Identification of agricultural crops by computer processing of ERTS-MSS data
Identification of agricultural crops in Dekalb, Ogle, and Lee counties in Illinois by computer processing of ERTS-1 MSS data
Bauer, M. E.↗
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Application of ERTS-1 data to analysis of agricultural crops and forests in Michigan
Application of ERTS-1 data to analysis of agricultural crops and forests in Michigan
Safir, G. R.↗
Procedures for the description of agricultural crops and soils in optical and microwave remote sensing studies
This paper describes procedures for characterizing agricultural crops and soils in remote sensing studies. The procedures are based on the accumulated experience of a number of researchers active in this field. Therefore, they represent a compromise between the theoretically desirable and the practically feasible, and should thus be an effective aid in further studies of this type. Although the guidelines were prepared specifically for microwave studies, adjustments were made to render the procedures applicable to optical studies as well. Given the increasing number of research teams involved in remote sensing applied to agriculture, there is an opportunity to acquire a broad data base on soils and crops in various geographic regions. To allow intercomparisons of such data, they must be obtained in a consistent manner. By following the proposed procedures and reporting results using the parameters described here, such intercomparisons should be possible on a continental or a global scale.
Cihlar, J.↗
Priorities for worldwide remote sensing of agricultural crops
The world's crops are ranked according to total harvested area, and comparisons are made among major world regions of differences in crops produced. The eight leading world crops are wheat, rice, corn, barley, millet, soybeans, sorghum, and cotton. Regionally, millet and sorghum are most important in Africa, wheat is the most extensively grown crop in north-central America, Europe, USSR, and Oceania; corn is the dominant crop in South America; and rice is the most extensively grown crop in Asia. Agriculture in the USA is considered in more detail to show the national economic impact of variations in value per hectare among crops. On the world scene, the cereals are the most important crops, but locally, such crops as tobacco can play a dominant role.
Bowker, D. E.↗
Interaction of electromagnetic energy with agricultural crops
Studying interaction of electromagnetic radiation with plants, soils, and water, and wavelengths and procedures for discriminating agricultural crops
Allen, W. A.↗
Application of ERTS-1 data to analysis of agricultural crops and forests in Michigan
The results reported are based on analysis of ERTS Frame 1033-15580 collected over southwestern Lower Michigan on August 25, 1972. Major agricultural crops such as corn and soybeans were approaching maturity at this data and forest canopies were dense. Extensive ground truth information was gathered by detailed field study of test strips. This detailed information was supplemented over larger areas by interpretation of RB-57 and C-47 photography and MSS imagery. Recognition processing of ERTS-1 MSS data was carried out on a digital computer. Fields and forest stands were selected as training sets and test areas. Aerial imagery was essential for locating the positions of these selected areas on ERTS digital tapes. The recognition process was successful for each type of vegetation which had a dense green canopy such as forests, corn, and soybeans. Bare soil was also recognizable as a category.
Safir, G. R.↗
Field spectroscopy of agricultural crops
The development of the full potential of multispectral data acquired from satellites, requires quantitative knowledge, and physical models of the spectral properties of specific earth surface features. Knowledge of the relationships between spectral-radiometric characteristics and important biophysical parameters of agricultural crops and soils can best be obtained by carefully controlled studies of fields or plots. It is important to select plots where data describing the agronomic-biophysical properties of the crop canopies and soil background are attainable, taking into account also the feasibility of frequent timely calibrated spectral measurements. The term 'field spectroscopy' is employed for this research. The present paper is concerned with field research which was sponsored by NASA as part of the AgRISTARS Supporting Research Project. Attention is given to field research objectives, field research instrumentation, measurement procedures, spectral-temporal profile modeling, and the effects of cultural and environmental factors on crop reflectance.
Bauer, M. E.↗
A preliminary study of the statistical analyses and sampling strategies associated with the integration of remote sensing capabilities into the current agricultural crop forecasting system
Extending the crop survey application of remote sensing from small experimental regions to state and national levels requires that a sample of agricultural fields be chosen for remote sensing of crop acreage, and that a statistical estimate be formulated with measurable characteristics. The critical requirements for the success of the application are reviewed in this report. The problem of sampling in the presence of cloud cover is discussed. Integration of remotely sensed information about crops into current agricultural crop forecasting systems is treated on the basis of the USDA multiple frame survey concepts, with an assumed addition of a new frame derived from remote sensing. Evolution of a crop forecasting system which utilizes LANDSAT and future remote sensing systems is projected for the 1975-1990 time frame.
