Comparison of estimates of daily solar radiation from air temperature range for application in crop simulations

Abraha, Michael; Savage, Michael J.

doi:10.1016/j.agrformet.2007.10.001

Cited by 144 publications

(89 citation statements)

References 33 publications

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“…Solar radiation is a dominant heating source for the surface, which then transfers heat to the immediate atmosphere [59]. A number of methods have been proposed to model spatio-temporal solar radiation, such as retrieval from satellite data [76], interpolation from surrounding weather stations [77,78], empirical estimation from other meteorological observations (e.g., diurnal air temperature range) [79,80], generation from weather models [81], and geometrical simulation accounting for shadows and clouds [82].…”

Section: The Seven Predictors and Complementary Predictorsmentioning

confidence: 99%

“…MCD43A3 contains BSA (i.e., directional hemispherical reflectance) and white-sky albedo (i.e., bi-hemispherical reflectance) for seven narrowbands and visible, near-infrared, and shortwave broadbands. Given that BSA and white-sky albedo are almost linearly correlated [47] and total solar energy reflected by the surface is commonly characterized by the shortwave broadband [59], BSA-shortwave was extracted and the pixels flagged with "processed, good quality" or "magnitude inversion [numobs ≥ 7]" were used.…”

Section: Ndvi and Isamentioning

confidence: 99%

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Enhanced Statistical Estimation of Air Temperature Incorporating Nighttime Light Data

et al. 2016

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Near surface air temperature (Ta) is one of the most critical variables in climatology, hydrology, epidemiology, and environmental health. In situ measurements are not efficient for characterizing spatially heterogeneous Ta, while remote sensing is a powerful tool to break this limitation. This study proposes a mapping framework for daily mean Ta using an enhanced empirical regression method based on remote sensing data. It differs from previous studies in three aspects. First, nighttime light data is introduced as a predictor (besides land surface temperature, normalized difference vegetation index, impervious surface area, black sky albedo, normalized difference water index, elevation, and duration of daylight) considering the urbanization-induced Ta increase over a large area. Second, independent components are extracted using principal component analysis considering the correlations among the above predictors. Third, a composite sinusoidal coefficient regression is developed considering the dynamic Ta-predictor relationship. This method was performed at 333 weather stations in China during 2001-2012. Evaluation shows overall mean error of −0.01 K, root mean square error (RMSE) of 2.53 K, correlation coefficient (R 2 ) of 0.96, and average uncertainty of 0.21 K. Model inter-comparison shows that this method outperforms six additional empirical regressions that have not incorporated nighttime light data or considered predictor independence or coefficient dynamics (by 0.18-2.60 K in RMSE and 0.00-0.15 in R 2 ).

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Section: The Seven Predictors and Complementary Predictorsmentioning

confidence: 99%

Section: Ndvi and Isamentioning

confidence: 99%

Enhanced Statistical Estimation of Air Temperature Incorporating Nighttime Light Data

et al. 2016

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“…The impact of estimated R s (R se ) on ecosystem models are mainly focused on the prediction of crop yields (cotton, maize, peanut, rice, etc.) around the world (Abraha and Savage, 2008;Garcia y Garcia et al, 2008;Phakamas et al, 2013;Trnka et al, 2007). However, fewer studies focused on the impact of R se on the calculation of GPP in forest ecosystems, although these systems play an important role in global terrestrial carbon cycle.…”

