Ayse Irmak

Estimation of crop coefficients using satellite remote sensing.

Crop coefficient ( Kc ) based estimation of crop evapotranspiration ( E Tc ) is one of the most commonly used methods for irrigation water management. The standardized FAO56 Penman-Monteith approach for estimating E Tc from reference evapotranspiration and tabulated generalized Kc values has been widely adopted worldwide to estimate E Tc . In this study, we presented a modified approach toward estimating Kc values from remotely sensed data. The surface energy balance algorithm for land model was used for estimating the spatial distribution of E Tc for major agronomic crops during the 2005 growing season in southcentral Nebraska. The alfalfa-based reference evapotranspiration ( E Tr ) was calculated using data from multiple automatic weather stations with geostatistical analysis. The Kc values were estimated based on E Tc and E Tr (i.e., Kc =E Tc /E Tr ). A land use map was used for sampling and profiling the Kc values from the satellite overpass for the major crops grown in southcentral Nebraska. Finally,...

ESTIMATING SPATIALLY VARIABLE SOIL PROPERTIES FOR APPLICATION OF CROP MODELS IN PRECISION FARMING

Crop models have been useful for identifying underlying causes of yield variability and evaluating management prescriptions. However, estimating the spatial soil inputs required to calibrate crop models to historic yields has proven to be challenging and time consuming. Currently, calibration techniques require excessive computer time when applied over many grid points within a field, and procedures for estimating unknown inputs are not well defined. The objectives of this research were: (1) to develop an efficient procedure for estimating spatially variable soil properties for the CROPGRO–Soybean model, and (2) to demonstrate its use in diagnosing areas in the field where excess water or water stress reduce soybean yield. A study was conducted for a 12–ha field in Linn County, Iowa, using soybean data collected during two years (1996 and 1998). Yield, soil type, topography, and soil characterization data were used to estimate spatial variations in soil drainage factors (saturated hydraulic conductivity of an impeding layer and tile drainage spacing), water availability (SCS curve number and maximum rooting depth), and a soil fertility factor. A procedure was developed to create a database of predicted yields for combinations of coefficients, and to search the database using rules based on soil classification, drainage class, and topography to guide the parameter estimation process. When rules based on drainage class were used, the CROPGRO–Soybean model explained 45% to 70% of the yield variability for 1996 and 1998, respectively. When rules based on soil water availability, drainage characteristics, and topography were used, good predictions were obtained in both years (r2 = 0.70 for 1996 and 0.80 for 1998), and RMSE was 2.8% of grid level yields. The data base approach required less than half the time that simulated annealing required for the field with 48 grids.

Artificial Neural Network Model as a Data Analysis Tool in Precision Farming

Spatial variation in landscape and soil properties combined with temporal variations in weather can result in yield patterns that change annually within a field. The complexity of interactions between a number of yield-limiting factors makes it difficult to accurately attribute yield losses to conditions that occur within a field. In this research, a back-propagation neural network (BPNN) model was developed to predict the spatial distribution of soybean yields and to understand the causes of yield variability. First, we developed a BPNN model by relating soybean yield to topography, soil, weather, and site factors and evaluated model predictions for the same field for independent years. We also explored the potential use of BPNN for predicting yields in independent fields. Finally, we evaluated the ability of the BPNN to attribute yield losses due to soybean cyst nematodes (SCN), soil pH, and weeds. A total of 14 input datasets with combinations of four controlling factors (topographic, soil fertility, weather, and site) were used. For each objective, data from fields in Iowa were used for training the BPNN, while a portion of the data was withheld to verify the accuracy of yield predictions. All BPNN models had fully connected feed-forward architecture with a back-propagation weight adjustment algorithm. When tested for a particular field, the BPNN captured the major patterns of yield variability in independent years; the root mean square error of prediction (RMSEP) was 14.2% of actual yield. When the BPNN was trained with inputs from five fields, the RMSEP at test sites was 11.2% of actual yield. When the BPNN was used to attribute yield losses to soil pH, SCN, and weed populations, standard errors were 92, 262, and 171 kg ha-1, respectively. The technique showed that the BPNN could predict spatial yield variability with an RMSEP of about 14%.

