José L. Chávez

Comparing Aircraft-Based Remotely Sensed Energy Balance Fluxes with Eddy Covariance Tower Data Using Heat Flux Source Area Functions

Abstract In an effort to better evaluate distributed airborne remotely sensed sensible and latent heat flux estimates, two heat flux source area (footprint) models were applied to the imagery, and their pixel weighting/integrating functionality was investigated through statistical analysis. Soil heat flux and sensible heat flux models were calibrated. The latent heat flux was determined as a residual from the energy balance equation. The resulting raster images were integrated using the 2D footprints and were compared to eddy covariance energy balance flux measurements. The results show latent heat flux estimates (adjusted for closure) with errors of (mean ± std dev) −9.2 ± 39.4 W m−2, sensible heat flux estimate errors of 9.4 ± 28.3 W m−2, net radiation error of −4.8 ± 20.7 W m−2, and soil heat flux error of −0.5 ± 24.5 W m−2. This good agreement with measured values indicates that the adopted methodology for estimating the energy balance components, using high-resolution airborne multispectral imagery, ...

Remote Sensing Based Energy Balance Algorithms for Mapping ET: Current Status and Future Challenges

Evapotranspiration (ET) is an essential component of the water balance and a major consumptive use of irrigation water and precipitation on cropland. Remote sensing based agrometeorological models are presently most suited for estimating crop water use at both field and regional scales. Numerous ET models have been developed in the last three decades to make use of visible, near-infrared (NIR), shortwave infrared (SWIR), and most importantly, thermal data acquired by sensors on airborne and satellite platforms. In this article, a literature review is done to evaluate numerous remote sensing based algorithms for their ability to accurately estimate regional ET. The remote sensing based models generally have the potential to accurately estimate regional ET; however, there are numerous opportunities to further improve them. The spatial and temporal resolution of currently available remote sensing data from the existing set of earth-observing satellite platforms are not sufficient enough to be used in the estimation of spatially distributed ET for on-farm irrigation scheduling purposes, especially at the field scale (~10 to 200 ha). This will be constrained further if the thermal sensors on future Landsat satellites are abandoned. Research opportunities exist to improve the spatial and temporal resolution of ET by developing algorithms to increase the spatial resolution of surface temperature data derived from ASTER/MODIS thermal images using same/other-sensor high-resolution visible, NIR, and SWIR images.

Surface Energy Balance Based Evapotranspiration Mapping in the Texas High Plains

Agriculture on the Texas High Plains (THP) uses approximately 89% of groundwater withdrawals from the Ogallala Aquifer. Consequently, groundwater levels are declining faster than the recharge rate. Therefore, efficient agricultural water use is essential for economic viability and sustainability of the THP. Accurate regional evapotranspiration (ET) maps would provide valuable information on actual crop water use. In this study, METRIC (Mapping Evapotranspiration at High Resolution using Internalized Calibration), a remote sensing based ET algorithm, was evaluated for mapping ET in the THP. Two Landsat 5 Thematic Mapper images acquired on 27 June (DOY 178) and 29 July (DOY 210) 2005 were used for this purpose. The performance of the ET model was evaluated by comparing the predicted daily ET with values derived from soil moisture budget at four commercial agricultural fields. Daily ET estimates resulted with a prediction error of 12.7±8.1% (mean bias error ± root mean square error) on DOY 178 and -4.7±9.4% on DOY 210 when compared with ET derived from measured soil moisture through the soil water balance. These results are good considering the prevailing advective conditions in the THP. METRIC have the potential to be used for mapping regional ET in the THP region. However, more evaluation is needed under different agroclimatological conditions.

