Monitoring anomalies on large-scale energy and water balance components by coupling remote sensing parameters and gridded weather data.

The SAFER (Simple Algorithm for Evapotranspiration Retrieving) algorithm was applied with MODIS images and gridded weather data from 2007 to 2021, to monitor the energy balance components and their anomalies, in the Atlantic Forest (AF) and Caatinga (CT) biomes inside the coastal agricultural growing zone, Northeast Brazil. Considering the long-term data, the Rn values between the biomes are not significantly different, however presenting distinct Rn partitions into latent (λE), sensible (H), and ground (G) heat fluxes between biomes. The Rn values annual averages are 9.40 ± 0.21 and 9.50 ± 0.23 MJ m-2 d-1, for AF and CT, respectively. However, for respectively AF and CT, they are respectively 5.10 ± 1.14 MJ m-2 d-1 and 4.00 ± 0.99 MJ m-2 d-1 for λE; 3.80 ± 1.12 MJ m-2 d-1 and 5.00 ± 1.00 MJ m-2 d-1 for H; 0.50 ± 0.12 MJ m-2 d-1 and 0.40 ± 0.10 MJ m-2 d-1 for G, yielding respective mean evaporative fraction (Ef = λE/(Rn - G) values of 0.60 ± 0.12 and 0.50 ± 0.15. Anomalies on λE, H, and Ef were detected through standardized index for these energy balance components by comparing the results for the years 2018 to 2021 with the long-term values from 2007 to each of these years, showing that the energy fluxes between surfaces and the lower atmosphere, and then the root-zone moisture conditions for both biomes, may strongly vary along seasons and years, with alternate positive and negative anomalies. These assessments are important for water policies as they can picture suitable periods and places for rainfed agriculture as well as the irrigation needs in irrigated agriculture, allowing rational agricultural environmental management while minimizing water competitions among other water users, under climate and land-use changes conditions.

Response of modified microclimates on growth, yield, and incidence of rhizome rot disease of ginger in Assam, India.

Field experiments were conducted at Biswanath, Assam, India (26° 42' N and 93° 15' E), during 2016, 2017, and 2018, to evaluate the effect of microclimates on growth, yield, and disease incidence in the ginger crop. The ginger variety Nadia was grown under six microclimates, viz., under shade net for the entire crop season (T1), under shade net from planting to mid-October (T2), with pigeon pea (T3), with maize (T4), with okra (T5), and as a sole crop (T6) in three replicated RBD. Photosynthetically active radiation (PAR), net radiation (Rn), temperature above the ginger canopy, soil temperature, and soil moisture were measured during the critical crop growth period under different microclimates. Recording of rhizome rot disease incidence was done periodically and genomic analysis of pathogen was carried out. PAR recorded above the ginger canopy under T6 was 1688.1 µ mol s-1 m-2, which was attenuated up to 80.1% in other microclimates. The Rn load of the ginger canopy was maximum (446.4 W m-2) under T6, which reduced to below 50 W m-2 under both T3 and T4. Both air temperatures above the ginger canopy and soil temperatures under T3 and T4 were reduced by 3.3 °C and 4.6 °C, respectively, as compared to T6. The pathogen causing the disease in the experimental site was identified as Fusarium oxysporum. Considerable increase in soil and air temperature and soil moisture favored disease incidence (90.3%) under shade net (T1 and T2) treatments, while opposite reason causing significant reduction in disease incidence (16.1%) was observed under T3 and T4. More yield of ginger recorded in treatments T3 (6.21 t ha-1) or T4 (6.48 t ha-1) was attributed to better crop growth and diminutive disease incidence, while the crop was almost damaged due to severe disease incidence under shade net (T1 and T2) treatments.

Machine Learning approach to Predict net radiation over crop surfaces from global solar radiation and canopy temperature data.

As the ground-based instruments for measuring net radiation are costly and need to be handled skillfully, the net radiation data at spatial and temporal scales over Indian subcontinent are scanty. Sometimes, it is necessary to use other meteorological parameters to estimate the value of net radiation, although the prediction may vary based on season, ground cover and estimation method. In this context, artificial intelligence can be used as a powerful tool for predicting the data considering past observed data. This paper proposes a novel method to predict the net radiation for five crop surfaces using global solar radiation and canopy temperature. This contribution includes the generation of real-time data for five crops grown in West Bengal state of India. After manual analysis and data preprocessing, data normalization has been done before applying machine learning approaches for training a robust model. We have presented the comparison in various machine learning algorithm such as ridge and spline regression, random forest, ensemble and deep neural networks. The result shows that the gradient boosting regression and ridge regression are outperforming other ML approaches. The estimated predictors enable to reduce the number of resources in terms of time, cost and manpower for proper net radiation estimation. Thus, the problem of predicting net radiation over various crop surfaces can be sorted out through ML algorithm.

