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Efficient landscape irrigation is an important water conservation strategy and reduces strain on limited water resources in inland Southern California. A three-year field irrigation trial was conducted in Riverside, California, to evaluate the response of 'Westcoaster' tall fescue (Festuca arundinacea Schreb.) to a wide range of autonomous irrigation scenarios implemented using a Weathermatic smart controller. The response of the turfgrass was assessed using visual rating (VR) and normalized difference vegetation index (NDVI) data collected with a handheld sensor. These experimental data were then used to develop a regression-based turfgrass water response function (TWRF), which was further employed to estimate the response of tall fescue to different irrigation levels during years with varying atmospheric evaporation demands, including extreme conditions. A strong correlation was observed between NDVI and VR (r = 0.88). A minimum NDVI threshold of 0.7 was identified to maintain an acceptable level of tall fescue quality for residential areas. During the trial periods, irrigation application rates below 100% of reference evapotranspiration (ETo) were insufficient to sustain the desired quality of tall fescue. The TWRF estimations suggested that 60% ETo can be applied for short periods (7 to 43 days, depending on the atmospheric evaporative demands) during the summer before the visual quality drops below the minimum threshold. Comparing different irrigation strategies, it was observed that on-demand irrigation, which adjusts irrigation frequency based on ETo demand, resulted in higher NDVI and VR values compared to a limited watering schedule of only a few days per week. Analysis of soil moisture data revealed that the primary root water uptake occurred mainly in the topsoil layer (<33 cm), and the applied irrigation water primarily replenished the shallow soil layer (<20 cm) due to the coarse sandy loam soil characteristics. The Weathermatic controller, equipped with on-site air temperature measurements, provided reliable ETo estimations and can be used for autonomous landscape irrigation scheduling in semiarid regions. [ABSTRACT FROM AUTHOR]

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]

Irrigation timing is crucial for achieving high cotton yields and lint quality. This irrigation timing is more challenging in the southeastern U.S. Coastal Plain region due to its spatial variable sandy soils with low water and nutrient holding capacities and rainfall variability during the growing season. To address these challenges, we conducted a 2-year (2017 and 2018) study evaluating two irrigation scheduling methods under a variable rate irrigation system. The two irrigation methods were: (1) a uniform irrigation management based on weekly crop water usage, and (2) spatial crop coefficients derived from normalized difference vegetative indices (NDVI). We compared cotton yields and water use efficiency using the two irrigation scheduling methods at two different planting densities. The two plant populations were 5 and 11.5 plants m2 to provide different NDVI readings and water requirements. In 2017, there were no significant differences in cotton yields due to the adequate rainfall during the growing season that required only three irrigations events. The mean irrigation depth for the NDVI method was significantly lower than the uniform method (56 and 64 mm, respectively, LSD = 4.2). In 2018, there was lower rainfall during the growing season requiring eight irrigation events and the cotton yields in the two irrigation treatments were significantly higher than the rainfed treatment. Irrigation depths in 2018 were not significantly different for the two irrigation methods. Water use efficiencies were not significantly different for the two irrigation methods. The planting density had little impact on the cotton yields, irrigation depth, water use efficiency, or cotton fiber quality. These results indicate that the NDVI-derived crop coefficient values were as effective in prescribing irrigation applications as the uniform irrigation method for irrigation management. The NDVI-derived crop coefficient irrigation method appears to be a useful tool for managing irrigation and developing irrigation prescriptions. [ABSTRACT FROM AUTHOR]

