Four fields of 117 ha area located at the University of Arizona's Maricopa Agricultural Center were selected for this study. Two soil series, the Casa Grande sandy clay loam and Trix clay loam occur. Surface samples (0-25 cm) were collected on a 98 m interval and 3 rows providing 47 sites per field. Sites were classified either as surveying (32) or testing (15) in each of the four fields. Additional samples at 25-50, 50-75, 75-100, and 100-125 cm were obtained with duplicate surface undisturbed cores at 5 sites per field. Soil parameters include bulk density, saturated hydraulic conductivity, moisture retention, particle size analysis, pH, EC, soluble cations, SAR, and ESP. A quantification of the spatial interdependence of samples was developed based on the variogram of soil parameters. A linear model was best fitted to the clay, EC, Ca²⁺, Mg²⁺, Na⁺, SAR and ESP, and a spherical model to the sand, silt, pH, and K⁺ observed variograms. A comparison of variograms obtained conventionally and with the robust estimation of Cressie and Hawkins (1980) for sand and Ca²⁺ were performed with a fixed couples number per class and with a fixed class size. Additionally, a negative log-likelihood function along with cross-validation criteria were used with the jackknifing method to validate and determine variogram parameters. Three interpolation techniques have been compared for estimating 11 soil properties at the test sites. The techniques include Arithmetic Mean, Inversely Weighted Average, and Kriging with various numbers of neighbor estimates. Using 4 point estimates resulted in nearly identical results, but the 8 point estimates gave more contrast for results among the alternative techniques. Jackknifing was used with 4, 8, 15, 25 neighbors for estimating 188 points of sand and Ca²⁺ with the three techniques. Sand showed a definite advantage of Kriging by lowering the Mean Square Error with increasing neighbor number. The simple interpolator Arithmetic Mean was comparable and sometimes even better than the other techniques. Kriging, the most complex technique, was not the absolute best interpolator over all situations as perhaps expected. The spatial dependence for the 11 soil variables was studied by preparing contour maps by punctual Kriging. Sand and Ca²⁺ were also mapped by block Kriging estimates.

Geostatistical methods were investigated in order to find efficient and accurate means for estimating a regionalized random variable in space based on limited sampling. The random variables investigated were (1) the bare soil temperature (BST) and crop canopy temperature (CCT) which were collected from a field located at the University of Arizona's Maricopa Agricultural Center, (2) the bare soil temperature and gravimetric moisture content (GMC) collected from a field located at the Campus Agricultural Center and (3) the electrical conductivity (EC) data collected by Al-Sanabani (1982). The BST was found to exhibit strong spatial auto-correlation (typically greater than 0.65 at 0⁺ lagged distance). The CCT generally showed a weaker spatial correlation (values varied from 0.15 to 0.84) which may be due to the length of time required to obtain an "instantaneous" sample as well as wet soil conditions. The GMC was found to be strongly spatially dependent and at least 71 samples were necessary in order to obtain reasonably well behaved covariance functions. Two linear estimators, the ordinary kriging and cokriging estimators, were investigated and compared in terms of the average kriging variance and the sum of squares error between the actual and estimated values. The estimate was obtained using the jackknifing technique. The results indicate that a significant improvement in the average kriging variance and the sum of squares could be expected by using cokriging for GMC and including 119 BST values in the analysis. A nonlinear estimator in one variable, the disjunctive kriging estimator, was also investigated and was found to offer improvements over the ordinary kriging estimator in terms of the average kriging variance and the sum of squares error. It was found that additional information at the estimation site is a more important consideration than whether the estimator is linear or nonlinear. Disjunctive kriging produces an estimator of the conditional probability that the value at an unsampled location is greater than an arbitrary cutoff level. This latter feature of disjunctive kriging is explored and has implications in aiding management decisions.

Soils darken upon wetting due to changes in the scattering properties of the individual soil particles. The objective of this research was to develop a procedure to map the distribution of wet soils using the radiance measurements acquired by a spaceborne imaging scanner. The soil-mapping procedure was designed for use in the regional exploration for ground water resources. The soil-mapping procedure was based upon the detection of reflectance changes in a comparison of Landsat 5 Thematic Mapper (TM) scenes acquired before and after a rain. The Stronghold watershed, which is situated on the western slopes of the Dragoon Mountains, Cochise County, Arizona, was chosen as the test site for the soil-mapping procedure. TM scenes depicting the watershed on 7 June 1985 and 14 November 1985 were used in the change-detection analysis. The region was dry at the time of the June overpass, the November overpass occurred two days after a rain. The recovery of reflectance from radiance requires knowledge of 1) the orientation of the surface relative to the sun and the satellite, 2) the exoatmospheric solar irradiance, 3) the atmospheric optical depth, and 4) the atmospheric path radiance. The orientation of the surface elements were defined through the use of a digital elevation model of the Stronghold watershed. The solar irradiance and atmospheric optical depth were obtained from the literature; the atmospheric path radiance was estimated from shadowed areas depicted in the images. Temporal changes in reflectance were detected by subtracting the November reflectance estimates from those recovered from the June radiance measurements. Changes significant at the 0.05 level were identified through use of the Student-t test. The identical significance level was used to identify temporal changes in the Perpendicular Vegetation Index, or PVI. A surface element was classified as an anomaly if there was a significant temporal change in reflectance with no attendant change in PVI. Field checks of the anomalies proved that wet soils could be mapped via the remote detection of changes in their reflectance. The majority of the false anomalies could be attributed to the disparity between the spatial resolutions of the radiance measurements and the topographic data.

This item was digitized as part of the Million Books Project led by Carnegie Mellon University and supported by grants from the National Science Foundation (NSF). Cornell University coordinated the participation of land-grant and agricultural libraries in providing historical agricultural information for the digitization project; the University of Arizona Libraries, the College of Agriculture and Life Sciences, and the Office of Arid Lands Studies collaborated in the selection and provision of material for the digitization project.

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Six pedons derived from volcanic cinders from the San Francisco Volcanic Field near Flagstaff, Arizona, were studied to evaluate their physical, chemical, and mineralogical properties for inclusion in the proposed soil order Andisol. All the pedons meet the requirements for the Andic soil properties and they are thus classified as either Typic Ustivitrand or Melanic Ustivitrand Subgroups of Andisol Order. The proposed classification is discussed with respect to the guidelines presented in the ninth International Committee of Classification on Andisols (ICOMAND) letter.

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