Reflectance-based Crop Coefficient Research Articles

Maize Evapotranspiration Estimates Using Planet Dove Mini-Satellites and Field-Level InfraRed Thermometers

Highlights Multispectral images from mini-satellites can be used in a reflectance-based crop coefficient model to estimate crop water use. Surface reflectance from mini-satellites and temperature from in-situ infrared thermometers can be used in a land surface energy balance model to more accurately estimate maize actual evapotranspiration. It is feasible to monitor daily maize actual evapotranspiration using Planet Dove mini-satellites on cloud-free days. Abstract. 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 ground-based 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. Keywords: Agricultural water management, Crop water use, Irrigation, Land surface energy balance, Reflectance-based crop coefficients, Remote sensing.

A Multi-sensor Analysis of Selected Reflectance-Based Crop Coefficient Models for Daily Maize Evapotranspiration Estimation

This study evaluated three reflectance-based crop coefficient models (RBCC) for daily maize actual evapotranspiration (ETa) estimates, using multispectral data from spaceborne, airborne, and proximal platforms. The goal was to identify the optimal multispectral sensor that gives more accurate daily ETa estimates. The remote sensing (RS) multispectral platforms included Landsat-8, Sentinel-2, Planet CubeSat, handheld multispectral radiometer (MSR), and unmanned aerial system or UAS, spatial resolution from 30 m to 0.03 m. Three RBCC models that use different vegetation indices as input variables were evaluated in the study. One RBCC uses the normalized difference vegetation index (NDVI). The second model uses the soil-adjusted vegetation index (SAVI), and the third model uses canopy cover (fc). The data for this study were from two maize research sites in Greeley and Fort Collins, Colorado, USA, collected in 2020 and 2021. The Greeley site had a subsurface drip system, while the Fort Collins site had surface irrigation (furrow). Daily maize ETa predictions were compared with observed daily maize ETa data from an Eddy Covariance system installed at each research site. Results indicated that, depending on the RS of ETa algorithm and platform, the optimal input RS data was different. The MSR sensor (1 m) provided the best remote sensing data (input) for the SAVI-based RBCC ETa model, with a maize ETa error (MBE±RMSE) of -0.13 (-3%)±0.67 (16%) mm/d. Sentinel-2 was the best sensor for the remaining two RBCC daily maize ETa algorithms, since the errors for the NDVI-based and fc-based RBCC models for maize ETa were 0.21 (5%)±0.78 (18%) mm/d and 0.59 (14%)±1.07 (25%) mm/d, respectively. These results indicate the need for methods to improve the spectral quality of the remote sensing data to improve spatial ETa estimates and advance sustainable irrigation water management.

Estimation of Crop Water Deficit in Lower Bari Doab, Pakistan Using Reflection-Based Crop Coefficient

There is a global realization in all governmental setups of the need to provoke the efficient appraisal of crop water budgeting in order to manage water resources efficiently. This study aims to use the satellite remote sensing techniques to determine the water deficit in the crop rich Lower Bari Doab Canal (LBDC) command area. Crop classification was performed using multi-temporal NDVI profiles of Landsat-8 imagery by distinguishing the crop cycles based on reflectance curves. The reflectance-based crop coefficients (Kc) were derived by linear regression between normalized difference vegetation index (NDVI) cycles of the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD13Q1 and MYD13Q1 products and Food and Agriculture Organization (FAO) defined crop coefficients. A MODIS 250 m NDVI product of the last 10 years (2004-2013) was used to identify the best performing crop cycle using Fourier filter method. The meteorological parameters including rainfall and temperature substantiated the reference evapotranspiration (ET0) calculated using the Hargreaves method. The difference of potential ET and actual ET, derived from the reflectance-based Kc calculated using reference NDVI and current NDVI, generates the water deficit. Results depict the strong correlation between ET, temperature and rainfall, as the regions having maximum temperature resulted in high ET and low rainfall and vice versa. The derived Kc values were observed to be accurate when compared with the crop calendar. Results revealed maximum water deficit at middle stage of the crops, which were observed to be particularly higher at the tail of the canal command. Moreover, results also depicted that kharif (summer) crops suffer higher deficit in comparison to rabi (winter) crops due to higher ET demand caused by higher temperature. Results of the research can be utilized for rational allocation of canal supplies and guiding farmers towards usage of alternate sources to avoid crop water stress.

