Estimation Of Net Radiation Research Articles

Multispectral Assessment of Net Radiations Using Comprehensive Multi-Satellite Data

Precise estimation of net radiation (Rn) is fundamental to understanding surface energy balance and is critical for accurately determining crop water requirements, especially using remote sensing and geospatial techniques. The core objective of this study is to evaluate multi-satellite-based net radiations on major cropped areas of the Punjab and Sindh provinces of Pakistan. In this study, overlapping scenes from the Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat 8, and Sentinel 2 were used from 2016 to 2020 along with three temperature products MOD11A1, Landsat 8 (brightness temperature), and ERA5. The multi-satellite-based net radiation estimations on overlapping days were compared with the Global Land Data Assimilation System (GLDAS) dataset. The models based on Landsat 8 and Sentinel 2 data exhibited good performance, with a Nash–Sutcliffe Efficiency (NSE) of 68.9%, a mean error (ME) of 13.918 W/m2, and a bias of 50.669 W/m2. The results indicated that Landsat 8 and Sentinel 2 data produced reliable estimations of net radiation, while MODIS data tended to overestimate due to its higher spatial resolution and broader coverage area. Landsat 8-based estimations are good compared to others, as it has good correlation coefficient and lower RMSE values. The study concludes that Landsat 8 provides the most reliable estimates of net radiation for determining crop water requirements, outperforming other datasets in accuracy. The findings underscore the importance of using high-resolution multi-satellite data for precise agricultural water management, recommending its use in future studies and water resource planning in Pakistan.

Surface Shortwave Net Radiation Estimation From Space: Emphasizing the Effects of Aerosol, Solar Zenith Angles, and DEM

Surface Shortwave Net Radiation Estimation From Space: Emphasizing the Effects of Aerosol, Solar Zenith Angles, and DEM

A double two-sources energy-water balance model for improving evapotranspiration estimates and irrigation management in fruit trees fields

Improving the use of water in irrigated agriculture is meaningful in a period of increasing water scarcity conditions. A more accurate estimate of evapotranspiration (ET) and its components thus becomes fundamental to better quantify the irrigation water volumes. Many existing models, based on different remote sensing data, provide daily estimates of latent heat flux (LE) from correlation between net radiation and instantaneous estimates of LE, computed as residual component of the energy balance equation or from a correlation of land surface temperature (LST) with vegetation indices., However, they mainly lack of solutions which are continuous in time (e.g. hourly), independent from satellite LST availability and a simultaneous estimation of soil moisture. Addressing this gap, a double two-sources energy-water balance model (FEST‐2×2‐EWB) is developed based on the decoupling of both water and energy fluxes between the bare soil or grass interrow and the trees rows. This novel parameterization approach enables the differentiation of water uptake from the root zone, which varies between tall trees and grass, considering the dynamics of soil moisture (SM) in the superficial and deep layers. Additionally, it provides partitioned values for transpiration and evaporation. The new model has been evaluated in two irrigated trees fields in the North of Italy, a walnut trees field from 2019 to 2021 and a pear trees field, for the year 2022. Results of the study showcased a root-mean-square-error (RMSE) of about 55 W m− 2 and a bias of about 40 W m− 2 for hourly latent and sensible heat fluxes when compared to the eddy covariance stations located in the fields, while a RMSE of 2 °C (bias of 1.5 °C) for LST and of 0.04 for SM. The FEST‐2×2‐EWB model significantly enhances the accuracy of ET simulation in fruits trees areas in respect to the original one source and one-layer version of the same model. Finally, the application of an irrigation optimization strategy with this new model, allowed to demonstrate its potentiality in water saving (about 90 mm in a year) in respect to farmers applied irrigation, and with a difference of about 60 mm between using the double two-sources model and single source one.

