Walter C. Bausch

Soil Background Effects on Reflectance-Based Crop Coefficients for Corn

A previously developed reflectance-based crop coefficient (Kcr) for corn, estimated from the normalized difference vegetation index (NDVI), has been shown to overestimate the basal crop coefficient (Kcb) for corn by 24% or more when used with a dark-colored soil. This overestimation occurs because the NDVI produces larger index values for the same vegetation amount over dark backgrounds. Thus, the purpose of this article was to investigate newer vegetation indices that have been developed to minimize soil background effects and to develop a reflectance-based crop coefficient for corn that applies over a wide range of agricultural soil reflectance. Two soils (light- and dark-colored) with red reflectance of 31% and 13%, respectively, were selected for this study. Reflectance data of the corn canopy were acquired with four combinations of these soils (light, dry; light, wet; dark, dry; and dark, wet) in trays inserted at the same place beneath the corn canopy. The soil adjusted vegetation index (SAVI), with an adjustment factor (L) set to 0.5, was found to adequately minimize soil background influences from sparse to dense vegetation conditions. A linear transformation between the Kcb for corn and the SAVI was used to convert the SAVI into the Kcr. The maximum difference for this Kcr between the light, dry soil background and the dark, wet soil background (extreme cases) was less than 6%. The Kcr based on the SAVI corrects for a wet soil surface and requires no additional calibration to estimate the basal crop coefficient for corn grown on most agricultural soils.

Remote sensing of crop coefficients for improving the irrigation scheduling of corn

Improved irrigation water management requires accurate scheduling of irrigations which in turn requires an accurate calculation of daily crop evapotranspiration (Et). Previous work by Neale et al. (1989) and Bausch (1993) have indicated that the reflectance-based crop coefficient (Kcr) for corn responded to crop growth anomalies and should improve irrigation scheduling. Thus, the purpose of this study was to develop a new procedure for using the Kcr in irrigation scheduling and present results of simulations comparing different basal crop coefficient (Kcb) curves for corn to evaluate their effects on estimated crop Et. Irrigation scheduling simulations were performed using SCHED, the USDA-ARS Irrigation Scheduling Program, and three Kcb curves (the one in SCHED, Wrights (1982) tabular data, and the Kcr-based Kcb). Simulated crop water use using the Kcb curve in SCHED was considerably greater during vegetative growth (60 to 100 mm) than simulated crop water use using Wrights Kcb or the Kcr derived Kcb curves for three growing seasons. Crop water use between the Kcr-basedKcb and Wrights Kcb were different by approximately 20 mm each growing season. Crop water use was less in 1990 and 1992 for the Kcr derived curve and greater for 1991; crop development was directly responsible for the differences. Although the differences between the Wright and Kcr basal crop curves were minimal, irrigations with the Kcr-based Kcb were more appropriately timed. Irrigations that are correctly timed minimize overirrigation as well as underirrigation.

Leaf area index estimation from visible and near-infrared reflectance data

A two-stream description of the interaction of radiation with vegetation and soil is tested with experimental data for a corn canopy. The results indicate that the two parameters of the theory (reflectance of a dense canopy and the attenuation coefficient for radiation in the canopy) can be estimated for the Thematic Mapper spectral bands. A collection of soil reflectance data is used to develop relationships between near-infrared reflectance and visible reflectances. Jointly the canopy and soil formulations verify the potential for estimating leaf area index from radiation measurements in the visible and near-infrared.