RELATIONSHIP BETWEEN PLANT AVAI LABLE SOIL WATER AND YIELD FOR EXPLAINING SOYBEAN YIELD VARIABILITY

Spatial patterns of crop yield differ from year to year because of spatial and temporal interactions that occur within a field. A clear understanding of spatial soil–water uptake by plant roots is fundamental to understand yield variability and to make management recommendations that maximize profit or minimize environmental impacts. The objective of this study was to investigate variations in water relations within and between soil map units in a field in order to explain spatial distribution of soybean yield. This research was conducted in a 20–ha field in Boone County, Iowa, in 2000. Spatial distribution of soil water was investigated in 30 sites across field using a tube–access TDR probe. Aerial digital photos were taken three times during the growing season to investigate the relationship between plant canopy and resulting yield. Results showed that soybean yield was greatly reduced in the field compared to an average year, probably due to the occurrence of a drier than normal year. The yield variation was about 24%, likely due variation in soil water during pod filling. Soil water balance calculations for selected sites showed that plants likely experienced water stress in mid–July, but the level of stress increased dramatically later in the season and reached its maximum at the end of August. The sites exposed to earlier water stress exhibited lower yield. There was a good correlation (r 2 > 0.48) between plant available soil water and yield for any date during the reproductive phase of the soybean crop. The soil water relations were able to explain more than 48% of yield variability in 30 sites. However, the vegetation index did not correlate well with yield for any of the dates on which remotely sensed images were taken. This poor relationship indicated the variable drought stress that dominated yield variability occurred after full canopy was reached and primarily affected pod numbers, not canopy biomass.

Treatment of anchor pixels in the METRIC model for improved estimation of sensible and latent heat fluxes

Abstract Reliable estimation of sensible heat flux (H) is important in energy balance models for quantifying evapotranspiration (ET). This study was conducted to evaluate the value of adding the Priestley-Taylor (PT) equation to the METRIC (Mapping Evapotranspiration at high Resolution with Internalized Calibration) model. METRIC was used to estimate energy fluxes for 10 Landsat images from the 2005, 2006 and 2007 crop growing seasons in south-central Nebraska, USA, where each image owing to recent rainfall exhibited high residual moisture content even at the hot pixel. The METRIC model performed satisfactorily for net radiation (Rn ) and soil heat flux (G) estimation with a root mean square error (RMSE) of 52 and 24 W m-2, respectively. A RMSE of 122 W m-2 for H indicated the limitation of the METRIC model in estimating H for high residual moisture content of the hot pixel (Alfalfa reference ET fraction, ET r F > 0.15). The modified METRIC model (wet METRIC or wMETRIC) incorporating the PT equation was applied to calculate H at the anchor pixels (hot and cold) for high residual moisture content of the hot pixel. The α coefficient of the PT equation was locally calibrated using hourly meteorological data from an automatic weather station and Rn and G data from a Bowen ratio flux tower. The mean α coefficient value was 1.14. The wMETRIC model reduced the RMSE of H from 122 to 64 W m-2 and that of latent heat flux, LE, from 163 to 106 W m-2. The RMSE of daily ET decreased from 1.7 to 1.1 mm d-1 with wMETRIC. The results indicate that treatment of anchor pixels for high residual moisture content with the PT approach gives improved estimation of H, LE and daily ET. It is recommended that the wMETRIC model be used for estimating ET if the hot pixel has high residual moisture (i.e. reference ET fraction > 0.15). Citation Singh, R. K. & Irmak, A. (2011) Treatment of anchor pixels in the METRIC model for improved estimation of sensible and latent heat fluxes. Hydrol. Sci. J. 56(5), 895–906.

Spatial Interpolation of Climate Variables in Nebraska

Temperature and rainfall are important climatological parameters, and knowledge of their temporal and spatial patterns is useful for researchers working in many disciplines. In this study, spatial interpolation techniques were implemented in a Geographic Information System (GIS) to study the spatial variability of climate variables (maximum air temperature, minimum air temperature, and seasonal and annual rainfall) in Nebraska. Thirty years (1971-2000) of climate data (average monthly maximum and minimum temperatures and rainfall) from 215 National Weather Service Cooperative Observer Network (COOP) weather stations distributed throughout Nebraska and surrounding states were used in the analyses. Literature suggests that there is no single preferred method of interpolation, and the selection of interpolation method is usually based on the available data, desired level of accuracy, and available resources. We analyzed three different commonly used interpolation methods (inverse distance weighted, spline, and kriging) and evaluated their performance. Overall, the summary of all statistical parameters showed no significant difference between interpolation techniques in predicting the spatial variability in 30-year climate normals. Investigation of interpolation errors at individual weather stations agreed with summary statistics. Spatial variability, in this instance, is likely smoothed due to long-term averaging of the data (30 years), resulting in similar errors for all the interpolation techniques. Subjective assessment of maps for all climate variables showed that the kriging method produced smoother maps compared to spline and inverse distance weighted. Considering the degree to which accurate spatial interpolation could be accomplished with relative ease and less bias, the spline method proves the better option.

