ice surfaces. The model performance would benefit from more accurate LST measurements, which are challenging to achieve with uncooled sensors in high mountain landscapes.

Evapotranspiration in Semi-Arid Climate: Remote Sensing vs. Soil Water Simulation

Estimating crop evapotranspiration (ETa) is an important requirement for a rational assessment and management of water resources. The various remote sensing products allow the determination of crops' biophysical variables integrated in the evaluation of ETa by using surface energy balance (SEB) models. This study compares ETa estimated by the simplified surface energy balance index (S-SEBI) using Landsat 8 optical and thermal infra-red spectral bands and transit model HYDRUS-1D. In semi-arid Tunisia, real time measurements of soil water content (θ) and pore electrical conductivity (ECp) were made in the crop root zone using capacitive sensors (5TE) for rainfed and drip irrigated crops (barley and potato). Results show that HYDRUS model is a fast and cost-effective assessment tool for water flow and salt movement in the crop root layer. ETa estimated by S-SEBI varies according to the available energy resulting from the difference between the net radiation and soil flux G0, and more specifically according to the assessed G0 from remote sensing. Compared to HYDRUS, the ETa from S-SEBI was estimated to have an R2 of 0.86 and 0.70 for barley and potato, respectively. The S-SEBI performed better for rainfed barley (RMSE between 0.35 and 0.46 mm·d-1) than for drip irrigated potato (RMSE between 1.5 and 1.9 mm·d-1).

Estimation of regional actual evapotranspiration based on the improved SEBAL model

The global food and water crises caused by climate warming are becoming increasingly significant. One of the important aspects of surface water transfer is evapotranspiration. The accurate estimation of evapotranspiration (ET) is of great significance in water resource management. However, accurate estimation of large-scale ET is challenging due to the adequate selection of the hot and cold pixels, the heterogeneity of the subsurface and the instability of the lower atmosphere. Therefore, in this paper, a surface energy balance model (Y-SEBAL) was proposed based on the SEBAL model and improved sensible heat flux calculation, and used to simulate large-scale actual evapotranspiration. Based on the principle of surface energy balance, this model introduced the empirical formula of sensible heat flux and simplifies the selection of limit to address the challenge of calculating the sensible heat flux of the crucial variable in the SEBAL model. The study simulated the spatial and temporal evolution of regional evapotranspiration with the model and verified the model simulation performance. In addition, the sensitivity of the model-driven data was quantified. The results showed that the simulation value of the Y-SEBAL model was highly consistent with the eddy covariance measurement data. In the verification results, the R value was 0.82, the index of agreement was 0.90, and the root mean square error was 0.81 mm·d−1. The performance verification indices were better than the SEBAL model, MOD16 model, and SSEBop model. And the model had the highest sensitivity to wind speed, reaching 0.714. The Y-SEBAL model improved the simulation performance of the original model and simulated the actual evapotranspiration. The study can provide a new solution for improving the retrieval of evapotranspiration by remote sensing at the regional scale.