Infrared Thermometry to Estimate Crop Water Stress Index and Water Use of Irrigated Maize in Northeastern Colorado

With an increasing demand of fresh water resources in arid/semi-arid parts of the world, researchers and practitioners are relying more than ever on remote sensing techniques for monitoring and evaluating crop water status and for estimating crop water use or crop actual evapotranspiration (ETa). In this present study, infrared thermometry was used in conjunction with a few weather parameters to develop non-water-stressed and non-transpiring baselines for irrigated maize in a semi-arid region of Colorado in the western USA. A remote sensing-based Crop Water Stress Index (CWSI) was then estimated for four hourly periods each day during 5 August to 2 September 2011 (29 days). The estimated CWSI was smallest during the 10:00-11:00 a.m. and largest during the 12:00-13:00 p.m. hours. Plotting volumetric water content of the topsoil vs. CWSI revealed that there is a high correlation between the two parameters during the analyzed period. CWSI values were also used to estimate maize actual transpiration (Ta). Ta estimates were more influenced by crop biomass rather than irrigation depths alone, mainly due to the fact that the effects of deficit irrigation were largely masked by the significant precipitation during the growing season. During the study period, applying an independent remotely sensed energy balance model showed that maize ETa was 159 mm, 30% larger than CWSI-Ta (122 mm) and 9% smaller than standard-condition maize ET (174 mm).

A Remote Irrigation Monitoring and Control System for continuous move systems. Part A: description and development

Continuous move irrigation systems have been modified since the 1990s to support variable rate irrigation. Most of these systems used PLC (Programmable Logic Controllers) technology that performed well for on-site control but were very expensive to add remote, real-time monitoring and control aspects that have been made possible by wireless sensor networks and the Internet. A new approach to the monitoring and control of continuous move irrigation systems is described. This system uses a Single Board Computer (SBC) using the Linux operating system to control solenoids connected to individual or groups of nozzles based on prescribed application maps. The main control box houses the SBC connected to a sensor network radio, a GPS (Global Positioning System) unit, and an Ethernet radio creating a wireless connection to a remote server. A C-software control program resides on the SBC to control the on/off time for each nozzle group using a “time on” application map developed remotely. The SBC also interfaces with the sensor network radio to record measurements from sensors on the irrigation system and in the field that monitor performance and soil and crop conditions. The SBC automatically populates a remote database on the server in real time and provides software applications to monitor and control the irrigation system through the Internet.

Using a Surface Energy Balance Model to Calculate Spatially Distributed Actual Evapotranspiration

Remote sensing algorithms are currently being used to estimate regional surface energy fluxes [e.g., latent heat flux or evapotranspiration (ET)]. Many of these surface energy balance models use information derived from satellite imagery such as Landsat, AVHRR, ASTER, and MODIS to estimate ET. The remote sensing approach to estimate ET provides advantages over traditional methods. One of the most important advantages is that it can provide regional estimates of actual ET at low cost. Most conventional methods are based on point measurements (e.g., soil water sensors, lysimeters, and weather station data), limiting their ability to capture the spatial variability of ET. Another advantage of remote sensing/surface energy balance ET models is that they are able to estimate the actual crop ET as a residual of the energy balance without the need of using reference crop ET and tabulated crop coefficients. This paper focuses on the application of the energy balance-based model “Remote Sensing of ET” (ReSET) that...

Intercomparison of Nine Micrometeorological Stations during the BEAREX08 Field Campaign

Land–atmosphere interactions play a critical role in regulating numerous meteorological, hydrological, and environmental processes. Investigating these processes often requires multiple measurement sites representing a range of surface conditions. Before these measurements can be compared, however, it is imperative that the differences among the instrumentation systems are fully characterized. Using data collected as a part of the 2008 Bushland Evapotranspiration and Agricultural Remote Sensing Experiment (BEAREX08), measurements from nine collocated eddy covariance (EC) systems were compared with the twofold objective of 1) characterizing the interinstrument variation in the measurements, and 2) quantifying the measurement uncertainty associated with each system. Focusing on the three turbulent fluxes (heat, water vapor, and carbon dioxide), this study evaluated the measurement uncertainty using multiple techniques. The results of the analyses indicated that there could be substantial variability in the uncertainty estimates because of the advective conditions that characterized the study site during the afternoon and evening hours. However, when the analysis was limited to nonadvective, quasi-normal conditions, the response of the nine EC stations were remarkably similar. For the daytime period, both the method of Hollinger and Richardson and the method of Mann and Lenschow indicated that the uncertainty in the measurements of sensible heat, latent heat, and carbon dioxide flux were approximately 13 W m 22 ,2 7 Wm 22 , and 0.10 mg m 22 s 21 , respectively. Based on the results of this study, it is clear that advection can greatly increase the uncertainty associated with EC flux measurements. Since these conditions, as well as other phenomena that could impact the measurement uncertainty, are often intermittent, it may be beneficial to conduct uncertainty analyses on an ongoing basis.

