Prasanna H. Gowda

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.

Hydrologic and Water Quality Models: Use, Calibration, and Validation

To provide a common background and platform for consensual development of calibration and validation guidelines, model developers and/or expert users of the commonly used hydrologic and water quality models globally were invited to write technical articles recommending calibration and validation procedures specific to their model. This article introduces a special collection of 22 research articles that present and discuss calibration and validation concepts in detail for 25 hydrologic and water quality models. The main objective of this introductory article is to introduce and summarize key aspects of the hydrologic and water quality models presented in this collection. The models range from field to watershed scales for simulating hydrology, sediment, nutrients, bacteria, and pesticides at temporal scales varying from hourly to annually. Individually, the articles provide model practitioners with detailed, model-specific guidance on model calibration, validation, and use. Collectively, the articles in this collection present a consistent framework of information that will facilitate development of a proposed set of ASABE model calibration and validation guidelines.

A review of downscaling methods for remote sensing-based irrigation management: part I

High-resolution daily evapotranspiration (ET) maps would greatly improve irrigation management. Numerous ET mapping algorithms have been developed to make use of thermal remote sensing data acquired by satellite sensors. However, adoption of remote sensing-based ET maps for irrigation management has not been feasible due to inadequate spatial and temporal resolution of ET maps. Data from a coarse spatial resolution image in agricultural fields often cause inaccurate ET estimation because of a high level of spatial heterogeneity in land use. Image downscaling methods have been utilized to overcome spatial and temporal scaling issues in numerous remote sensing applications. In the field of hydrology, the image downscaling method has been used to improve spatial resolution of remote sensing-based ET maps for irrigation scheduling purposes and thus improves estimation of crop water requirements. This paper (part I) reviews downscaling methods to improve spatial resolution of land surface characteristics such as land surface temperature or ET. Each downscaling method was assessed and compared with respect to their capability of downscaling spatial resolutions of images. The companion paper (part II) presents review of image fusion methods that are also designed to increase spatial resolutions of images by integrating multi-spectral and panchromatic images.

Water quality modeling of fertilizer management impacts on nitrate losses in tile drains at the field scale

Nitrate losses from subsurface tile drained row cropland in the Upper Midwest U.S. contribute to hypoxia in the Gulf of Mexico. Strategies are needed to reduce nitrate losses to the Mississippi River. This paper evaluates the effect of fertilizer rate and timing on nitrate losses in two (East and West) commercial row crop fields located in south-central Minnesota. The Agricultural Drainage and Pesticide Transport (ADAPT) model was calibrated and validated for monthly subsurface tile drain flow and nitrate losses for a period of 1999-2003. Good agreement was found between observed and predicted tile drain flow and nitrate losses during the calibration period, with Nash-Sutcliffe modeling efficiencies of 0.75 and 0.56, respectively. Better agreements were observed for the validation period. The calibrated model was then used to evaluate the effects of rate and timing of fertilizer application on nitrate losses with a 50-yr climatic record (1954-2003). Significant reductions in nitrate losses were predicted by reducing fertilizer application rates and changing timing. A 13% reduction in nitrate losses was predicted when fall fertilizer application rate was reduced from 180 to 123 kg/ha. A further 9% reduction in nitrate losses can be achieved when switching from fall to spring application. Larger reductions in nitrate losses would require changes in fertilizer rate and timing, as well as other practices such as changing tile drain spacings and/or depths, fall cover cropping, or conversion of crop land to pasture.

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.

Comparing the Subsurface Drainage Flow Prediction of the DRAINMOD and ADAPT Models for a Cold Climate

The DRAINMOD computer model has been widely used for simulating the performance of subsurface drainage systems. The ADAPT model was created by merging components of DRAINMOD and GLEAMS and has evolved for over ten years, including the recent addition of soil freeze/thaw processes. DRAINMOD was also recently modified for soil freeze/thaw processes for application in cold climates. Computational time step, method of ET estimation, and soil freeze/thaw processes are examples of ways in which current versions of these models differ from one another. Previous comparisons of these models were made for warmer climates, before the addition of cold–climate hydrology to both models. The performances of DRAINMOD and ADAPT were compared for cold–climate conditions, calibrated using two years of observed data from a 23–ha farm field in southern Minnesota. Model performance was evaluated and compared for seasonal, monthly, daily, and event–based time scales and during snowmelt runoff periods. Observed data showed that 60% and 20% of annual subsurface drainage runoff occurred during the transition period between winter and spring (snowmelt period) in 1998 and 1999, respectively. DRAINMOD overpredicted drainage by 11% and 25% for these periods, and ADAPT’s results were within 10% of observed values for the snowmelt periods. Both models performed well at simulating the number and timing of drainage events in both snowmelt and later–season periods. The models performed best on a cumulative basis over the 2–year simulation period, where DRAINMOD overpredicted cumulative subsurface drainage by 1.7%, and ADAPT underestimated cumulative drainage by 0.2%. The models diverged in their abilities to predict the largest daily drainage events: DRAINMOD overpredicted and ADAPT underpredicted these events. Substantially more effort was required to calibrate ADAPT because of the increased complexity of the model.

