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To solve problems such as low nitrogen use efficiency during corn intertillage and topdressing and the presence of spatial differences in corn growth, a method for zoning and division based on the normalized difference vegetation index (NDVI) and the improved self-organizing map (SOM)-K-means algorithm was proposed. First, the GreenSeeker spectrum sensor was utilized to acquire the NDVI of the corn canopy in the vegetative V6-V10 stage during the intertillage period. Second, the acquired data were screened, and the SOM-K-means algorithm was used to perform a cluster analysis of the processed data. Finally, the clustering performance was analyzed. The initial clustering center was acquired with an SOM neural network, the clustering center was used in K-means clustering, and zoning was performed. The optimal number of zones was 4 according to the Davies‒Boulding Index (DBI), the silhouette coefficient, and a silhouette analysis of the differences in corn growth. With four zones, the DBI and the silhouette coefficient were 0.569 and 0.537, respectively. Clusters 1 and 4 and clusters 2 and 3 in the silhouette map displayed similar thicknesses, with large differences between clusters and small differences within clusters. A comparison of the SOM-K-means algorithm, K-means algorithm, and SOM neural network algorithm revealed that the run time of the SOM-K-means clustering algorithm was 4.880 s, its inertia was 9.2118, and the run time of the SOM neural network was 4.715 s. The overall coefficient of variation of corn growth in the unzoned test area was 15.24%, and the overall coefficient of variation of corn growth after zoning was 6.94%. This method provides a new approach for understanding issues related to variable-rate fertilization zoning and online real-time zoning during corn intertillage based on the NDVI. [ABSTRACT FROM AUTHOR]

Closed-loop control for precision irrigation represents an effective method to provide water savings in this resource-intensive activity. Typically, implementation of these systems uses an on-off control approach with soil moisture as the feedback variable. In these cases, no process modeling is required due to the simplicity of the implemented control algorithm. However, the amount of water consumed by irrigation globally presents an interesting application area for the control discipline. Therefore, to obtain greater water savings and to improve crop productivity through control theory, an irrigation model is required that integrates the three main elements in the irrigation process: soil, crop, and weather. This article proposes a hybrid automata model for a closed-loop irrigation system applied to a grass-covered area. The main measured variables used to represent the process dynamics are soil moisture, vegetation index, and evapotranspiration. The hybrid automata approach allows a complex system to be modeled as a finite state machine in which there is a specific linear model for each state. When certain conditions are met, the system transitions from one state to another, and the new behavior is represented by a different linear model. To develop the proposed model, experimental data were obtained from an irrigation process, the data were analyzed to produce a model using the hybrid automata approach, and finally the model was validated with a new set of measured data. [ABSTRACT FROM AUTHOR]

Cotton producers aim for optimal nitrogen (N) fertilizer application rates that are correctly timed. Supplemental N is only used at the growth stage when and where it can be most effective. One management strategy monitors N levels using leaf spectral reflectance throughout the growing season. However, when the canopy is sparse or absent, a mobile implement-mounted spectral sensor is subject to influence from the exposed soil and stubble background. The results are misleading readings. This exploratory research project evaluated two vegetation index (VI) algorithms. The optimal soil-adjusted vegetation index (OSAVI) and normalized difference vegetation index (NDVI) values were selectively trimmed for improving a commercial NDVI sensing system's ability to discriminate between plant biomass and the ground surface. Varying plant populations, especially in early growth, can lead to erroneous real-time readings during mobile applications when passing over exposed ground surfaces. The goal of this study was to improve the real-time monitoring of cotton N status, both spatially and at the field scale. A large-scale field experiment statistically established a distribution of small contiguous plots in rows. A plot unit was a combination of one of three seeding rates, one of three cotton varieties, and one of four N application rates. Plant height, leaf N, and VI values were collected and analyzed. Both VI algorithms were found to reduce the varying plant population effect on NDVI. Plant population affected raw NDVI values throughout the season, which confirmed the effect of soil background exposure on NDVI values. Research findings suggest that after applying the two algorithms and comparing results with the non-filtered data analysis, both algorithms detected the differences due to variety, seeding rate, and N rate treatments. However, the N effect was detected earliest in the season by NDVI. [ABSTRACT FROM AUTHOR]

