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Classification and technology development for area estimation of corn, soybeans, wheat, barley, and sunflowers are outlined. Supporting research for corn and soybean foreign commodity production forecasting is highlighted. Graphs profiling the greenness and brightness of the crops are presented.
Optimal LANDSAT image dates for 1980 were identified based on the weekly crop-weather reports for Colorado, New Mexico, South Dakota, Texas, Oklahoma, Kansas, Nebraska, and Wyoming. The 1979 agricultural statistics data were entered into computer files and a revised questionnaire was developed and mailed to ASCS county agents. A set of computer programs was developed to allow the preparation of computer-assisted graphic displays of much of the collected data.
The experiment design, data acquisition and preprocessing, data base management, analysis results and development of instrumentation for the AgRISTARS Supporting Research Project, Field Research task are described. Results of several investigations on the spectral reflectance of corn and soybean canopies as influenced by cultural practices, development stage and nitrogen nutrition are reported as well as results of analyses of the spectral properties of crop canopies as a function of canopy geometry, row orientation, sensor view angle and solar illumination angle are presented. The objectives, experiment designs and data acquired in 1980 for field research experiments are described. The development and performance characteristics of a prototype multiband radiometer, data logger, and aerial tower for field research are discussed.
A combined Landsat/radar approach to classification of remotely sensed data, with emphasis on crops, was undertaken. Radar data were obtained by microwave radar spectrometers over fields near Eudora, Kansas and Landsat image data were obtained for the same test site. After Landsat digital images were registered and test-cells extracted, a comparable set of radar image pixels were simulated to match the Landsat pixels. The combined data set is then used for classification, and the results are examined with the best combination of sensor variables identified. Finally, the usefulness of radar in a simulated cloud-cover situation is demonstrated. The major conclusion derived from this study is that the combination of radar/optical sensors is superior to either one alone.
An investigation was conducted to evaluate the effect of several sampling alternatives on the accuracy of crop area estimates made from classification of Landsat Multispectral Scanner (MSS) data. The specific objective was to assess the precision and the bias associated with alternative sampling schemes involving different numbers of several sampling unit sizes. The estimates achieved using the 5 by 6 nm segments were found to have the least precision of any sampling scheme tested. The estimates become more precise as the segment size decreases and more segments are taken. The precision of the 5 by 6 nm segments was significantly less than that of the pixel samples. None of the sampling schemes was significantly biased on the average, and none of the average estimates differed significantly from the population parameter. The maximum absolute deviation, however, was directly related to sampling unit size and should be considered in selection of a sampling unit.
Thermal images from an aircraft-mounted scanner are used to evaluate the effectiveness of crop-freeze protection devices. Data from flights made while using fuel oil heaters, a wind machine and an undercanopy irrigation system are compared. Results show that the overall protection provided by irrigation (at approximately 2 C) is comparable to the less energy-efficient heater-wind machine combination. Protection provided by the wind machine alone (at approximately 1 C) was found to decrease linearly with distance from the machine by approximately 1 C/100 m. The flights were made over a 1.5 hectare citrus grove at an altitude of 450 m with an 8-14 micron detector. General meteorological conditions during the experiments, conducted during the nighttime, were cold (at approximately -6 C) and calm with clear skies.
The interactions and support functions required between the early warning/crop condition assessment (EW/CCA) project and soil moisture (SM) project are defined. The EW Project aims to develop, test and evaluate techniques and procedures for adapting remote sensing technology to provide early warning of events and the timely assessment of those factors which affect the quality and quantity of production of economically important crops. Those techniques to augment and reinforce the current assessment activities are to be developed to improve the definition of the relationship between the plant(s) and its environment. This assessment and evaluation will certainly include the need for soil moisture measurement and estimation. The SM Project aims to develop, test, and evaluate techniques and procedures to measure or predict soil moisture in the root zone using both contact and remote sensors.
The most probable current U.S. taxonomic classification of the soils estimated to dominate world soil map units (WSM)) in selected crop producing states of Argentina and Brazil are presented. Representative U.S. soil series the units are given. The map units occurring in each state are listed with areal extent and major U.S. land resource areas in which similar soils most probably occur. Soil series sampled in LARS Technical Report 111579 and major land resource areas in which they occur with corresponding similar WSM units at the taxonomic subgroup levels are given.
