Field-scale Evapotranspiration Research Articles

Evaluation of Partitioned Evaporation and Transpiration Estimates within the DisALEXI Modeling Framework over Irrigated Crops in California

Accurate characterization of evapotranspiration (ET) is imperative in water-limited cropping systems such as California vineyards and almond orchards. Satellite-based ET modeling techniques, including the atmosphere–land exchange inverse model (ALEXI) and associated flux disaggregation technique (DisALEXI), have proven reliable in determining field scale ET. However, validation efforts typically focus on ET and omit an evaluation of partitioned evaporation (E) and transpiration (T). ALEXI/DisALEXI is based on the two-source energy balance (TSEB) model, making it uniquely qualified to derive E and T individually. The current study evaluated E and T estimates derived using two formulations of DisALEXI; one based on Priestley-Taylor (DisALEXI-PT) and the other on Penman-Monteith (DisALEXI-PM). The modeled values were validated against partitioned fluxes derived from the conditional eddy covariance (CEC) approach using EC flux towers in three wine grape vineyards and three almond orchards for the year 2021. Modeled estimates were derived using Landsat 8 Collection 2 thermal infrared and surface reflectance imagery as well as Harmonized Landsat and Sentinel-2 surface reflectance datasets as input into DisALEXI. The results indicated that the modeled total ET fluxes were similar between the two methods, but the partitioned values diverged, with DisALEXI-PT overestimating E and slightly underestimating T when compared to CEC estimates. Conversely, DisALEXI-PM agreed better with CEC-derived E and overestimated T estimates under non-advective conditions. Compared to one another, DisALEXI-PM estimated canopy temperatures ~5 °C cooler and soil temperatures ~5 °C warmer than DisALEXI-PT, causing differences in E and T of −2.6 mm day−1 and +2.6 mm day−1, respectively. The evaluation of the iterative process required for DisALEXI indicates DisALEXI-PM ET values converge on ALEXI ET with proportionate adjustments to E and T, while DisALEXI-PT convergence is driven by adjustments to E. The analysis presented here can potentially drive improvements in the modeling framework to provide specific soil and canopy consumptive water use information in unique canopy structures, allowing for improved irrigation and water use efficiencies in these water-limited systems.

Improved Daily Evapotranspiration Estimation Using Remotely Sensed Data in a Data Fusion System

Evapotranspiration (ET) represents crop water use and is a key indicator of crop health. Accurate estimation of ET is critical for agricultural irrigation and water resource management. ET retrieval using energy balance methods with remotely sensed thermal infrared data as the key input has been widely applied for irrigation scheduling, yield prediction, drought monitoring and so on. However, limitations on the spatial and temporal resolution of available thermal satellite data combined with the effects of cloud contamination constrain the amount of detail that a single satellite can provide. Fusing satellite data from different satellites with varying spatial and temporal resolutions can provide a more continuous estimation of daily ET at field scale. In this study, we applied an ET fusion modeling system, which uses a surface energy balance model to retrieve ET using both Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data and then fuses the Landsat and MODIS ET retrieval timeseries using the Spatial-Temporal Adaptive Reflectance Fusion Model (STARFM). In this paper, we compared different STARFM ET fusion implementation strategies over various crop lands in the central California. In particular, the use of single versus two Landsat-MODIS pair images to constrain the fusion is explored in cases of rapidly changing crop conditions, as in frequently harvested alfalfa fields, as well as an improved dual-pair method. The daily 30 m ET retrievals are evaluated with flux tower observations and analyzed based on land cover type. This study demonstrates improvement using the new dual-pair STARFM method compared with the standard one-pair STARFM method in estimating daily field scale ET for all the major crop types in the study area.

Operational daily evapotranspiration mapping at field scale based on SSEBop model and spatiotemporal fusion of multi-source remote sensing data.

