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The state and crop reporting district crop calendars for Iowa, Illinois, and Indiana are presented. Crop calendars for corn, soybeans, sorghum, oats, wheat, barley, clover, flax, sugar beets, and tobacco are included.
An overall approach to crop spectral understanding is presented which serves to maintain a strong link between actual plant responses and characteristics and spectral observations from ground based and spaceborne sensors. A specific technique for evaluating field reflectance data, as a part of the overall approach, is also described. Results of the application of this technique to corn and soybeans reflectance data collected by and at Purdue/LARS indicate that a number of common cultural and environmental factors can significantly affect the temporal spectral development patterns of these crops in tasseled cap greenness (a transformed variable of LANDSAT MSS signals).
A coherent optical technique for the diffraction analysis of an image is presented. Developments in radar remote sensing shows a need to understand plant geometry and its relationship to plant moisture, soil moisture, and the radar backscattering coefficient. A corn plant changes its leaf angle distribution, as a function of time, from a uniform distribution to one that is strongly vertical. It is shown that plant and soil moisture may have an effect on plant geometry.
Red, photographic infrared, near infrared spectral data of corn, cotton, soybeans, sugar beets, sorghum, sunflowers and tobacco were collected throughout the entire growing season by using a three band handheld radiometer. Different radiance patterns were found among these crops based on their morphology, green biomass duration and leaf size. Results show near infrared radiance is a good indicator of water content in plant tissue under small scale experimental conditions.
A physical model is presented, which has been derived from multitemporal-multispectral data acquired by Landsat satellites to describe the behavior and new features that are crop specific. A feasibility study over 40 sites was performed to classify the segment pixels into those of corn, soybeans, and others using the new features and a linear classifier. Results agree well with other existing methods, and it is shown the multitemporal-multispectral scanner data can be transformed into two parameters that are closely related to the target of interest and thus can be used in classification. The approach is less time intensive than other techniques and requires labeling of only pure pixels.
Some essential processes for the development of a green-number-based logic for identifying (labeling) crops in LANDSAT imagery are documented. The supporting data and subsequent conclusions that resulted from development of a specific labeling logic for corn and soybean crops in the United States are recorded.
Models based on multiple regression were developed to estimate corn and soybean yield from weather data for agrophysical units (APU) in Iowa. The predictor variables are derived from monthly average temperature and monthly total precipitation data at meteorological stations in the cooperative network. The models are similar in form to the previous models developed for crop reporting districts (CRD). The trends and derived variables were the same and the approach to select the significant predictors was similar to that used in developing the CRD models. The APU's were selected to be more homogeneous with respect crop to production than the CRDs. The APU models are quite similar to the CRD models, similar explained variation and number of predictor variables. The APU models are to be independently evaluated and compared to the previously evaluated CRD models. That comparison should indicate the preferred model area for this application, i.e., APU or CRD.
An evaluation was made of Thompson-Type models which use trend terms (as a surrogate for technology), meteorological variables based on monthly average temperature, and total precipitation to forecast and estimate corn yields in Iowa, Illinois, and Indiana. Pooled and unpooled Thompson-type models were compared. Neither was found to be consistently superior to the other. Yield reliability indicators show that the models are of limited use for large area yield estimation. The models are objective and consistent with scientific knowledge. Timely yield forecasts and estimates can be made during the growing season by using normals or long range weather forecasts. The models are not costly to operate and are easy to use and understand. The model standard errors of prediction do not provide a useful current measure of modeled yield reliability.
A soil plant atmosphere model for corn (Zea mays L.) together with the scaling theory for soil hydraulic heterogeneity are used to study the sensitivity of spatial variation of canopy temperature to field averaged soil texture and crop rooting characteristics. The soil plant atmosphere model explicitly solves a continuity equation for water flux resulting from root water uptake, changes in plant water storage and transpirational flux. Dynamical equations for root zone soil water potential and the plant water storage models the progressive drying of soil, and day time dehydration and night time hydration of the crop. The statistic of scaling parameter which describes the spatial variation of soil hydraulic conductivity and matric potential is assumed to be independent of soil texture class. The field averaged soil hydraulic characteristics are chosen to be representative of loamy sand and clay loam soils. Two rooting characteristics are chosen, one shallow and the other deep rooted. The simulation shows that the range of canopy temperatures in the clayey soil is less than 1K, but for the sandy soil the range is about 2.5 and 5.0 K, respectively, for the shallow and deep rooted crops.
Attention is given to the development of methods for combining spectral and meteorological data in crop yield models which are capable of providing accurate estimates of crop condition and yields throughout the growing season. The present investigation is concerned with initial tests of these concepts using spectral and agronomic data acquired in controlled experiments. The data were acquired at the Purdue University Agronomy Farm, 10 km northwest of West Lafayette, Indiana. Data were obtained throughout several growing seasons for corn and soybeans. Five methods or models for predicting yields were examined. On the basis of the obtained results, it is concluded that estimating intercepted solar radiation using spectral data is a viable approach for merging spectral and meteorological data in crop yield models.
A technique for evaluating crop temporal-spectral development patterns is described and applied to the analysis of cropping practices and environmental conditions as they affect reflectance characteristics of corn and soybean canopies. Typical variations in field conditions are shown to exert significant influences on the spectral development patterns, and thereby to affect the separability of the two crops.