Section: Introductionmentioning

confidence: 99%

Impact of estimated solar radiation on gross primary productivity simulation in subtropical plantation in southeast China

et al. 2015

Solar Energy

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Sunshine duration is widely used to estimate solar radiation, but this estimated inherently contains some uncertainties, limiting its applications. This study investigated the impacts of the estimated solar radiation on simulated gross primary productivity (GPP), which were obtained using ecosystem models -light use efficiency model (LUE) and process-based model -Boreal Ecosystem Productivity Simulator (BEPS) at an evergreen coniferous forest ecosystem in southeast China. The models for solar radiation and diffuse radiation estimation were calibrated through observation data from nearby meteorological stations. The results showed that the established model could be successfully used to estimate solar radiation with high coefficient of determination (0.92) and low root mean square error (2.18 MJ m À2 day À1 ), but the solar radiation was overestimated when the clearness index was less than 0.15 and underestimated when it was within the range of 0.2-0.35 or greater than 0.6. The estimated solar radiation has significant influence on the diffuse radiation estimation and GPP simulation comparing with using observations. The two ecosystem models reacted differently to the errors of estimated solar radiation. For the LUE model, the estimated solar radiation led to the underestimated GPP in growing season (May-October), and overestimated GPP during non-growing season (November-April) with the bias ranged from À11% to 10% depending on the month of a year. For the BEPS model, estimated solar radiation resulted in overestimated GPP in most months with the bias ranged from À6% to 20%. The difference between the simulated GPP based on these two sources of solar radiation could be counteracted to some extent at the annual scale, especially for LUE model.

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“…Daily solar radiation incident (Rs) data are used in crop yield simulation (Bellocchi et al, 2003;Abraha and Savage, 2008;Mavromatis, 2008;Borges et al, 2010;Wang et al, 2015) and evapotranspiration requirements (Bandyopadhyay et al, 2008;Conceição, 2010;Carvalho et al, 2011;El Nesr et al, 2011). However, because of instrument cost and calibration requirements (Hossain et al 2014), Rs is not widely measured like commonly used air temperature and rainfall data (Weiss and Hays, 2004).…”

Section: Introductionmentioning

confidence: 99%

Temperature-based solar radiation models for use in simulated soybean potential yield

Silva¹,

Silva²,

Almorox³

et al. 2016

Aust J Crop Sci

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Solar radiation is the main meteorological element required for crop yield simulation. However, it is not widely measured as air temperature and rainfall. This study evaluated some temperature-based solar radiation models for estimation of daily solar radiation (Rs), and how the estimates may affect soybean yield potential. The evaluated models were Annandale (AN), Hargreaves (HA), Modified Hargreaves (HA-1), Hunt (HU), Bristow and Campbell (BC), Chen (CH), Donatelli and Campbell (DC) and De Jong and Stewart (JS). This research was carried out using historical data from six sites in the Triangulo Mineiro region, where measured values of Rs, minimum and maximum air temperature and rainfall were available. The dataset (2009)(2010)(2011)(2012)(2013)(2014) was separated into two sub-data sets, one for calibration (2009) and the other for evaluation of performance (2010)(2011)(2012)(2013)(2014). The Rs estimated data were used in SoySim software to estimate potential soybean yield. Statistical indexes: (a) root mean square error (RMSE), (b) relative root mean square error (RRMSE), (c) coefficient of determination (R 2 ) and (d) mean error (ME) were used as indicators of the agreement between observed and estimated Rs data. After evaluating the performance, Rs estimated values for each model were used to simulate the soybean potential yield. Although the eight models have presented similar performance for estimating Rs values, when these data were used for simulation of the potential soybean yield, the performances diverged considerably. In this way, only the BC, CH, DC and JS models showed satisfactory performance in yield simulation with R 2 and RRMSE varying from 0.76 to 0.80 and 3 to 4%, respectively. Therefore, the findings suggest that, before choosing the model to estimate Rs, it is important to define the purpose of use of solar radiation data.

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Comparison of estimates of daily solar radiation from air temperature range for application in crop simulations

Cited by 144 publications

References 33 publications

Enhanced Statistical Estimation of Air Temperature Incorporating Nighttime Light Data

Enhanced Statistical Estimation of Air Temperature Incorporating Nighttime Light Data

Impact of estimated solar radiation on gross primary productivity simulation in subtropical plantation in southeast China

Temperature-based solar radiation models for use in simulated soybean potential yield

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