LINKING MULTIPLE LAYERS OF INFORMATION FOR DIAGNOSING CAUSES OF SPATIAL YIELD VARIABILITY IN SOYBEAN

Soybean yields are highly variable across fields because of the complex interaction of many factors, including weather, management, soil properties, fertility, pests, and weeds. However, only limited progress has been made on techniques for diagnosing reasons for yield variability and for identifying and managing different areas of the field to maximize profit or minimize environmental risk. The objective of this study was to develop a crop model–based technique to attribute yield losses due to water stress, soybean cyst nematodes (SCN), soil pH, and weeds that may cause soybean yield variability. The procedure computes yield as a function of: (a) site–specific soil water parameter inputs to a crop model, and (b) residual site growth and yield–reducing factors not accounted for by the crop model and its spatially variable soil water parameters. We used a two–year data set from Iowa (1995 and 1997) to estimate parameters and evaluate yield predictions using a combined crop model–statistical regression approach. The data were modified by imposing pH, SCN, and weed stress to 8 of 60 grids and a combination of these stresses to 8 grids. Thus, the dataset was constructed based on the both natural (field measured) and artificial yield variation. This was done to evaluate the accuracy with which the proposed technique could quantify losses. The model indicated that water stress reduced soybean yields by an average 1092 and 710 kg ha –1 for 1995 and 1997, respectively. After taking into account water stress, pH, SCN, and weeds, the combined approach was able to explain 96% of yield variability over two years of data. The RMSE was 143 kg ha –1 . Our technique was able to reproduce the quantities of yield loss for each grid and attribute them to the correct causes. Standard errors of attribution accuracy were 185, 220, and 163 kg ha –1 for soybean yield losses due to soil pH, SCN, and weeds, respectively. The combined crop model–regression technique was able to reproduce an observed spatial grid of yield according to both natural and imposed yield variation, and to quantify losses due to several factors.

Operational Remote Sensing of ET and Challenges

Satellite imagery now provides a dependable basis for computational models that determine evapotranspiration (ET) by surface energy balance (EB). These models are now routinely applied as part of water and water resources management operations of state and federal agencies. They are also an integral component of research programs in land and climate processes. The very strong benefit of satellite-based models is the quantification of ET over large areas. This has enabled the estimation of ET from individual fields among populations of fields (Tasumi et al. 2005) and has greatly propelled field specific management of water systems and water rights as well as mitigation efforts under water scarcity. The more dependable and universal satellite-based models employ a surface energy balance (EB) where ET is computed as a residual of surface energy. This determination requires a thermal imager onboard the satellite. Thermal imagers are expensive to construct and more a required for future water resources work. Future moderate resolution satellites similar to Landsat need to be equipped with moderately high resolution thermal imagers to provide greater opportunity to estimate spatial distribution of actual ET in time. Integrated ET is enormously valuable for monitoring effects of water shortage, water transfer, irrigation performance, and even impacts of crop type and variety and irrigation type on ET. Allen (2010b) showed that the current 16-day overpass return time of a single Landsat satellite is often insufficient to produce annual ET products due to impacts of clouds. An analysis of a 25 year record of Landsat imagery in southern Idaho showed the likelihood of producing annual ET products for any given year to increase by a factor of NINE times (from 5% probability to 45% probability) when two Landsat systems were in operation rather than one (Allen 2010b). Satellite-based ET products are now being used in water transfers, to enforce water regulations, to improve development and calibration of ground-water models, where ET is a needed input for estimating recharge, to manage streamflow for endangered species management, to estimate water consumption by invasive riparian and desert species, to estimate ground-water consumption from at-risk aquifers, for quantification of native

Comparison and Analysis of Empirical Equations for Soil Heat Flux for Different Cropping Systems and Irrigation Methods

We evaluated the performance of four models for estimating soil heat flux density (G) in maize (Zea mays L.) and soybean (Glycine max L.) fields under different irrigation methods (center-pivot irrigated fields at Mead, Nebraska, and subsurface drip irrigated field at Clay Center, Nebraska) and rainfed conditions at Mead. The model estimates were compared against measurements made during growing seasons of 2003, 2004, and 2005 at Mead and during 2005, 2006, and 2007 at Clay Center. We observed a strong relationship between the G and net radiation (Rn) ratio (G/Rn) and the normalized difference vegetation index (NDVI). When a significant portion of the ground was bare soil, G/Rn ranged from 0.15 to 0.30 and decreased with increasing NDVI. In contrast to the NDVI progression, the G/Rn ratio decreased with crop growth and development. The G/Rn ratio for subsurface drip irrigated crops was smaller than for the center-pivot irrigated crops. The seasonal average G was 13.1%, 15.2%, 10.9%, and 12.8% of Rn for irrigated maize, rainfed maize, irrigated soybean, and rainfed soybean, respectively. Statistical analyses of the performance of the four models showed a wide range of variation in G estimation. The root mean square error (RMSE) of predictions ranged from 15 to 81.3 W m-2. Based on the wide range of RMSE, it is recommended that local calibration of the models should be carried out for remote estimation of soil heat flux.

Status and continuing challenges in operational remote sensing of ET

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