An estimation of net radiation from global solar radiation in the main regions of Thailand

Net radiation can be used for different purposes, especially for studying the energy or radiation balance, which can be further analysed to investigate a global warming. In order to utilize these applications, it is necessary to know the amount of net radiation in that area. This can be done by installing a net radiometer for measuring the net radiation. However, there are few monitoring stations of net radiation compared to a global solar radiation in Thailand. Therefore, this research aims to analyse a statistical characteristic of the measured net radiation and to develop a model for estimating the net radiation from the global solar radiation at four solar monitoring stations in the main regions of Thailand, namely Chiang Mai, Ubon Ratchathani, Songkhla and Nakhon Pathom during the year of 2017 to 2021. The results showed that the most net radiation of these stations had a range of 8 to 14 MJ/m2. The relationship between daily and monthly average daily net radiation and global solar radiation was found to be a linear. After that, the developed model was validated by comparing the estimated and measured net radiation. The discrepancy between the calculated net radiation and that obtained from the measurements was presented in terms of root mean square difference (RMSD) and mean bias difference (MBD) ranged from 6.98% to 16.03% and -1.82% to 7.09%, respectively.

Estimation of fine spatial resolution all-sky surface net shortwave radiation over mountainous terrain from Landsat 8 and Sentinel-2 data

Net shortwave radiation (NSR) plays an essential role in surface energy budget and has been reported to exhibit low estimation accuracy when ignoring topographic effects. Several algorithms have been developed to estimate clear-sky NSR in mountains, but none have estimated NSR in mountainous terrain under all-sky (i.e., both clear and cloudy) conditions. Moreover, surface heterogeneity is considerable in mountains, and coarse-spatial-resolution satellites may not provide sufficient details for fine-scale applications. In this study, to address these challenges, we developed a method for fine-spatial-resolution (i.e., from 20 m to 30 m resolution) all-sky NSR estimation in mountains directly from Landsat 8 and Sentinel-2 top-of-atmosphere (TOA) observations with mountainous radiative transfer scheme (herein denoted ‘TOPO’). For comparison, we also created the model neglecting topography (herein called ‘FLAT’). The validation of the TOPO model in complex terrain against Chengde ground-based pyrometers in China was good, with an R2 of 0.91 and a root-mean-square error (RMSE) of 80.9 W/m2, which was an improvement of 46.1% (reduction of RMSE) against the FLAT model. Meanwhile, the consistency of NSR generated from Landsat 8 and Sentinel-2 using TOPO model was satisfactory. Time-series comparisons of NSR derived from TOPO and FLAT models showed that topographic effects were substantial both in clear sky (maximum deviation of 643.4 W/m2) and cloudy sky (270.2 W/m2) when NSR estimates from the two models were compared. The evaluation of the FLAT model with a dataset simulated from the mountainous radiative transfer (created for the TOPO model) showed that neglecting topographic effects over mountainous terrain could lead to RMSEs of 541.7 W/m2 (relative RMSE = 159.5%) and 201.4 W/m2 (relative RMSE = 217.1%) for NSR estimation under clear and cloudy skies, respectively. This study provides a straightforward method of deriving fine-spatial-resolution all-sky NSR with topographic consideration, which can be further extended to the global mapping of NSR over mountainous terrain.

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.

Estimation of turbulent fluxes over almond orchards using high-resolution aerial imagery with one and two-source energy balance models

