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The author has identified the following significant results. An initial demonstration was made of the capability to make direct production forecasts for winter wheat using early season LANDSAT data. The approach offers the potential to make production forecasts quickly and simply, possibly avoiding some of the complexities of alternate procedures.
The ECON work and the results of the independent reviews are summarized. Attempts are made to put this information into layman's terms and to present the benefits that can realistically be expected from a LANDSAT-type remote sensing system. Further the mechanisms by which these benefits can be expected to accrue are presented. The benefits are given including the nature of expected information improvements, how and why they can lead to benefits to society, and the estimated magnitude of the expected benefits. A brief description is presented of the ECON models, how they work, their results, and a summary of the pertinent aspects of each review. The ECON analyses show that substantial benefits will accrue from implementation of an improved wheat crop information system based on remote sensing.
For abstract, see volume 1 N77-30577. The enclosed maps indicate the areal coverage of the various models for spring (durum and other spring) and winter wheat. The given regions are the combination of several climatic divisions and many times comprise an entire state.
This report presents the results of experiments which were performed to evaluate procedures for estimating wheat acreage in intensive test sites (ITS's) in Kansas. An analyst/interpreter (AI) selected and labeled fields from Landsat-1 satellite imagery. Statistics were generated for each selected ITS, and the imagery was classified using a maximum likelihood classifier. Various components of the classification process were tested.
The relationship between airborne and ground-based measurements of soil and crop canopy temperatures is investigated for a partial crop canopy. Daily ground-based measurements using a wide-field-of-view radiometer oriented towards the nadir at a height of 1.5 m and airborne thermal imagery at two-week intervals were obtained throughout the entire growing season of a stand of wheat. When corrected for atmospheric effects, the airborne measurements were found to be virtually identical to ground-based measurements, with a regression line slope of 0.985, a standard deviation of 1.8 C and a correlation coefficient of 0.97.
The major research conducted during the three years of LACIE to solve problems associated with segment wheat area estimation is reviewed. Topics covered include proportion estimation, clustering, feature extraction, and signature extension. It would appear that LANDSAT-1 and LANDSAT-2 data do not contain enough information to discriminate between crop types perfectly all the time and, therefore, a basic problem arises when no ground truth data on crop types in the area are available. New approaches are needed to reduce labeling error. Perhaps better use of multiyear LANDSAT data, a more detailed understanding of the cropping practices in the area, better crop calendar prediction, and a better understanding of the limiting sources of error in LANDSAT data related to crop discrimination may provide the insight required to develop improved designs.
The adaptation of the law of the minimum (LOM) to wheat yield estimation is discussed. It is demonstrated through a trial application that the LOM concept is a valuable tool for model building when regression tools are inadequate.
The merits and shortcomings of LACIE wheat development models are described. The effects of planting dates, spatial errors in the variables, and model forms on accuracy are discussed.
The author has identified the following significant results. Red and photographic-infrared spectral data collected on 21 dates over the growing season with a hand-held radiometer was quantitatively correlated with total dry-matter accumulation in winter wheat. The spectral data were found to be highly related to vigor and condition of the plant canopy. Two periods of drought stress and subsequent recovery from it were readily apparent in the spectral data. Simple ratios of the spectral data compensated for variations in solar intensities and, when integrated over the growing season, explained 79% of the variation in total above-ground accumulation of dry matter.
The object of the paper is to assess the results of the Large Area Crop Inventory Experiment (LACIE) and closely related research on yield estimation techniques based on remote sensing variables. The exploratory research conducted during LACIE substantiated the hypothesis of yield related information contained in Landsat multispectral scanner data and indicated some of its empirical characteristics. It is noted that leaf area and possibly other foliage features can be derived from spectral data for yield estimation through agrometeorological models and that multiple vegetative and grain related features may be discernable by Landsat derived wheat spectra at different points in the crop development.
The state crop calendars for the principal spring wheat producing states within the United States are presented. These crop calendars are an update of those produced for the large area crop inventory experiment multilabeling task during 1978and are compiled for the foreign commodity production forecasting (FCPF) project of the agriculture and resources inventory surveys through aerospace remote sensing program.
An algorithm for estimating spectral crop calendar shifts of spring small grains was applied to 1978 spring wheat fields. The algorithm provides estimates of the date of peak spectral response by maximizing the cross correlation between a reference profile and the observed multitemporal pattern of Kauth-Thomas greenness for a field. A methodology was developed for estimation of crop development stage from the date of peak spectral response. Evaluation studies showed that the algorithm provided stable estimates with no geographical bias. Crop development stage estimates had a root mean square error near 10 days. The algorithm was recommended for comparative testing against other models which are candidates for use in AgRISTARS experiments.
The Badwar profile similarity classification technique used successfully for classification of corn was applied to spring wheat classifications. The software programs and the procedures used to generate full-scene classifications are presented, and numerical results of the acreage estimations are given.
Procedures and techniques for providing analyses of meteorological conditions at segments during the growing season were developed for the U.S./Canada Wheat and Barley Exploratory Experiment. The main product and analysis tool is the segment-level climagraph which depicts temporally meteorological variables for the current year compared with climatological normals. The variable values for the segment are estimates derived through objective analysis of values obtained at first-order station in the region. The procedures and products documented represent a baseline for future Foreign Commodity Production Forecasting experiments.
Experiments using ground-based measurements of canopy temperatures have shown that plant temperatures are good indicators of plant water stress, and thus are useful for assessing water requirements and predicting yields. An intensive 23-day airborne- and ground-measurement program was conducted in Phoenix, Arizona in 1977 to compare airborne-acquired wheat canopy temperatures with simultaneous ground measurements. For canopies that covered at least 85 percent of the soil surface, airborne measurements differed from ground measurements of plant temperature by less than 2 C. Regardless of the amount of plant cover, the airborne measurements were virtually identical to ground-nadir measurements, and thus represent a combination of plant temperature and solid background temperature.
Red and photographic-infrared spectral data collected on 21 dates over the growing season with a hand-held radiometer were quantitatively correlated with total dry-matter accumulation in winter wheat. The spectral data were found to be highly related to vigor and condition of the plant canopy. Two periods of drought stress and subsequent recovery from it were readily apparent in the spectral data. Simple ratios of the spectral radiance data compensated for variations in solar intensities and, when integrated over the growing season, explained 79% of the variation in total above-ground accumulation of dry matter. A satellite system is proposed to provide large-area assessment of total dry accumulation or net primary production from terrestrial vegetation.
During the past six years several thousand reflectance spectra of corn, soybean, and wheat canopies were acquired and analyzed. The relationships of biophysical variables, including leaf area index, percent soil cover, chlorophyll and water content, to the visible and infrared reflectance of canopies are described. The effects on reflectance of cultural, environmental, and stress factors such as planting data, seeding rate, row spacing, cultivar, soil type and nitrogen fertilization are also examined. The conclusions are that several key agronomic variables including leaf area index, development stage and degree of stress are strongly related to spectral reflectance and that it should be possible to estimate these descriptions of crop condition from satellite acquired multispectral data.
Automatic classification of LANDSAT data supported by aerial photography for identification and estimation of wheat growing areas was evaluated. Data covering three regions in the State of Rio Grande do Sul, Brazil were analyzed. The average correct classification of IMAGE-100 data was 51.02% and 63.30%, respectively, for the periods of July and of September/October, 1979.