Daily evapotranspiration estimates from extrapolating instantaneous airborne remote sensing ET values

In this study, six extrapolation methods have been compared for their ability to estimate daily crop evapotranspiration (ET_d) from instantaneous latent heat flux estimates derived from digital airborne multispectral remote sensing imagery. Data used in this study were collected during an experiment on corn and soybean fields, covering an area of approximately 12 × 22 km, near Ames, Iowa. ET_d estimation errors for all six methods and both crops varied from −5.7 ± 4.8% (MBE ± RMSE) to 26.0 ± 15.8%. Extrapolated ET_d values based on the evaporative fraction (EF) method better compared to eddy covariance measured ET values. This method reported an average corn ET_d estimate error of −0.3 mm day⁻¹, with a corresponding error standard deviation of 0.2 mm day⁻¹, i.e., about 5.7 ± 4.8% average under prediction when compared to average ET_d values derived from eddy covariance energy balance systems. A solar radiation-based ET extrapolation method performed relatively well with ET_d estimation error of 2.2 ± 10.1% for both crops. An alfalfa reference ET-based extrapolation fraction method (ET_rF) yielded an overall ET_d overestimation of about 4.0 ± 10.0% for both crops. It is recommended that the average daily soil heat flux not be neglected in the calculation of ET_d when utilizing method EF. These results validate the use of the airborne multispectral RS-based ET methodology for the estimation of instantaneous ET and its extrapolation to ET_d. In addition, all methods need to be further tested under a variety of vegetation surface homogeneity, crop growth stage, environmental and climatological conditions.

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Multi-Year Measurements of Field-Scale Metolachlor Volatilization

Volatilization is a critical pathway for herbicide loss from agricultural fields, and subsequently deposited downwind from the edge of the field. To better understand the volatilization process, field-scale turbulent volatilization fluxes of metolachlor (2-chloro-N-(2-ethyl-6-methylphenyl)-N-(2-methoxy-1-methylethyl) acetamide) were quantified for 13 consecutive years using a combination of herbicide concentration profiles and eddy diffusivities derived from turbulent fluxes of heat and water vapor. Site location, type of herbicides, and agricultural management practices remained unchanged during this study in order to evaluate the effect of soil moisture on metolachlor volatilization. Twenty gravimetric surface soil moisture samples (0–5 cm) were collected immediately after herbicide application and then at 0430 hours each morning to determine the impact of surface moisture on herbicide volatilization. Five days after application, cumulative herbicide volatilization ranged from 5 to 63% of that applied for metolachlor. Metolachlor volatilization remained an important loss process more than 5 days after application when the soil surface was moist. Conversely, if the soil surface was dry, negligible volatilization occurred beyond 5 days. Furthermore, the total amount of metolachlor volatilized into the atmosphere increased exponentially with surface soil water content during application (r ²?=?0.78). Metolachlor volatility was found to be governed largely by surface soil moisture.     

A comparison of operational remote sensing-based models for estimating crop evapotranspiration

The integration of remotely sensed data into models of evapotranspiration (ET) facilitates the estimation of water consumption across agricultural regions. To estimate regional ET, two basic types of remote sensing approaches have been successfully applied. The first approach computes a surface energy balance using the radiometric surface temperature for estimating the sensible heat flux (H), and obtaining ET as a residual of the energy balance. This paper compares the performance of three different surface energy balance algorithms: an empirical one-source energy balance model; a one-source model calibrated using inverse modeling of ET extremes (namely ET = 0 and ET at potential) which are assumed to exist within the satellite scene; and a two-source (soil + vegetation) energy balance model. The second approach uses vegetation indices derived from canopy reflectance data to estimate basal crop coefficients that can be used to convert reference ET to actual crop ET. This approach requires local meteorological and soil data to maintain a water balance in the root zone of the crop. Output from these models was compared to sensible and latent heat fluxes measured during the soil moisture–atmosphere coupling experiment (SMACEX) conducted over rain-fed corn and soybean crops in central Iowa. The root mean square differences (RMSD) of the estimation of instantaneous latent and heat fluxes were less than 50 W m⁻² for the three energy balance models. The two-source energy balance model gave the lowest RMSD (30 W m⁻²) and highest r² values in comparison with measured fluxes. In addition, three schemes were applied for upscaling instantaneous flux estimates from the energy balance models (at the time of satellite overpass) to daily integrated ET, including conservation of evaporative fraction and fraction of reference ET. For all energy balance models, an adjusted evaporative fraction approach produced the lowest RMSDs in daily ET of 0.4–0.6 mm d⁻¹. The reflectance-based crop coefficient model yielded RMSD values of 0.4 mm d⁻¹, but tended to significantly overestimate ET from corn during a prolonged drydown period. Crop stress can be directly detected using radiometric surface temperature, but ET modeling approaches-based solely on vegetation indices will not be sensitive to stress until there is actual reduction in biomass or changes in canopy geometry.     