THE SENSITIVITY OF ADAPT MODEL PREDICTIONS OF STREAMFLOWS TO PARAMETERS USED TO DEFINE HYDROLOGIC RESPONSE UNITS

The responses of ADAPT, a daily water table management simulation model, to variations in the principal input parameters which define hydrologic response units on a watershed was evaluated. The study was conducted on a small agricultural watershed in Ohio. The results suggest that useful estimates of monthly flows could be obtained by using NRCS soils information, land use, and tillage information estimated from Landsat TM data, a 30-meter digital elevation model, and readily available information on the prevailing farming systems. Water quality and quantity responses were sensitive to combinations of the slope, soil type, land use, tillage, crop rotations, and drainage practice and should be taken into account in defining the hydrologic response units. The sediment load predictions were sensitive to the field size. Approximations of when planting occurred did not affect the flow and sediment predictions but had an impact on nitrate and pesticide predictions.

A review of potential image fusion methods for remote sensing-based irrigation management: part II

Satellite-based sensors provide data at either greater spectral and coarser spatial resolutions or lower spectral and finer spatial resolutions due to complementary spectral and spatial characteristics of optical sensor systems. In order to overcome this limitation, image fusion has been suggested to obtain higher spatial and spectral resolution images at the same time. Image fusion has been a valuable technique in digital image analysis and comparison because of the availability of multi-spatial and multispectral images from satellite and airborne sensors. It has been applied to merge coarser spatial resolution of multispectral images with a finer spatial resolution panchromatic image to enhance visual apprehension and to provide images that are more informative. Part I companion paper presented and discussed the image downscaling methods. In this paper (part II), the main objective is to review existing image fusion methods for their capability to downscale coarser spatial resolution images for irrigation management applications. A literature review indicated that image fusion methods have not been actively used in obtaining high-resolution land surface temperature (LST) and evapotranspiration (ET) images for irrigation management. However, there is a great potential for applying image fusion methods to retrieve finer LST and ET images from coarser thermal images by fusing them with finer non-thermal color or panchromatic images for irrigation scheduling and management purposes.

Evapotranspiration of Corn and Forage Sorghum for Silage

In the U.S. Southern High Plains, dairies have expanded and have increased the regional demand for forage and silage. The objectives were to measure water use and determine crop coefficients for corn (Zea mays L.) and forage sorghum (Sorghum bicolor (L.) Moench) produced for silage on the Southern High Plains. Water use was measured with large, precision weighing lysimeters in 2006 and 2007. Both growing seasons had normal to above normal rainfall. The 2006 season was more advective with greater mean daily reference evapotranspiration (ET) rates. Seasonal ET was 671 mm for forage sorghum with a yield of 1.48 kg m -2 in 2006 and 489 mm in 2007 with a yield of 1.70 kg m -2 ; water productivity was 2.21 kg m -3 in 2006 and 3.47 kg m -3 in 2007. Seasonal ET was 418 mm for corn for silage with a yield of 1.52 kg m -2 in 2006 and 671 mm in 2007 with a yield of 2.44 kg m -2 ; water productivity was 3.63 kg m -3 in 2006 and 3.64 kg m -3 in 2007. Using the 2007 season as a better species comparison, forage sorghum can achieve comparable water productivity as corn with less ET (~73% of corn ET) and irrigation requirement although with a reduced yield (~62% of corn dry matter).

ADAPT: Model Use, Calibration, and Validation

This article presents an overview of the Agricultural Drainage and Pesticide Transport (ADAPT) model and a case study to illustrate the calibration and validation steps for predicting subsurface drainage and nitrate-N losses from an agricultural system. The ADAPT model is a daily time step, field-scale water table management model that was developed as an extension of the GLEAMS model. The GLEAMS algorithms were augmented with algorithms for subsurface drainage, subsurface irrigation, deep seepage, and related water quality processes. Recently, a frost depth algorithm was incorporated to enhance the model’s capability to predict flow during spring and fall months. In addition to the normal GLEAMS output, ADAPT gives estimates of pesticides and nutrients in drainage. The model has four components: hydrology, erosion, nutrient transport, and pesticide transport. Predictions of surface runoff and subsurface drainage by ADAPT are very sensitive to hydrology input parameters, such as NRCS curve number, hydraulic conductivity, depth of the impeding layer, and hydraulic conductivity of the impeding layer. In the erosion component, slope, hydraulic length, and crop management are the most sensitive factors. Nutrients generally follow the trends in surface runoff and subsurface drainage. In addition, nitrogen and phosphorus concentrations in soil horizons are sensitive to nutrient losses. Recently, the ADAPT model was further calibrated and validated in southern Minnesota to evaluate impacts of subsurface drain spacing and depth, rate and timing of nitrogen application, and precipitation changes on water quality. ADAPT is written in FORTRAN, and the source code is available to interested model users. Considering the limited technical support and text editor-based input files, development of a user-friendly interface to create input files would greatly enhance ADAPT’s acceptability by users involved in modeling agricultural systems equipped with subsurface drains.