Net ecosystem CO2 exchange from jute crop (Corchorus olitorius L.) and its environmental drivers in tropical Indo-Gangetic plain using open-path eddy covariance technique.

Present study is a maiden attempt to assess net ecosystem exchange (NEE) of carbon dioxide (CO2) flux from jute crop (Corchorus olitorius L.) in the Indo-Gangetic plain by using open-path eddy covariance (EC) technique. Diurnal variations of NEE were strongly influenced by growth stages of jute crop. Daytime peak NEE varied from - 5 µmol m-2 s-1 (in germination stage) to - 23 µmol m-2 s-1 (in fibre development stage). The ecosystem was net CO2 source during nighttime with an average NEE value of 5-8 µmol m-2 s-1. Combining both daytime and nighttime CO2 fluxes, jute ecosystem was found to be a net CO2 sink on a daily basis except the initial 9 days from date of sowing. Seasonal and growth stage-wise NEEs were computed, and the seasonal total NEE over the jute season was found to be - 268.5 gC m-2 (i.e. 10.3 t CO2 ha-1). In different jute growth stages, diurnal variations of NEE were strongly correlated (R2 > 0.9) with photosynthetic photon flux density (PPFD). Ecosystem level photosynthetic efficiency parameters were estimated at each growth stage of jute crop using the Michaelis-Menten equation. The maximum values of photosynthetic capacity (Pmax, 63.3 ± 1.15 µmol CO2 m-2 s-1) and apparent quantum yield (α, 0.072 ± 0.0045 µmol CO2 µmol photon-1) were observed during the active vegetative stage, and the fibre development stage, respectively. Results of the present study would significantly contribute to understanding of the carbon flux from the Indian agro-ecosystems, which otherwise are very sparse.

Grazing effects on surface energy fluxes in a desert steppe on the Mongolian Plateau.

Quantifying the surface energy fluxes of grazed and ungrazed steppes is essential to understand the roles of grasslands in local and global climate and in land use change. We used paired eddy-covariance towers to investigate the effects of grazing on energy balance (EB) components: net radiation (Rn ), latent heat (LE), sensible heat (H), and soil heat (G) fluxes on adjacent grazed and ungrazed areas in a desert steppe of the Mongolian Plateau for a two-year period (2010-2012). Near 95% of Rn was partitioned as LE and H, whereas the contributions of G and other components of the EB were 5% at an annual scale. H dominated the energy partitioning and shared ~50% of Rn . When comparing the grazed and the ungrazed desert steppe, there was remarkably lower Rn and a lower H, but higher G at the grazed site than at the ungrazed site. Both reduced available energy (Rn - G) and H indicated a "cooling effect" feedback onto the local climate through grazing. Grazing reduced the dry year LE but enhanced the wet year LE. Energy partitioning of LE/Rn was positively correlated with the canopy conductivity, leaf area index, and soil moisture. H/Rn was positively correlated with the vapor pressure deficit but negatively correlated with the soil moisture. Boosted regression tree results showed that LE/Rn was dominated by soil moisture in both years and at both sites, while grazing shifted the H/Rn domination from temperature to soil moisture in the wet year. Grazing not only caused an LE shift between the dry and the wet year, but also triggered a decrease in the H/Rn because of changes in vegetation and soil properties, indicating that the ungrazed area had a greater resistance while the grazed area had a greater sensitivity of EB components to the changing climate.

An Improved Approach for Estimating Daily Net Radiation over the Heihe River Basin.

