This paper reviews methods for estimating evaporation from landscapes, regions and larger geographic extents, with remotely sensed surface temperatures, and highlights uncertainties and limitations associated with those estimation methods. Particular attention is given to the validation of such approaches against ground based flux measurements. An assessment of some 30 published validations shows an average root mean squared error value of about 50 W m−2 and relative errors of 15–30%. The comparison also shows that more complex physical and analytical methods are not necessarily more accurate than empirical and statistical approaches. While some of the methods were developed for specific land covers (e.g. irrigation areas only) we also review methods developed for other disciplines, such as hydrology and meteorology, where continuous estimates in space and in time are needed, thereby focusing on physical and analytical methods as empirical methods are usually limited by in situ training data. This review also provides a discussion of temporal and spatial scaling issues associated with the use of thermal remote sensing for estimating evaporation. Improved temporal scaling procedures are required to extrapolate instantaneous estimates to daily and longer time periods and gap-filling procedures are needed when temporal scaling is affected by intermittent satellite coverage. It is also noted that analysis of multi-resolution data from different satellite/sensor systems (i.e. data fusion) will assist in the development of spatial scaling and aggregation approaches, and that several biological processes need to be better characterized in many current land surface models.

Estimating land surface evaporation: a review of methods using remotely sensed surface temperature data | NOVA. The University of Newcastle's Digital Repository

Measuring sustainability in cotton farming systems: towards a guidance framework.

To properly manage subsurface drip irrigation (SDI), and increase the efficiency of the water use while reducing water losses due to evaporation, the precise distribution of water around the emitters must be known. In this paper, we’ll evaluate how different irrigation depths applied with SDI affected the redistribution of soil moisture in the semiarid climate of Tunisia. Data shows that with suitable management, SDI at 35cm depth can achieve higher efficiency rates with limited water to maximize yields. The objective of this work was to evaluate soil moisture distribution bef ore and after irrigation in an experiment carried out in the Higher Institute of Agronomy of Chott Meriem under subsurface drip irrigation. The results show that soil moisture is relatively more stable for subsurface drip irrigation buried at 35 cm (T3) than those buried at 5 (T1) and 20cm (T2) with a slight difference except of water ’s contributions. There was greater increase in volumetric soil water content for T3 than for T1 and T2 with statistically significant increases .

Effect of subsurface drip irrigation system depth on soil water content distribution at different depths and different times after irrigation

883 Leaf area index is a fundamental plant physiological variable, but is one of the most challenging to measure or estimate (Bréda, 2003). For most applications involving broadleaf plants (i.e., crops), LAI is defi ned as the one-sided leaf tissue area per unit ground area (Watson, 1947). Leaf area index interacts with practically every aspect of the mass and energy balance of vegetated surfaces. Th erefore, LAI has direct bearing on the radiation balance, net primary production, evapotranspiration (ET), and carbon and other gas exchange (Norman and Campbell, 1989). It follows that the quality of LAI measurements or estimates will impact any model involving the mass or energy balance of vegetated surfaces. Ground-based or in situ methods to quantify LAI have been broadly described as direct, semi-direct, or indirect (Chen et al., 1997; Bréda, 2003; Jonckheere et al., 2004; Weiss et al., 2004). Direct and semi-direct methods include destructive plant harvesting and nondestructive methods, such as litter collection and allometric methods. Destructive plant harvesting is the most direct and usually accurate method, but it is limited to smaller plants (i.e., crops, pasture, shrubs) and limited areas (i.e., a few point samples in a fi eld due to its destructive nature and substantial labor requirements). Litter collection, while nondestructive, is limited to deciduous species and requires careful siting and maintenance of litter traps. Allometric methods are also nondestructive, have been successfully used for many vegetation types, and can be applied to larger areas. However, allometric methods are based on empirical relationships with some other (more easily measured) morphometric variable(s), such as leaf size, leaf shape, dry matter, or diameter at breast height, and hence may be siteand season-specifi c (Bréda, 2003). In contrast with direct and semi-direct methods, indirect methods usually entail non-contact approaches, such as irradiance extinction or optical analysis (e.g., hemispherical photography), and infer LAI based Allometric Method to Estimate Leaf Area Index for Row Crops

Allometric Method to Estimate Leaf Area Index for Row Crops

Estimating preseason irrigation losses by characterizing evaporation of effective precipitation under bare soil conditions using large weighing lysimeters

Plant canopy temperature and multi-spectral reflectance are measured with a wireless multi-band sensor, and the temperature data are qualified and the spectral reflectance measurements are classified. The multi-band sensor includes sensors for measuring plant canopy temperature radiation and spectral reflectance over five bands, a microprocessor to receive and store measured data, and a wireless transmitter for transmitting data from the microprocessor to a remote receiver, all enclosed within a single housing. The data are used to detect variations in spectral signature due to plant stress (e.g., disease, water stress) and due to soil background and to qualify temperature data accordingly. The data provide information for decision support algorithms related to the initiation of automatic irrigation scheduling as a function of crop canopy cover, qualification of temperature data used in automatic irrigation scheduling algorithms, and detection of diseased crops for the purpose of withholding irrigations when yield potential is compromised.

