Thomas R. Clarke

COTTON IRRIGATION SCHEDULING USING REMOTELY SENSED AND FAO-56 BASAL CROP COEFFICIENTS

Multispectral vegetation indices calculated from canopy reflectance measurements have been used to simulate real-time basal crop coefficients (Kcb), which have been validated to improve evapotranspiration (ETc) estimation for several crops. In this article, an application of the approach was evaluated for cotton using remote sensing observations of the normalized difference vegetation index (NDVI) to estimate Kcb as a function of NDVI. The dual crop coefficient procedures of FAO Paper 56 (FAO-56) were used to calculate ETc and determine irrigation scheduling using Kcb estimates from remote sensing (NDVI treatment) as well as from time-based Kcb curves (FAO treatment), which were developed locally for standard crop conditions using FAO-56 procedures. Two cotton experiments, conducted in 2002 and 2003 in central Arizona, included sub-treatments of three levels of plant density and two levels of nitrogen management to impose a wide range of crop development and water use. The NDVI-Kcb relationships used for 2002, developed from previous data for a different cotton cultivar, row orientation, and soil type, substantially underestimated ETc, resulting in significantly less irrigation water applied and lower lint yields for NDVI compared to the FAO treatment. The 2002 data were used to recalibrate the NDVI-Kcb relationships, which were then used for the NDVI treatments in 2003. The FAO Kcb curve used in 2002 described ETc and irrigation scheduling reasonably well for sparse plots, but consistently underestimated water use and soil water depletion for the higher plant densities during the first half of the season. Consequently, an adjusted FAO Kcb curve, based on 2002 results, was employed for the FAO treatment in 2003. For the 2003 experiment, estimated cotton ETc for the NDVI treatment resulted in a mean absolute error of 9% compared to 10% for the FAO treatment, where the difference was not significant between treatments. However, the NDVI-Kcb relations used in 2003 greatly improved estimates for ETc compared to the previous year, where the mean absolute error for the NDVI treatment in 2002 was 22%. Predicted ETc using the FAO Kcb curve of 2003 for typical planting density and high nitrogen conditions resulted in a mean absolute error of 10% compared to 15% in 2002. Final lint yields for 2003 were not significantly different between the two Kcb methods. Although additional research is needed to validate remote sensing Kcb estimation for other conditions than those in these experiments, this study did not show significant advantages for the NDVI approach over a carefully derived single FAO Kcb application. However, the NDVI approach has the potential to further extend our present crop coefficient estimation capabilities when weather, plant density, or other factors cause cotton canopy development and water use patterns to depart from typical conditions.

Wheat Irrigation Management Using Multispectral Crop Coefficients: I. Crop Evapotranspiration Prediction

A method widely used for irrigation management determines crop evapotranspiration (ETc) from reference evapotranspiration (ETo) calculations and estimated crop coefficients. However, standard time-based crop coefficients may fail to represent the actual crop water use, for example, when deviations in weather or agronomic constraints appreciably change crop development patterns from typical conditions. In this study, the FAO-56 dual crop coefficient procedures were applied during experiments with wheat to calculate the estimated ETc for irrigation scheduling. The objective of this research was to determine whether basal crop coefficients (Kcb) determined from a normalized difference vegetation index (NDVI treatment) improve the prediction of ETc over a standard application with a locally developed time-based Kcb curve (FAO treatment). The experiments conducted for two seasons in central Arizona included subtreatments, equally replicated within the NDVI and FAO treatments, of three plant densities (typical, dense, and sparse) and two nitrogen levels (high and low) to provide a range of crop development and water use conditions. The effects of plant density and N level resulted in significant differences in measured seasonal ETc. Large variations that occurred in the observed Kcb and ETc trends between subtreatments were better correlated with the NDVI than the FAO treatment. The mean absolute percent difference for predicted ETc was significantly smaller for NDVI than FAO during both seasons. The treatment difference was 5% for the first season, but 10% for the second season when an unexpected early decline in ETc and Kcb was effectively predicted by the NDVI treatment but not by the FAO treatment. NDVI appears to be a robust approach for Kcb estimation of wheat, able to reliably predict actual ETc for both typical and abnormal water use conditions.