Temporal and spatial variations of irrigation water use for commercial corn fields in Central Nebraska

The increasing pressure on water resources in Nebraska-US and other agricultural areas requires the implementation of innovative tools and solutions for the governance of water resources and the analysis of water use efficiency. In this vein, this paper presents the application of a remote sensing based soil water balance for the study of water use in agricultural areas. The specific objectives were the identification of the temporal and spatial behavior of the irrigation water use based on the quantification of the water use deviation (irrigation water applied minus irrigation water requirements), as the main indicator and the comparative analysis of the irrigation productivity (crop yield under irrigated field minus crop yield under rainfed condition per volume of water applied by irrigation), WPi, water productivity (harvestable grain per total volume of water applied considering precipitation plus irrigation), WP, and finally water productivity based on evapotranspiration (harvested grain per total volume of water evapotranspired), WPET, in the various management zones analyzed. Additionally, we examined the impact of soil types, local weather and irrigation system (center pivot and furrow irrigation) on these indicators. The study was carried out in three Natural Resources District (Tri-Basin, Central Platte and Lower Niobrara) across Central Nebraska for the period 2004–2012 and comprised over 2000 irrigated corn fields per year. Crop water requirements were estimated using the reflectance-based crop coefficient approach developed in previous research (see Campos et al., 2017) and the field data were reported for each field monitored through cropland data layer by National Agricultural Statistics Service of USDA. The difference between modeled irrigation water requirements and field level irrigation application was significant (p < 0.001) being the water use deviation in generally positive (over irrigation). These results were consistently higher for furrow irrigated fields during the whole analyzed period, reaching up to three times more water applied compared to the required amount. This was expected as surface irrigation systems typically require a higher application depth. This trend changed for the central pivot irrigated fields depending on the climatic conditions, especially in dry years. The analysis of the water use deviation with respect to soil types and weather conditions revealed that the water use deviation is not justified by the biophysical conditions alone. The estimated values of WP and WPi for furrow system was lower compared to center pivot in both NRD’s reaching the maximum value of 1.37 kg m-3 and 3.06 kg m-3 for WP and WPi in Tri-basin respectively for center pivot. In general, the results suggested potential to improve water management in these NRDs in Central Nebraska and reduce pumping potentially saving groundwater resources for drought years and other uses monitoring soil type, weather data and switching to sprinklers system.

Evaluation of variable rate irrigation using a remote-sensing-based model

Improvements in soil water balance modeling can be beneficial for optimizing irrigation management to account for spatial variability in soil properties and evapotranspiration (ET). A remote-sensing-based ET and water balance model was tested for irrigation management in an experiment at two University of Nebraska-Lincoln research sites located near Mead and Brule, Nebraska. Both fields included a center pivot equipped with variable rate irrigation (VRI). The study included maize in 2015 and 2016 and soybean in 2016 at Mead, and maize in 2016 at Brule, for a total of 210 plot-years. Four irrigation treatments were applied at Mead, including: VRI based on a remote sensing model (VRI-RS); VRI based on neutron probe soil water content measurement (VRI-NP); uniform irrigation based on neutron probe measurement; and rainfed. Only the VRI-RS and uniform treatments were applied at Brule. Landsat 7 and 8 imagery were used for model input. In 2015, the remote sensing model included reflectance-based crop coefficients for ET estimation in the water balance. In 2016, a hybrid component of the model was activated, which included energy-balance-modeled ET as an input. Both 2015 and 2016 had above-average precipitation at Mead; subsequently, irrigation amounts were relatively low. Seasonal irrigation was greatest for the VRI-RS treatment in all cases because of drift in the water balance model. This was likely caused by excessive soil evaporation estimates. Irrigation application for the VRI-NP at Mead was about 0 mm, 6 mm, and –12 mm less in separate analyses than for the uniform treatment. Irrigation for the VRI-RS was about 40 mm, 50 mm, and –98 mm greater in separate analyses than the uniform at Mead and about 18 mm greater at Brule. For maize at Mead, treatment effects were primarily limited to hydrologic responses (e.g., ET), with differences in yield generally attributed to random error. Rainfed soybean yields were greater than VRI-RS yields, which may have been related to yield loss from lodging, perhaps due to over-irrigation. Regarding the magnitude of spatial variability in the fields, soil available water capacity generally ranked above ET, precipitation, and yield. Future research should include increased cloud-free imagery frequency, incorporation of soil water content measurements into the model, and improved wet soil evaporation and drainage estimates.