In this study, we used visible/near-infrared (Vis/NIR) hyperspectral imaging technology to measure the reflectance and gray-level texture features of grape leaf surfaces, and we predicted water content based on those features for more effective irrigation control. Grape vine specimens were potted in the actual field environment, and five levels of water treatment with two replicates were applied to create controlled water stresses. The reflectance and gray-level co-occurrence matrix (GLCM) texture features were used to develop partial least squares (PLS) models for water content prediction. The results showed that when using both reflectance and GLCM texture features, the results achieved a correlation coefficient (rp), root mean square error of prediction (RMSEP), bias, and residual predictive deviation (RPD) of 0.900, 0.826, -2.213e-04, and 2.084, respectively. The experimental results indicated that using a Vis/NIR hyperspectral imaging system could enable this combined method of reflectance and GLCM texture to predict water content of grape vines and be beneficial for grape vine management. [ABSTRACT FROM AUTHOR]

Remote sensing has increasingly been used to estimate evapotranspiration (ET) and its supporting parameters in a rapid, accurate, and cost-effective manner. The goal of this study was to develop remote sensing-based models for estimating ET and the biophysical parameters canopy conductance (gc), upper-canopy temperature, and soil moisture for a mature loblolly pine forest (Pinus taeda L.) in the Parker Tract in eastern North Carolina. To validate the remote sensing approach, we acquired long-term on-site eddy flux measurements, including micrometeorological variables and water and energy fluxes. Other measured and derived ET-associated parameters include forest gc, leaf area index, canopy absorbed radiation, canopy temperature, and soil moisture. Multi-temporal cloud-free (≤10% cloud cover) Landsat 7 ETM+ satellite images from 2006-2012 were acquired. Field data for the 2 h (12:00 noon to 2:00 p.m.) means were used in the model, coinciding with the image acquisition time. Individual Landsat bands (1 through 7) and developed image vegetation indices (NDVI, SAVI, and VVI) for the study site were obtained through automated geospatial models and were correlated to measured ET flux and related parameters. An excellent coefficient of determination (R2 = 0.93, n = 42) was obtained for the upper-canopy temperature versus band 6 (thermal infrared) model. However, a low correlation (R2 = 0.36, n = 6) was obtained for gc versus band 5 (mid-infrared) model. The correlation for soil moisture versus band 7 was poor (R2 = 0.05, n = 42), perhaps due to heavy canopy and pine litter ground cover. However, the ET estimation model with multiple image information variables, such as bands 5 and 7, provided a good correlation (R2 = 0.55, n = 35) with less spatial and temporal variation in the datasets, along with no data mining application in model building. Therefore, this study suggests that the remote sensing approach is promising for estimating ET with good accuracy (average model prediction residual error = 25.46 W m-2, 6.18% of the average ET values used in the analysis) for a mature homogenous pine forest. Further work is needed to develop robust remote sensing-based ET models by including spatial variability, sound data mining, high-resolution imagery, and advanced image processing to account for potential modeling uncertainties. [ABSTRACT FROM AUTHOR]

The complex nature of coupled human-natural systems often hinders the identification of forces and mechanisms causing observed environmental changes. The analysis of long-term time series can allow better understanding of those interactions and hence inform more adapted restoration and management programs. However, long-term time series of ground-measured vegetation variables are often not readily available due to the tediousness of the work and the financial and time investment required, especially for large-scale wetland systems. Remote sensing can help overcome this issue by providing more than 40 years of Earth cover images, but until recently the processing and analysis of these images was restricted to experts in remote sensing. The new Google Earth Engine (GEE) opens the remote sensing information mine to engineers or scientists without advanced knowledge in the field, including easy access to petabytes of publicly available remote sensing data and their spatial analysis in the cloud. To illustrate its capabilities, we used GEE to generate time series of the normalized difference vegetation index (NDVI) for the human-impacted Tempisque watershed with its severely degraded downstream Palo Verde wetland in northwest Costa Rica. We detail the processing and analysis steps to facilitate replication to any other case study. After defining the boundary of a study area, any user can generate a list and a video of the Landsat and MODIS images available for the area, NDVI maps, and a time series of the NDVI values spatially aggregated over the study area or a set of previously delineated polygons. One of the challenges we address is discriminating between the multitude of image collections available in the GEE catalog for vegetation mapping. We evaluate and compare the results obtained from five selected Landsat and MODIS image collections and identify the collections that give the best quality results for our case study. We conclude that MODIS is more appropriate for this tropical region because of the higher temporal resolution and hence higher probability to record cloud-free images. NDVI time series from Landsat images demonstrate a significant number of missing values. Landsat maps of NDVI suggest that the entire watershed and protected wetland witnessed an overall increase in vegetation greenness and hence cover since 1986, matching the abandonment of cattle ranching and the known degradation of the wetland by cattail invasion. Within-season vegetation variability can be tracked using MODIS images despite their coarser spatial resolution, showing high variability linked to precipitation patterns in this region. This GEE application illustrates new opportunities for biosystems engineers and other scientists to integrate historical vegetation and land cover data into comprehensive datasets to understand human impacts on ecosystems, and provides the guidance to do so. [ABSTRACT FROM AUTHOR]