Estimation of actual crop evapotranspiration (ETc) using high-resolution aerial remote sensing data is important to detect water stress, map, and manage water resources in precision agriculture. High-resolution ETc can be estimated using land surface energy balance models and remotely sensed land surface temperatures (LST) obtained using manned or unmanned aerial vehicles equipped with thermal cameras. In this study, three remote sensing ETc models were compared, i.e., Two-Source Energy Balance (TSEB) model, Mapping Evapotranspiration with Internalized Calibration (METRIC), and Surface Energy Balance Algorithm for Land (SEBAL) models. Thermal images were obtained using an airplane flying daily over almond orchards in California during the 2020 growing season. The LST images were obtained at 0.5 m spatial resolution. Model comparisons indicated that all three models produced latent heat fluxes and net radiation estimates that agreed with eddy covariance measurements in the order of TSEB (R2 of 0.89 for LE and 0.88 for Rn), METRIC (R2 of 0.81 for LE and 0.86 for Rn), and SEBAL (R2 of 0.81 for LE and 0.83 for Rn). However, METRIC and SEBAL overestimated the latent heat fluxes while underestimating the sensible heat fluxes as compared to the two-source model (TSEB). The root mean square error (RMSE) of the instantaneous latent and sensible heat fluxes were less than 38 W m−2 for TSEB, and were within 58 W m−2 for METRIC and SEBAL models. The TSEB model’s good performance can be attributed to its partitioning of surface temperature between soil, crop cover in the inter rows, and almond tree canopies. Overall, the results suggest that both one and two-source surface energy models originally developed for satellite imagery are able to estimate instantaneous turbulent fluxes and spatial variability in ETc using high-resolution imagery. In addition, systematic variations in LST due to variable rate irrigation scheduling as depicted in the high spatial resolution imagery provided confidence in the spatially distributed latent heat flux maps estimated by the energy balance models. This study shows that high-resolution aerial imagery combined with energy balance models originally developed for satellite remote sensing can be used to accurately estimate site specific ETc that is critical to achieving precision irrigation management in almond orchards and other crops.

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.

Analysis and Modelling of Temperature at the Water – Atmosphere Interface of a Lake by Energy Budget and ANNs Models

Different processes of lake surface water temperature (Tsw) are considered in this work. The Tsw estimation is based on the energy budget method and Artificial Neural Networks (ANNs models).These processes were applied to Vegoritis Lake in northern Greece. For the analysis, daily meteorological data, lake characteristics, as well as simulation results from an integrated heat transfer model were used for two complete years. The simulation results from the heat transfer model are considered as the reference and more accurate procedure to estimate Tsw. These results are used to compare the performance of the other processes. The examined processes include:(a) models of heat storage changes in relation to net radiation values Qt(Rn); (b) net radiation estimation using different approaches, such as the process of Slob’s equation with adjusted coefficients to lake data; and (c) ANN models with various architectures and input variables. The results show that the surface water energy budget model accurately describes the temperature (r2 = 0.916, RMSE = 2.422 °C). The ANN(5,6,1) model in which Tsw(i-1) is incorporated in the input variables was considered the best performing compared to all other ANN structures (r2 = 0.995, RMSE = 0.490 °C). Using different approaches for simulating net radiation (Rn) and Qt(Rn) in the equation of surface water temperature estimation provides results with lower accuracy.

Estimation of Daily All-Wave Surface Net Radiation With Multispectral and Multitemporal Observations From GOES-16 ABI