Alfalfa is an important forage crop grown for hay, silage, and pasture production. Accurate yield estimations before harvest or grazing are crucial for producers to optimize forage utilization. The goal of this research was to develop an aboveground dry biomass prediction function for alfalfa using remote sensing from an unmanned aerial vehicle (UAV). A DJI Mavic Pro equipped with RGB and NDVI cameras captured aerial images. High-resolution orthomosaics and digital surface models were obtained using Structure from Motion (SfM) with Agisoft Metashape. An equation to estimate wet biomass was developed using three variables: change in canopy height (∆H) from digital elevation and crop surface models, and NDVI based canopy density index (CDI) data. The dry biomass yield was estimated as the product of a wet biomass prediction function and a correlation equation to estimate the dry matter fraction (DMF). The best correlation equation for wet biomass (BMwet) only required SfM methods to measure ΔH. The linear regression equation for BMwet had an R² of 0.963 and the mean coefficient of variation (CV) was ±23%. The best prediction equation for DMF was a quadratic equation with an R² of 0.642 with a mean CV of ±11%. It was also determined that the DMF varied significantly by season of harvest. As a result, the dry biomass could be estimated using the DMF equation or seasonal mean DMF values. Comparison of the observed dry biomass measurements with the empirically determined biomass prediction function that used the NDVI data to estimate the DMF showed excellent agreement. The model underpredicted the dry biomass by 1.1% with a standard error of the estimate of 236 kg DM/ha (CV = ±26%). The model predictions using the seasonal mean DMF values were not significantly different [ABSTRACT FROM AUTHOR]

A normalized difference vegetation index (NDVI) sensor can be used for nondestructively assessing the nitrogen (N) status of crops. When deployed as a mobile ground-based sensor for variable-rate cropping applications, its readings can be influenced by the irregular or sparse canopy coverages of the ground surface. The soil and ground stubble backgrounds that are periodically exposed within the sensor's view will generate erroneous readings. The objective of this research was to develop an algorithm that improves an NDVI-based instrumentation system's ability to discriminate between cotton plant biomass and the ground surface, thereby providing a more accurate N status for the crop. The research focused on N concentration assessment as a measure of N status; hence, eliminating the exposed soil background became essential. Algorithms were developed to correct the errors resulting from irregular cotton populations. A statistical analysis of field data taken using manipulated crop populations highlighted two effective algorithms. [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]

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]

The use of predictive yield maps is an important tool for the delineation of within-field management zones. In particular, appropriate placement in the field of organically grown produce, as is the case for the processing tomato crop in this study, will ensure higher crop yield. Accurate estimation of yield can be used to plan the best time for harvesting and transport for industrial processing. Apparent electrical conductivity (ECa) and vegetation indices based on crop reflectance are two tools that can be used to help attain these objectives. Developments in the use of sensors have enabled massive georeferenced data sampling of these parameters. The aim of this article is to assess the ECa and normalized difference vegetation index (NDVI) using geostatistical techniques to optimize their use. Principal component analysis was used to evaluate the predictive yield maps developed. ECa was a reasonably good indicator of crop production potential throughout the plot as a whole, but NDVI was the best indicator, offering a better resolution than ECa and a reasonable estimation of yield distribution over the extensive tested crop surface area. [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]

Many weather monitoring networks such as the Oklahoma Mesonet provide estimates of reference evapotranspiration (ETref) to facilitate weather-informed irrigation decisions. However, weather stations that collect the required input data to estimate ETref using the widely applied ASCE standardized ETref equation are not typically installed over a reference surface, defined as a large expanse of dense, well-watered, stress-free grass or alfalfa having a specified height, surface resistance, and albedo. The deviation of actual surface conditions in the surrounding environment of the weather stations from the reference condition creates station aridity effects that can lead to overestimation of ETref. Daily hydroclimate datasets for a period of 20 years (2000-2019) were used to evaluate the prevalence and spatiotemporal characteristics of station aridity across the Oklahoma Mesonet. Station aridity was characterized based on mean dew point deviation (MDD = Tmin - Tdew), maximum relative humidity (RHmax), and normalized difference vegetation index (NDVI). Results demonstrate that station aridity is prevalent and highly variable in both space and time across the Oklahoma Mesonet, as it increases from southeast to northwest in the Oklahoma Panhandle. Larger average seasonal MDD (up to 13°C), lower RHmax (e.g., 57%), and lower NDVI (e.g., 0.22) were observed during extreme to exceptional drought of 2011 in western Oklahoma, where a majority of the state’s irrigated agriculture (88%) is located. Spatiotemporal patterns of station aridity demonstrate the profound effect of wet and dry periods that influence the utility of ETref estimates to improve agricultural water conservation during high irrigation requirement times in water-scarce irrigated areas. [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]