Influenced by such factors as air temperature, humidity, solar radiation, and soil moisture, the normalized difference vegetation index (NDVI) for remote sensing of vegetation always changes dynamically within a day. Therefore, choosing the proper time to monitor the index is very important so as to minimize the effects of these factors. In this study, the NDVIs of soybean, cotton, pasture, regular grass, and weeds were measured with a GreenSeeker handheld NDVI sensor for six consecutive days. Furthermore, SPSS statistical software was used to test the differences in the NDVI values between different times for the five types of vegetation. The optimum monitoring time was determined as the period with minimum NDVI change by investigating the NDVI changes within a day and during the days after rainfall. The results showed that the most suitable time within a day for monitoring the NDVI was from 10:00 to 12:00 in the morning, and the best time was from 10:00 to 11:00. The most suitable time for monitoring the NDVI after saturating rainfall was from the 2nd day to the 4th day, and the best time was from the 3rd day to the 4th day. [ABSTRACT FROM AUTHOR]

Knowledge of phenological events in grapevines is essential for successful vineyard management. Conventional ground-observed phenological measurements are limited mainly due to their spatial coverage. However, satellite data provide access to global spatial coverage with the potential for high temporal resolution. The goal of this study was to evaluate the ability of vegetation indices based on remote sensing to estimate the growing season length of grapes in central Washington State. Several phenological metrics for vineyards located in the Columbia Valley region were derived from the satellite time series of the Moderate Resolution Imaging Spectroradiometer (MODIS) using the normalized difference vegetation index (NDVI). The methodology included exponential smoothing and a moving average to compute both the onset of greenness and the end of greenness. The MODIS NDVI values were evaluated with aerial NDVI images of the same vineyard from August 2011. The average bias was -0.08, the average root mean squared error (RMSE) was 0.16, and the coefficient of determination (R²) was 0.5 (p = 0.06). The results revealed an average growing season duration of 216 days for grapevines in this region for a period of five years. The average starting date of the growing season was April 2, and the computed end of the growing season was November 4. The highest NDVI value was 0.55, which coincided with July 12. On average, the lowest NDVI value was 0.3, and the average range of NDVI was 0.25. The preliminary results of this study showed that MODIS NDVI can be used to monitor vineyard vegetation dynamics in the Columbia Valley and has potential to be applied to other grape-growing regions in the U.S. and across the globe. [ABSTRACT FROM AUTHOR]

Determination of cover crop termination rate has been based exclusively on visual evaluation of color by a trained evaluator to describe the life state of the plant. However, visual color-based assessment is a subjective method and can vary from one evaluator to another. If several skilled individuals are involved in the plant evaluation process in the same field, most likely a deviation associated with the cover crop evaluation will occur due to differences in eye response to colors. To remedy this problem, two experiments over three growing seasons were conducted in Alabama to evaluate cover crop termination rates using three different evaluation methods: (1) the visual method and manual data generation, (2) a chlorophyll meter with built-in data logger, and (3) an active light sensor (Greenseeker meter) with an iPAQ to store data. The instruments' have the advantage of performing these evaluations quickly and effectively, and assessments can be performed by relatively unskilled personnel in the field. A linear correlation procedure was employed to develop relationships between observed termination rates and data collected with the instruments. The goal was to establish relationships between visual determination and instrument readouts, and to use these results for developing a procedure for future cover crop senescence assessment (termination rate in percentage of cover crop). Two cover crops were evaluated: cereal rye (Secale cereale L.) and crimson clover (Trifolium incarnatum L.). Results from three years of data (2009-2011)from two sites showed that there were strong linear relationships between visual observation and data obtained from the instruments, with R² values ranging from 0.713 to 0.945. Models were developed to predict termination rates for the two cover crops using the Greenseeker and chlorophyll meters, which are being used in agricultural research for different plant evaluation purposes. [ABSTRACT FROM AUTHOR]