Net radiation plays an essential role in determining the thermal conditions of the Earth's surface and is an important parameter for the study of land-surface processes and global climate change. In this paper, an improved satellite-based approach to estimate the daily net radiation is presented, in which sunshine duration were derived from the geostationary meteorological satellite (FY-2D) cloud classification product, the monthly empirical as and bs Angstrom coefficients for net shortwave radiation were calibrated by spatial fitting of the ground data from 1997 to 2006, and the daily net longwave radiation was calibrated with ground data from 2007 to 2010 over the Heihe River Basin in China. The estimated daily net radiation values were validated against ground data for 12 months in 2008 at four stations with different underlying surface types. The average coefficient of determination (R²) was 0.8489, and the averaged Nash-Sutcliffe equation (NSE) was 0.8356. The close agreement between the estimated daily net radiation and observations indicates that the proposed method is promising, especially given the comparison between the spatial distribution and the interpolation of sunshine duration. Potential applications include climate research, energy balance studies and the estimation of global evapotranspiration.

Use of MODIS Sensor Images Combined with Reanalysis Products to Retrieve Net Radiation in Amazonia.

In the Amazon region, the estimation of radiation fluxes through remote sensing techniques is hindered by the lack of ground measurements required as input in the models, as well as the difficulty to obtain cloud-free images. Here, we assess an approach to estimate net radiation (Rn) and its components under all-sky conditions for the Amazon region through the Surface Energy Balance Algorithm for Land (SEBAL) model utilizing only remote sensing and reanalysis data. The study period comprised six years, between January 2001-December 2006, and images from MODIS sensor aboard the Terra satellite and GLDAS reanalysis products were utilized. The estimates were evaluated with flux tower measurements within the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) project. Comparison between estimates obtained by the proposed method and observations from LBA towers showed errors between 12.5% and 16.4% and 11.3% and 15.9% for instantaneous and daily Rn, respectively. Our approach was adequate to minimize the problem related to strong cloudiness over the region and allowed to map consistently the spatial distribution of net radiation components in Amazonia. We conclude that the integration of reanalysis products and satellite data, eliminating the need for surface measurements as input model, was a useful proposition for the spatialization of the radiation fluxes in the Amazon region, which may serve as input information needed by algorithms that aim to determine evapotranspiration, the most important component of the Amazon hydrological balance.

Earth's energy imbalance since 1960 in observations and CMIP5 models.

Observational analyses of running 5 year ocean heat content trends (Ht) and net downward top of atmosphere radiation (N) are significantly correlated (r ∼ 0.6) from 1960 to 1999, but a spike in Ht in the early 2000s is likely spurious since it is inconsistent with estimates of N from both satellite observations and climate model simulations. Variations in N between 1960 and 2000 were dominated by volcanic eruptions and are well simulated by the ensemble mean of coupled models from the Fifth Coupled Model Intercomparison Project (CMIP5). We find an observation-based reduction in N of - 0.31 ± 0.21 W m-2 between 1999 and 2005 that potentially contributed to the recent warming slowdown, but the relative roles of external forcing and internal variability remain unclear. While present-day anomalies of N in the CMIP5 ensemble mean and observations agree, this may be due to a cancelation of errors in outgoing longwave and absorbed solar radiation. KEY POINTS: Observed maximum in ocean heat content trend in early 2000s is likely spuriousNet incoming radiation (N) reduced by 0.31 ± 0.21 W m-2 during the warming pausePresent-day estimates of N may contain opposing errors in radiative components.

Machine learning models for daily net radiation prediction across different climatic zones of China.

Net radiation (Rn), a critical component in land surface energy cycling, is calculated as the difference between net shortwave radiation and longwave radiation at the Earth's surface and holds significant importance in crop models for precision agriculture management. In this study, we examined the performance of four machine learning models, including extreme learning machine (ELM), hybrid artificial neural networks with genetic algorithm models (GANN), generalized regression neural networks (GRNN), and random forests (RF), in estimating daily Rn at four representative sites across different climatic zones of China. The input variables included common meteorological factors such as minimum and maximum temperature, relative humidity, sunshine duration, and shortwave solar radiation. Model performance was assessed and compared using statistical parameters such as the correlation coefficient (R2), root mean square errors (RMSE), mean absolute errors (MAE), and Nash-Sutcliffe coefficient (NS). The results indicated that all models slightly underestimated actual Rn, with linear regression slopes ranging from 0.810 to 0.870 across different zones. The estimated Rn was found to be comparable to observed values in terms of data distribution characteristics. Among the models, the ELM and GANN demonstrated higher consistency with observed values, exhibiting R2 values ranging from 0.838 to 0.963 and 0.836 to 0.963, respectively, across varying climatic zones. These values surpassed those of the RF (0.809-0.959) and GRNN (0.812-0.949) models. Additionally, the ELM and GANN models showed smaller simulation errors in terms of RMSE, MAE, and NS across the four climatic zones compared to the RF and GRNN models. Overall, the ELM and GANN models outperformed the RF and GRNN models. Notably, the ELM model's faster computational speed makes it a strong recommendation for Rn estimates across different climatic zones of China.

