Search

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. [ABSTRACT FROM AUTHOR]

The Zoige wetland located in the eastern margin of the Tibet Plateau, is very important for the runoff regulation and biodiversity in the Yellow River basin. Due to the large non-uniformity of the alpine wetland surface, the use of higher spatial resolution satellite remote sensing data is essential to match or validate the results of ground observation. The land evapotranspiration in this survey was estimated by using the GF-4 satellite data from 2016 to 2020 into the Two Source Energy Balance (TSEB) model, and the estimated evapotranspiration is compared to the ground observation. The results show that: (1) the regression analysis of the actual observed evapotranspiration and the evapotranspiration estimated by the TSEB model shows that the actual evapotranspiration and the evapotranspiration calculated by the TSEB model have a good positive linear correlation (R2 > 0.8) at the Maqu observation site and the Huahu wetland observation site. Therefore, the accuracy of the evapotranspiration calculated by the TSEB model is high. (2) The spatio-temporal distribution of evapotranspiration and relevant land surface variables is generally characterized by high in the southeast and low in the northwest over the Zoige wetland basin. (3) The mean daily evapotranspiration for different GF-4 observation over the Zoige area is as follows: 1.8 mm on 8 April 2018, 3.6 mm on 20 July 2016, 1.4 mm on 29 November 2020 and 0.9 mm on 15 December 2017, and the magnitudes of evapotranspiration of the different surface are as follows: waterbody > grassland > bare land. (4) The histogram of the frequency distribution of evapotranspiration show that 3.2–4.0 mm on 20 July 2016, 0–0.8 mm on 15 December 2017, 0.8–1.6 mm on 8 April 2018 and 1.6–2.4 mm on 29 November 2020 are the maximum evapotranspiration range over the Zoige wetlands area. [ABSTRACT FROM AUTHOR]

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.

Evapotranspiration (ET) plays an important role in water and energy exchange between the atmosphere and the land surface. ET at regional scale is of great importance in hydrology, meteorology, and agriculture. The remotely sensed land surface temperature (LST)‐based two‐source energy balance (TSEB) model is widely used to monitor ET. However, due to the influence of clouds, the quality of remotely sensed LST products is declined and there are also many gaps in the LST products, which make it very difficult to estimate spatio‐temporally continuous ET based on remote sensing data. In this study, we proposed a method to generate hourly continuous ET using Fengyun‐2F geostationary images. First, the hourly continuous real LST are obtained using a clear‐sky LST reconstruction algorithm and radiometric correction algorithm, and then the reconstructed real LST is used to the estimation of latent heat flux using the TSEB model. The results in the Heihe River Basin show that the proposed method can successfully generate hourly continuous ET compared with three eddy covariance sites with different underlying surface. The estimated ET achieves acceptable performance with RMSE = 81.88 W/m2 and R2 = 0.67 at hourly scale and with RMSE = 1.48 mm/d and R2 = 0.65 at daily scale. We believe this can help the community to better understand the water and energy exchange between the atmosphere and the land surface at fine spatio‐temporal scale, especially on the hourly scale. Key Points: Fengyun‐2F (FY‐2F) real land surface temperature (LST) was reconstructed under cloudy conditionSpatio‐temporally continuous hourly Evapotranspiration (ET) was obtained by the two‐source energy balance ET model derived by reconstructed FY‐2F real LSTSimulated ET is comparable with the observations at hourly and daily scales [ABSTRACT FROM AUTHOR]

One of the objectives of many studies conducted by breeding programs is to characterize and select rootstocks well-adapted to drought conditions. In recent years, field high-throughput phenotyping methods have been developed to characterize plant traits and to identify the most water use efficient varieties and rootstocks. However, none of these studies have been able to quantify the behavior of crop evapotranspiration in almond rootstocks under different water regimes. In this study, remote sensing phenotyping methods were used to assess the evapotranspiration of almond cv. “Marinada” grafted onto a rootstock collection. In particular, the two-source energy balance and Shuttleworth and Wallace models were used to, respectively, estimate the actual and potential evapotranspiration of almonds grafted onto 10 rootstock under three different irrigation treatments. For this purpose, three flights were conducted during the 2018 and 2019 growing seasons with an aircraft equipped with a thermal and multispectral camera. Stem water potential (Ψstem) was also measured concomitant to image acquisition. Biophysical traits of the vegetation were firstly assessed through photogrammetry techniques, spectral vegetation indices and the radiative transfer model PROSAIL. The estimates of canopy height, leaf area index and daily fraction of intercepted radiation had root mean square errors of 0.57 m, 0.24 m m–1 and 0.07%, respectively. Findings of this study showed significant differences between rootstocks in all of the evaluated parameters. Cadaman® and Garnem® had the highest canopy vigor traits, evapotranspiration, Ψstem and kernel yield. In contrast, Rootpac® 20 and Rootpac® R had the lowest values of the same parameters, suggesting that this was due to an incompatibility between plum-almond species or to a lower water absorption capability of the rooting system. Among the rootstocks with medium canopy vigor, Adesoto and IRTA 1 had a lower evapotranspiration than Rootpac® 40 and Ishtara®. Water productivity (WP) (kg kernel/mm water evapotranspired) tended to decrease with Ψstem, mainly in 2018. Cadaman® and Garnem® had the highest WP, followed by INRA GF-677, IRTA 1, IRTA 2, and Rootpac® 40. Despite the low Ψstem of Rootpac® R, the WP of this rootstock was also high.