Irrigation Performance Assessment in Table Grape Using the Reflectance-Based Crop Coefficient

In this paper, we present the results of our study on the operational application of the reflectance-based crop coefficient for assessing table grape irrigation requirements. The methodology was applied to provide irrigation advice and to assess the irrigation performance. The net irrigation water requirements (NIWR) simulated using the reflectance-based basal crop coefficient were provided to the farmer during the growing season and compared with the actual irrigation volumes applied. Two treatments were implemented in the field, increasing and reducing the irrigation doses by 25%, respectively, compared to the regular management. The experiment was carried out in a commercial orchard during three consecutive growing seasons in Northern Chile. The NIWR based on the model was approximately 900 mm per season for the orchard at tree maturity. The experimental results demonstrate that the regular irrigation applied covered only 76% of the NIWR for the whole season, and the analysis of monthly and weekly accumulated values indicates several periods of water shortage. The regular management system tended to underestimate the water requirements from October to January and overestimate the water requirements after harvest from February to April. The level of the deficit of water was quantified using such plant physiological parameters as stem water potential, vegetative development (coverage), and fruit productivity. The estimated NIWR was roughly covered in the treatment where the irrigation dose was increased, and the analyses of the crop production and fruit quality point to the relative advantage of this treatment. Finally, we conclude that the proposed approach allows the analysis of irrigation performance on the scale of commercial fields. These analytic capabilities are based on the well-demonstrated relationship of the crop evapotranspiration with the information provided by satellite images, and provide valuable information for irrigation management by identifying periods of water shortage and over-irrigation.

Reflectance-based crop coefficients REDUX: For operational evapotranspiration estimates in the age of high producing hybrid varieties

Methodologies based on earth observation remote sensing allow for a precise estimation of actual water requirements for irrigated crops across large areas. In spite of the many number of experiments using or analyzing the relationship between the basal crop coefficient (Kcb) and the soil adjusted vegetation index (SAVI) for maize, the development of new maize hybrid varieties with modifications related to canopy architecture suggest a possible change of the leaf area index (LAI) for maximum Kcb and its relationship with the SAVI or other vegetation indices. In addition, we noted a lack of analysis of these relationships for cultivated soybean. The objective of this paper is to analyze the Kcb, SAVI and LAI relationships in maize and soybean based on the non-linear relationships proposed by Choudhury et al. (1994). In addition, we propose a modification of the Choudhury et al. (1994) approach based on field-based experimental evidence of a minimum Kcb greater than 0. For sites with limited field data, we also analyze the utility of a simple linear regression based on the Kcb and SAVI values for bare soil and maximum Kcb values. The resulting Kcb-SAVI relationships are assimilated into a remote sensing based soil water balance model. The results of the model are analyzed in terms of irrigation requirements and crop evapotranspiration (ETa) for 11 growing seasons in two fields cultivated with irrigated and rain-fed maize and soybean in eastern Nebraska. Comparisons of measured and modelled ETa values indicate a good agreement, with RMSE lower than 0.7mmd−1 for weekly averaged values. The comparison of actual irrigation applied and irrigation requirements indicate the central pivot systems could not supply adequate water in some growing seasons with higher demands.

Comparisons of satellite-based models for estimating evapotranspiration fluxes

Two different types of remote sensing-based techniques were applied to assess the mass and energy exchange process within the continuum soil–plant-atmosphere of a typical Mediterranean crop. The first approach computes a surface energy balance using the radiometric surface temperature (Ts) for estimating the sensible heat flux (H), and obtaining the evapotranspiration fluxes (ET) as a residual of the energy balance. In the paper, the performance of two different surface energy balance approaches (i.e. one-source and two-source (soil + vegetation)) was compared. The second approach uses vegetation indices (VIs), derived from the canopy reflectance, within the FAO-based soil water balance approach to estimate basal crop coefficients to adjust reference ET0 and compute crop ET. Outputs from these models were compared to fluxes of sensible (H) and latent (LE) heat directly measured by the Eddy Covariance method, through a long micrometeorological monitoring campaign carried out in the area of interest. The two-source (2S) model gave the best performance in terms of surface energy fluxes and ET rate estimation, although the overall performance of the three approaches was appreciable. The reflectance-based crop coefficient model has the advantages to do not require any upscaling of the instantaneous ET fluxes from the energy balance models to daily integrated ET. However, its results may be less sensitive to detect crop water stress conditions respect to approaches based on the radiometric surface temperature detection.