Monitoring Energy Balance, Turbulent Flux Partitioning, Evapotranspiration and Biophysical Parameters of Nopalea cochenillifera (Cactaceae) in the Brazilian Semi-Arid Environment.

The in-situ quantification of turbulent flux and evapotranspiration (ET) is necessary to monitor crop performance in stressful environments. Although cacti can withstand stressful conditions, plant responses and plant-environment interactions remain unclear. Hence, the objective of our study was to investigate the interannual and seasonal behaviour of components of the surface energy balance, environmental conditions, morphophysiological parameters, biomass yield and water relations in a crop of Nopalea cochenillifera in the semi-arid region of Brazil. The data were collected from a micrometeorological tower between 2015 and 2017. The results demonstrate that net radiation was significantly higher during the wet season. Latent heat flux was not significant between the wet season and dry season. During the dry-wet transition season in particular, sensible heat flux was higher than during the other seasons. We observed a large decline in soil heat flux during the wet season. There was no difference in ET during the wet or dry seasons; however, there was a 40% reduction during the dry-wet transition. The wet seasons and wet-dry transition showed the lowest Evaporative Stress Index. The plants showed high cladode water content and biomass during the evaluation period. In conclusion, these findings indicate high rates of growth, high biomass and a high cladode water content and explain the response of the cactus regarding energy partitioning and ET.

Estimating Bulk Stomatal Conductance in Grapevine Canopies.

In response to changes in their environments, grapevines regulate transpiration using various physiological mechanisms that alter conductance of water through the soil-plant-atmosphere continuum. Expressed as bulk stomatal conductance at the canopy scale, it varies diurnally in response to changes in vapor pressure deficit and net radiation, and over the season to changes in soil water deficits and hydraulic conductivity of both the soil and plant. To help with future characterization of this dynamic response, a simplified method is presented for determining bulk stomatal conductance based on the crop canopy energy flux model by Shuttleworth and Wallace using measurements of individual vine sap flow, temperature and humidity within the vine canopy, and estimates of net radiation absorbed by the vine canopy. The methodology presented respects the energy flux dynamics of vineyards with open canopies, while avoiding problematic measurements of soil heat flux and boundary layer conductance needed by other methods, which might otherwise interfere with ongoing vineyard management practices. Based on this method and measurements taken on several vines in a non-irrigated vineyard in Bordeaux France, bulk stomatal conductance was estimated on 15-minute intervals from July to mid-September 2020 producing values similar to those presented for vineyards in the literature. Time-series plots of this conductance show significant diurnal variation and seasonal decreases in conductance associated with increased vine water stress as measured by predawn leaf water potential. Global sensitivity analysis using non-parametric regression found transpiration flux and vapor pressure deficit to be the most important input variables to the calculation of bulk stomatal conductance, with absorbed net radiation and bulk boundary layer conductance being much less important. Conversely, bulk stomatal conductance was one of the most important inputs when calculating vine transpiration, emphasizing the usefulness of characterizing its dynamic response for the purpose of estimating vine canopy transpiration in water use models.

Atmospheric water vapor and soil moisture jointly determine the spatiotemporal variations of CO2 fluxes and evapotranspiration across the Qinghai-Tibetan Plateau grasslands.