One of the objectives of many studies conducted by breeding programs is to characterize and select rootstocks well-adapted to drought conditions. In recent years, field high-throughput phenotyping methods have been developed to characterize plant traits and to identify the most water use efficient varieties and rootstocks. However, none of these studies have been able to quantify the behavior of crop evapotranspiration in almond rootstocks under different water regimes. In this study, remote sensing phenotyping methods were used to assess the evapotranspiration of almond cv. "Marinada" grafted onto a rootstock collection. In particular, the two-source energy balance and Shuttleworth and Wallace models were used to, respectively, estimate the actual and potential evapotranspiration of almonds grafted onto 10 rootstock under three different irrigation treatments. For this purpose, three flights were conducted during the 2018 and 2019 growing seasons with an aircraft equipped with a thermal and multispectral camera. Stem water potential (Ψ s t e m ) was also measured concomitant to image acquisition. Biophysical traits of the vegetation were firstly assessed through photogrammetry techniques, spectral vegetation indices and the radiative transfer model PROSAIL. The estimates of canopy height, leaf area index and daily fraction of intercepted radiation had root mean square errors of 0.57 m, 0.24 m m–1 and 0.07%, respectively. Findings of this study showed significant differences between rootstocks in all of the evaluated parameters. Cadaman® and Garnem® had the highest canopy vigor traits, evapotranspiration, Ψ s t e m and kernel yield. In contrast, Rootpac® 20 and Rootpac® R had the lowest values of the same parameters, suggesting that this was due to an incompatibility between plum-almond species or to a lower water absorption capability of the rooting system. Among the rootstocks with medium canopy vigor, Adesoto and IRTA 1 had a lower evapotranspiration than Rootpac® 40 and Ishtara®. Water productivity (WP) (kg kernel/mm water evapotranspired) tended to decrease with Ψ s t e m , mainly in 2018. Cadaman® and Garnem® had the highest WP, followed by INRA GF-677, IRTA 1, IRTA 2, and Rootpac® 40. Despite the low Ψ s t e m of Rootpac® R, the WP of this rootstock was also high. [ABSTRACT FROM AUTHOR]

Estimates of turbulent fluxes (i.e., sensible and latent heat fluxes H and LE) over heterogeneous surfaces is not an easy task. The heterogeneity caused by the contrast in vegetation, hydric and soil conditions can generate a large spatial variability in terms of surface–atmosphere interactions. This study considered the issue of using a thermal-based two-source energy model (TSEB) driven by MODIS (Moderate resolution Imaging Spectroradiometer) and MSG (Meteosat Second Generation) observations in conjunction with an aggregation scheme to derive area-averaged H and LE over a heterogeneous watershed in Niamey, Niger (Wankama catchment). Data collected in the context of the African Monsoon Multidisciplinary Analysis (AMMA) program, including a scintillometry campaign, were used to test the proposed approach. The model predictions of area-averaged turbulent fluxes were compared to data acquired by a Large Aperture Scintillometer (LAS) set up over a transect about 3.2 km-long and spanning three vegetation types (millet, fallow and degraded shrubs). First, H and LE fluxes were estimated at the MSG-SEVIRI grid scale by neglecting explicitly the subpixel heterogeneity. Moreover, the impact of upscaling the model’s inputs was investigated using in-situ input data and three aggregation schemes of increasing complexity based on MODIS products: a simple averaging of inputs at the MODIS resolution scale, another simple averaging scheme that considers scintillometer footprint extent, and the weighted average of inputs based on the footprint weighting function. The H and LE simulated using the footprint weighted method were more accurate than for the two other aggregation rules despite the heterogeneity of the landscape. The statistical values are: correlation coefficient (R) = 0.71, root mean square error (RMSE) = 63 W/m2 and mean bias error (MBE) = −23 W/m2 for H and an R = 0.82, RMSE = 88 W/m2 and MBE = 45 W/m2 for LE. This study opens perspectives for the monitoring of convective and evaporative fluxes over heterogeneous landscape based on medium resolution satellite products.