Numerous existing spectral-based soil heat flux (G) models have shown wide variation in performance for maize and soybean cropping systems in Nebraska, indicating the need for localized calibration and model development. The objectives of this article are to develop a semi-empirical model to estimate G from a normalized difference vegetation index (NDVI) and net radiation (Rn) for maize (Zea mays L.) and soybean (Glycine max L.) fields in the Great Plains, and present the suitability of the developed model to estimate G under similar and different soil and management conditions. Soil heat fluxes measured in both irrigated and rainfed fields in eastern and south-central Nebraska were used for model development and validation. An exponential model that uses NDVI and Rn was found to be the best to estimate G based on r² values. The effect of geographic location, crop, and water management practices were used to develop semi-empirical models under four case studies. Each case study has the same exponential model structure but a different set of coefficients and exponents to represent the crop, soil, and management practices. Results showed that the semi-empirical models can be used effectively for G estimation for nearby fields with similar soil properties for independent years, regardless of differences in crop type, crop rotation, and irrigation practices, provided that the crop residue from the previous year is more than 4000 kg ha-1. The coefficients calibrated from particular fields can be used at nearby fields in order to capture temporal variation in G. However, there is a need for further investigation of the models to account for the interaction effects of crop rotation and irrigation. Validation at an independent site having different soil and crop management practices showed the limitation of the semi-empirical model in estimating G under different soil and environment conditions. [ABSTRACT FROM AUTHOR]

Advanced remote sensing technologies provide researchers an innovative way to collect spatial data in precision agriculture. Sensor information and spatial analysis together allow for a detailed understanding of the spatial complexity of a field and its crop. The objective of the study was to describe field variability in the normalized difference vegetation index (NDVI) and characterize the spatial structure of NDVI data by geostatistical variogram analysis. Data sets at three different sampling densities were investigated and compared to characterize NDVI variation within the specified study area. Variograms were computed by Matheron's method of moments (MoM) estimator and fitted by theoretical models'. The fitted spherical model was determined to be the best model for the data analysis in the study. The range of spatial dependence of the NDVI data was 40 m for a sampling area of 4 m x 3 m. Knowing the amount of remotely sensed data needed to characterize the spatial variation of the field with NDVI allows us to save sampling costs and prescribe site-specific nitrogen and other agrichemical applications. [ABSTRACT FROM AUTHOR]

A radio-controlled unmanned helicopter-based low-altitude remote sensing (LARS) platform was used to acquire quality images of high spatial and temporal resolution in order to estimate yield and total biomass of a rice crop (Oriza sativa L.). Fifteen rice field plots with five N treatments (0, 33, 66, 99, and 132 kg ha-1) having three replicates each were arranged in a randomized complete block design for estimating yield and biomass as a function of applied N. Images were obtained by image acquisition sensors mounted on the LARS platform operating at the height of 20 m over experimental plots. The rice yield and total biomass for the five N treatments were found to be significantly different at the 0.05 and 0.1 levels of significance, respectively, and normalized difference vegetation index (NDVI) values at panicle initiation stage were highly correlated with yield and total biomass with regression coefficients (r²) of 0.728 (RMSE = 0.458 ton ha-1) and 0.760 (RMSE = 0.598 ton ha-1), respectively. The study demonstrated the suitability of using LARS images as a substitute for satellite images for estimating leaf chlorophyll content in terms of NDVI values (r² = 0.897, RMSE = 0.012). The LARS system described has potential to evaluate areas that require additional nutrients at critical growth stages to improve final yield in rice cropping. [ABSTRACT FROM AUTHOR]

This study assessed the impact of potential future climate change on the inflow to an agricultural reservoir and its downstream streamflow from reservoir operation for paddy irrigation using SLURP (semi-distributed land use-based runoff process), a physically based hydrological model. For a 366.5 km² watershed that includes two agricultural reservoirs located in the upper middle part of South Korea, the SLURP model was calibrated using six years (1998-2003) of daily streamfow records and validated using three years (2004-2006) of streamflow data, before the future analysis. The CCCma CGCM2 data of the SRES (special report on emissions scenarios) A2 and B2 scenarios of the IPCC (Intergovernmental Panel on Climate Change) was adopted for the future climate condition, and then the change factor method was used to downscale the future weather data through bias-correction. In addition, the future land covers were predicted with a modified CA (cellular automata)-Markov technique using a time series of Landsat land cover data, and the future vegetation information was predicted by linear regression between the monthly NDVI (normalized difference vegetation index) from NOAA AVHRR (advanced very high resolution radiometer) images and the monthly mean temperature data. The results for the future impact on reservoir storage showed that it would not have recovered to 100% by the end of the year (December) because of a decrease in precipitation and an increase in evaporation from July to September. Therefore, this reduced reservoir storage could bring a spring drought that causes an insufficient guarantee of water for rice paddy transplanting at the end of May. The future impact on the monthly streamflow of the watershed showed that the future streamflow for the three months of April, May, and June would increase because of a future increase in precipitation, and that the future streamflow for the next three months (July, August, and September) would decrease because of a future decrease in precipitation and increase in temperature. Considering the future decrease in the summer and autumn streamflows, the reservoir operation has to be more conservative when supplying water for paddy irrigation during this period and in the decision making process for reservoir releases during storm events. [ABSTRACT FROM AUTHOR]

