Assessing crop coefficients of sunflower and canola using two-source energy balance and thermal radiometry

Sánchez, Juan Manuel; López-Urrea, R.; Rubio, E.; González-Piqueras, José; Caselles, Vicente

doi:10.1016/j.agwat.2014.02.002

Cited by 56 publications

(37 citation statements)

References 26 publications

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“…Values of T a , T c and T s are in˝K. Equation (9) follows the methodology indicated by [16] The soil and canopy contributions to total sensible heat were estimated using the following equations [44,45]:…”

Section: Rseb Algorithm Adapted For a Helicopter-based Uavmentioning

confidence: 99%

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Estimation of Energy Balance Components over a Drip-Irrigated Olive Orchard Using Thermal and Multispectral Cameras Placed on a Helicopter-Based Unmanned Aerial Vehicle (UAV)

et al. 2016

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A field experiment was carried out to implement a remote sensing energy balance (RSEB) algorithm for estimating the incoming solar radiation (Rsi), net radiation (Rn), sensible heat flux (H), soil heat flux (G) and latent heat flux (LE) over a drip-irrigated olive (cv. Arbequina) orchard located in the Pencahue Valley, Maule Region, Chile (35˝25 1 S; 71˝44 1 W; 90 m above sea level). For this study, a helicopter-based unmanned aerial vehicle (UAV) was equipped with multispectral and infrared thermal cameras to obtain simultaneously the normalized difference vegetation index (NDVI) and surface temperature (T surface ) at very high resolution (6 cmˆ6 cm). Meteorological variables and surface energy balance components were measured at the time of the UAV overpass (near solar noon). The performance of the RSEB algorithm was evaluated using measurements of H and LE obtained from an eddy correlation system. In addition, estimated values of Rsi and Rn were compared with ground-truth measurements from a four-way net radiometer while those of G were compared with soil heat flux based on flux plates. Results indicated that RSEB algorithm estimated LE and H with errors of 7% and 5%, respectively. Values of the root mean squared error (RMSE) and mean absolute error (MAE) for LE were 50 and 43 W m´2 while those for H were 56 and 46 W m´2, respectively. Finally, the RSEB algorithm computed Rsi, Rn and G with error less than 5% and with values of RMSE and MAE less than 38 W m´2. Results demonstrated that multispectral and thermal cameras placed on an UAV could provide an excellent tool to evaluate the intra-orchard spatial variability of Rn, G, H, LE, NDVI and T surface over the tree canopy and soil surface between rows.

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Section: Rseb Algorithm Adapted For a Helicopter-based Uavmentioning

confidence: 99%

“…The partitioning of instantaneous net radiation, which represented a mixture of canopy and soil was estimated as follows [43][44][45][46]:…”

Section: Rseb Algorithm Adapted For a Helicopter-based Uavmentioning

confidence: 99%

Estimation of Energy Balance Components over a Drip-Irrigated Olive Orchard Using Thermal and Multispectral Cameras Placed on a Helicopter-Based Unmanned Aerial Vehicle (UAV)

et al. 2016

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“…Moreover, it is a critical input parameter to crop monitoring systems that rely on agrometeorologic models [9,10]. Finally, R s is essential for diverse socioeconomic sectors, such as the production of energy from solar energy systems [2,[11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…Knowledge of spatiotemporal distributions of this parameter is essential for understanding numerous processes in the climate system, such the water cycle or plant photosynthesis [2]. In this regard, Rs is crucial in hydrology, playing a key role in the modelling of evapotranspiration (ET) as well as air temperature [3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Seasonal Characterization of Solar Radiation Estimates Obtained from a MSG-SEVIRI-Derived Dataset and a RAMS-Based Operational Forecasting System over the Western Mediterranean Coast