GROUND-BASED REMOTE SENSING OF WATER AND NITROGEN STRESS

A ground–based remote sensing system (Agricultural Irrigation Imaging System, or AgIIS) was attached to a linear–move irrigation system. The system was used to develop images of a 1–ha field at 1 U 1 m resolution to address issues of spatial scale and to test the ability of a ground–based remote sensing system to separate water and nitrogen stress using the coefficient of variation (CV) for water and nitrogen stress indices. A 2 U 2 Latin square water and nitrogen experiment with four replicates was conducted on cotton for this purpose. Treatments included optimal and low nitrogen with optimal and low water. ANOVA was not an adequate method to assess the statistical variation between treatments due to the large number of data points. In general, the coefficient of variation of water and nitrogen stress indices increased with water and nitrogen stress. In fact, the coefficient of variation of stress indices was a more reliable measurement of water and nitrogen status than the mean value of the indices. Differences in coefficient of variation of stress indices between treatments were detectable at 3 m grid resolution and finer for water stress and at 7 m grid resolution and finer for nitrogen stress.

REMOTE SENSING OF COTTON NITROGEN STATUS USING THE CANOPY CHLOROPHYLL CONTENT INDEX (CCCI)

Various remote sensing indices have been used to infer crop nitrogen (N) status for field-scale nutrient management. However, such indices may indicate erroneous N status if there is a decrease in crop canopy density influenced by other factors, such as water stress. The Canopy Chlorophyll Content Index (CCCI) is a two-dimensional remote sensing index that has been proposed for inferring cotton N status. The CCCI uses reflectances in the near-infrared (NIR) and red spectral regions to account for seasonal changes in canopy density, while reflectances in the NIR and far-red regions are used to detect relative changes in canopy chlorophyll, a surrogate for N content. The primary objective of this study was to evaluate the CCCI and several other remote sensing indices for detecting the N status for cotton during the growing season. A secondary objective was to evaluate the ability of the indices to appropriately detect N in the presence of variable water status. Remote sensing data were collected during the 1998 (day of year [DOY] 114 to 310) and 1999 (DOY 106 to 316) cotton seasons in Arizona, in which treatments of optimal and low levels of N and water were imposed. In the 1998 season, water treatments were not imposed until late in the season (DOY 261), well after full cover. Following an early season N application in 1998 for the optimal (DOY 154) but not the low N treatment, the CCCI detected significant differences in crop N status between the N treatments starting on DOY 173, when canopy cover was about 30%. A common vegetation index, the ratio of NIR to red (RVI), also detected significant separation between N treatments, but RVI detection occurred 16 days after the CCCI response. After an equal amount of N was applied to both optimal and low N treatments on DOY 190 in 1998, the CCCI indicated comparable N status for the N treatments on DOY 198, a trend not detected by RVI. In the 1999 season, both N and water treatments were imposed early and frequently during the season. The N status was poorly described by both the CCCI and RVI under partial canopy conditions when water status differed among treatments. However, once full canopy was obtained in 1999, the CCCI provided reliable N status information regardless of water status. At full cotton cover, the CCCI was significantly correlated with measured parameters of N status, including petiole NO 3 -N (r = 0.74), SPAD chlorophyll (r = 0.65), and total leaf N contents (r = 0.86). For well-watered cotton, the CCCI shows promise as a useful indicator of cotton N status after the canopy reaches about 30% cover. However, further study is needed to develop the CCCI as a robust N detection tool independent of water stress.

Energy Balance Estimation of Evapotranspiration for Wheat Grown Under Variable Management Practices in Central Arizona

