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Land cover data sets were developed for 1997, 1999, 2003, 2005, 2006, and 2007 for the Little Washita River and Fort Cobb Reservoir experimental watersheds (LWREW and FCREW respectively), located in southwestern Oklahoma, to support remote sensing based studies of soil water content. A previously unpublished retrospective land cover analysis covering the years 1974, 1981, 1985, 1989, and 1994 was conducted to complement these data sets to gain a sense of the dynamics of land cover in both the LWREW and FCREW over the 33 yr. Each of these studies used satellite-based sensors of various spatial, radiometric, and spectral resolutions, but the number of images used, image date, and thods used to analyze the images varied from study to study. Our purpose was to document the details of the retrospective land cover study, to compare land cover between watersheds with time, and to compare findings from the various studies to elucidate changes or trends in land cover in each watershed during the 33 yr the data sets represent. Information on how to access to the data sets is also given. The LWREW was a grassland watershed that changed little during the study period. The FCREW was divided between grassland and cropland, but the cropland portion exhibited dynamic behavior that appeared correlated with peanut (Arachis hypogaea L.) price supports and Conservation Reserve programs. Dynamic land use information coupled with information concerning conservation practices will enhance assessment of conservation practice effectiveness as well as improve modeling of the fate and transport of chemicals and nutrients in watersheds.

Standard data products from NASA’s moderate resolution imaging spectroradiometer (MODIS) were available at launch (collection 3) and have undergone two revisions (collections 4 and 5) during the continuing Terra and Aqua missions. In 2000, a research project was conducted in large fields of corn and soybean to evaluate MODIS leaf area index (LAI) and land cover type (MOD15 and MOD12 data products, respectively) as input to crop yield models. Our objective was to compare collections 3, 4 and 5 with the ground data to determine data product improvement. Classification of land cover type for collections 3, 4 and 5 were similar to the USDA-NASS Cropland Data Layer. The collection 5 MOD15 LAI was considerably improved over earlier collections when the quality assurance flags indicated good LAI retrievals. Land surface reflectances (MOD09) of MODIS band 2 (near-infrared, 250-m) were used as inputs to an inversion of the scattering by arbitrarily inclined leaves (SAIL) model. Compared to the collection 5 MOD15 p...

Including incident light sensors (ILS) with multispectral sensors is an important development for agricultural remote sensing because spectral reflectances are necessary for accurate determination of plant biophysical variables such as leaf area index and leaf chlorophyll content. Effects of different aircraft flight conditions on accuracy of surface reflectances retrieved using an ILS are not known. The objectives of this study were to assess the effects of ILS orientation with respect to sun and aircraft altitude. A Tetracam Miniature Multiple Camera Array (Mini-MCA) was mounted on a fixed-wing unmanned aircraft system (UAS) with the ILS mounted on top of the aircraft&rsquo, s fuselage. On two dates the aircraft flew over six 50-ha agricultural fields with center-pivot irrigation at three different altitudes (450, 650 and 1800 m above ground level (AGL)). Ground reflectances were estimated using atmospherically corrected Landsat 8 Operational Land Imager data acquired at or near the time of the aircraft overflights. Because the aircraft had a positive pitch during flight, the ILS pointed opposite to the flight direction. The first date had flight lines closely oriented towards and away from the sun. The second date had flight lines oriented perpendicularly to the solar azimuth. On the first date, red and near-infrared (NIR) reflectances were significantly higher when the ILS was oriented away from the sun, whereas ILS orientation had little effect on the second date. For both dates, red and near-infrared reflectances were significantly greater at 450 m compared to 1800 m. Both the effects of ILS orientation and flight altitude are correctable during image processing because the physical basis is well known.