Accurate understanding of daily evapotranspiration (ET) at field scale is of great significance for agricultural water resources management. The operational simplified surface energy balance (SSEBop) model has been applied to estimate field scale ET with Landsat satellite imagery. However, there is still uncertainty in the ET time reconstruction for cloudy days based on limited clear days’ Landsat ET fraction (ETf) computed by SSEBop. The Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data can provide daily surface observation over clear-sky areas. This paper presented an enhanced gap-filling scheme for the SSEBop ET model, which improved the temporal resolution of Landsat ETf through the spatio-temporal fusion with SSEBop MODIS ETf on clear days and increased the time reconstruction accuracy of field-scale ET. The results were validated with the eddy covariance (EC) measurements over cropland in northwestern China. It indicated that the improved scheme performed better than the original SSEBop Landsat approach in daily ET estimation, with higher Nash-Sutcliffe efficiency (NSE, 0.75 vs. 0.70), lower root mean square error (RMSE, 0.95 mm·d-1 vs. 1.05 mm·d-1), and percent bias (PBias, 16.5% vs. 25.0%). This fusion method reduced the proportion of deviation (13.3% vs. 25.5%) in the total errors and made the random error the main proportion, which can be reduced over time and space in regional ET estimation. It also evidently improved the underestimation of crop ET by the SSEBop Landsat scheme during irrigation before sowing and could more accurately describe the synergistic changes of soil moisture and cropland ET. The proposed MODIS and Landsat ETf fusion can significantly improve the accuracy of SSEBop in estimating field-scale ET.

Proximal-Sensing-Powered Modelling of Energy-Water Fluxes in a Vineyard: A Spatial Resolution Analysis

Spatial resolution is a key parameter in energy–water surface flux modelling. In this research, scale effects are analyzed on fluxes modelled with the FEST-EWB model, by upscaling both its inputs and outputs separately. The main questions are: (a) if high-resolution remote sensing images are necessary to accurately model a heterogeneous area; and (b) whether and to what extent low-resolution modelling provides worse/better results than the upscaled results of high-resolution modelling. The study area is an experimental vineyard field where proximal sensing images were obtained by an airborne platform and verification fluxes were measured via a flux tower. Modelled fluxes are in line with those from alternative energy-balance models, and quite accurate (NSE = 0.78) with respect to those measured in situ. Field-scale evapotranspiration has resulted in both the tested upscaling approaches (with relative error within ±30%), although fewer pixels available for low-resolution calibration may produce some differences. When working at low resolutions, the model has produced higher relative errors (20% on average), but is still within acceptable bounds. This means that the model can produce high-quality results, partially compensating for the loss in spatial heterogeneity associated with low-resolution images.

Evaluation of SEBS, METRIC-EEFlux, and QWaterModel Actual Evapotranspiration for a Mediterranean Cropping System in Southern Italy

Remote sensing-based evapotranspiration (ET) models with various levels of sophistication have emerged recently with the possibilities of user-defined model calibrations. Their application for water resources management and climate studies from regional to global scale has been rapidly increasing, which makes it important to validate field scale ET in a complex crop assemblage before operational use. Based on in situ flux-tower measurements by the eddy-covariance (EC) system, this study tested three single-source energy balance models for estimating daily ET from fennel/maize/ryegrass-clover cropland rotations in a Mediterranean context in southern Italy. The sensitivity of three user-friendly ET models (SEBS, QWaterModel, and METRIC-EEFlux) with reference to the EC system over a center pivot irrigated cropland is discussed in detail. Results in terms of statistical indicators revealed that SEBS and METRIC-EEFlux showed reasonable agreements with measured ET (r2 = 0.59SEBS, RMSE = 0.71 mm day−1; r2 = 0.65METRIC, RMSE = 1.13 mm day−1) in terms of trends and magnitudes. At 30 m spatial resolution, both models were able to capture the in-field variations only during the maize development stage. The presence of spurious scan lines due to sensor defects in Landsat L7 ETM+ can contribute to the qualities of the METRIC-Efflux’s ET product. In our observation, the QWaterModel did not perform well and showed the weakest congruency (r2 = 0.08QWaterModel) with ground-based ET estimates. In a nutshell, the study evaluated these automated remote sensing-based ET estimations and suggested improvements in the context of a generic approach used in their underlying algorithm for robust ET retrievals in rotational cropland ecosystems.