Alpine grasslands play important functions in mitigating climate change and regulating water resources. However, the spatiotemporal variability of their carbon and water budgets remains unquantified. Here, 47 site-year observations of CO2 and water vapor fluxes (ET) are analyzed at sites situated along a hydrothermal gradient across the Qinghai-Tibetan Plateau, including an alpine wetland (wettest), an alpine shrub (coldest), an alpine meadow, an alpine meadow-steppe, and an alpine steppe (driest and warmest). The results show that the benchmarks for annual net ecosystem exchange (NEE) are -79.3, -77.8, -66.7, 20.2, and 100.9 g C m-2 year-1 at the meadow, shrub, meadow-steppe, steppe, and wetland, respectively. The peak daily NEE normalized by peak leaf area index converges to 0.93 g C m-2 d-1 at the 5 sites. Except in the wetland (722.8 mm), the benchmarks of annual ET fluctuate from 511.0 mm in the steppe to 589.2 mm in the meadow. Boosted regression trees-based analysis suggests that the enhanced vegetation index (EVI) and net radiation (Rn) determine the variations of growing season monthly CO2 fluxes and ET, respectively, although the effect is to some extent site-specific. Inter-annual variability in NEE, ecosystem respiration (RES), and ET are tightly (R2 > 0.60) related to the inter-growing season NEE, RES, and ET, respectively. Both annual RES and annual NEE are significantly constrained by annual gross primary productivity (GPP), with 85% of the per-unit GPP contributing to RES (R2 = 0.84) and 15% to NEE (R2 = 0.12). Annual GPP significantly correlates with annual ET alone at the drier sites of the meadow-steppe and the steppe, suggesting the coupling of carbon and water is moisture-dependent in alpine grasslands. Over half of the inter-annual spatial variability in GPP, RES, NEE, and ET is explained by EVI, atmospheric water vapor, topsoil water content, and bulk surface resistance (rs), respectively. Because the spatial variations of EVI and rs are strongly regulated by atmospheric water vapor (R2 = 0.48) and topsoil water content (R2 = 0.54), respectively, we conclude that atmospheric water vapor and topsoil water content, rather than the expected air/soil temperatures, drive the spatiotemporal variations in CO2 fluxes and ET across temperature-limited grasslands. These findings are critical for improving predictions of the carbon sequestration and water holding capacity of alpine grasslands.

Variability and role of long-wave radiation fluxes in the formation of net radiation and thermal features of grassy and bare soil active surfaces in Wroclaw.

The paper examines the variability of long-wave radiation fluxes in two contrasting types of urban active surfaces - grassy surface and surface without plants (bare soil) in Wroclaw (Poland) within a 12-year period (August 2007-July 2019). The study used net radiation and heat balance formulas to calculate the share of individual radiation fluxes in these balances, and then utilized the Stefan-Boltzmann formula to calculate the effective temperatures of researched surfaces. The analysis showed the temporal variability of these fluxes against the background of weather and climatic conditions and in relation to the variability of short-wave radiation fluxes. The role of long-wave radiation fluxes in forming net radiation was examined in detail to show the buffering role of vegetation surfaces regarding the variability of solar radiation fluxes and their heat effects. The mean monthly values of outgoing long-wave radiation fluxes change from 309.0 W·m-2 for bare soil, 309.8 W·m-2 for grassy surface, and 288.8 W·m-2 for downward atmospheric radiation to respectively 435.8, 425.0 and 369.4 W·m-2 in July. The coefficient of variability for long-wave radiation daily fluxes are approximately one order of magnitude lower than for the short-wave radiation. The differences between values of long-wave radiation fluxes for bare soil and grassy surfaces vary from slight negative values in winter to relatively sizable positive values during the vegetation period (March-October). The weakening of the buffering effect for grassy surface and how air temperature then changes considerably compared to the effective temperature of the active surfaces were explained using the dry summer period of August 2015 as example. The obtained results are important, as they provide empirical arguments for urban planning to extend plant areas' share in big cities as well as to introduce there a friendly environmental system of irrigation in these areas using renewable solar energy.

Biophysical effect of conversion from croplands to grasslands in water-limited temperate regions of China.

The biophysical effect of land use and land cover change (LUCC) on regional climatic regulation is currently of growing interest. However, in water-limited temperate regions, the net biophysical effect of conversion from croplands to grasslands on regional climatic regulation remains poorly understood to date. To answer this concern, a modified land surface model (mEASS) and two different land use scenarios in a typical study area of the Loess Plateau of China were used in this study. We first validated the performances of mEASS model by using observations from six flux tower sites with different land cover and three metrics of the coefficient of determination (R2), the root mean square error (RMSE) and the difference between the simulated and observed data (bias). Subsequently, the biophysical effect of conversion from croplands to grasslands was investigated. Results indicated that mEASS model could well capture the seasonal dynamics of net radiation and latent heat with high R2 and lower RMSE and bias at grassland, forest and cropland sites. In the context of semi-arid and semi-humid climatic conditions, conversion from croplands to grasslands caused the cooling effect (-0.3 W/m2) at the annual scale. Similar cooling effects were found in spring (-0.4 W/m2), autumn (-0.8 ±â€¯0.1 W/m2) and winter (-0.9 ±â€¯0.1 W/m2). The decreased latent heat (inducing warming effects) were completely offset by decreased net radiation (inducing cooling effects), which were responsible for the net cooling effects. However, a warming effect with 1.0 ±â€¯0.1 W/m2 was observed in summer. This is because that magnitude of decreased latent heat is greater than that of decreased net radiation in summer. These findings will enrich our understanding for the biophysical effect of conversion from croplands to grasslands in water-limited temperate regions.