A preliminary description of a crop forecasting system is presented. Ground and satellite observation are the main sources of data collection.
The content, format, and storage of data bases developed for the Foreign Commodity Production Forecasting project and used to produce normal crop calendars are described. In addition, the data bases may be used for agricultural meteorology, modeling of stage sequences and planting dates, and as indicators of possible drought and famine.
Both C- and L-band radar data acquired over a test site near Colby, Kansas during the summer of 1978 were used to identify three types of vegetation cover and bare soil. The effects of frequency, polarization, and the look angle on the overall accuracy of recognizing the four types of ground cover were analyzed. In addition, multidate data were used to study the improvement in recognition accuracy possible with the addition of temporal information. The soil moisture conditions had changed considerably during the temporal sequence of the data; hence, the effects of soil moisture on the ability to discriminate between cover types were also analyzed. The results provide useful information needed for selecting the parameters of a radar system for monitoring crops.
The performance of the USDAs EDITOR system is evaluated. The system processes Landsat imagery and estimates crop hectarage for large areas based on a regression estimator developed on a sample with known ground truth. It is found that use of multitemporal data over unitemporal significantly improves the hectarage estimates, and a 15% reduction in the r-squared of the regression occurs when independent and jackknifed test sets are used to evaluate the performance of the estimator. When an alternative clustering algorithm, CLASSY, is substituted for the current EDITOR method, estimator performance is improved with reduced need for analyst decisions. It is recommended that the CLASSY clustering algorithm and some form of jackknifing be implemented on EDITOR.
A study has been carried out in order to determine the angular limits which should be imposed upon NOAA06 and -7 satellite advanced very high-resolution radiometer (AVHRR) data used in vegetation assessment. The study involved simulations of anticipated sensor output over a wheat crop and empirical investigation of sensor data over vegetated areas. The conclusion drawn from both approaches is that less than 10% variation in the simulated vegetative index may be anticipated across the swath of imagery used if only the central 512 pixels are utilized.
Models based on multiple regression were developed to estimate barley yield and spring wheat yield from weather data for Agrophysical units(APU) in North Dakota. 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 models show sight improvements in some of the statistics of the models, e.g., explained variation. These models are to be independently evaluated and compared to the previously evaluated CRD models. The comparison will indicate the preferred model area for this application, i.e., APU or CRD.
A classification procedure for assessing crop areal proportion in multispectral scanner image is discussed. The procedure is into four parts: labeling; classification; proportion estimation; and evaluation. The procedure also has the following characteristics: multitemporal classification; the need for a minimum field information; and verification capability between automatic classification and analyst labeling. The processing steps and the main algorithms involved are discussed. An outlook on the future of this technology is also presented.
A wide spectrum of technology pertaining to the inventory of crops using LANDSAT without in situ training data is addressed. Methods considered include Bayesian based through-the-season methods, estimation technology based on analytical profile fitting methods, and expert-based computer aided methods. Although the research was conducted using U.S. data, the adaptation of the technology to the Southern Hemisphere, especially Argentina was considered.
Improvements in crop discrimination can be realized by using mid-IR bands (1.55 -1.75 and 2.08 -2.35 microns) which are sensitive to canopy moisture content. Analyses of data from two growing seasons in Webster County, Iowa clearly indicate that corn and soybeans are highly separable in the mid-IR from early season through harvest. This contrasts sharply with visible and near-IR bands where corn and soybeans are confused throughout much of the growing season. The mid-IR temporal reflectance behavior appears to result from differences between C4 monocot and C3 dicot internal leaf structure. If this hypothesis holds, mid-IR observations should improve discrimination in other instances where similar differences in internal leaf structure are present.
A vegetation and soil-moisture experiment was conducted in order to examine the microwave emission and backscattering from vegetation canopies and soils. The data-acquisition methodology used in conjunction with the mobile radar scatterometer (MRS) systems is described and associated ground-truth data are documented. Test fields were located in the Kansas River floodplain north of Lawrence, Kansas. Ten fields each of wheat, corn, and soybeans were monitored over the greater part of their growing seasons. The tabulated data summarize measurements made by the sensor systems and represent target characteristics. Target parameters describing the vegetation and soil characteristics include plant moisture, density, height, and growth stage, as well as soil moisture and soil-bulk density. Complete listings of pertinent crop-canopy and soil measurements are given.