As a vital parameter describing the Earth surface energy budget, surface all-wave net radiation ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{n}$ </tex-math></inline-formula> ) drives many physical and biological processes. Remote estimation of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{n}$ </tex-math></inline-formula> using satellite data is an effective approach to monitor the spatial and temporal dynamics of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{n}$ </tex-math></inline-formula> . Accurate daily <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{n}$ </tex-math></inline-formula> estimation typically depends on the spatio-temporal resolutions of satellite data. There are currently few high-spatial-resolution daily <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{n}$ </tex-math></inline-formula> products from polar-orbiting satellite data, and they exhibit limited accuracy due to sparse diurnal observations. In addition, traditional estimation approaches typically require cloud mask and clear-sky albedo as inputs and ignore the length ratio of daytime (LRD), which may lead to large errors. To overcome these challenges and obtain <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{n}$ </tex-math></inline-formula> data with improved spatial resolution and accuracy, an operational approach was proposed in this study to derive daily 1-km <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{n}$ </tex-math></inline-formula> , which takes the advantages from a radiative transfer model, a machine learning algorithm, and multispectral and dense diurnal temporal information of geostationary satellite observations. An improved all-sky hybrid model (AHM) coupling radiative transfer simulations with a random forest (RF) model was first developed to estimate the shortwave net radiation ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{ns}$ </tex-math></inline-formula> ). Then, another RF model was developed to estimate the daily <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{n}$ </tex-math></inline-formula> from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{ns}$ </tex-math></inline-formula> , incorporating the LRD, which is called extended hybrid model (EHM). Data from the Advanced Baseline Imager (ABI) onboard the new-generation Geostationary Operational Environmental Satellite (GOES)-16 with a 5-min temporal resolution and a 1-km spatial resolution were used to test the proposed method. Compared to traditional lookup table (LUT) algorithms, the results show that AHM not only makes the process of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{ns}$ </tex-math></inline-formula> estimation simple and efficient but also has high accuracy in estimating instantaneous all-sky <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{ns}$ </tex-math></inline-formula> . Benefiting from high spatio-temporal resolutions, our daily <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{ns}$ </tex-math></inline-formula> estimates using GOSE-16 data exhibited superior performance compared to using the 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) and 1° Clouds and the Earth’s Radiant Energy System (CERES) product. Using accurate daily <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{ns}$ </tex-math></inline-formula> estimates and LRD as inputs, the EHM model shows reasonably good results for estimating <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{n}$ </tex-math></inline-formula> ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> , RMSE, and bias of 0.91, 20.95 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , and −0.05 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , respectively). Maps of 1-km <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{ns}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{n}$ </tex-math></inline-formula> exhibit similar spatial patterns to those from the 1° CERES product, but with substantially more spatial details. Overall, the proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{n}$ </tex-math></inline-formula> retrieval scheme can accurately estimate all-sky 1-km <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{ns}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{n}$ </tex-math></inline-formula> at mid- to low-latitudes and can be easily adapted and applied to other GOES- 16-like satellites, such as Himawari-8, Meteosat Third Generation (MTG), and Fenyun-4. This study demonstrates the advantages of estimating <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{n}$ </tex-math></inline-formula> using geostationary satellites with improved accuracy and resolutions.

Estimation of the All-Wave All-Sky Land Surface Daily Net Radiation at Mid-Low Latitudes from MODIS Data Based on ERA5 Constraints

The surface all-wave net radiation (Rn) plays an important role in the energy and water cycles, and most studies of Rn estimations have been conducted using satellite data. As one of the most commonly used satellite data sets, Moderate Resolution Imaging Spectroradiometer (MODIS) data have not been widely used for radiation calculations at mid-low latitudes because of its very low revisit frequency. To improve the daily Rn estimation at mid-low latitudes with MODIS data, four models, including three models built with random forest (RF) and different temporal expansion models and one model built with the look-up-table (LUT) method, are used based on comprehensive in situ radiation measurements collected from 340 globally distributed sites, MODIS top-of-atmosphere (TOA) data, and the fifth generation of European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) data from 2000 to 2017. After validation against the in situ measurements, it was found that the RF model based on the constraint of the daily Rn from ERA5 (an RF-based model with ERA5) performed the best among the four proposed models, with an overall validated root-mean-square error (RMSE) of 21.83 Wm−2, R2 of 0.89, and a bias of 0.2 Wm−2. It also had the best accuracy compared to four existing products (Global LAnd Surface Satellite Data (GLASS), Clouds and the Earth’s Radiant Energy System Edition 4A (CERES4A), ERA5, and FLUXCOM_RS) across various land cover types and different elevation zones. Further analyses illustrated the effectiveness of the model by introducing the daily Rn from ERA5 into a “black box” RF-based model for Rn estimation at the daily scale, which is used as a physical constraint when the available satellite observations are too limited to provide sufficient information (i.e., when the overpass time is less than twice per day) or the sky is overcast. Overall, the newly-proposed RF-based model with ERA5 in this study shows satisfactory performance and has strong potential to be used for long-term accurate daily Rn global mapping at finer spatial resolutions (e.g., 1 km) at mid-low latitudes.