Accurately estimating the yield is one of the most important research projects for orchard management. Growers need to estimate the yield of trees at an early stage to make smart decisions for field management. However, methods to predict the yield at the individual tree level are currently not available because of the complexity and variability of each tree. To improve the accuracy of yield prediction, the authors evaluate the performance of an unmanned aerial vehicle (UAV)-based remote sensing system and machine learning (ML) approaches for tree-level pomegranate yield estimation. A lightweight multispectral camera was mounted on the UAV platform to acquire high-resolution images. Eight features were extracted from the UAV images, including the normalized difference vegetation index (NDVI), the green normalized vegetation index (GNDVI), the red-edge normalized difference vegetation index (NDVIre), red-edge triangulated vegetation index (RTVIcore), individual tree canopy size, the modified triangular vegetation index (MTVI2), the chlorophyll index-green (CIg), and the chlorophyll index-rededge (CIre). Correlation coefficients (R2 ) were calculated between these vegetation indices and tree yield. Classic machine learning approaches were applied with the extracted features to predict the yield at the individual tree level. Results showed that the decision tree classifier had the best prediction performance, with an accuracy of 85%. The study demonstrated the potential of using UAV-based remote sensing methods, coupled with ML algorithms, for pomegranate yield estimation. Predicting the yield at the individual tree level will enable the stakeholders to manage the orchard at different scales, thus improving field management efficiency. [ABSTRACT FROM AUTHOR]

Accurate knowledge of plant and field characteristics is crucial for irrigation management. Irrigation can potentially be better managed by utilizing data collected from various sensors installed on different platforms. The accuracy and repeatability of each data source are important considerations when selecting a sensing system suitable for irrigation management. The objective of this study was to compare data from multispectral (red and near-infrared bands) and thermal (long wave thermal infrared band) sensors mounted on different platforms to investigate their comparative usability and accuracy. The different sensor platforms included stationary posts fixed on the ground, the lateral of a center pivot irrigation system, unmanned aircraft systems (UAS), and Planet (PlanetScope multispectral imager, Planet Labs, Inc., San Francisco, Calif.) satellites. The surface reflectance data from multispectral (MS) sensors were used to compute the Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI). The experimental plots were managed with rainfed and irrigated treatments. Irrigation was applied according to a spatial evapotranspiration model informed with Planet satellite imagery. The NDVI and SAVI curves computed from the different sensing systems exhibited similar patterns and were able to capture differences between the rainfed and irrigated treatments when the crops were approaching senescence. Strong correlations were observed for canopy temperature measurements between the stationary and pivot-mounted infrared thermometer (IRT) sensors (p-value of less than 0.01 for the correlations) when canopy were scanned with no irrigation application (dry scans). The best correlation was obtained for the irrigated maize, which yielded r2 of 0.99, RMSE of 0.4°C, and MAE of 0.3°C. The correlation for the canopy temperature data collected during dry scan between UAS and pivot-mounted thermal sensors was weak with r2 = 0.26 to 0.28, larger RMSE values of 3.7°C and MAE values of 3.4°C. Secondary analysis between thermal data from stationary and pivot-mounted IRTs collected during wet scans (during an irrigation event) demonstrated reduced canopy temperature from pivot-mounted IRTs by approximately 2°C for irrigated soybean due to wetting of the canopy by the irrigation. Understanding the performance of these sensor systems is valuable in configuring practical design and operational considerations when using sensor feedback for irrigation management. [ABSTRACT FROM AUTHOR]

Plant yield and productivity are significantly affected by abiotic stresses such as water or nutrient deficiency. Automated timely detection of plant stress can mitigate stress development, thereby maximizing productivity and fruit quality. A multi-modal sensing system was developed and evaluated to identify the onset and severity of plant stress in young apple trees (cultivar 'Gale Gala') under five different water treatments in a greenhouse. The multi-modal sensors include a multispectral camera, an NDVI sensor, a digital camera, an ultrasonic range finder, and a thermal imager. Photosynthesis measurements for each water treatment group were recorded to determine photosynthesis reduction due to water stress and compared with multi-modal sensor responses. Data analysis determined that spectral signature (NDVI) and canopy temperature are highly correlated to plant water stress. The highest correlation to photosynthesis reduction was found for canopy temperature (r² = 0.83), followed by GreenSeeker NDVI (r² = 0.76) and multispectral camera (r² = 0.64). [ABSTRACT FROM AUTHOR]