Sand, F.↗
Airborne Hyperspectral Imagery for the Detection of Agricultural Crop Stress
Multispectral digital imagery from aircraft or satellite is presently being used to derive basic assessments of crop health for growers and others involved in the agricultural industry. Research indicates that narrow band stress indices derived from hyperspectral imagery should have improved sensitivity to provide more specific information on the type and cause of crop stress, Under funding from the NASA Earth Observation Commercial Applications Program (EOCAP) we are identifying and evaluating scientific and commercial applications of hyperspectral imagery for the remote characterization of agricultural crop stress. During the summer of 1999 a field experiment was conducted with varying nitrogen treatments on a production corn-field in eastern Nebraska. The AVIRIS (Airborne Visible-Infrared Imaging Spectrometer) hyperspectral imager was flown at two critical dates during crop development, at two different altitudes, providing images with approximately 18m pixels and 3m pixels. Simultaneous supporting soil and crop characterization included spectral reflectance measurements above the canopy, biomass characterization, soil sampling, and aerial photography. In this paper we describe the experiment and results, and examine the following three issues relative to the utility of hyperspectral imagery for scientific study and commercial crop stress products: (1) Accuracy of reflectance derived stress indices relative to conventional measures of stress. We compare reflectance-derived indices (both field radiometer and AVIRIS) with applied nitrogen and with leaf level measurement of nitrogen availability and chlorophyll concentrations over the experimental plots (4 replications of 5 different nitrogen levels); (2) Ability of the hyperspectral sensors to detect sub-pixel areas under crop stress. We applied the stress indices to both the 3m and 18m AVIRIS imagery for the entire production corn field using several sub-pixel areas within the field to compare the relative sensitivity of each stress index; and (3) Comparative sensitivity of stress indices to realistic measurement uncertainties. We compare the stress indices calculated with several levels of spectral uncertainty (by shifting the wavelengths) and reflectance uncertainty (by systematically varying the reflectance retrieval code initialization).
Cassady, Philip E.↗
Field size distributions for selected agricultural crops in the United States and Canada
Digitized agricultural field boundary taken in the United States and Canada during the LACIE and AgRISTARS programs, in 1977 through 1980, were used to construct histograms showing the distributions of field area, width, and length for crops for which there were data for 700 or more fields per state. The observed distributions of area and width for fields of 10 crops grown in 13 states of the United States and Canada were compared with best-fit inverse Gaussian distributions and with log-normal distributions. For 28 distributions of area and 16 distributions of width there was found to be a probability of greater than .01 of their being inverse Gaussian. There were 10 distributions of area for which there was probability of greater than .005 of their being log-normal. Distributions of area and width stratified by state and crop type appear to be unique. The inverse Gaussian, which represents a wide range of statistical distributions from skewed to almost symmetrical, can provide a useful model for distributions of field area.
Ferguson, M. C.↗
The tasselled cap - A graphic description of the spectral-temporal development of agricultural crops as seen by Landsat
The time trajectories of agricultural data points as seen in Landsat signal space form a pattern suggestive of a tasselled woolly cap. Most of the important crop phenomena can be described using this three dimensional construct: the distribution of signals from bare soil, the processes of green development, yellow development, and shadowing and harvesting. A linear preprocessing transformation which isolates green development, yellow development and soil brightness is used to reduce the dimension of the signal space. Specific measurable pattern elements of the tasselled cap are used to estimate and correct atmospheric haze and moisture effects.
Kauth, R. J.↗
Selected examples of dispersal of arthropods associated with agricultural crop and animal production
The economic importance of arthropods in agricultural production systems and the possibilities of using dispersal behavior to develop and manipulate control are examined. Examples of long and short distance dispersal of economic insect pests and beneficial species from cool season host reservoirs and overwintering sites are presented. Significant dispersal of these species often occurring during crop and animal production is discussed.
Henneberry, T. J.↗
Impact of nowcasting on the production and processing of agricultural crops
The value was studied of improved weather information and weather forecasting to farmers, growers, and agricultural processing industries in the United States. The study was undertaken to identify the production and processing operations that could be improved with accurate and timely information on changing weather patterns. Estimates were then made of the potential savings that could be realized with accurate information about the prevailing weather and short term forecasts for up to 12 hours. This weather information has been termed nowcasting. The growing, marketing, and processing operations of the twenty most valuable crops in the United States were studied to determine those operations that are sensitive to short-term weather forecasting. Agricultural extension specialists, research scientists, growers, and representatives of processing industries were consulted and interviewed. The value of the crops included in this survey and their production levels are given. The total value for crops surveyed exceeds 24 billion dollars and represents more than 92 percent of total U.S. crop value.