Spatial Allocation Method from Coarse Evapotranspiration Data to Agricultural Fields by Quantifying Variations in Crop Cover and Soil Moisture

Cropland evapotranspiration (ET) is the major source of water consumption in agricultural systems. The precise management of agricultural ET helps optimize water resource usage in arid and semiarid regions and requires field-scale ET data support. Due to the combined limitations of satellite sensors and ET mechanisms, the current high-resolution ET models need further refinement to meet the demands of field-scale ET management. In this research, we proposed a new field-scale ET estimation method by developing an allocation factor to quantify field-level ET variations and allocate coarse ET to the field scale. By regarding the agricultural field as the object of the ET parcel, the allocation factor is calculated with combined high-resolution remote sensing indexes indicating the field-level ET variations under different crop growth and land-surface water conditions. The allocation ET results are validated at two ground observation stations and show improved accuracy compared with that of the original coarse data. This allocated ET model provides reasonable spatial results of field-level ET and is adequate for precise agricultural ET management. This allocation method provides new insight into calculating field-level ET from coarse ET datasets and meets the demands of wide application for controlling regional water consumption, supporting the ET management theory in addressing the impacts of water scarcity on social and economic developments.

Evaluation of the Surface Renewal Method for Estimating Cotton Evapotranspiration in South Carolina

HighlightsThe Surface Renewal (SR), Eddy Covariance (EC), and FAO56 methods were used to estimate crop evapotranspiration.An independently calibrated SR approach was used with a low-cost cup anemometer and assumed energy balance closure.All three approaches (SR, EC, and FAO56) resulted in similar crop ET values during later stages of growth (R2 = 0.87).SR has the potential to lower field-scale ET measurement costs and equipment requirements.Abstract. Evapotranspiration (ET) is important for many agricultural and hydrological applications, but its measurement normally requires expensive instrumentation. The Surface Renewal method (SR) has been proposed as a relatively low-cost alternative to measure ET, but the need for local calibration has been cited as a shortcoming. Castellví (2004)(2004) proposed a similarity-based SR approach to address this shortcoming, but field comparisons evaluating the accuracy of the approach using low-cost anemometry are currently lacking. The objective of this study was to evaluate the reliability of the similarity-based SR approach using low-cost anemometry (cup anemometer) for estimating cotton ET in comparison to the Eddy Covariance (EC) and FAO56 methods (Allen et al., 1998). Concurrent estimates of cotton ET were taken in 2019 at two adjacent cotton fields in South Carolina using the SR, EC, and FAO56 methods. SR and EC measurements of sensible heat flux were replicated using two in-field tower locations in the north field and ET was derived using the energy balance closure approach. An analysis was made to ensure =90% of the EC and SR seasonal data came from the field area (Kljun et al., 2015), with additional filtering to ensure that the higher-mounted EC had >90% footprint from the field area for each half-hour of analysis. In general, the results showed very good agreement between the daily crop ET values measured by SR and EC (R2north = 0.93, R2south = 0.97) for all of the season, and good agreement between SR and FAO56 measurements when crop height was more developed (R2north = 0.92, R2south = 0.87), near or above a canopy height of 0.75 m. There was disagreement between the non-continuous north tower SR measurements and FAO56 measurements in earlier stages of growth (June and July R2 = 0.09), which may have been due to uncertainty with the FAO56 local single crop coefficients or with a lack of energy balance closure. The results suggest that the use of low-cost anemometry with the independently calibrated SR approach is a useful alternative to measure crop ET under the conditions of this study, but further research may be necessary to evaluate the use of low-cost anemometry for direct SR ET measurements. Keywords: Cotton irrigation, Crop coefficients, Eddy covariance, Evapotranspiration, ET, Surface renewal.