Inconsistencies in net radiation estimates from use of several models of instruments in a desert environment

Studies of surface energy and water balance generally require an accurate estimate of net radiation and its spatial distribution. A project quantifying both short term and seasonal water use of shrub and grass vegetation in the Jornada Experimental Range in New Mexico prompted a study to compare net radiation observations using two types of net radiometers currently being used in research. A set of 12 REBS net radiometers were compared with each other and one Swissteco, over wet and dry surfaces in an arid landscape under clear skies. The set of REBS exhibited significant differences in output over both surfaces. However, they could be cross calibrated to yield values within 10 W m⁻², on average. There was also a significant bias between the REBS and Swissteco over a dry surface, but not over a wet one. The two makes of instrument could be made to agree under the dry conditions by using regression or autoregression techniques. However, the resulting equations would induce bias for the wet surface condition. Thus, it is not possible to cross calibrate these two makes of radiometer over the range of environmental conditions observed. This result indicates that determination of spatial distribution of net radiation over a variable surface should be made with identical instruments which have been cross calibrated. The need still exists for development of a radiometer and calibration procedures which will produce accurate and consistent measurements over a range of surface conditions.     

A reexamination of the crop water stress index

Summary Hand-held infrared radiometers, developed during the past decade, have extended the measurement of plant canopy temperatures from individual leaves to entire plant canopies. Canopy temperatures are determined by the water status of the plants and by ambient meteorological conditions. The crop water stress index (CWSI) combines these factors and yields a measure of plant water stress. Two forms of the index have been proposed, an empirical approach as reported by Idso et al. (1981), and a theoretical approach reported by Jackson et al. (1981). Because it is simple and requires only three variables to be measured, the empirical approach has received much attention in the literature. It has, however received some criticism concerning its inability to account for temperature changes due to radiation and windspeed. The theoretical method is more complicated in that it requires these two additional variables to be measured, and the evaluation of an aerodynamic resistance, but it will account for differences in radiation and windspeed. This report reexamines the theoretical approach and proposes a method for estimating an aerodynamic resistance applicable to a plant canopy. A brief history of plant temperature measurements is given and the theoretical basis for the CWSI reviewed.     

Evaluating unoccupied aerial systems (UAS) imagery as an alternative tool towards cotton-based management zones

Unoccupied aerial system (UAS) imagery may serve as an additional tool towards management zone delineation. This is because UAS data collection is relatively flexible. However, it is unclear how useful UASs can be towards generating management zones, relative to preexisting tools (e.g. apparent soil electrical conductivity or EC_a). The purpose of this study, therefore, was to evaluate UAS imagery, relative to EC_a, in terms of their ability to: 1) predict cotton traits (i.e. height, seed cotton yield), and 2) define cotton management zones based on these traits. Single-season UAS images from multispectral/thermal sensors were collected and processed into Normalized Difference Vegetation Index (NDVI) and radiometric surface temperature (T_r), respectively. Management zones were also delineated using digital camera (RGB) imagery collected at periods before planting and near harvest. RGB management zones were delineated by a novel open boll mapping approach. In-season NDVI and T_r layers were significant (P?<?0.01) predictors of canopy height. Additionally, NDVI and T_r maps produced statistically different management zones during flowering and boll filling growth stages in terms of yield (P?=?0.001 or less). Open boll layers were all more accurate predictors of cotton seed yield than EC_a data—these two layers also produced statistically distinct management zones. ANOVA tests revealed that, given EC_a alone, adding UAS information via the RGB open boll map resulted in a significantly different yield prediction model (P?<?0.001). These results suggest that UAS imagery can offer valuable information for cotton management zone delineation that other techniques cannot.

     

Estimating energy balance fluxes above a boreal forest from radiometric temperature observations

The great areal extent of boreal forests confers these ecosystems potential to impact on the global surface-atmosphere energy exchange. A modelling approach, based on a simplified two-source energy balance model, was proposed to estimate energy balance fluxes above boreal forests using thermal infrared measurements. Half-hourly data from the Solar-Induced Fluorescence Experiment, carried out in a Finnish boreal forest, was used to evaluate the performance of the model. Energy balance closure, determined by linear regression, found all fluxes to underestimate available energy by 9% (r² = 0.94). Significance in the energy balance of the heat storage in the air and in the soil terms was also analyzed. Canopy temperatures, measured by a CIMEL Electronique CE 312 radiometer, together with ancillary meteorological variables and vegetation characteristics, were used to run the model. Comparison with ground measurements showed errors lower than ±15 W m^?2 for the retrieval of net radiation, soil heat flux and storage heat flux, and about ±50 W m^?2 for the sensible and latent heat fluxes. A sensitivity analysis of the approach to typical operational uncertainties in the required inputs was conducted showing the necessity of accurate measurements of the target radiometric surface temperature.