Evapotranspiration mapping for agricultural water management: An overview

Evapotranspiration (ET) is an essential component of the water balance. Any attempt to improve water use efficiency must be based on reliable estimates of ET, which includes water evaporation from land and water surfaces and transpiration by vegetation. ET varies regionally and seasonally according to weather and wind conditions. Remote sensing based agro-meteorological models are presently most suited for estimating crop water use at both field and regional scales. Numerous ET algorithms have been developed to make use of remote sensing data acquired by sensors on airborne and satellite platforms. The use of remote sensing to estimate ET is presently being developed along two approaches: (a) land surface energy balance (EB) method and (b) Reflectance based crop coefficient and reference ET approach. The reported estimation accuracy varied from 67 to 97% for daily ET and above 94% for seasonal ET indicating that they have the potential to estimate regional ET accurately. Automated contours are not confined to specific pre-determined geographic areas (as in MLRA), require less time and cost. The spatial and temporal remote sensing data from the existing set of earth observing satellite platforms are not sufficient enough to be used in the estimation of spatially distributed ET for on-farm irrigation management purposes, especially at a field scale level (~10 to 200 ha). However, research opportunities exist to improve the spatial and temporal resolution of ET by developing algorithms to increase the spatial resolution of reflectance and surface temperature data derived from K1VHRR/Landsat/ASTER/MODIS images using same/other-sensor high resolution multi-spectral images.

Hyperspectral remote sensing for growth-stage-specific water use in wheat

Precise application of irrigation water to crops requires an accurate calculation of daily crop evapotranspiration (ET), which has always remained a challenge to the scientific community. Reflectance-based crop coefficients approach has a strong theoretical base, as both the crop coefficient [ratio of actual crop (ETc) and reference ET (ET0)] and remote sensing of crop follow a similar response curve mediated by crop growth stages and crop health conditions. This paper investigates the feasibility of linking the evolution of basal crop coefficient (Kcb) of wheat to the hyperspectral remote sensing derived vegetation indices, through leaf area index (LAI), the principal plant growth parameter. Two years field experiments were conducted with three cultivars of wheat (Triticum aestivum L.) under adequate (6-cm each irrigation) and limited (4-cm each irrigation) water supply. Ground based observations on profile water balance components, hyperspectral remote sensing, fractional ground coverage, LAI, water potential and relative water content in leaves were monitored periodically. Biomass and yields were recorded at harvest. Limited water application forced the crop to attain its peak crop coefficients and LAI values early (at flowering, 80–95DAS), compared to at milking stage (90–105DAS) under adequate water supply. Basal crop coefficients (Kcb), indicative of transpiration in plants were able to generate a better estimate of the stage-specific crop water use. The prospect of its retrieval through hyperspectral remote sensing was demonstrated. A reduction in Kcb could be primarily due to reduction in LAI in wheat, especially when soil moisture was not a limiting factor. Exclusion of residual evaporation and minimizing background effect of soil made the evolution of Kcb similar to LAI and LAI similar to Soil Adjusted Vegetation Index (SAVI). These imply that the transpiration and light absorption profile of the crop increase or decrease with nearly the same rate throughout its growth period. The LAI saturated at a value of 3 in limited and 4 in adequate irrigation treatments suggesting that once the canopy coverage is complete, further increase in LAI might not lead to an increase in single crop coefficient values. Interestingly, SAVI showed a linear response to Kcb, and also did not saturate before the LAI reached to 4.5 (LAI>4.0 is reported in full developed canopies of wheat). This makes SAVI superior than NDVI (Normalized Difference Vegetation Index), which saturates at LAI>3.5, for retrieving crop coefficient; and improving the accuracy in predicting crop water use at specific stages. These relations have high potential at an operational scale for irrigation scheduling over extended wheat growing areas like Indo-Gangetic Plains, through use of high resolution earth observation satellite data.