A field experiment was conducted utilizing artificial arrangements of plant components during the summer of 1982 to examine the effects of corn canopy structure and plant water content on microwave emission. Truck-mounted microwave radiometers at C (5 GHz) and L (1.4 GHz) band sensed vertically and horizontally polarized radiation concurrent with ground observations of soil moisture and vegetation parameters. Results indicate that the orientation of cut stalks and the distribution of their dielectric properties through the canopy layer can influence the microwave emission measured from a vegetation/soil scene. The magnitude of this effect varies with polarization and frequency and with the amount of water in the plant, disappearing at low levels of vegetation water content. Although many of the canopy structures and orientations studied in this experiment are somewhat artificial, they serve to improve understanding of microwave energy interactions within a vegetation canopy and to aid in the development of appropriate physically based vegetation models.
The quantity of radiation potentially available for photosynthesis that is captured by the crop is best described as absorbed photosynthetically active radiation (PAR). Absorbed PAR (APAR) is the difference between descending and ascending fluxes. The four components of APAR were measured above and within two planting densities of corn (Zea mays L.) and several methods of measuring and estimating APAR were examined. A line quantum sensor that spatially averages the photosynthetic photon flux density provided a rapid and portable method of measuring APAR. PAR reflectance from the soil (Typic Argiaquoll) surface decreased from 10% to less than 1% of the incoming PAR as the canopy cover increased. PAR reflectance from the canopy decreased to less than 3% at maximum vegetative cover. Intercepted PAR (1 - transmitted PAR) generally overestimated absorbed PAR by less than 4% throughout most of the growing season. Thus intercepted PAR appears to be a reasonable estimate of absorbed PAR.
Knowledge of the parameters of amyloplast sedimentation is crucial for an evaluation of proposed mechanisms of root graviperception. Early estimates of the rate of root amyloplast sedimentation were as low as 1.2 micron/min which may be too slow for many amyloplasts to reach the vicinity of the new lower wall within the presentation time. On this basis, Haberlandt's classical statolith hypothesis involving amyloplast stimulation of a sensitive surface near the new lower wall was questioned. The aim was to determine the kinetics of amyloplast sedimentation with reference to the presentation time in living and fixed corn rootcap cells as compared with coleoptiles of the same variety.
The multitemporal classification approach based on the greenness profile derived from Landsat Multispectral Scanner (MSS) spectral bands has proved successful in effectively separating and identifying corn, soybean, and other ground cover classes. Features derived from these profiles have been shown to carry virtually all the information contained in the original data and, in addition, have been shown to be stable over a large geographic area of the United States. The objective of this investigation was to determine if the same features derived from multitemporal Thematic Mapper (TM) data would also prove effective in separating these two crop types, and, in fact, if algorithms developed for MSS could be directly applied to TM. It is shown that this is indeed the case. In addition, because of greater spatial and spectral resolution, the accuracy of TM classifications is better than in MSS.
The effects of sun and view angles on reflectance factors of corn (Zea mays L.) canopies ranging from the six leaf stage to harvest maturity were studied on the Purdue University Agronomy Farm by a multiband radiometer. The two methods of acquiring spectral data, the truck system and the tower systrem, are described. The analysis of the spectral data is presented in three parts: solar angle effects on reflectance factors viewed at nadir; solar angle effects on reflectance factors viewed at a fixed sun angle; and both sun and view angles effect on reflectance factors. The analysis revealed that for nadir-viewed reflectance factors there is a strong solar angle dependence in all spectral bands for canopies with low leaf area index. Reflectance factors observed from the sun angle at different view azimuth angles showed that the position of the sensor relative to the sun is important in determining angular reflectance characteristics. For both sun and view angles, reflectance factors are maximized when the sensor view direction is towards the sun.
Reflectance and agronomic Thematic Mapper (TM) data were analyzed to determine possible data transformations for evaluating several plant parameters of corn. Three transformation forms were used: the ratio of two TM bands, logarithms of two-band ratios, and normalized differences of two bands. Normalized differences and logarithms of two-band ratios responsed similarly in the equations for estimating the plant growth parameters evaluated in this study. Two-term equations were required to obtain the maximum predictability of percent ground cover, canopy moisture content, and total wet phytomass. Standard error of estimate values were 15-26 percent lower for two-term estimates of these parameters than for one-term estimates. The terms log(TM4/TM2) and (TM4/TM5) produced the maximum predictability for leaf area and dry green leaf weight, respectively. The middle infrared bands TM5 and TM7 are essential for maximizing predictability for all measured plant parameters except leaf area index. The estimating models were evaluated over bare soil to discriminate between equations which are statistically similar. Qualitative interpretations of the resulting prediction equations are consistent with general agronomic and remote sensing theory.
Immunofluorescence techniques have been used to study the distribution of calmodulin in several tissues in etiolated corn (Zea mays, var. Bear Hybrid) seedlings. Uniform staining was seen in the background cytoplasm of most cell types. Cell walls and vacuoles were not stained. In coleoptile mesophyll cells the nucleoplasm of most nuclei was stained as was the stroma of most amyloplasts. The lumen border of mature tracheary elements in coleoptiles also stained. In the rootcap the most intensely stained regions were the cytoplasms of columella cells and of the outermost cells enmeshed in the layer of secreted slime. Nuclei in the rootcap cells did not stain distinctly, but those in all cell types of the root meristem did. Also in the root meristem, the cytoplasm of metaxylem elements stained brightly. These results are compared and contrasted with previous data on the localization of calmodulin in pea root apices and epicotyls and discussed in relation to current hypotheses on mechanisms of gravitropism.