Radiometric surface temperature calibration effects on satellite based evapotranspiration estimation

Agriculture on the Texas High Plains (THP) uses approximately 89% of groundwater withdrawals from the Ogallala Aquifer, leading to steady decline in water table levels. Therefore, efficient water management is essential for sustaining agricultural production in the THP. Accurate evapotranspiration (ET) maps provide critical information on actual spatio‐temporal crop water use. METRIC (Mapping Evapotranspiration at High Resolution using Internalized Calibration) is a remote sensing based energy balance method that uses radiometric surface temperature (T s) for mapping ET. However, T s calibration effects on satellite based ET estimation are less known. Further, METRIC has never been applied for the advective conditions of the semi‐arid THP. In this study, METRIC was applied and predicted ET was compared with measured values from five monolithic weighing lysimeters at the USDA‐ARS Conservation and Production Research Laboratory in Bushland, Texas, USA. Three different levels of calibration were applied on a Landsat 5 Thematic Mappers thermal image acquired on 23 July 2006 to derive T s. Application of METRIC on a MODTRAN calibrated image improved the accuracy of distributed ET prediction. In addition, ET estimates were further improved when a THP‐specific model was used for estimating leaf area index. Results indicated that METRIC performed well with ET mean bias error±root mean square error of 0.4±0.7 mm d−1.

Remote sensing of contrasting tillage practices in the Texas Panhandle

Tillage information is crucial in environmental modelling as it has a direct impact on water holding capacity, evapotranspiration, carbon sequestration and water quality. In this study, a set of Landsat Thematic Mapper (TM)‐based linear logistic models were developed for mapping tillage practices and verified with an independent dataset. For data collection purposes, 35 and 41 commercial fields were randomly selected in Moore and Ochiltree counties, respectively, in the Texas Panhandle. Tillage survey was planned and conducted to coincide with Landsat 5 satellite overpasses during the 2005 planting season and two TM scenes were acquired. Using the Moore County dataset, seven logistic regression models were developed and these were evaluated with the data collected from Ochiltree County. The overall classification accuracy of the models varied from 86% to 91% with the Moore County dataset. These models were evaluated against independent Ochiltree County dataset and resulted in somewhat less accurate (classification accuracy of 67–85%) but still useful results. Analysis of these results indicates that logistic regression models that have indices derived from the combination of TM band 5 with bands 4 or 6 may provide consistent and acceptably accurate results when they are applied in the same geographic region.

Optical and Thermal Remote Sensing of Turfgrass Quality, Water Stress, and Water Use under Different Soil and Irrigation Treatments

Optical and thermal remote sensing data were acquired at ground level over several turfgrass species under different soil and irrigation treatments in northern Colorado, USA. Three vegetation indices (VIs), estimated based on surface spectral reflectance, were sensitive to the effect of reduced water application on turfgrass quality. The temperature-based Grass Water Stress Index (GWSI) was also estimated by developing non-transpiring and non-water-stressed baselines. The VIs and the GWSI were all consistent in (i) having a non-linear relationship with the water application depth; and, (ii) revealing that the sensitivity of studied species to water availability increased in order from warm season mix to Poa pratensis L. and then Festuca spp.. Implemented soil preparation treatments had no significant effect on turfgrass quality and water stress. The differences between GWSI-based estimates of water use and the results of a complex surface energy balance model (METRIC) were not statistically significant, suggesting that the empirical GWSI method could provide similar results if the baselines are accurately developed under the local conditions of the study area.