Illustrations of soil moisture variability in selected areas and plots of different sizes

Hills, Rodney C.; Reynolds, S. G.

doi:10.1016/0022-1694(69)90029-8

Cited by 97 publications

(72 citation statements)

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“…In addition the data from the N ►. '-& Reynolds (1969) are plotted for comparison and to extend the moisture range of the data. It should be noted that their data were for smaller pots, (31 X 31 m was the largest), and the sample depth was 0-8 cm.…”

Section: Integrated Layersmentioning

confidence: 99%

“…Inherent to this study was the desire to develop a statistically based sampling system relative to soil moisture variability which would accurately define the soil moisture regime of a given area. Hills and Reynolds (1969), in a study of soil moisture variability for various size plots have found that the Coefficient of Variation (CV = standard deviation/mean) ranges from 6 to 161 7v for 60 samples from I X 1 m plots and from 8 to 15% for 30 X 30 m plots. The average soil moisture ranged from 30 to 7010 for these plots.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of surface moisture variations within large‐field sites

Bell¹,

Blanchard²,

Schmugge³

et al. 1980

Water Resources Research

173

116

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In the past several years, 1974–1977, NASA has conducted several research studies to develop an extensive collection of ground truth soil moisture data. As a result of these experiments, moisture data were available from 58 ‘large‐field sites,’ each being 400 × 400 m (40 acre). The field locations were 1 field in Phoenix, Arizona (sampled four times), 28 fields in Jefferson County, Kansas, 23 fields in Finney County, Kansas, and 5 fields in Hand County, South Dakota. At the first three locations, samples were taken in specific vertical increments or horizons (i.e., 0–1; 1–2, 2–5, 5–9, and 9–15 cm). In the South Dakota study, moisture samples were taken in increments from the surface ( i.e., 0–2.5, 0–5, and 0–10 cm). A detailed statistical analysis was made to define the general relationship and ranges of values of the field moisture relative to both the variance (standard deviation) and coefficient of variation (CV) for a given test site and depth increment. On the basis of the results of the variability study it was concluded that (1) moisture variations within any given‘large‐field’ area are inherent and are normally distributed about the mean, (2) neither a single (constant) value of the standard deviation nor coefficient of variation uniquely define the variability over the complete range of mean field moisture contents examined, and (3) using an upper bound standard deviation parameter clearly defines the maximum range of anticipated moisture variability. It was found that 87% of all large‐ moisture content standard deviations were less than 3%, while about 96% of all the computed values had an upper bound of s = 4% for these intensively sampled fields. Using these upper bound magnitudes as an estimate of the population standard deviation and a preselected confidence level, limit of error curves of mean soil moisture measurements for large‐field sites relative to the required number of samples were determined.

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Section: Integrated Layersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of surface moisture variations within large‐field sites

Bell¹,

Blanchard²,

Schmugge³

et al. 1980

Water Resources Research

173

116

View full text Add to dashboard Cite

show abstract

“…However, this approach has provided contradictory conclusions in earlier publications. For example, Hills and Reynolds (1969), Loague (1992), Schmugge and Jackson (1996), and Mohanty and others (2000) have reported little spatial correlation of soil moisture. In contrast, other researchers have found spatial correlation.…”

Section: Introductionmentioning

confidence: 98%

Geostatistical analysis of soil moisture variability on Da Nangou catchment of the loess plateau, China

Wang

Yang

et al. 2001

Env Geol

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To achieve a better understanding of environmental issues related to soil erosion,¯ood-ing, and solute transport, it is necessary to understand the variability of soil moisture under different climatic and geological conditions and at different spatio-temporal scales. Moisture patterns based on ®ve depths (0±5, 15±20, 25±30, 45±50, and 70±75 cm) were determined in the Da Nangou catchment of the loess plateau of China from May to September 1999. Spatial structure of pro®le-averaged and layer-averaged soil moisture was analysed using semivariograms. The results indicated that soil moisture exhibits changing spatial dependence with time and depth. For pro®le-averaged soil moisture, high sills [4.6±14 %(v/v) 2 ] and high ranges (135±160 m) were observed during dry conditions. The sills [2.6±6.3 (%v/v) 2 ] and ranges (140 m) were small during wet conditions. However, soil moisture was described by linear semivariograms without a sill following a signi®cant rain event. For layeraveraged soil moisture, the sills increased with depth, but not for the ranges. The nuggets tended to increase with higher sills. This analysis provides insights into the variability of soil moisture on a large scale and interpolation strategies for extrapolating point measurements across the catchment.

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“…Since the first half of last century, the problem of obtaining representative sampling of agricultural fields has led to the development of new sampling schemes. Initially, scientists based their strategies on classical statistical concepts, which were later complemented with geoestatistics and time-space series analyses, and more recently neural networks (Hills and Reynolds 1969;Mohanty and Mousli 2000;Western et al 2002;Timm et al 2006;Hu et al 2008).…”

Section: Introductionmentioning

confidence: 99%

Temporal variability of soil water storage evaluated for a coffee field

Timm

Neto²,

Bacchi³

et al. 2011

Soil Res.

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Abstract. Sampling field soils to estimate soil water content and soil water storage (S) is difficult due to the spatial variability of these variables, which demands a large number of sampling points. Also, the methodology employed in most cases is invasive and destructive, so that sampling in the same positions at different times is impossible. However, neutron moderation, time domain reflectrometry, and, more recently, frequency domain reflectrometry methodologies allow measurements at the same points over long time intervals. This study evaluates a set of neutron probe data, collected at 15 positions placed randomly along a coffee crop contour line, over 2 years at 14-day intervals. The temporal stability of S was again demonstrated, so that wetter or dryer locations remain so over time, and the definition of such positions in the field reduces the number of sampling points in future S evaluations under similar conditions. An analysis was made to determine the minimum number of sampling points to obtain the average S of the field within a chosen level of significance. Classical statistical analysis indicated that the 15 measurement positions could be reduced to four or even to one position to obtain a reliable field S average. State-time analysis showed S estimations depend more on previous measurements of rainfall P (52%) than on evapotranspiration ET (28%) and S (20%). The analysis also showed that ET was not realistically estimated from previous measurements of S; it was more dependent on previous measurements of ET (59%) than on P (30%) and S (9%). This statistical procedure showed great advantages over classical multiple regressions. Future studies of this type should be carried out at regularly spaced observation points in a grid, in order to allow a 2-D and 3-D state-space-time analysis.

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Illustrations of soil moisture variability in selected areas and plots of different sizes

Cited by 97 publications

References 1 publication

Analysis of surface moisture variations within large‐field sites

Analysis of surface moisture variations within large‐field sites

Geostatistical analysis of soil moisture variability on Da Nangou catchment of the loess plateau, China

Temporal variability of soil water storage evaluated for a coffee field

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