Plant stress has been estimated by spectral signature using both passive and active sensors. As optical sensors measure reflected light from a target, changes in illumination conditions critically affect sensor response. Active spectral sensors minimize the illumination effects by producing their own illumination, which is reflected from the target and measured by the detector. Although active sensors use modulated radiation that can be differentiated from ambient illumination, sensor performance characteristics must be well understood and examined in different target conditions of plant foliage in order to validate the data and increase the accuracy. In this article, the performance of a commercial active spectral sensor, GreenSeeker, was evaluated to study the effects of: partial canopy coverage, target off-center, standoff distance, target surface tilting, wetness of target surface, illumination and temperature, bidirectional solar angle, and diurnal solar radiation. Experiments examined a valid range of sensor responses and identified a major effect of relative humidity that was amplified by moistened surfaces, resulting in an increase of NDVI response up to 41%. These evaluations illustrate the potentials and limitations of active spectral sensors for plant sensing and provide a guideline to understanding sensor performance in order to improve measurement accuracy. [ABSTRACT FROM AUTHOR]

Canopy temperature provides valuable information for efficient irrigation management and for detecting drought injury in a fast and non-destructive way. It also helps quantify the trade-offs between water conservation and the cooling benefit of the irrigated urban landscape, a critical issue in semiarid inland southern California with limited available water resources. Two adjacent hybrid bermudagrass and tall fescue irrigation trials were conducted to determine canopy temperature changes under a wide range of irrigation treatments from 2017 to 2019 in Riverside, California, USA. The canopy temperature data were also used to develop an empirical crop water stress index (CWSI) for each species. The CWSI values ranged from -0.15 to 0.71 for tall fescue and from -0.28 to 0.54 for hybrid bermudagrass. We observed a moderate correlation between visual rating scores (VR) and CWSI values for tall fescue (r = -0.68) and hybrid bermudagrass (r = -0.61). The fitted linear regression between VR and CWSI data suggested CWSI thresholds close to zero for both species to maintain their acceptable visual quality. The irrigation level consistently showed a significant effect on canopy temperature for both species. On average, a 10% decrease in irrigation application increased the canopy temperature of tall fescue and hybrid bermudagrass by 1.3°C and 0.9°C, respectively. Therefore, deficit irrigation might reduce the cooling benefits of irrigated urban lawns in the semiarid climate of inland southern California. [ABSTRACT FROM AUTHOR]

Water scarcity, climate change, and the need to sustain irrigated agriculture demand improved methods to manage irrigation water. Irrigation scheduling can be improved when remote sensing (RS) images are used because RS captures the actual crop field conditions. Mini-satellites (mS) are small spaceborne RS platforms that can provide high spatial and temporal resolution multispectral (MS) images. These multispectral images can be used to estimate maize actual evapotranspiration (ETa) on a daily basis. Thus, the main objective of this study was to assess the accuracy of maize ETa estimates when mS MS images were used in two different ETa algorithms. This study was conducted at two research sites in northern Colorado, USA. A reflectance-based crop coefficient and a one-source surface energy balance (OSSEB) models were applied, using MS Planet Dove images, to map maize ETa. An Eddy Covariance energy balance system was used to assess the accuracy of distributed ETa estimates. Results indicate that the reflectance-based crop coefficient method can be used with mS MS images yielding acceptable estimates. Both ETa models performed better in the case of the sub-surface irrigated maize. Further, OSSEB-based estimates of ETa model were more accurate over the surface irrigated maize and homogeneous soils; when surface reflectance images were paired with radiometric surface temperature data from groundbased infrared thermometers. These results imply that potentially maize ETa could be mapped, on a daily basis with good accuracy, using Planet Dove mS-based MS reflectance images and ground-based radiometric surface temperatures. [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]

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]