INNOVATIVE REMOTE SENSING TECHNIQUES TO INCREASE NITROGEN USE EFFICIENCY OF CORN

Nitrogen (N) fertilizer recommendations made without adequate knowledge of the N supply capability of a soil can lead to ineffi-cient use of N. Proper crediting of N from manure and legumes as well as mineralization of N from organic matter is difficult. Remote sensing techniques that use the crop to indicate its N status show considerable promise for improving N management. Objectives of this paper were twofold: 1) to compare the N Reflectance Index (NRI) calculated from ground-based radiometer measurements acquired over irrigated corn (Zea mays L.) at a nadir view (0°) and an oblique view (75°) with measured plant N and 2) to evaluate the NRI obtained from both view angles for correcting in-season N deficiencies in a commercial corn field. The NRI calculated from canopy reflectance was not representative of plant N at the sixth leaf growth stage (V6) for either view angle because of the soil background influence on canopy reflectance. However, the oblique view NRI was a good predictor of plant N at V9 and V12 as was the nadir view NRI at V12. The nadir view NRI was not as sensitive as the oblique view NRI at the V9 growth stage because soil was still visible through the canopy. Consequently, the nadir view NRI provides a conservative estimate of plant N prior to complete canopy cover. Use of the nadir view NRI to detect in-season corn N deficiencies for the 1999 growing season reduced N application during the growing season by 39.2 kg N ha−1without reducing grain yield. If the oblique view NRI would have been used to assess the plant N status, the first fertigation would not have been recommended which would have saved additional N.

Potential Use of Nitrogen Reflectance Index to estimate Plant Parameters and Yield of Maize

Abstract Estimating the spatial variability of various plant parameters during the growing season can assist in timely correction of stress conditions within a field. This research illustrates that the nitrogen reflectance index (NRI) developed to estimate plant nitrogen status can be used to estimate plant parameters and yield potential. The study was conducted on two experimental maize sites. Selected maize hybrids were ‘Pioneer 3790’, which was a planophile canopy architecture and ‘NC+ 1598’ with an erectophile canopy architecture. The first site consisted of six non-replicated fertiliser plots. Data from these plots were used to develop the relationships between reflectance data and the plant parameters. The second site contained four plots with various nitrogen (N) and water treatments on which the developed relationships were verified. Leaf area, biomass, and plant reflectance data were collected almost weekly from both sites during the 1996 growing season. Measured and estimated yield, leaf area index (LAI) and dry matter were mapped in ArcVIEW geographical information system. Results showed that the NRI was a comparable estimator of potential yield to the normalised difference vegetation index or to the modified soil adjusted vegetation index. For the LAI and biomass, all vegetation indices produced similar coefficients of determination. Results showed that the NRI could be used to estimate the within-field variation of yield potential and plant parameters.

QuickBird satellite versus ground-based multi-spectral data for estimating nitrogen status of irrigated maize

In-season nitrogen (N) management of irrigated maize (Zea mays L.) requires frequent acquisition of plant N status estimates to timely assess the onset of crop N deficiency and its spatial variability within a field. This study compared ground-based Exotech nadir-view sensor data and QuickBird satellite multi-spectral data to evaluate several green waveband vegetation indices to assess the N status of irrigated maize. It also sought to determine if QuickBird multi-spectral imagery could be used to develop plant N status maps as accurately as those produced by ground-based sensor systems. The green normalized difference vegetation index normalized to a reference area (NGNDVI) clustered the data for three clear-day data acquisitions between QuickBird and Exotech data producing slopes and intercepts statistically not different from 1 and 0, respectively, for the individual days as well as for the combined data. Comparisons of NGNDVI and the N Sufficiency Index produced good correlation coefficients that ranged from 0.91 to 0.95 for the V12 and V15 maize growth stages and their combined data. Nitrogen sufficiency maps based on the NGNDVI to indicate N sufficient (≥0.96) or N deficient (<0.96) maize were similar for the two sensor systems. A quantitative assessment of these N sufficiency maps for the V10–V15 crop growth stages ranged from 79 to 83% similarity based on areal agreement and moderate to substantial agreement based on the kappa statistics. Results from our study indicate that QuickBird satellite multi-spectral data can be used to assess irrigated maize N status at the V12 and later growth stages and its variability within a field for in-season N management. The NGNDVI compensated for large off-nadir and changing target azimuth view angles associated with frequent QuickBird acquisitions.