Neural network-based approach for estimation of downwelling longwave radiation flux under cloudy-sky conditions

Net surface radiation defines the availability of radiation energy on and near the surface to drive many physical and physiological processes such as latent heat, sensible heat fluxes, and evapotranspiration. One of the prime challenges of modeling radiation budget is estimation of net longwave radiation. Incoming or downwelling longwave radiation (LWin) flux is one of the two key components of net longwave radiation. Its estimation in cloudy conditions has always been a challenge due to lack of instrumentation and regular measurements at different spatial scales. In this study, two artificial neural network (ANN) multi-layer perceptron (MLP) models were developed for LWin flux estimation under cloudy-sky during daytime and nighttime using half-hourly flux measurements over different agro-climatic settings and several atmospheric parameters from measurements, satellite-based observations, and model outputs. A comparative evaluation was made between existing or newly developed multivariate linear regression (MVR) models and ANN-based models. The latter set of models were found to be superior to the best MVR model during both daytime and nighttime. The ANN models were found to have consistent performance across different sites and cloud types except less accuracy in sub-humid or humid climate and in deep convection cloud. The ANN models showed overall accuracies of 2.7% and 3.3% of measured mean and R2 of 0.86 and 0.85 for daytime and nighttime, respectively, when compared with independent data of in-situ measurements.

Seasonal Net Shortwave Radiation of Bare Arable Land in Poland and Israel According to Roughness and Atmospheric Irradiance

Tillage of arable fields, using for instance a smoothing harrow, may increase the magnitude of albedo of such soil surfaces depending on the location, the sun’s illumination and atmospheric components. As these soil surfaces absorb less shortwave radiation compared to plowed soils, the result is an atmospheric cooling and a positive effect on the Earth’s climate. This paper is the follow-on of a previous study aimed at quantifying the seasonal dynamics of net shortwave radiation reflected by bare air-dried arable land areas located in contrasting environments, i.e. Poland and Israel. Soil tillage includes a plow, a disk harrow, and a smoothing harrow. Previous work concentrated on the estimate of net shortwave radiation under clear-sky theoretical scenarios, whereas the present study deals with a realistic atmosphere throughout the year 2014. This latter is characterized by the observations of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on board the Meteosat Second Generation (MSG). The variations of the net shortwave radiation for the selected bare arable land areas were assessed in combining observations from Landsat 8 images and digital maps of land use and soil, plus model equations that calculate the diurnal variations of the broadband blue-sky albedo with roughness inclusive. The daily amount of net shortwave radiation for air-dried bare arable land in Poland and Israel for the time their spatial coverage is the largest was found to be about 40–50% and 10% lower, respectively, in cloudy-sky conditions compared to clear-sky conditions.

Evapotranspiration partitioning assessment using a machine-learning-based leaf area index and the two-source energy balance model with sUAV information.

Accurate quantification of the partitioning of evapotranspiration (ET) into transpiration and evaporation fluxes is necessary to understanding ecosystem interactions among carbon, water, and energy flux components. ET partitioning can also support the description of atmosphere and land interactions and provide unique insights into vegetation water status. Previous studies have identified leaf area index (LAI) estimation as a key descriptor of biomass conditions needed for the estimation of transpiration and evaporation. LAI estimation in clumped vegetation systems, such as vineyards and orchards, has proven challenging and is strongly related to crop phenological status and canopy management. In this study, a feature extraction model based on previous research was built to generate a total of 202 preliminary variables at a 3.6-by-3.6-meter-grid scale based on submeter-resolution information from a small Unmanned Aerial Vehicle (sUAV) in four commercial vineyards across California. Using these variables, a machine learning model called eXtreme Gradient Boosting (XGBoost) was successfully built for LAI estimation. The XGBoost built-in function requires only six variables relating to vegetation indices and temperature to produce high-accuracy LAI estimation for the vineyard. Using the six-variable XGBoost-based LAI map, two versions of the Two-Source Energy Balance (TSEB) model, TSEB-PT and TSEB-2T were used for energy balance and ET partitioning. Comparing these results with the Eddy-Covariance (EC) tower data, showed that TSEB-PT outperforms TSEB-2T on the estimation of sensible heat flux (within 13% relative error) and surface heat flux (within 34% relative error), while TSEB-2T outperforms TSEB-PT on the estimation of net radiation (within 14% relative error) and latent heat flux (within 2% relative error). For the mature vineyard (north block), TSEB-2T performs better than TSEB-PT in partitioning the canopy latent heat flux with 6.8% relative error and soil latent heat flux with 21.7% relative error; however, for the younger vineyard (south block), TSEB-PT performs better than TSEB-2T in partitioning the canopy latent heat flux with 11.7% relative error and soil latent heat flux with 39.3% relative error.