A validation of eddy covariance technique for measuring crop evapotranspiration on different time scales in the North China Plain

Accurate determination of evapotranspiration (ET) has tremendous potential in guiding irrigation and improving the efficiency of water resources utilization in the North China Plain. Eddy covariance (EC) method is currently a popular method for determining field-scale ET. However, due to varying foot print and unclosed energy balance, the applicability of EC in different regions needs to be tested and corrected. In present work, we compared the ET of the winter wheat – summer maize rotation cropland measured by the EC method with the ET measured by large-scale lysimeters on different time scales. The degree of energy balance closure of EC measurements in this region is 78%. After adjusted by using Bowen ratio forced closure method, the ET monitored by EC is comparable with those monitored by large-scale lysimeters. The results also indicated that the consistency of the observed ET by the EC and lysimeters got better with an increasing time scale, especially for the multi-year average ET values with a relative deviation of less than 1%. The short-time events such as irrigation and precipitation and the mismatch of the varying footprint area of the EC and the small fixed source area of the lysimeter should be responsible for the discrepancy of ET in two methods on daily scale. However, the factors of crop biomass, total available water, and local climate condition exert more effects on the observed ET on large time scale. Overall, the EC technique is responsible for ET measurement of winter wheat – summer maize rotation cropland of the North China Plain.

Modelling High-Resolution Actual Evapotranspiration through Sentinel-2 and Sentinel-3 Data Fusion

The Sentinel-2 and Sentinel-3 satellite constellation contains most of the spatial, temporal and spectral characteristics required for accurate, field-scale actual evapotranspiration (ET) estimation. The one remaining major challenge is the spatial scale mismatch between the thermal-infrared observations acquired by the Sentinel-3 satellites at around 1 km resolution and the multispectral shortwave observations acquired by the Sentinel-2 satellite at around 20 m resolution. In this study we evaluate a number of approaches for bridging this gap by improving the spatial resolution of the thermal images. The resulting data is then used as input into three ET models, working under different assumptions: TSEB, METRIC and ESVEP. Latent, sensible and ground heat fluxes as well as net radiation produced by the models at 20 m resolution are validated against observations coming from 11 flux towers located in various land covers and climatological conditions. The results show that using the sharpened high-resolution thermal data as input for the TSEB model is a sound approach with relative root mean square error of instantaneous latent heat flux of around 30% in agricultural areas. The proposed methodology is a promising solution to the lack of thermal data with high spatio-temporal resolution required for field-scale ET modelling and can fill this data gap until next generation of thermal satellites are launched.

Daily Evapotranspiration Estimation Using HJ-1 Data for Irrigation Water Management in a Desert-Oasis Irrigation District, Northwest China

Irrigation of agricultural land is the main water consumer in the arid and semiarid regions. The accurate time series of daily evapotranspiration (ET) at the field scale is crucial for irrigation water management. Here, we presented an integrated approach to field-scale ET mapping by combing METRIC (Mapping Evapotranspiration at High Resolution with Internalized Calibration) with trapezoidal framework of vegetation fraction and land surface temperature (fv-Trad). This approach fed with Chinese satellite HJ-1 (environmental and disaster monitoring and forecasting with a small satellite constellation) images was used to map daily ET over the desert-oasis irrigation fields in the middle of the Heihe River Basin, Northwest China. The results showed that time series of daily ET derived from HJ-1 were well consistent with the in situ measurements (coefficient of determination (R2) of 0.95, root mean square error (RMSE) of 0.31 mmd–1, mean absolute percentage error (MAPE) of 12.0% at Daman station; R2 of 0.89, RMSE of 0.16 mmd–1, and MAPE of 13.2% at Huazhaizi station). The rapid variation of ET could be precisely captured by HJ-1, especially when rainfall or irrigation events occurred between successive acquisitions of satellite data. Meanwhile, the spatiotemporal variation of monthly ET is closely related to crop growth. Daily ET at field scale will support a variety of local interests in water use and irrigation management for both planning and regulatory purposes.