Vegetation indices (VIs) derived from remotely sensed imagery are commonly used to estimate crop yields. Spectral angle mapper (SAM) provides an alternative approach to quantifying the spectral differences among all pixels in an image and therefore has the potential for mapping yield variability. The objective of this study was to apply the SAM technique to airborne hyperspectral imagery for mapping yield variability. Airborne hyperspectral imagery was acquired from two grain sorghum fields in south Texas, and yield data were collected using a grain yield monitor SAM images were generated from the hyperspectral images based on six reference spectra extracted directly from the hyperspectral images and four reflectance spectra measured on the ground. Statistical analysis showed that the ten SAM images for each field produced similar correlation coefficients with yield. For comparison, all 5151 possible narrow-band normalized difference vegetation indices (NDVIs) were derived from the 102-band images and related to yield. Results showed that the SAM images based on the soil reference spectra provided higher correlation coefficients with yield than 75% and 92% of the 5151 narrow-band NDVIs for fields 1 and 2, respectively. Like an NDVI image, a SAM image can be easily generated from a hyperspectral image to characterize the spatial variability in yield. Moreover, since the best NDVI typically varies with yield datasets, a SAM image based on a single reference spectrum can be a better representation of yield variability if actual yield data are not available for the identification of the best NDVI. The results from this study indicate that the SAM technique can be used alone or in conjunction with other VIs for yield estimation from hyperspectral imagery. [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]

Cover crops are crops that occupy the soil between the end of a cash crop and the beginning of the next one, providing both agronomic and environmental benefits. To allow for the subsequent cultivation of the cash crop, cover crops must undergo an appropriate termination process based on winter frost, herbicide chemical treatments, or mechanical methods. For this latter approach, previous published research has only examined the effectiveness of termination obtained with individual mechanical tools without considering the possible synergistic effects of multiple tools with complementary actions. To address this gap, a modular multi-tool prototype has been developed in this work. It allows for the evaluation of the effects of different types of tools, both individually and in combination. The working modules of the prototype include: 1) tine cultivator; 2) disk harrow; and 3) crimper roller. Field trials were carried out in 2021 and 2022 on three fields of approximately 6000 m2, which were divided into 60 plots each and sown with vetch and barley. The plots were mechanically terminated with the prototype in different configurations, and the effectiveness of the different modules and their combinations was subsequently evaluated by measuring the NDVI index using ground sensing technology. The results indicate that the disk harrow 15°, used alone and in combination with other tools considered (crimper roller and tine cultivator for barley, crimper roller for vetch), offered the best termination effectiveness for both barley and vetch, with a termination rate of greater than or equal to 85%. [ABSTRACT FROM AUTHOR]

Water scarcity, climate change, and the need to sustain irrigated agriculture demand improved methods to manage irrigation water. Irrigation scheduling can be improved when remote sensing (RS) images are used because RS captures the actual crop field conditions. Mini-satellites (mS) are small spaceborne RS platforms that can provide high spatial and temporal resolution multispectral (MS) images. These multispectral images can be used to estimate maize actual evapotranspiration (ETa) on a daily basis. Thus, the main objective of this study was to assess the accuracy of maize ETa estimates when mS MS images were used in two different ETa algorithms. This study was conducted at two research sites in northern Colorado, USA. A reflectance-based crop coefficient and a one-source surface energy balance (OSSEB) models were applied, using MS Planet Dove images, to map maize ETa. An Eddy Covariance energy balance system was used to assess the accuracy of distributed ETa estimates. Results indicate that the reflectance-based crop coefficient method can be used with mS MS images yielding acceptable estimates. Both ETa models performed better in the case of the sub-surface irrigated maize. Further, OSSEB-based estimates of ETa model were more accurate over the surface irrigated maize and homogeneous soils; when surface reflectance images were paired with radiometric surface temperature data from groundbased infrared thermometers. These results imply that potentially maize ETa could be mapped, on a daily basis with good accuracy, using Planet Dove mS-based MS reflectance images and ground-based radiometric surface temperatures. [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]

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]