Modeling and optimization method of an indirectly irradiated solar receiver.

This work presents the modeling and optimization of an indirectly irradiated solar receiver. A numerical model of the cavity-absorber block is put forward with the coupling of the net-radiation method using infinitesimal areas and a CFD code. An iterative method with a relaxation factor made it possible to obtain the temperature distribution and the developed code was implemented in the form of UDF and used as boundary conditions in the CFD model of the absorber to simulate the flow of air and heat transfer. The good ability of the receiver to transfer heat to the fluid is proved with a 92% thermal efficiency obtained. Then the combination of the Kriging surface response method and the MOGA allowed the mathematical optimization of the receiver. The multi-objective optimization made it possible to obtain 3 candidates giving the best combinations of design parameters from the fixed objectives. Three bullet points, highlighting the customization of the procedure. •A practical analysis using the net-radiation method using infinitesimal areas is applied for cavity radiative exchange model.•The code developed for the cavity is implemented in the boundary conditions at the level of the ANSYS Fluent CFD model allowing the simulation of the conjugated transfers within the absorber.•The optimization method proposed is the combination of the Kriging surface response method for quantitative and qualitative analysis of the design parameters and MOGA to obtain different combinations seeking to maximize or to minimize the chosen parameters.

Validação do balanço de radiação obtido a partir de dados MODIS/TERRA na Amazônia com medidas de superfície do LBA / Validation of net radiation obtained from MODIS/TERRA data in Amazonia with LBA surface measurements

This study aims to estimate the components of net radiation in two regions located in the state of Rondônia (southwest of the Brazilian Amazon), using Moderate Resolution Imaging Spectroradiometer (MODIS/TERRA) data based on Surface Energy Balance Algorithms for Land (SEBAL) model, and to validate the results with information acquired by the micrometeorological towers of LBA under the conditions of pasture (Fazenda Nossa Senhora Aparecida) and forest (Reserva Biológica do Jaru). Implementation of SEBAL model was performed directly on the MODIS data and included steps involving the computation of vegetation indices, albedo and atmospheric transmittance. Comparison between estimates from MODIS data and the observations showed relative errors for the condition of pasture between 0.2 and 19.2%, and for the condition of forest ranging between 0.8 and 15.6%. The integration of data at different scales was a useful proposition for the estimation and spatialization of the radiation fluxes in the Amazon region, which may contribute to a better understanding of the interaction between Amazon rainforest and atmosphere, and generate input information needed to the surface models coupled to atmospheric general circulation models.

Este estudo tem como objetivo estimar os componentes do balanço de radiação em duas regiões do estado de Rondônia (sudoeste da Amazônia brasileira), a partir de dados do Moderate Resolution Imaging Spectroradiometer (MODIS/TERRA) por intermédio do modelo Surface Energy Balance Algorithms for Land (SEBAL), e validar os resultados com informações adquiridas por torres micrometeorológicas do projeto LBA sob as condições de pastagem (Fazenda Nossa Senhora Aparecida) e floresta (Reserva Biológica do Jaru). A implementação do modelo SEBAL foi realizada diretamente sobre os dados MODIS e incluiu etapas envolvendo o cômputo de índices de vegetação, albedo e transmitância atmosférica. A comparação das estimativas geradas a partir de dados MODIS com as observações resultou em erros relativos para a condição de pastagem variando entre 0,2 e 19,2%, e para a condição de floresta variando entre 0,8 e 15,6%. A integração de dados em diferentes escalas constituiu uma proposição útil para a estimativa e espacialização dos fluxos de radiação na região amazônica, o que pode contribuir para a melhor compreensão da interação entre a floresta tropical e a atmosfera e gerar informações de entrada necessárias aos modelos de superfície acoplados aos modelos de circulação geral da atmosfera.