Hydrologic and water quality impacts of biofuel feedstock production in the Ohio River Basin

AbstractThis study addresses the uncertainties related to potential changes in land use and management and associated impacts on hydrology and water quality resulting from increased production of biofuel from the conventional and cellulosic feedstock. The Soil Water Assessment Tool (SWAT) was used to assess the impacts on regional and field scale evapotranspiration, soil moisture content, stream flow, sediment, and nutrient loadings in the Ohio River Basin. The model incorporates spatially and temporally detailed hydrologic, climate and agricultural practice data that are pertinent to simulate biofuel feedstock production, watershed hydrology and water quality. Three future biofuel production scenarios in the region were considered, including a feedstock projection from the DOE Billion‐Ton (BT2) Study, a change in corn rotations to continuous corn, and harvest of 50% corn stover. The impacts were evaluated on the basis of relative changes in hydrology and water quality from historical baseline and future business‐as‐usual conditions of the basin. The overall impact on water quality is an order of magnitude higher than the impact on hydrology. For all the three future scenarios, the sub‐basin results indicated an overall increase in annual evapotranspiration of up to 6%, a decrease in runoff up to 10% and minimal change in soil moisture. The sediment and phosphorous loading at both regional and field levels increased considerably (up to 40–90%) for all the biofuel feedstock scenario considered, while the nitrogen loading increased up to 45% in some regions under the BT2 Study scenario, decreased up to 10% when corn are grown continuously instead of in rotations, and changed minimally when 50% of the stover are harvested. Field level analyses revealed significant variability in hydrology and water quality impacts that can further be used to identify suitable locations for the feedstock productions without causing major impacts on water quantity and quality.

Satellite-based water use dynamics using historical Landsat data (1984–2014) in the southwestern United States

Remote sensing-based field-scale evapotranspiration (ET) maps are useful for characterizing water use patterns and assessing crop performance. The relative impact of climate variability and water management decisions are better studied and quantified using historical data that are derived using a set of consistent datasets and methodology. Historical (1984–2014) Landsat-based ET maps were generated for major irrigation districts in California, i.e., Palo Verde and eight other sub-basins in parts of the middle and lower Central Valley. A total of 3396 Landsat images were processed using the Operational Simplified Surface Energy Balance (SSEBop) model that integrates weather and remotely sensed images to estimate monthly and annual ET within the study sites over the 31years. Model output evaluation and validation using gridded-flux data and water balance ET approaches indicated relatively good correspondence (R2 up to 0.88, root mean square error as low as 14mm/month) between SSEBop ET and validation datasets. In a pairwise comparison, annual variability of agro-hydrologic parameters of actual evapotranspiration (ETa), land surface temperature (Ts), and runoff (Q) were found to be more variable than their corresponding climatic counterparts of atmospheric water demand (ETo), air temperature (Ta), and precipitation (P), revealing process differences between regional climatic drivers and localized agro-hydrologic responses. However, only Ta showed a consistent increase (up to 1.2K) over study sites during the 31years, whereas other climate variables such as ETo and P showed a generally neutral trend. This study demonstrates a useful application of “Big Data” science where large volumes of historical Landsat and weather datasets were used to quantify and understand the relative importance of water management and climate variability in crop water use dynamics in regards to the linkages among water management decisions, hydrologic processes and economic transactions. Irrigation district-wide ETa estimates were used to compute historical crop water use volumes and monetary equivalents of water savings for the Palo Verde Irrigation District (PVID). During the peak crop fallowing year in PVID, the water saved reached a maximum of ~107,200acre-feet in 2011 with an estimated monetary payout value of $20.5 million. A significant decreasing trend in actual ET despite an increasing atmospheric demand in PVID highlights the role of management decisions in affecting local hydrologic processes. This study has importance for planning water resource allocation, managing water rights, sustaining agricultural production, and quantifying impacts of climate and land use/land cover changes on water resources. With increased computational efficiency, similar studies can be conducted in other parts of the world to help policy and decision makers understand and quantify various aspects of water resources management.