Multi-band photodiode sensor

Crop responses to MESA (mid-elevation spray application), LESA (low-elevation spray applicator), LEPA, (low energy precision application), and SDI (subsurface drip irrigation) were compared for full and deficit irrigation rates in the Texas Panhandle. Crops included three seasons of grain sorghum and one season of cotton; crop responses consisted of economic yield, seasonal water use, and water use efficiency (WUE). Irrigation rates were I0, I25, I50, I75, and I100 (where the subscript denotes the percentage of full irrigation, and I0 is dryland). Yield and WUE was greatest for SDI and least for spray at the I25 and I50 rates, and greatest for spray at the I100 rate. Yield and WUE trends were not consistent at the I75 rate. Seasonal water use was not significantly different in most cases between irrigation methods within a given irrigation rate. For cotton, the irrigation method did not influence boll maturity rates, but SDI resulted in higher fiber quality at the I25, I50, and I100 rates.

Comparison of spray, lepa, and sdi for cotton and grain sorghum in the texas panhandle 1

It is known that irrigation application method can impact crop water use and water use efficiency, but the mechanisms involved are incompletely understood, particularly in terms of the water and energy balances during the growing season from pre‐irrigation through planting, early growth and yield development stages. Grain corn (Zea mays L.) and sorghum (Sorghum bicolor L. Moench) were grown on four large weighing lysimeters at Bushland, Texas in 2013 (corn) and 2014 (sorghum). Two of the lysimeters and surrounding fields were irrigated by subsurface drip irrigation (SDI) and the other two were irrigated by mid elevation spray application (MESA). Crop evapotranspiration (ETc) was measured using the weighing lysimeters and soil water content was measured using the neutron probe and electromagnetic sensors. Periodic measurements of plant height, width, leaf area index and biomass were made, and final biomass and yield were measured. Micrometeorological measurements included incoming and outgoing short and long wave radiation, soil heat flux, precipitation, air temperature and humidity and wind speed. Irrigation amounts were metered. Compared with MESA irrigation, using SDI saved from 2.5 to 2.2 inches of water that was lost to evaporation early in the season (pre‐plant to 25 days after planting) in 2013 and 2014, respectively. While sorghum, particularly short season sorghum, is not a crop ordinarily considered for SDI, it was grown successfully using SDI with yields averaging 120 bu/acre, comparable to others reported for short season sorghum at Bushland. In the relatively dry 2013 season, SDI increased corn yields by 35 bu/acre (20%) compared with MESA irrigation, while reducing overall corn water use by 3.6 inches.

https://www.k-state.edu/irrigate/oow/p15/Evett_15R.pdf

Corn and Sorghum Performance As Affected by Irrigation Application Method: SDI versus Mid-elevation Spray Irrigation

Application and validation of many thermal remote sensing-based energy balance models involve the use of local meteorological inputs of incoming solar radiation, wind speed and air temperature as well as accurate land surface temperature (LST), vegetation cover and surface flux measurements. For operational applications at large scales, such local information is not routinely available. In addition, the uncertainty in LST estimates can be several degrees due to sensor calibration issues, atmospheric effects and spatial variations in surface emissivity. Time differencing techniques using multi-temporal thermal remote sensing observations have been developed to reduce errors associated with deriving the surface-air temperature gradient, particularly in complex landscapes. The Dual-Temperature-Difference (DTD) method addresses these issues by utilizing the Two-Source Energy Balance (TSEB) model of Norman et al. (1995) [1], and is a relatively simple scheme requiring meteorological input from standard synoptic weather station networks or mesoscale modeling. A comparison of the TSEB and DTD schemes is performed using LST and flux observations from eddy covariance (EC) flux towers and large weighing lysimeters (LYs) in irrigated cotton fields collected during BEAREX08, a large-scale field experiment conducted in the semi-arid climate of the Texas High Plains as described by Evett et al. (2012) [2]. Model output of the energy fluxes (i.e., net radiation, soil heat flux, sensible and latent heat flux) generated with DTD and TSEB using local and remote meteorological observations are compared with EC and LY observations. The DTDmethod is found to be significantly more robust in flux estimation compared to the TSEB using the remote meteorological observations. However, discrepancies between model and measured fluxes are also found to be significantly affected by the local inputs of LST and vegetation cover and the representativeness of the remote sensing observations with the local flux measurement footprint. Published by Elsevier Ltd.

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Abolishing activity against ascorbate in a cytosolic ascorbate peroxidasefrom switchgrass

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Multi-band photodiode sensor

Comparison of spray, lepa, and sdi for cotton and grain sorghum in the texas panhandle 1

Corn and Sorghum Performance As Affected by Irrigation Application Method: SDI versus Mid-elevation Spray Irrigation

Abolishing activity against ascorbate in a cytosolic ascorbate peroxidasefrom switchgrass