Estimating and monitoring the spatial distribution of evapotranspiration (ET) over irrigated crops is becoming increasingly important for managing crop water requirements under water scarce conditions. The usual point-based approaches for estimating ET, however, do not provide enough data for precision farming applications, whereby irrigation schedules could be customized by crop conditions at sub-field scales. Needed in addition are spatially distributed ET modeling approaches, obtainable only through remote sensing, which can observe ET-related surface properties such as vegetation density and surface temperature. Although research using remote sensing to estimate ET has been pursued for many years, there are still few ground-validated, full-season ET studies at fine spatial scales. In this study, we assessed the ability of a remote sensing model to retrieve daily ET throughout an entire growing season for wheat. Image data with 0.5 m resolution were collected in 2005 over an irrigation scheduling research site in central Arizona (Maricopa). The 1.3 ha study area (FISE05) contained 32 leveled, flood-irrigated plots with treatments for irrigation scheduling, planting density, and fertilization. Daily ET was modeled using a two-source energy balance (TSEB) approach and airborne image observations in visible, near-infrared, and thermal infrared wavelengths for six dates throughout the growing season. Using independent soil water depletion observations, modeled daily ET values were accurate to within 0.4 mm d-1 for most of the FISE05 season and sensitive to changes in wheat canopy changes. Late-season ET estimates were less satisfactory, with accuracies to within 1.3 mm d-1. The significance of these results was supported by verifying agreement between ground and airborne estimates of vegetation indices, wheat canopy cover, and surface temperature. Results from FISE05 also showed that the value of this particular implementation of ET remote sensing was limited by inadequate temporal sampling, seasonal dependence of vegetation density estimators, and uncertain parameterization for the late-season. In each case, modeled and projected daily ET estimate errors could exceed 1 mm d-1. These ET/energy balance results can be improved, however, by revising the vegetation density estimators and by combining episodic high-spatial-resolution image data with continuous high-temporal-resolution ground data.

Evaluation of the CSM-CROPSIM-CERES-Wheat Model as a Tool for Crop Water Management

Development and implementation of improved methodologies for crop water management will conserve valuable water resources in agricultural regions that depend on irrigation. To address this problem for conditions in central Arizona, we have evaluated the CSM-CROPSIM-CERES-Wheat model using measured wheat growth and soil water data from plot-level irrigation scheduling experiments conducted during the winters of 2003-2004 and 2004-2005. During each season, wheat plots were managed using two FAO-56-based irrigation scheduling approaches at three planting densities (~75, ~150, and ~300 plant m-2) and at two nitrogen application rates (~80 and ~215 kg ha-1 year-1). For these treatments, the calibrated model simulated wheat yield with relative root mean squared errors (RRMSE) of 7.4% and 1.7% for the 2003-2004 and 2004-2005 seasons, respectively. Time series plots of measured and simulated Zadoks number, leaf number, leaf mass, stem mass, spike mass, and green leaf area index demonstrated favorable wheat development and growth responses to experimental treatments and seasonal weather and management variability. The model was able to quantify average soil water contents in eight soil layers to a depth of 210 cm with RRMSEs ranging from 3.3% to 18.9% for the 2003-2004 season and from 2.7% to 11.3% for the 2004-2005 season. Evapotranspiration was simulated with RRMSEs of 2.4% and 3.2% for the 2003-2004 and 2004-2005 seasons, respectively. Deficiencies were demonstrated in the ability of the models automatic irrigation routines to reproduce the FAO-56 irrigation schedules devised during field experimentation. With further development, the CSM-CROPSIM-CERES-Wheat model could become a valuable central component for decision tools designed to evaluate alternative water management scenarios and improve water management for irrigated agricultural systems.

AgIIS, Agricultural Irrigation Imaging System

Ground-based remote sensing can provide data at spatial resolutions, repeat frequencies, and turnaround times that are suitable for daily farm management at the field scale, whereas present satellite and airborne platforms do not meet these criteria. Remotely sensed data, when combined with other ancillary data, can provide spatially distributed maps of vegetation vigor, evapotranspiration, crop water stress, and nitrogen status. The objective of this paper was to describe the design, operation, and data processing of a ground-based remote sensing system called the Agricultural Irrigation Imaging System (AgIIS) that uses a self-propelled lateral move irrigation system as the transport platform. AgIIS consists of a cart that contains four nadir-looking sensors that measure reflected irradiance in the green (555 nm), red (670 nm), red-edge (720 nm), and near infrared (790 nm), all filtered to a 10-nm band pass at the band centers, and an infrared thermometer that measures directional radiometric surface temperature. The cart moves along a track, which is mounted to the overhead pipe of the lateral move system. Surface reflectance and temperature measurements were resampled to 1- m x 1-m raster grids, which were used to construct maps of vegetation, water, and nitrogen stress indices. These indices were correlated to field measurements of leaf area index, (r2 = 0.81 to 0.92), soil water deficit index (r2 = 0.76 to 0.86), and leaf petiole nitrogen content (r2 = 0.17 to 0.55), where experimental treatments consisted of two rates of irrigation and nitrogen applications for a cotton and broccoli crop.