Responses of field evapotranspiration to the changes of cropping pattern and groundwater depth in large irrigation district of Yellow River basin

To solve the problems of decreasing water resources from the Yellow River, the renovation project of water-saving and controlling water distribution was implemented in the Hetao Irrigation District of North China since 1999. The local hydrological cycle is expected to be influenced or changed during the past fifteen years through the project. Jiefangzha Irrigation Region, the second large partition of the Hetao Irrigation District, was selected as the typical area to explore the relationship between agricultural water consumption, water distribution, cropping pattern and changing groundwater level. In this paper, the SEBS (Surface Energy Balance System) model and the ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) algorithm were applied to produce daily field-scale evapotranspiration (ET) based on Landsat and MODIS data from 2000 to 2015 at intervals. The results indicate that the cropping pattern had changed greatly, while the water table descended through the years. The total agriculture ET stayed relatively stable, with the average annual consumption of 8.9×108m3. The temporal variation of agriculture ET was not obviously sensitive to the adjustment of cropping pattern. There was no significant difference in April–October ET for different crops, in spite of the clear distinction of ET in each crop-specific growing season. The spatial distribution of agricultural ET did not change in spite of the great adjustment of cropping pattern during fifteen years, which has a close inverse correlation with the groundwater depth. Groundwater depth descended from 1.76m to 2.33m during the operation period of the water-saving project, which might reduce the soil evaporation and have a positive effect on soil salinization effectively. Meanwhile, the net irrigation water use efficiency was improved from 0.59 to 0.66. These effects indicate the positive impact of the implemented water-saving renovation project on water management and environment. However, the actual crop coefficient Kc,a decreased slightly with the gradually increasing reference evapotranspiration, indicating possible drought stress as an effect of the continuing reduction of the availability of net irrigation water and declining groundwater table.

Quantifying variability in field-scale evapotranspiration measurements in an irrigated agricultural region under advection

This study compares evapotranspiration (ET) measurements from eddy covariance (EC), lysimetry (LY), and water balance using a network of neutron probe (NP) sensors and investigates the role of within-field variability in the vegetation density in explaining the differences among the various techniques. Measurements were collected over irrigated cotton fields during a period of rapid crop growth under advective conditions. Using NP-based ET estimates as reference, differences in cumulative ET measurements from the EC systems and NP ranged between 2 and 14 %, while differences between LY and NP ranged from 22 to 25 %. The discrepancy in the ET between the three methods was largely attributed to variations in vegetation cover within the source areas of the sensors, which was reliably assessed using high-resolution remote sensing imagery. This analysis indicates that the source area contributing to the measurements must be considered, even in instances where one might consider field conditions uniform. Consequently, differences in measured ET require accounting for variability of vegetation cover conditions in measurement source areas, particularly when used for model validation. This point concerning model validation is exemplified by the difference in performance of a thermal-based energy balance model in estimating ET evaluated using LY versus EC measurements.

Mapping daily evapotranspiration at field scales over rainfed and irrigated agricultural areas using remote sensing data fusion