Dancer, W. S.↗
Argentina Food Security & Agriculture: Crop Monitoring and Forecasting for Argentina using NASA Satellite Observations
Early harvest information helps guide agricultural commodity assessments in Argentina, providing valuable planning information to identify potentially food-insecure regions, anticipate transportation and storage demands, predict price fluctuations, and project commodity trends. However, crop yield estimates are currently subjective, based on interviews with qualified informants (i.e., farmers, agribusiness actors). In partnership with the Buenos Aires Grain Exchange, we leveraged Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Soil Moisture Active Passive (SMAP), and Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM IMERG) NASA Earth observations to develop a Google Earth Engine (GEE) toolset to monitor vegetation growth. The first component of the toolset produces spatial and temporal maps of temperature, precipitation, soil moisture, and the Normalized Difference Vegetation Index (NDVI), allowing users to visualize the influence of the region’s climate and weather. Next, we developed an autoregressive model to predict NDVI several months in advance. Lastly, we created a linear regression model of crop yield and NDVI for soybeans, corn, and wheat, and input the forecasted NDVI to generate a predicted crop yield output. The NDVI forecasting model produced accurate predictions at two, four, and six months when examining the most recent growing season. In the crop yield model, soybeans exhibited moderately strong correlation, wheat had consistent weak correlation, and corn varied from weak to strong correlation depending on zone. This information is vital for vegetation growth monitoring by identifying areas of high growth and allocating resources to areas of lower growth to efficiently maximize crop yields.
DEVELOP Tech Paper↗
Multi-frequency and polarimetric radar backscatter signatures for discrimination between agricultural crops at the Flevoland experimental test site
We describe the calibration and analysis of multi-frequency, multi-polarization radar backscatter signatures over an agriculture test site in the Netherlands. The calibration procedure involved two stages: in the first stage, polarimetric and radiometric calibrations (ignoring noise) were carried out using square-base trihedral corner reflector signatures and some properties of the clutter background. In the second stage, a novel algorithm was used to estimate the noise level in the polarimetric data channels by using the measured signature of an idealized rough surface with Bragg scattering (the ocean in this case). This estimated noise level was then used to correct the measured backscatter signatures from the agriculture fields. We examine the significance of several key parameters extracted from the calibrated and noise-corrected backscatter signatures. The significance is assessed in terms of the ability to uniquely separate among classes from 13 different backscatter types selected from the test site data, including eleven different crops, one forest and one ocean area. Using the parameters with the highest separation for a given class, we use a hierarchical algorithm to classify the entire image. We find that many classes, including ocean, forest, potato, and beet, can be identified with high reliability, while the classes for which no single parameter exhibits sufficient separation have higher rates of misclassification. We expect that modified decision criteria involving simultaneous consideration of several parameters increase performance for these classes.
Freeman, A.↗
Spectral properties of agricultural crops and soils measured from space, aerial, field and laboratory sensors
It is pointed out that in order to develop the full potential of multispectral measurements acquired from satellite or aircraft sensors to monitor, map, and inventory agricultural resources, increased knowledge and understanding of the spectral properties of crops and soils are needed. The present state of knowledge is reviewed, emphasizing current investigations of the multispectral reflectance characteristics of crops and soils as measured from laboratory, field, aerial, and satellite sensor systems. The relationships of important biological and physical characteristics to their spectral properties of crops and soils are discussed. Future research needs are also indicated.
Bauer, M. E.↗
Separability of agricultural crops with airborne scatterometry
Backscattering measurements were acquired with airborne scatterometers over a site in Cass County, North Dakota on four days in the 1981 crop growing season. Data were acquired at three frequencies (L-, C- and Ku-bands), two polarizations (like and cross) and ten incidence angles (5 degrees to 50 degrees in 5 degree steps). Crop separability is studied in an hierarchical fashion. A two-class separability measure is defined, which compares within-class to between-class variability, to determine crop separability. The scatterometer channels with the best potential for crop separability are determined, based on this separability measure. Higher frequencies are more useful for discriminating small grains, while lower frequencies tend to separate non-small grains better. Some crops are more separable when row direction is taken into account. The effect of pixel purity is to increase the separability between all crops while not changing the order of useful scatterometer channels. Crude estimates of separability errors are calculated based on these analyses. These results are useful in selecting the parameters of active microwave systems in agricultural remote sensing.
Mehta, N. C.↗
Mapping Agricultural Crops with AVIRIS Spectra in Washington State
Spectroscopy is used in the laboratory to measure the molecular components and concentrations of plant constituents to answer questions about the plant type, status, and health. Imaging spectrometers measure the upwelling spectral radiance above the Earth's surface as images. Ideally, imaging spectrometer data sets should be used to understand plant type, plant status, and health of plants in an agricultural setting. An Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data set was acquired over agricultural fields near Wallula, Washington on July 23rd, 1997. AVIRIS measures upwelling radiance spectra through 224 spectral channels with contiguous 10-nm sampling from 400 to 2500 run in the solar-reflected spectrum. The spectra are measured as images of 11 by up to 800 km with 20-m spatial resolution. The spectral images measured by AVIRIS represent the integrated signal resulting from: the solar irradiance; two way transmittance and scattering of the atmosphere; the absorptions and scattering of surface materials; as well as the spectral, radiometric and spatial response functions of AVIRIS. This paper presents initial research to derive properties of the agricultural fields near Wallula from the calibrated spectral images measured by AVIRIS near the top of the atmosphere.
Green, Robert O.↗