Understanding water and energy fluxes in the Amazonia: Lessons from an observation-model intercomparison.

Tropical forests are an important part of global water and energy cycles, but the mechanisms that drive seasonality of their land-atmosphere exchanges have proven challenging to capture in models. Here, we (1) report the seasonality of fluxes of latent heat (LE), sensible heat (H), and outgoing short and longwave radiation at four diverse tropical forest sites across Amazonia-along the equator from the Caxiuanã and Tapajós National Forests in the eastern Amazon to a forest near Manaus, and from the equatorial zone to the southern forest in Reserva Jaru; (2) investigate how vegetation and climate influence these fluxes; and (3) evaluate land surface model performance by comparing simulations to observations. We found that previously identified failure of models to capture observed dry-season increases in evapotranspiration (ET) was associated with model overestimations of (1) magnitude and seasonality of Bowen ratios (relative to aseasonal observations in which sensible was only 20%-30% of the latent heat flux) indicating model exaggerated water limitation, (2) canopy emissivity and reflectance (albedo was only 10%-15% of incoming solar radiation, compared to 0.15%-0.22% simulated), and (3) vegetation temperatures (due to underestimation of dry-season ET and associated cooling). These partially compensating model-observation discrepancies (e.g., higher temperatures expected from excess Bowen ratios were partially ameliorated by brighter leaves and more interception/evaporation) significantly biased seasonal model estimates of net radiation (Rn ), the key driver of water and energy fluxes (LE~0.6 Rn and H~0.15 Rn ), though these biases varied among sites and models. A better representation of energy-related parameters associated with dynamic phenology (e.g., leaf optical properties, canopy interception, and skin temperature) could improve simulations and benchmarking of current vegetation-atmosphere exchange and reduce uncertainty of regional and global biogeochemical models.

Improvement in the Estimation of Daily Net Surface Radiation in China

AbstractEarth’s surface net radiation (Rn) governs sensitive and latent heat fluxes, plant photosynthesis, and the terrestrial carbon cycle. However, Rn records are scarce in China. The accurate qu...

Estimation of Net Surface Shortwave Radiation From Remotely Sensed Data Under Dust Aerosol Conditions

Due to the complexity and heterogeneity mechanism of aerosol scattering and absorption, there is no systematic operational net surface shortwave radiation (NSSR) retrieval algorithm for considering the forces of different aerosols. This study is to estimate NSSR under dust aerosol conditions. A parameterized model is proposed to quantify the net shortwave radiative forcing (ASRF) of aerosol. Considering the particularity of dust aerosol on shortwave radiation and its mechanisms, aerosol optical properties and atmospheric water vapor content (WVC) are used to quantify the ASRF, and the previous Tang’s parameterization model for estimating NSSR is refined to estimate NSSR under dust aerosol conditions. On this basis, a more accurate radiative transfer model MODTRAN 5 is primitively utilized to simulate and fit the complex relationship. Moreover, the coefficients for parameterization are recalculated by an improved algorithm under diverse types of aerosols. Finally, the NSSR measurements in the study areas and the NSSR products from the MODIS satellites are utilized to verify the retrieved NSSR. The root-mean-square error (RMSE) is below 11 W/m2 under various land cover types and the research findings can significantly increase the estimation accuracy of NSSR around 40 W/m2 under dust aerosol conditions.