Depending on management, soil organic carbon (SOC) is a potential source or sink for atmospheric CO 2 . We used the EPIC model to study impacts of soil and crop management on SOC in corn (Zea mays L.) and soybean (Glycine max L. Merr.) croplands of Iowa. The National Agricultural Statistics Service crops classification maps were used to identify corn-soybean areas. Soil properties were obtained front a combination of SSURGO and STATSGO databases. Daily weather variables were obtained from first order meteorological stations in Iowa and neighboring states. Data on crop management, fertilizer application and tillage were obtained from publicly available databases maintained by the NRCS, USDA-Economic Research Service (ERS), and Conservation Technology Information Center. The EPIC model accurately simulated state averages of crop yields during 1970-2005 (R 2 = 0.87). Simulated SOC explained 75% of the variation in measured SOC. With current trends in conservation tillage adoption, total stock of SOC (0-20 cm) is predicted to reach 506 Tg by 2019, representing an increase of 28 Tg with respect to 1980. In contrast, when the whole soil profile was considered, EPIC estimated a decrease of SOC stocks with time, from 1835 Tg in 1980 to 1771 Tg in 2019. Hence, soil depth considered for calculations is an important factor that needs further investigation. Soil organic C sequestration rates (0-20 cm) were estimated at 0.50 to 0.63 Mg ha -1 yr -1 depending on climate and soil conditions. Overall, combining land use maps with EPIC proved valid for predicting impacts of management practices on SOC. However, more data on spatial and temporal variation in SOC are needed to improve model calibration and validation.

Management of plant litter or crop residues in agricultural fields is an important consideration for reducing soil erosion and increasing soil organic C. Current methods of quantifying crop residue cover are inadequate for characterizing the spatial variability of residue cover within fields or across large regions. Our objectives were to evaluate several spectral indices for measuring crop residue cover using satellite multispectral and hyperspectral data and to categorize soil tillage intensity in agricultural fields. Landsat Thematic Mapper (TM) and EO-1 Hyperion imaging spectrometer data were acquired over agricultural fields in central Iowa in May and June 2004. Crop residue cover was measured in corn (Zea mays L.) and soybean (Glycine max Merr.) fields using line-point transects. Spectral residue indices using Landsat TM bands were weakly related to crop residue cover. With the Hyperion data, crop residue cover was linearly related to the cellulose absorption index (CAI), which measures the relative intensity of cellulose and lignin absorption features near 2100 nm. Coefficients of determination (r2) for crop residue cover as a function of CAI were 0.85 for the May and 0.77 for the June Hyperion data. Three tillage intensity classes, corresponding to intensive ( 30% cover) tillage, were correctly identified in 66-68% of fields. Classification accuracy increased to 80-82% for two classes, corresponding to conventional (intensive + reduced) and conservation tillage. By combining information on previous season's (2003) crop classification with crop residue cover after planting in 2004, an inventory of soil tillage intensity by previous crop type was generated for the whole Hyperion scene. Regional surveys of soil management practices that affect soil conservation and soil C dynamics are possible using advanced multispectral or hyperspectral imaging systems.

NOAA AVHRR has been used extensively for monitoring vegetation condition and changes across the United States. Integration of crop growth models with MODIS imagery at 250 m resolution from the Terra Satellite potentially offers an opportunity for operational assessment of the crop condition and yield at both field and regional scales. The primary objective of this research was to evaluate the quality of the MODIS 250 m resolution data for retrieval of crop biophysical parameters that could be integrated in crop yield simulation models. A secondary objective was evaluating the potential use of MODIS 250 m resolution data for crop classification. A field study (24 fields) was conducted during the 2000 crop season in McLean County, Illinois, in the U.S. Midwest to evaluate the applicability of the MODIS 8-day, 250 m resolution composite imagery (version 4) for operational assessment of crop condition and yields. Ground-based canopy and leaf reflectance and leaf area index (LAI) measurements were used to calibrate a radiative transfer model to create a look up table (LUT) that was used to simulate LAI. The seasonal trend of MODIS derived LAI was used to find crop model parameters by adjusting the LAI simulated from the climate-based crop yield model. Other intermediate products such as crop phenological events were adjusted from the LAI seasonal profile. Corn (Zea mays L.) and soybean (Glycine max (L.) Merr.) yield simulations were conducted on a 1.6 × 1.6 km2 spatial resolution grid and the results integrated to the county level. The results were within 10% of county yields reported by the USDA National Agricultural Statistics Service (NASS).