Wheat Irrigation Management Using Multispectral Crop Coefficients: II. Irrigation Scheduling Performance, Grain Yield, and Water Use Efficiency

Current irrigation scheduling is based on well-established crop coefficient and reference evapotranspiration procedures to estimate daily crop evapotranspiration (ETc). Effective irrigation scheduling and efficient irrigation water use can occur when ETc is calculated with crop coefficients representative of actual crop water use conditions. The objective of this research was to evaluate irrigation scheduling using two approaches to estimate the basal crop coefficient (Kcb) during wheat experiments conducted in 2003-2004 and 2004-2005 at Maricopa, Arizona. Each Kcb approach (main treatment) included six subtreatment combinations (three plant densities and two N managements) imposed to create spatial and temporal variations in water use among experimental plots. The first approach (NDVI treatment) estimated Kcb separately for each plot based on normalized difference vegetation index (NDVI) data obtained by frequent canopy reflectance measurements. The second approach (FAO treatment) estimated Kcb uniformly for all plots based on a Kcb curve developed for standard wheat conditions. The Kcb estimates were incorporated within the FAO-56 dual crop coefficient procedures to calculate daily ETc and root zone soil water depletion (Dr). Plot irrigations were provided when the predicted Dr reached 45% of the available soil water. During both wheat experiments, considerable variations in measured soil water depletion were observed for subtreatments due to differences in crop water use rates. For the FAO treatment, mean absolute percent difference (MAPD) for predicted Dr was 27% and 40% for 2003-2004 and 2004-2005, respectively. Prediction of Dr was improved significantly for NDVI for both experiments where treatment MAPD was 17% (2003-2004) and 18% (2004-2005). Although mean irrigation application efficiency for NDVI (89%) and FAO (88%) was similar for 2003-2004, it was significantly higher for NDVI (86%) than FAO (77%) for 2004-2005. Differences for irrigation scheduling resulted in significantly lower seasonal irrigation water use for the NDVI than FAO treatment, 8% (2003-2004) and 13% (2004-2005), but did not result in appreciable treatment differences for seasonal ETc, final grain yield, and crop water use efficiency (yield per unit ETc). Consequently, a primary outcome for both experiments was significantly higher irrigation water use efficiency (yield per unit irrigation water) for NDVI than FAO. Incorporating Kcb estimates based on NDVI within existing crop coefficient algorithms provides an opportunity to improve wheat irrigation scheduling strategies for conserving irrigation water while maintaining grain yield potentials.

Combining Remotely Sensed Data and Ground-Based Radiometers to Estimate Crop Cover and Surface Temperatures at Daily Time Steps

Estimation of evapotranspiration (ET) is important for monitoring crop water stress and for developing decision support systems for irrigation scheduling. Techniques to estimate ET have been available for many years, while more recently remote sensing data have extended ET into a spatially distributed context. However, remote sensing data cannot be easily used in decision systems if they are not available frequently. For many crops ET estimates are needed at intervals of a week or less, but unfortunately due to cost, weather, and sensor availability constraints, high resolution ( <100 m ) remote sensing data are usually available no more frequently than 2 weeks. Since resolution of this problem is unlikely to occur soon, a modeling approach has been developed to extrapolate remotely sensed inputs needed to estimate ET. The approach accomplishes this by combining time-series observations from ground-based radiometers and meteorological instruments with episodic visible, near infrared, and thermal infrared ...

Estimating Crop Water Use for Camelina with Remote Sensing

Camelina (Camelina sativa [L.] Crtz.) is an oilseed crop with apparently low water requirements and therefore could be very attractive for growers in arid lands. Verifying this potential for environments such as the U.S. Southwest, however, requires field experiments that test yield response to different irrigation schedules. By adapting evapotranspiration (ET) methodologies previously developed for more conventional crops (i.e., wheat and cotton), consumption of water by Camelina could be assessed in a spatial context. Using remote sensing observations collected in 2007 and 2008 over a 1.3 ha plot in Maricopa, Arizona, daily ET over Camelina was estimated with a surface energy balance approach. The observations included ground-based imagery spanning visible, near infrared, and thermal infrared wavelengths. Crop treatments included four level of water depletion for 32 plots and one level of water-stress for 6 other plots. Modeled transpiration generally agreed well with independently obtained soil moisture depletions. Preliminary results indicate that crop coefficients, adapted by vegetation indices, provide sufficient guidance for effective irrigation scheduling and that canopy surface temperature changes can be a reliable indicator of ET.