Evaluation and Refinement of the Nitrogen Reflectance Index (NRI) for Site-Specific Fertilizer Management

Environmental and economic concerns have collectively created a demand for more efficient agricultural fertilizer application. Remote sensing of plant parameters has recently shown the potential to improve fertilizer application efficiency by showing corn (Zea mays L.) producers when and where to apply fertilizers based on crop needs. Previous studies have indicated that a normalized near-infrared over green (nir/green) waveband reflectance ratio, called the Nitrogen Reflectance Index (NRI), could be used to assess in-season corn N status (CNS) in small plots. This study evaluated the NRI using small plot data and then tested possible index improvements in a larger area. Data from four small plot site years indicated that the NRI explained > 70% of the variability occurring with in-season CNS for canopies with Leaf Area Index (LAI) ³ 2. These results led to the development of an early-season feasibility indicator (ESFI) – a non-invasive method of assessing LAI. This created a simplistic guideline for appropriate use of the NRI based on minimum ground-cover. The ESFI, calculated by subtracting the green and red reflectance, utilized a minimum threshold level of 0.01 to remove measurements where LAI < 2. Another proposed improvement to the NRI illustrated that the cumulative density function of the nir/green ratio could be used to develop N variability maps and improve fertilizer management. Further testing within a 22.5 ha area was used to determine platform compatibility and performance comparisons of the NRI versus other indices. After filtering with the ESFI, results showed strong compatibility between aerial imagery and ground-based radiometer measurements. Comparing the NRI to the NDVI, Green NDVI, SAVI, and MSAVI showed that all of these indices accurately assessed N variability based on total N uptake measurements collected.

WATER PRODUCTION FUNCTIONS FOR CENTRAL PLAINS CROPS

Sustaining irrigated agriculture with limited water supplies requires maximizing productivity per unit of water. Relationships between crop production and water consumed are basic information required to maximize productivity. This information can be used to determine if deficit irrigation is economically desirable and how to best manage limited water supplies. Field trials of corn, sunflower, dry bean, and wheat production with six levels of water application were used to develop water production functions based on consumptive use and to better understand water timing effects and crop responses to stress. Initial results indicate linear relationships between yield and crop ET and transpiration. The field data are being used to improve and validate crop models so they can be used to generalize the field results for other climate and soil characteristics.

LOW GROUND-COVER FILTERING TO IMPROVE RELIABILITY OF THE NITROGEN REFLECTANCE INDEX (NRI) FOR CORN N STATUS CLASSIFICATION

Recent advancements in low-cost remote sensing equipment and techniques have proven that crop parameter classification can be practical in agricultural situations. One problem still remaining is how to account for in-field variability of mixed ground-cover areas. This study focused on finding a non-invasive method of screening low ground-cover areas before assessing crop nitrogen (N) status. Four years of plot data were used where intense ground sampling was coupled with nadir-viewing remotely sensed data from a ground-based remote sensing system. Data were collected over irrigated corn (Zea mays L.) with both light and dark soil backgrounds to develop a relationship between leaf area index (LAI) and remotely sensed data. An index based on subtracting the red from the green reflectance (green - red) was shown to be strongly related to LAI. A concurrent assessment of the N reflectance index (NRI) over the four-year study indicated that the corn N status classification improved with increasing LAI. Using the (green - red) index and a screening value corresponding to an LAI of 2.5, the data from the four-year study were screened and the NRI was reassessed. The initial screening removed all bare-soil and many of the measurements collected before the V12 growth stage. In this example, 94.0% of the data associated with LAI below 2.5 was correctly filtered out, and 95.4% of the data collected over areas with LAI at or above 2.5 correctly passed the filter. This method of filtering was shown to be highly effective in screening low ground-cover and bare-soil areas before classification. Future incorporation of this technique into on-the-go filtering algorithms could improve crop parameter classification from remotely sensed measurements in early-season and mixed vegetative cover situations.

Robotic Data Acquisition of Directional Reflectance Factors

Abstract A data collection platform for rapid and repeatable positioning of a down-looking radiometer was constructed using commercially available instru mentation and hardware. The platform also accommodated a second radiometer to measure irradiance at the same instant the other radiometer measured target radiance. Stepper motors position the down-looking radiometer at programmed view zenith and view azimuth angles. A Polycorder and stepper motor interface control the stepper motors while data acquisition is the sole responsibility of the Polycorder. Less than 15 min is required to measure target radiance from a full set of measurements consisting of 182 combinations of view zenith and view azimuth angles. Positioning accuracy of the viewing radiometer is within ± 0.2° for a nominal 15° angular movement. The system has proven to be dependable, easy to use, highly mobile, and adaptable.