Monitoring crop condition and yields at regional scales using imagery from operational satellites remains a challenge because of the problem in scaling local yield simulations to the regional scales. NOAA AVHRR satellite imagery has been traditionally used to monitor vegetation changes that are used indirectly to assess crop condition and yields. Additionally, the 1-km spatial resolution of NOAA AVHRR is not adequate for monitoring crops at the field level. Imagery from the new MODIS sensor onboard the NASA Terra satellite offers an excellent opportunity for daily coverage at 250-m resolution, which is adequate to monitor field sizes are larger than 25 ha. A field study was conducted in the predominantly corn and soybean area of Iowa to evaluate the applicability of the 8-day MODIS composite imagery in operational assessment of crop condition and yields. Ground-based canopy reflectance and leaf area index (LAI) measurements were used to calibrate the models. The MODIS data was used in a radiative transfer model to estimate LAI through the season. LAI was integrated into a climate-based crop simulation model to scale from local simulation of crop development and responses to a regional scale. Simulations of corn and soybean yields at a 1.6×1.6-km2 grid scale were comparable to county yields reported by the USDA–National Agricultural Statistics Service (NASS). Weekly changes in soil moisture for the top 1-m profile were also simulated as part of the crop model as one of the critical parameters influencing crop condition and yields.

Monitoring crop condition and production estimates at the state and county level is of great interest to the U.S. Department of Agriculture. The National Agricultural Statistical Service (NASS) of the U.S. Department of Agriculture conducts field interviews with sampled farm operators and obtains crop cuttings to make crop yield estimates at regional and state levels. NASS needs supplemental spatial data that provides timely information on crop condition and potential yields. In this research, the crop model EPIC (Erosion Productivity Impact Calculator) was adapted for simulations at regional scales. Satellite remotely sensed data provide a real-time assessment of the magnitude and variation of crop condition parameters, and this study investigates the use of these parameters as an input to a crop growth model. This investigation was conducted in the semi-arid region of North Dakota in the southeastern part of the state. The primary objective was to evaluate a method of integrating parameters. retrieved from satellite imagery in a crop growth model to simulate spring wheat yields at the sub-county and county levels. The input parameters derived from remotely sensed data provided spatial integrity, as well as a real-time calibration of model simulated parameters during the season, to ensure that the modeled and observed conditions agree. A radiative transfer model, SAIL (Scattered by Arbitrary Inclined Leaves), provided the link between the satellite data and crop model. The model parameters were simulated in a geographic information system grid, which was the platform for aggregating yields at local and regional scales. A model calibration was performed to initialize the model parameters. This calibration was performed using Landsat data over three southeast counties in North Dakota. The model was then used to simulate crop yields for the state of North Dakota with inputs derived from NOAA AVHRR data. The calibration and the state level simulations are compared with spring wheat yields reported by NASS objective yield surveys.

Iowa is the largest corn acreage state with 14.2 million acres which is an increase of 1.6 million acres from 2006. Two sources of data were used for this study. First, NASS calculates the number of acres planted each year for each county. This data is useful to determine general trends; however it will not show where the crops were planted within a county. Secondly, National Agricultural Statistics Service (NASS) has created a land cover classification image each year from either Landsat TM or Advanced Wide Field Sensor (AWiFS) imagery from 2001 through 2007. These images show the location of where each crop is being planted and when combined with each other can be used to determine crop rotations. By analyzing the NASS state acreage reports from 2001-2007, it does not appear that new areas are being put into cultivation. The total area used in corn and soybean production has remained the same throughout this time period. Thus increases in corn acreages have come from decreases in soybean acreages. Based on the NASS land cover classification, the area for the state in a continuous corn rotation has increased from 15.2% in 2001-2002 to 20.9% in 2006-2007. Areas in continuous soybean rotation have diminished from 8.1% in 2001-2002 to 4.3% in 2006-2007. There are thirty three counties which have consistently high accuracies for the 2001-2007 time period. These counties also show that the increase in corn acreages have come from soybean acreages, not from increasing overall agricultural areas.

Landcover classification is essential in studies of landcover change, climate, hydrology, carbon sequestration, and yield prediction. The potential for using NASA's MODIS sensor at 250-meter resolution was investigated for USDA's operational programs. This research was conducted over Iowa and Illinois to classify corn and soybean crops. Multitemporal 8-day composite 250-meter-resolution surface reflectance product time series were used to generate the NDVI data, which were used to differential between corn and soybean crops in the U.S. Corn Belt. The results of the MODIS-based classification were compared with the Landsat-based classification for the 2-year period. The overall classification accuracy for Iowa was 82%, and for Illinois 75%. In conclusion, this method has been used successively during the 2002-2006 years to develop crop classifications and products for crop conditions and potential yield maps for Iowa and Illinois.