2016

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Solar radiation is a key factor in the Earth's energy balance and it is used as a crucial input parameter in many disciplines such as ecology, agriculture, solar energy and hydrology. Thus, accurate information of the global downward surface shortwave flux integration into the grid is of significant importance. From the different strategies used for grid integration of the surface solar radiation estimates, satellite-derived and numerical weather prediction forecasts are two interesting alternatives. In the current work, we present a comprehensive evaluation of the global downward solar radiation forecasts provided by the Regional Atmospheric Modeling System (RAMS) and the Downwelling Surface Shortwave Flux (DSSF) product, derived from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI). Both solar radiation estimates are compared to thirteen ground-based weather station measurements for the winter 2010-2011 and the summer 2011 seasons. For these periods, the most recent versions of RAMS (4.4 and 6.0) were running in parallel within the real-time weather forecasting system implemented over the Valencia Region. The solar radiation performance and accuracy are evaluated for these datasets segmented into two atmospheric conditions (clear and cloudy skies) and two terrain classes (flat and hilly). DSSF shows a very good agreement over the study area. Statistical daily evaluations show that corresponding errors vary between seasons, with absolute bias ranging from´30 to 40 W¨m´2, absolute root mean square errors (RMSE) from 25 to 60 W¨m´2, relative bias ranging from´11% to 7% and relative RMSE from 7% to 22%, depending on the sky condition and the terrain location as well, thus reproducing the observations more faithfully than RAMS, which produces higher errors in comparison to the measurements. In this regard, statistical daily evaluations show absolute bias values varying from´50 to 160 W¨m´2, absolute RMSE from 60 to 240 W¨m´2, relative bias ranging from´30% to 40% and relative RMSE from 10% to 80%, also depending on the daily initialization and the forecast horizon. This bias variability demonstrates that there is a different trend in the deviation of the model results in relation to the observations, both for the DSSF product and RAMS forecasts, and considering the summer and the winter seasons independently. In this regard, although there is an overestimation of the observed solar radiation within the summer months, this magnitude is underestimated during the winter. Finally, comparing this solar radiation estimates for different atmospheric conditions and different terrain classes, the best results are found under clear skies over flat terrain. This result is achieved using both methodologies.

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“…The second category involves separate estimation of ET c and ET 0 via direct measurement or modeling. Methods for estimating ET c include water balance assessments using weighing lysimeters [3][4][5][6][7] or soil water measurements [3,8], energy-conservation approaches using the Bowen ratio [9,10], eddy covariance techniques [11][12][13][14][15][16][17] or remote sensing retrievals (based on one-source [18] and two-source energy balance models) [19][20][21]. Methods for modeling ET 0 can be roughly divided into three categories [22,23], i.e., temperature models [24,25], radiation-temperature models [26,27] and combination models [1,28].…”

Section: Introductionmentioning

confidence: 99%

Temporal Downscaling of Crop Coefficients for Winter Wheat in the North China Plain: A Case Study at the Gucheng Agro-Meteorological Experimental Station

Wang

Qiu

Huo

et al. 2017

Water

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Abstract:The crop coefficient (K c ) is widely used for operational estimation of actual evapotranspiration (ET a ) and crop water requirements. The standard method for obtaining K c is via a lookup table from FAO-56 (Food and Agriculture Organization of the United Nations Irrigation and Drainage Paper No. 56), which broadly treats K c as a function of four crop-growing stages. However, the distinctive physiological characteristics of overwintering crops, such as winter wheat (Triticum aestivum L.), which is extensively planted in the North China Plain (NCP), are not addressed in this method. In this study, we propose a stage-wise method that accounts for K c variations for winter wheat at each critical phenological stage, thereby estimating K c at finer temporal scales. Compared with the conventional FAO method, the proposed stage-wise method successfully captures the bimodal pattern in K c time series for winter wheat, which is shown at both ten-day and phenological time scales. In addition, the accuracies of the proposed stage-wise K c method and the FAO method were evaluated using micro-meteorological measurements of ET a collected at the Gucheng agro-meteorological experimental station in the NCP. Using a leave-one-out strategy, the evaluation revealed that the stage-wise method significantly outperformed the FAO method at both daily and critical phenological time scales, with root-mean-square errors in ET a for the stage-wise method and the FAO method being 0.07 mm·day −1 and 0.16 mm·day −1 , respectively, at the daily time scale, and 0.01 mm·day −1 and 0.27 mm·day −1 at the critical phenological time scale. Generally, the FAO method underestimates ET a during the initial stage and overestimates ET a during both the development and mid-season stages. It is shown that the proposed stage-wise method is important for the water-stressed NCP where precision irrigation is highly desirable, especially during the critical phenological stages. Results from this study provide insight into accurate estimation of water requirements for winter wheat at phenological time scales.

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Assessing crop coefficients of sunflower and canola using two-source energy balance and thermal radiometry

Cited by 56 publications

References 26 publications

Estimation of Energy Balance Components over a Drip-Irrigated Olive Orchard Using Thermal and Multispectral Cameras Placed on a Helicopter-Based Unmanned Aerial Vehicle (UAV)

Estimation of Energy Balance Components over a Drip-Irrigated Olive Orchard Using Thermal and Multispectral Cameras Placed on a Helicopter-Based Unmanned Aerial Vehicle (UAV)

Seasonal Characterization of Solar Radiation Estimates Obtained from a MSG-SEVIRI-Derived Dataset and a RAMS-Based Operational Forecasting System over the Western Mediterranean Coast

Temporal Downscaling of Crop Coefficients for Winter Wheat in the North China Plain: A Case Study at the Gucheng Agro-Meteorological Experimental Station

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