Continuous monitoring of daily evapotranspiration (ET) at field scale can be achieved by combining thermal infrared remote sensing data information from multiple satellite platforms, given that no single sensor currently exists today with the required spatiotemporal resolution. Here, an integrated approach to field-scale ET mapping is described, combining multi-scale surface energy balance evaluations and a data fusion methodology, namely the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), to optimally exploit spatiotemporal characteristics of image datasets collected by the Landsat and Moderate resolution Imaging Spectroradiometer (MODIS) sensors, as well as geostationary platforms. Performance of this methodology is evaluated over adjacent irrigated and rainfed fields, since mixed conditions are the most challenging for data fusion procedures, and in two different climatic regions: a semi-arid site in Bushland, TX and a temperate site in Mead, NE. Daytime-total ET estimates obtained for the Landsat overpass dates suggest that the intrinsic model accuracy is consistent across the different test sites (and on the order of 0.5mmd−1) when contemporaneous Landsat imagery at 30-m resolution is available. Comparisons between tower observations and daily ET datastreams, reconstructed between overpasses by fusing Landsat and MODIS estimates, provide a means for assessing the strengths and limitations of the fused product. At the Mead site, the model performed similarly for both irrigated and rainfed fields, with an accuracy of about 0.9mmd−1. This similarity in performance is likely due to the relatively large size of the fields (≈50ha), suggesting that the soil moisture dynamics of the irrigated fields are reasonably well captured at the 1-km MODIS thermal pixel scale. On the other hand, the accuracy of daily retrievals for irrigated fields at the Bushland site was lower than that for the rainfed field (errors of 1.5 and 1.0mmd−1, respectively), likely due to the inability of the model to capture ET spikes right after irrigation events for fields substantially smaller than MODIS data resolution. At this site, the irrigated fields were small (≈5ha) compared to the MODIS pixel size, and sparsely distributed over the landscape, so sporadic contributions to ET from soil evaporation due to irrigation were not captured by the 1-km MODIS ET retrievals. However, due the semiarid environment at Bushland, these irrigation-induced spikes in soil evaporation are not long-lived and these underestimations generally affect the irrigation dates only and they do not seem to influence negatively the estimates at the seasonal scale. ET data fusion is expected to perform better over agricultural areas where irrigation is more spatially continuous, resulting in moisture fluxes that are more uniform at the MODIS pixel scale. Overall, the model accurately reproduces the ET temporal dynamics for all the experimental sites, and is able to capture the main differences that were observed between irrigated and rainfed fields at both daily and seasonal time scales.

Estimating high spatiotemporal resolution evapotranspiration over a winter wheat field using an IKONOS image based complementary relationship and Lysimeter observations

Mapping high spatiotemporal resolution evapotranspiration (ET) over large areas is important for water resources planning, precision irrigation and monitoring water use efficiency. However, both traditional field measurement and aerodynamic estimation mainly focus on obtaining local ET. Remote sensing observations usually can be used to retrieve instantaneous ET at a low spatial resolution over region or global scale. Therefore, using field measurements and high resolution image data to generate high spatiotemporal resolution ET is becoming an important research direction. In this study, the complementary relationship model (CR) was tested together with meteorological data to estimate actual ET, and the results were validated by the Lysimeter observation. Furthermore, CR model combined with high resolution IKONOS data was used to estimate instantaneous field scale ET that was then transferred to daily ET. The cumulative evapotranspiration (ET) of winter wheat during the reproductive period from March through June of 2011 was 469.12mm, essentially corresponding to the annual precipitation in the study area. The highest accuracy of estimating ET by CR model with remote sensing data was in May (R2=0.863, RMSE=0.103mm). The transferred daily ET by a self-preservation of evaporative fraction (EF) approach from the CR modeling instantaneous ET was consistent with The Lysimeter measurements for all four months, March through June, 2011 (R2=0.937, RMSE=0.668mm). The experimental results demonstrate that CR model can be used to accurately estimate actual ET with both meteorological data and high resolution remote sensing data at a regional scale.

Assessing the Performance of a Cold Region Evapotranspiration Landfill Cover Using Lysimetry and Electrical Resistivity Tomography

In order to test the efficacy of a cold-region evapotranspiration (ET) landfill cover against a conventional compacted clay (CCL) landfill cover, two pilot scale covers were constructed in side-by-side basin lysimeters () at a site in Anchorage, Alaska. The primary basis of comparison between the two lysimeters was the percolation of moisture from the bottom of each lysimeter. Between 30 April 2005 and 16 May 2006, 51.5 mm of water percolated from the ET lysimeter, compared to 50.6 mm for the the CCL lysimeter. This difference was not found to be significant at the 95% confidence level. As part of the project, electrical resistivity tomography (ERT) was utilized to measure and map soil moisture in ET lysimeter cross sections. The ERT-generated cross sections were found to accurately predict the onset and duration of lysimeter percolation. Moreover, ERT-generated soil moisture values demonstrated a strong linear relationship to lysimeter percolation rates (R–Squared = 0.92). Consequently, ERT is proposed as a reliable tool for assessing the function of field scale ET covers in the absence of drainage measurement devices.