Brazil has become a major player in world soybean markets, second only to the U.S. Brazil Crop area is about 10 million hectares and is now rapidly expanding into the Brazilian savannah (Cerrado) and the Amazonian region where forested area is being converted to cropland. There is a need for accurate updated information on the newly expanded agricultural areas in Brazil and the current total production. The objective of this research was to develop an operational method for assessing soybean crop area that would facilitate developing remote sensing based algorithms for assessing crop yields in major producing areas. The moderate resolution imaging spectroradiometer (MODIS) onboard the Terra satellite offers a good potential for assessing crop area as well as provide opportunity to retrieve crop condition parameters that can be used to assess crop yields. A three-year MODIS data set was acquired for the study and this research describes the methods used for processing the 8-day composite reflectance data from bands 1 and 2 and its use in developing the classification of soybean crop area in four major soybean producing areas in Brazil. The results suggest methods that can be used for operational application of MODIS 250m data for classification as well as potential use in crop yield assessment.

Using NOAA AVHRR or MODIS imagery to create land-use classifications has been attempted for many years. Unfortunately, most of these classifications do not differentiate crop types. If one is to extract pixel information as input to a crop model it is critical that the pixel be correctly identified with its crop type. In this study a Landsat TM classification was aggregated to the 250-meter pixel size of MODIS. Only pixels that were at least 80% of a particular crop were used as ground truth for the MODIS classification. The MODIS classification was performed using both standard techniques and decision tree techniques. The MODIS classifications were compared at the county, agricultural statistics district and state levels to the 80% mask and to the original TM land-use classification. When compared with the 80% mask, 60-80% accuracies were obtainable at the state level. However, the MODIS classification was only approximately 40% accurate when compared with the Landsat TM classification. It does appear that decision tree methodology resulted in better accuracies in most cases.

Monitoring crop condition and yields at regional scales using traditional operational NOAA AVHRR data remains challenging. The 1 km spatial resolution with two primary spectral bands is not adequate for development of field level canopy parameters. MODIS imagery offers an opportunity for daily coverage and adequate resolution required in operational applications. The objective of this research is to investigate the applicability of the 8-day MODIS composite data in the operational programs for crop condition and yield assessment. A field study was conducted in the predominantly corn and soybean area of Iowa in the Upper Midwest U.S. Crop yields were simulated at 250 m resolution and results were mapped for the study area.

Monitoring regional agricultural crop condition has traditionally been accomplished using NOAA AVHRR (1 km) data. New methods are developed for assessing crop yields by retrieving biophysical parameters from remotely sensed imagery and integrating with crop simulation models. The MODIS imagery with its 250 m resolution and a potentially daily coverage offers an opportunity for operational applications. The objective of this research was to assess the potential application of MODIS data for operational crop condition and yield estimates. A field study was conducted during the 2000 crop season in McLean county Illinois (IL), USA. Twenty corn and soybean fields were monitored with measurements for crop reflectance, Leaf area Index (LAI) and other crop growth parameters. A radiative transfer model was used to independently develop the LAI from the MODIS 250-m data. Crop growth parameters retrieved from the imagery were integrated in a crop yield simulation model. The magnitude and spatial variability of estimated LAI and the NASA product was partly due to differences in the classification of crop type and the pixel resolutions. A comparison with the NASA derived MODIS vegetation parameters and independently derived parameters are presented.

This research was part of a 1997 NASA-USDA collaborative research study for regional mapping of soil moisture in the US southern Great Plains. The study was conducted during a transition from winter to summer crops in June and July. Classification techniques were developed using Landsat TM data to separate the transitional vegetation from the natural vegetation. Areal photography and associated ground truth data for selected areas were used in developing the spectral signatures for each vegetation class. Classification of the entire region used these signatures. This research suggests techniques to be used in mapping vegetation classes in a very mixed land use area and suggests ways of using multi temporal satellite imagery for separating vegetation classes and minimizing cloud cover problems.