Modelling Net Radiative Measurement of Meteorological Parameters Using MERRA-2 Data in Sub-Sahara African Town

In this study, the net radiation was estimated using a simple straightforward expression proposed by different researchers, which is based on the principle of the Fourier Series Technique. The estimation of net radiation of Iwo ( ) from the data collected from the archive of HelioClim satellite MERRA- 2 (i.e. global solar radiation and air temperature) was done on the real and imaginary measurements. The result of both real and imaginary radiation at maximum revealed ( ) and minimum at about ( ), while solar radiation and temperature revealed about ( ) and 299K maximum and minimum ( ) and 297.7K, respectively. Statistically, the result indicated that the regression coefficient of 3.959 with t- statistics of 3.34 and p < 0.05 indicates that for every 1K increase in air temperature, solar radiation will increase by 3.959, which shows that both solar radiation and temperature have a significant effect on net radiation. Therefore, the researchers concluded that Iwo had maximum real net radiation in February with months such as January, March, July, August, October and December as minimum radiation while imaginary radiation had its maximum and minimum in September and August respectively.

Evaluation of satellite-retrieved evapotranspiration based on a nonparametric approach over an arid region

ABSTRACT Daily evapotranspiration (ET) controlled by latent energy (LE) is an important variable used in the study of hydrology, meteorology, and ecology. A constant solar radiation ratio approach was introduced to the LE retrieval algorithm based on a nonparametric ET approach to upscale instantaneous LE temporally. On the basis of the proposed algorithm, this study retrieved the daily and eight-day LE using Moderate-resolution Imaging Spectroradiometer (MODIS) and China Meteorological Administration Land Data Assimilation System products and evaluated the daily and eight-day LE retrieval by using the ground observations and MOD16 product in Zhangye City during 25 June to 15 September 2012. Results show that the temporal-spatial distribution of retrieved result was reliable. The retrieved daily/eight-day LE could capture the dynamic change of ground observations, whereas the MOD16 LE was invalid. The retrieved daily (eight-day) LE was underestimated at all four (two) sites, with bias, root mean square error, and R2 of −7.9 to −2.2 W m−2 (approximately −5 W m−2), 19.8 to 39.0 W m−2 (approximately 10 W m−2), and 0.72 to 0.84 (approximately 0.9), respectively. The proposed approach showed more satisfactory performance than the triangle approach under clear sky condition. The eight-day LE retrieval was considerably better than MOD16 LE and had (no/slight) influence on MOD16 LE (eight-day retrieval). In addition, the accurate estimation of net radiation, soil heat flux, land surface temperature, and air temperature could aid in improving LE retrieval. Future studies would focus on the improvement of the retrieval algorithm, input accuracy, and residuals.

Estimating Near Real-Time Hourly Evapotranspiration Using Numerical Weather Prediction Model Output and GOES Remote Sensing Data in Iowa

This study evaluates the applicability of numerical weather prediction output supplemented with remote sensing data for near real-time operational estimation of hourly evapotranspiration (ET). Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR) systems were selected to provide forcing data for a Penman-Monteith model to calculate the Actual Evapotranspiration (AET) over Iowa. To investigate how the satellite-based remotely sensed net radiation ( R n ) estimates might potentially improve AET estimates, Geostationary Operational Environmental Satellite derived R n (GOES- R n ) data were incorporated into each dataset for comparison with the RAP and HRRR R n -based AET evaluations. The authors formulated a total of four AET models—RAP, HRRR, RAP-GOES, HRRR-GOES, and validated the respective ET estimates against two eddy covariance tower measurements from central Iowa. The implementation of HRRR-GOES for AET estimates showed the best results among the four models. The HRRR-GOES model improved statistical results, yielding a correlation coefficient of 0.8, a root mean square error (mm hr−1) of 0.08, and a mean bias (mm hr−1) of 0.02 while the HRRR only model results were 0.64, 0.09, and 0.04, respectively. Despite limited in situ observational data to fully test a proposed AET estimation, the HRRR-GOES model clearly showed potential utility as a tool to predict AET at a regional scale with high spatio-temporal resolution.