Utility of thermal image sharpening for monitoring field‐scale evapotranspiration over rainfed and irrigated agricultural regions

The utility of a thermal image sharpening algorithm (TsHARP) in providing fine resolution land surface temperature data to a Two‐Source‐Model for mapping evapotranspiration (ET) was examined over two agricultural regions in the U.S. One site is in a rainfed corn and soybean production region in central Iowa. The other lies within the Texas High Plains, an irrigated agricultural area. It is concluded that in the absence of fine (sub‐field scale) resolution thermal data, TsHARP provides an important tool for monitoring ET over rainfed agricultural areas. In contrast, over irrigated regions, TsHARP applied to kilometer‐resolution thermal imagery is unable to provide accurate fine resolution land surface temperature due to significant sub‐pixel moisture variations that are not captured in the sharpening procedure. Consequently, reliable estimation of ET and crop stress requires thermal imagery acquired at high spatial resolution, resolving the dominant length‐scales of moisture variability present within the landscape.

Combining remote sensing-simulation modeling and genetic algorithm optimization to explore water management options in irrigated agriculture

We present an innovative approach to explore water management options in irrigated agriculture considering the constraints of water availability and the heterogeneity of irrigation system properties. The method is two-folds: (i) system characterization using a stochastic data assimilation procedure where the irrigation system properties and operational management practices are estimated using remote sensing (RS) data; and (ii) water management optimization where we explored water management options under various levels of water availability. We set up a soil–water–atmosphere–plant model (SWAP) in a deterministic–stochastic mode for regional modeling. The distributed data, e.g. sowing dates, irrigation practices, soil properties, depth to groundwater and water quality, required as inputs for the regional modeling were estimated by minimizing the residuals between the distributions of field-scale evapotranspiration (ET) simulated by the regional application of SWAP, and by surface energy balance algorithm for land (SEBAL) using two Landsat7 ETM+ images. The derived distributed data were used as inputs in exploring water management options. Genetic algorithm was used in data assimilation and water management optimizations. The case study was conducted in Bata minor (lateral canal), Kaithal, Haryana, India during 2000–2001 rabi (dry) season. Our results showed that under limited water condition, regional wheat yield could improve further if water and crop management practices are considered simultaneously and not independently. Adjusting sowing dates and their distribution in the irrigated area could improve the regional yield, which also complements the practice of deficit irrigation when water availability is largely a constraint. This result was also found in agreement with the scenario that water is non-limited with the exception that the farmers have more degrees of freedom in their agricultural activities. An improvement of the regional yield to 8.5% is expected under the current scenario.

Uncertainties in Leaching Risk Assessments Due to Field Averaged Transfer Function Parameters

The transfer function model is widely used to estimate solute transport patterns at the field scale. Leaching risk assessments with the transfer function model may be influenced by spatial variability in the net applied water (NAW) distribution, but few researchers have investigated this possibility. The objective of this study was to evaluate the impact of spatial variability in the NAW distribution on leaching risk assessment and identification of leaching risk categories at the field scale. Bromide concentration profiles, irrigation depths, bulk densities, and soil moisture contents were measured in 40 plots across a 57-ha potato (Solanum tuberosum L.) farm, along with field-scale evapotranspiration estimates, to estimate solute transport parameters. Values for field- and plot-scale means and standard deviations for the NAW distribution were estimated using the stochastic convective lognormal transfer function (CLT) model. The probability of NO 3 - leaching below a depth of 2 m was then estimated using field-averaged versus plot-scale estimates for the mean and standard deviation of the NAW distribution. For 30-cm NAW, NO 3 - leaching risks estimated with the CLT model and plot-scale means and standard deviations were very high in 0.4 ha of the field, high in 1.8 ha, moderate in 8.7 ha, low in 23.0 ha, and none in 23.1 ha. In contrast, when field-scale average estimates of NAW were used, there was a low risk of NO 3 - leaching for the entire field. Thus, when estimating leaching risks using the CLT, information about spatial variability of the NAW distribution is important.