Soil water content estimation using a remote sensing based hybrid evapotranspiration modeling approach

Remote sensing of evapotranspiration (ET) has evolved over the last 20 years with the development of more robust energy balance approaches and the availability of timely remotely sensed imagery from satellite sensors. This has allowed the use of remote sensing for near-real time water management in irrigated systems in the western United States. In this paper a hybrid ET approach is applied to irrigated and non-irrigated cotton fields at the BEAREX08 experimental site using airborne remote sensing inputs under highly advective conditions, taking advantage of the available root zone soil water content measurements for verification of model output. The modeling approach is based on coupling the Two-Source-Energy Balance (TSEB) and the reflectance-based crop coefficient models. The TSEB model provides estimates of real crop ET while the reflectance-based crop coefficient approach allows for updating the basal crop coefficient and the interpolation and extrapolation of ET between the dates of remote sensing inputs facilitating the maintenance of a soil water balance in the root zone of the crop. Actual ET estimates using the TSEB model were compared with measured ET using eddy covariance systems deployed in four cotton fields during the BEAREX08 experiment. Estimates of soil water content in the soil profile of both irrigated and rain fed cotton fields were compared with measurements at different depths using neutron probe observations. Data assimilation techniques were applied to update soil water content values using estimates based on actual ET from the TSEB model. Results indicate that the hybrid ET modeling approach using data assimilation produced reliable daily ET interpolated between remote sensing observations and significantly improved soil water content estimates throughout the root zone profile compared to applying the crop-coefficient technique in a water balance model without the actual ET inputs.

A comparison of operational remote sensing-based models for estimating crop evapotranspiration

The integration of remotely sensed data into models of evapotranspiration (ET) facilitates the estimation of water consumption across agricultural regions. To estimate regional ET, two basic types of remote sensing approaches have been successfully applied. The first approach computes a surface energy balance using the radiometric surface temperature for estimating the sensible heat flux ( H), and obtaining ET as a residual of the energy balance. This paper compares the performance of three different surface energy balance algorithms: an empirical one-source energy balance model; a one-source model calibrated using inverse modeling of ET extremes (namely ET = 0 and ET at potential) which are assumed to exist within the satellite scene; and a two-source (soil + vegetation) energy balance model. The second approach uses vegetation indices derived from canopy reflectance data to estimate basal crop coefficients that can be used to convert reference ET to actual crop ET. This approach requires local meteorological and soil data to maintain a water balance in the root zone of the crop. Output from these models was compared to sensible and latent heat fluxes measured during the soil moisture–atmosphere coupling experiment (SMACEX) conducted over rain-fed corn and soybean crops in central Iowa. The root mean square differences (RMSD) of the estimation of instantaneous latent and heat fluxes were less than 50 W m −2 for the three energy balance models. The two-source energy balance model gave the lowest RMSD (30 W m −2) and highest r 2 values in comparison with measured fluxes. In addition, three schemes were applied for upscaling instantaneous flux estimates from the energy balance models (at the time of satellite overpass) to daily integrated ET, including conservation of evaporative fraction and fraction of reference ET. For all energy balance models, an adjusted evaporative fraction approach produced the lowest RMSDs in daily ET of 0.4–0.6 mm d −1. The reflectance-based crop coefficient model yielded RMSD values of 0.4 mm d −1, but tended to significantly overestimate ET from corn during a prolonged drydown period. Crop stress can be directly detected using radiometric surface temperature, but ET modeling approaches-based solely on vegetation indices will not be sensitive to stress until there is actual reduction in biomass or changes in canopy geometry.

Development and validation of canopy reflectance-based crop coefficient for potato

The present paper describes the development and validation of canopy reflectance-based crop coefficients for potato using canopy reflectance (measured using hand-held radiometers and high resolution multispectral digital imagery), and extensive crop biophysical sampling in selected potato growing fields in and around Kimberly, Idaho, during 1998 and 1999 seasons. Daily crop evapotranspiration was estimated using basal and canopy reflectance-based crop coefficients, and a hydrologic water balance was conducted in the plant root zone. Independent measurements of actual soil moisture measurements were made by neutron probe and gravimetric methods, and used to validate the simulated results. The results validate the reflectance-based crop coefficient method. High resolution multispectral aerial imagery was used to highlight the spatial variability of actual crop water demand in the study fields.