Growers in the inland Pacific Northwest face numerous challenges in managing cropping systems. Climate variability, soil degradation, and topography all lead to significant spatial and temporal variability in yield. Often, yield modeling approaches such as deep learning can be "black boxes" or suffer from parameter uncertainty and instability, as with process-based crop models. To explore an alternative, we examined nearly two decades of crop rotation data from the R.J. Cook Agronomy Farm, along with soil properties, topography, weather, and multispectral data. We then tested two modeling approaches to estimate yield: linear modeling (LM) and Bayesian hierarchical modeling (BHM). We found BHM with spatial and temporal random effects performed best in predicting relative yield, both using soil variables as predictors or remotely sensed data. Since the BHM approach handles missing data, offers the possibility of farmer knowledge to be incorporated into prior probabilities, and gives uncertainty, this methodology lends itself well to decision support tools and on-farm study design. [ABSTRACT FROM AUTHOR]

There is a rapidly growing interest in methods for automatic plant identification in agricultural research. Cotton (Gossypium spp.) is a crop well-suited to precision agriculture and its inherent goals of increasing yields while minimizing environmental impacts. Ten cotton (G. hirsutum and G. barbadense,) cultivars with differing leaf characteristics were evaluated in a greenhouse environment. Hyperspectral data collected with a handheld spectroradiometer were used to distinguish among the cultivars. The features extracted by principal component analysis and stepwise selection approaches were used for discriminant analysis. The best discrimination accuracy by selected wavelengths was 90.4% for G. hirsutum cultivars, 100% for G. barbadense cultivars, and 91.6% for pooled cultivars of the two species. Spectral wavelengths at 550 and 760 nm were most relevant to the discrimination between these two cotton species. Two vegetation indices, NDVI and PRI, were also investigated for any significant differences across cotton cultivars. The results demonstrated that hyperspectral radiometry has good potential for discrimination of G. hirsutum and G. barbadense cotton cultivars in early stages of growth. [ABSTRACT FROM AUTHOR]

Soil compaction can cause yield reductions in cotton (Gossypium hirsutum). This study investigated the effect of soil compaction on canopy spectral reflectance, soil electrical conductivity (EC), and cotton yield. Field experiments were conducted during 2003-2005 using a completely randomized block design with four soil compaction treatments. The treatments were no subsoiling (control); subsoiled, disked, and bedded (conventional); subsoiled and compacted (compaction I); and compacted with no subsoiling (compaction II). Field data were collected on soil resistance, canopy reflectance, soil EC, and cotton yield. Comparison of means showed differences between treatments in reflectance in 2003 and 2004, soil compaction parameters in 2004 and 2005, and soil EC and yield in 2005. The depth and thickness of the hardpan were significantly correlated to green NDVI on 16 September (R² = 0.53) in 2003. Depth, average resistance of hard pan, and EC all showed relationships with yield in 2005. Their combination as independent variables could explain 65% of the variability in cotton yield in 2005. These results verified that compaction affected canopy reflectance and reduced cotton yield in Arkansas. The practical implications of the outcome of this study are the potential use of EC and canopy reflectance to infer crop yield and extent of soil compaction. However, a multi-site and multi-year study is necessary to confirm this possibility. [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]

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]

Concerns over water availability and water quality increasingly require more efficient use of water resources in irrigated agriculture. Center pivot (CP) systems designed with variable rate irrigation (VRI) technology have the potential to improve application and water-use efficiency while reducing environmental impacts from excess runoff and poor water quality. The objective of this study was to develop a computer model (MOPivot) to automate management zone delineation for irrigation scheduling based on the minimum area that can be controlled by the CP system (based on sprinkler spacing and location, and wetted diameter) matched to VRI capabilities and GIS site-specific grid-based field data affecting water storage and runoff. To ensure that the CP was capable of meeting scheduled VRI prescriptions, application uniformity was validated along the lateral length for sprinkler pulse percentages of 20 to 100 percent using catch collectors spaced 3-m apart along thirteen radial lines (82 m long, beginning 68 m from the pivot point and covering 10° of pivot rotation) for both clockwise and counter-clockwise rotation. Results indicated that the system achieved high application uniformity over a range of VRI application depths. The MOPivot management zones were in close agreement with GIS grid-based soil survey geographic database (SSURGO) and soil apparent electrical conductivity (ECa) derived soil texture input data (82.8% and 93.5% overall accuracy, respectively). The model successfully established the minimum azimuthal angle and sector length for VRI zone-control VRI and automatically delineated management zones useful in developing VRI irrigation prescriptions. [ABSTRACT FROM AUTHOR]