Using NOAA AVHRR or MODIS imagery to create land-use classifications has been attempted for many years. Unfortunately, most of these classifications do not differentiate crop types. Crop models require that vegetation characteristics extracted from an image be the correct crop type. This study compares four techniques to create land-use classifications using MODIS data. These classifications were compared to the ground data that had been set aside, to a mask where each MODIS sized pixel were at least 80% of a single land-used based on a Landsat TM classification for the same year, and to the Landsat TM classification. Using a decision tree method and comparing the classification to an 80% mask resulted in an accuracy of 73% which was the highest accuracy obtained in this study. The study showed that accuracies could range from 37% to 73% depending on the classification process and if segment data, an 80% mask, or a Landsat TM classification were used for accuracy assessment.

A new family of dispersants for agrochemical suspension concentrate (SC) formulations has been created by reacting an anhydride-bearing resin with a methyl capped polyether amine. The result is a moderate molecular weight graft polyamide, with the desirable “comb” geometry. From this family of materials, four were chosen for comparison to three commercially available benchmark dispersants in several typical pesticide suspension concentrates. The criteria used to compare the new to the old dispersants were: ability to reduce suspension viscosity, zeta potential, syneresis, and tendency of the concentrate to “hardpack.” The study demonstrated that the new dispersants are generally very effective dispersants, but care must be used in selecting the best dispersant/wetting agent combination for a given pesticide formulation.

A study was conducted to determine whether the efficacy of pyrethroid insecticides, formulated with Biodac2 (an inert carrier granule derived from paper recycling waste) could be enhanced through the addition of surfactants. Thus, a series of formulations of permethrin on Biodac was prepared so that the effect of several nonionic surfactants on granule efficacy could be studied. To compare the efficacy of the formulations, the granules were tested on imported red fire ants, Solenopsis invicta, under laboratory conditions. Formulations with surfactant outperformed the control formulation (no surfactant) by a wide margin.

Nitrogen and sulfur-substituted dioxetanes exhibit dramatically lower activation energies for decomposition compared to the corresponding oxygen-bearing dioxetane. A mechanism involving intramolecular electron-transfer processes is proposed for the cleavage of these unstable dioxetanes.

Synthese par oxydation electrochimique de (t-butyldimethyl siloxy)-4 et (t-butyl diphenyl siloxy)-4 phenols. Hydrolyse des bis-acetals obtenus

Tocqueville described the eventual progress of equality as inevitable; today its prospects seem less assured. The main engines of equality's modest advance are to be found in contemporary welfare states, where politics concerns who gets what and why. Governments are deeply concerned with these matters even when beguiling themselves, as well as the rest of us, into overlooking the cumulative results of their actions. By shoring up and gradually reshaping stratification systems, they help provide frameworks within which we live and plan our futures. Ordinary citizens are more attuned to the facts of inequality than to speculations about its origins. This essay investigates when and how citizens learn about stratification in England and the United States.

Synthese par oxydation electrochimique de (t-butyldimethyl siloxy)-4 et (t-butyl diphenyl siloxy)-4 phenols. Hydrolyse des bis-acetals obtenus

Complexation of quinone monoacetals with methylaluminium bis-(2,6-di-t-butyl-4-methylphenoxide), followed by addition of organolithium reagents, gives products from 1,4-addition of the organolithium reagent to the quinone monoacetal.

There was a time when American students of political parties could operate with the simple assumption that, in contrast to our own, European parties were cohesive, homogeneous, and disciplined. With the exception of a few deviant, old-fashioned parties of notables, the assumption ran, the European mass party had effectively solved the problem of internal dissidence and could turn to the challenge of external competition with "a punctilious ideological and principled rigidity."'l In contrast to parties in the United States, the problem of factions seemed to have been solved in Europe (1) because of the more streamlined and hierarchical organization of European mass parties, (2) because of their apparently higher degree of programmatic homogeneity, and (3) because of the greater institutional centralization of European political systems. The monolithic view of the European mass party outlined above ignores many of the historical cleavages within these parties, especially during the period between the two world wars.2 And, in recent years, this perspective seems particularly inappropriate as factions have not only flourished, but have buttressed themselves with the very tools of organization and propaganda that the mass parties once developed to insure