Understanding the spatial variability in highly heterogeneous natural environments such as savannas and river corridors is an important issue in characterizing and modeling energy fluxes, particularly for evapotranspiration (ET) estimates. Currently, remote-sensing-based surface energy balance (SEB) models are applied widely and routinely in agricultural settings to obtain ET information on an operational basis for use in water resources management. However, the application of these models in natural environments is challenging due to spatial heterogeneity in vegetation cover and complexity in the number of vegetation species existing within a biome. In this research effort, small unmanned aerial systems (sUAS) data were used to study the influence of land surface spatial heterogeneity on the modeling of ET using the Two-Source Energy Balance (TSEB) model. The study area is the San Rafael River corridor in Utah, which is a part of the Upper Colorado River Basin that is characterized by arid conditions and variations in soil moisture status and the type and height of vegetation. First, a spatial variability analysis was performed using a discrete wavelet transform (DWT) to identify a representative spatial resolution/model grid size for adequately solving energy balance components to derive ET. The results indicated a maximum wavelet energy between 6.4 m and 12.8 m for the river corridor area, while the non-river corridor area, which is characterized by different surface types and random vegetation, does not show a peak value. Next, to evaluate the effect of spatial resolution on latent heat flux (LE) estimation using the TSEB model, spatial scales of 6 m and 15 m instead of 6.4 m and 12.8 m, respectively, were used to simplify the derivation of model inputs. The results indicated small differences in the LE values between 6 m and 15 m resolutions, with a slight decrease in detail at 15 m due to losses in spatial variability. Lastly, the instantaneous (hourly) LE was extrapolated/upscaled to daily ET values using the incoming solar radiation (Rs) method. The results indicated that willow and cottonwood have the highest ET rates, followed by grass/shrubs and treated tamarisk. Although most of the treated tamarisk vegetation is in dead/dry condition, the green vegetation growing underneath resulted in a magnitude value of ET. [ABSTRACT FROM AUTHOR]

The accurate quantification of surface heat and water vapor fluxes is significantly essential for understanding water balance dynamics. In this study, 15-m spatial resolution turbulent fluxes (H and LE) in the Zhangye oasis situated the middle reaches of the Heihe River Basin (HRB) were estimated by the remote sensing-based two-source energy balance model (TSEB). The TSEB model uses temperature including land surface temperature (LST) and air temperature (T a) as the main input variable to compute turbulent fluxes but their spatial resolution is rather limited. To overcome this shortcoming, the 15-m spatial resolution LST and T a were obtained by using the back-propagation neural network (BPNN). The results indicated that the BPNN was able to obtain finer spatial resolution and LST and T a ; the root mean square error (RMSE) values of LST and T a are 1.99 K and 0.50 K, respectively. The remotely sensed H and LE predicted by TSEB model utilizing the LST and T a modeled by BPNN. The results showed that H and LE agreed well with the flux observations from multi-set eddy covariance (EC) systems installed at a number of sites and covering all representative land cover types; particularly for the latent heat flux, its estimates produced mean absolute percent errors (MAPE) of 8.76% for maize, 20.17% for vegetable, 29.06% for residential area, and 16.12% for orchard. This study obtained surface heat and water vapor fluxes at finer spatial resolution than the other flux estimates from the remote sensing models that have been used in the Zhangye oasis. The results produced by combining the TSEB model and BPNN can provide more information for drafting reliable sustainable water resource management schemes and improving the irrigation water use efficiency in arid and semi-arid regions. Unlabelled Image • A finer spatial resolution surface heat and water vapor fluxes have been obtained. • H and LE from TSEB is in good agreement with measurements. • The downscaled LST based on BPNN have a consistency with observation. • BPNN is able to obtain high accuracy and finer spatial resolution Ta. • It is an effective method to obtain regional H and LE combining TSEB and BPNN. [ABSTRACT FROM AUTHOR]

The retrieval of canopy and soil component temperatures for estimating evapotranspiration in the two source energy balance (TSEB) model depends on a relatively accurate partitioning of soil/substrate evaporation and canopy transpiration along with the soil and vegetation temperature components. To avoid the need for a Priestley-Taylor based transpiration formulation, this study applies the TSEB model using radiometric land surface temperature observations at multiple view angles from an airborne sensor for estimating soil and canopy temperatures directly. This direct partitioning between soil and canopy temperatures applied with the TSEB formulation improved the agreement between observed and modeled surface heat fluxes, reducing mean absolute percentage error (MAPE) in latent heat fluxes (LE) with flux tower observations from nearly 20% using the original Priestley-Taylor based TSEB model (TSEB-PT) to 15% using TSEB with thermal infrared observations from two substantially different view angles (TSEB-2AG) to nearly 5% using multiple (~6) view angles (TSEB-6AG). Moreover, TSEB-6AG is shown to compute physically realistic spatially-distributed LE for a range of vegetation cover and environmental conditions over the imaged domain. Values of MAPE for sensible heat (H) tended to be larger for all three models due to the fact that tower measurements tended to be located in well irrigated and densely vegetated sites having relatively low H values. This increased accuracy of soil and vegetation component temperature separation using multiangle radiometric temperature observations is useful for evaluating the utility of single and dual view angle thermal radiometer measurements currently available for applying the TSEB model. • A multiangle component temperature partition approach has been integrated into TSEB model. • The TSEB model can make use of airborne multiangle observations. • This multi-angle approach has low sensitivity in soil and vegetation separation. [ABSTRACT FROM AUTHOR]