Analysis and simulation of watershed-scale processes requires spatial characterization of land use, including differentiation among crop types. If this crop type information could be obtained accurately from remote sensing data, the effort required would be significantly reduced, especially for large watersheds. The objective of this study was to compare two methods using multiple satellite remote sensing datasets to differentiate land cover, including crop type, for the Salt River/Mark Twain Lake basin in northeast Missouri. Method I involved unsupervised classification of Landsat visible and near-infrared satellite images obtained at multiple dates in the growing season, followed by traditional, manual class identification. Method 2, developed in this research, employed the same unsupervised classification but also used normalized difference vegetation index (ND VI) maps obtained on a 16-day cycle from MODIS satellite images as ancillary data to derive seasonal NDVI trends for each class in the classification map. Tree analysis was applied to the NDVI trend data to group similar classes into clusters, and crop type for each cluster was determined from ground-truth data. Additional ground-truth data were used to assess the accuracy of the procedure, and crop acreage estimates were compared to county-level statistics. The overall classification accuracy of Method 2 was 3% higher than that of Method 1. Method 2 was also more efficient in terms of analyst time and ground-truth data requirements. Therefore, this method, employing variations in seasonal ND VI trends, is suggested for differentiation of crop type. The 30-m resolution crop type maps developed using this process will be useful as input data to environmental analysis models. [ABSTRACT FROM AUTHOR]

Farmers can use aerial images to assess crop conditions. Crop parameters, such as leaf area index (LAI) and normalized difference vegetation index (ND VI), are calculated from aerial images and used to determine crop status and infer other characteristics. However, direct comparison of images taken at different times in the growing season is not possible, as the images have to be calibrated to true reflectance or normalized to a standard. Various methods of image normalization have been proposed. Most of these methods rely on reflectance from standardized panels included in the image, which are often not available under farming conditions. The soil line is a relation between the red and near-infrared bands of a multispectral image and can be used to characterize bare soil pixels. Therefore, the soil line transformation (SLT) technique was developed to normalize images based on the soil line's characteristics. Utilizing the soil line concept, images can be normalized to a reference soil line, so that crop parameters from multi-temporal images can be compared directly. The objective was to verify that the SLT could effectively normalize images and that the NDVI extracted from SLT transformed images would be directly comparable to the NDVI values from true reflectance images. NDVI was calculated for a series of ten images that were all available as raw, true reflectance, and SLT transformed forms. The SLT program was used on a sub-image known to contain cotton and bare soil pixels. There was no significant improvement of the NDVI values when calculated from the SLT transformed images compared to the values from the raw images. Improvements to the SLT technique that may provide better performance are suggested. Normalization of images would be useful for comparison of multi-temporal data, but the existing SLT technique requires further development. [ABSTRACT FROM AUTHOR]

The presence of nitrogen in soil and chlorophyll in plants are directly related; thus, chlorophyll may be used as an indirect indicator of nitrogen levels in fertilizer management systems. This research investigated a non-destructive method of determining chlorophyll content and concentration in field-grown spinach. Biomass was estimated with percent vegetation coverage based on images taken with a multispectral imaging system. Chlorophyll yield was estimated using reflectance-based NDVI from a GreenSeekerTM hand-held optical sensor and a multispectral imaging system. Strong correlations were found between reflectance-based NDVI and chlorophyll yield (R2 = 0.92), between NDVI and biomass (R2 = 0.94), and between biomass and projected plant area (172 = 0.96). Combined parameter estimators of chlorophyll concentration had low correlation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Chlorophyll is an indirect indicator of nitrogen status and is used in optical reflectance-based variable-rate chemical application technology. Chlorophyll concentration relates strongly to tire photosynthetic potential of a plant and to the physiological and metabolic status of the plant. This research investigated a non-destructive method of determining chlorophyll content and concentration at the individual plant level in Spinacea oleracea (spinach). A multispectral imaging system was used to determine spectral reflectance and to estimate top-view surface area. An ultrasonic distance sensor provided vegetation height estimates. Surface area estimates and height data were included in calculations to estimate plant biomass. The relationships between reflectance, estimated biomass, and laboratory-measured chlorophyll content and concentration were investigated. The product of biomass estimate and normalized difference vegetative index (NDVI670) provided the best estimate of chlorophyll content per plant (R² = 0.91), while estimates of chlorophyll concentration per unit leaf mass were less accurate (R² = 0.30). [ABSTRACT FROM AUTHOR]