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 ( E t). Previous work by Neale et al. (1989) and Bausch (1993) have indicated that the reflectance-based crop coefficient ( K cr) 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 K cr in irrigation scheduling and present results of simulations comparing different basal crop coefficient ( K cb) curves for corn to evaluate their effects on estimated crop E t. Irrigation scheduling simulations were performed using SCHED, the USDA-ARS Irrigation Scheduling Program, and three K cb curves (the one in SCHED, Wright's (1982) tabular data, and the K cr-based K cb). Simulated crop water use using the K cb curve in SCHED was considerably greater during vegetative growth (60 to 100 mm) than simulated crop water use using Wright's K cb or the K cr derived K cb curves for three growing seasons. Crop water use between the K cr-based K cb and Wright's K cb were different by approximately 20 mm each growing season. Crop water use was less in 1990 and 1992 for the K cr 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 K cr-based K cb were more appropriately timed. Irrigations that are correctly timed minimize overirrigation as well as underirrigation.

Soil background effects on reflectance-based crop coefficients for corn

A previously developed reflectance-based crop coefficient ( K cr ) for corn, estimated from the normalized difference vegetation index (NDVI), has been shown to overestimate the basal crop coefficient ( K cb ) 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 K cb for corn and the SAVI was used to convert the SAVI into the K cr . The maximum difference for this K cr between the light, dry soil background and the dark, wet soil background (extreme cases) was less than 6%. The K cr 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.

Development of Reflectance-Based Crop Coefficients for Corn

ABSTRACT Concurrent measurements of reflected canopy radia-tion and the basal crop coefficient (K^b) for corn were conducted throughout a season in order to develop a reflectance-based crop coefficient model. Reflectance was measured in Landsat Thematic Mapper bands TM3 (0.63 - 0.69 um) and TM4 (0.76 - 0.90 um) and used in the calculation of a vegetation index called the normalized difference (ND). A linear transformation of the ND was used as the reflectance-based crop coefficient (Kcr). The transformation equates the ND for dry bare soil and the ND at effective cover, to the basal crop coefficient for dry soil evaporation and at effective cover, respectively. Basal crop coefficient values for com were obtained from daily evapotranspiration measurements of corn and alfalfa, using hydraulic weighing lysimeters. The Richards growth curve function was fitted to both sets of data. The K^b values were determined to be within -2.6% and 4.7% of the K^^ values. The date of effective cover obtained from the K^b data was within four days of the date on which the ND curve reached its maxima according to the Richards function. A comparison of the Kcr with basal crop curves from the literature for several years of data indicated good agreement. Reflectance-based crop coefficients are sensitive to periods of slow and fast growth induced by weather conditions, resulting in a real time coefficient, independent from the traditional time base parameters based on the day of planting and effective cover.

Spectral Inputs Improve Corn Crop Coefficients and Irrigation Scheduling

ABSTRACT An algorithm was developed to shift the basal crop coefficient (K^b) curve with respect to its time axis to obtain a K^b in accordance with the real time reflectance-based crop coefficient for corn. Adjustment of the emergence and effective cover dates during vegetative growth made these dates converge on their actual occurrence. Impact of adjustments to the K^b curve on irrigation dates was simulated using the USDA-ARS irrigation scheduling model for two seasons. Simulated results for the 1983 growing season showed one or two-day differences in the occurrence of irrigation dates. Prior to effective cover, irrigations occurred earlier for the simulation using feedback (adjusted K^b). After effective cover, irrigation dates lagged for the with-feedback simulation compared to the without-feedback simulation. An extra irrigation was required prior to effective cover in 1983 for the simulation using feedback. In 1986, one less irrigation was required for the simulation using feedback to the irrigation scheduling model. This was due to K^b curve adjustments that accounted for hail damage and ensuing cool weather that slowed plant growth during early vegetative growth. The first two irrigations were delayed by two and three days, respectively, due to these events for the with-feedback simulation when compared to simulated results obtained not using feedback. Results from the simulation indicated the following: 1) revised or adjusted K^b curves derived from spectral inputs are unique to the individual growing season, 2) conventional crop coefficients contribute to underestimation as well as overestimation of crop ET because they cannot account for variable crop growth rates, and 3) adjusting the K^b curve in response to actual crop growth allows proper timing of irrigations to ensure that soil moisture conditions are ideal throughout the growing season.