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In low‐relief areas, easily observed landscape features such as terrain and vegetation often do not spatially co‐vary with soil conditions to the level that they can be effectively used in predictive soil mapping. This paper proposes an approach to predicting soil spatial variation over such areas at a coarse resolution by constructing environmental covariates from the dynamic feedbacks of land surface in response to solar radiation. These feedbacks are captured by the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature observations as a time series of four temperatures per day (1:30 AM, 10:30 AM, 1:30 PM, 10:30 PM) converted into five covariates representing the features of the time series. The approach is demonstrated by mapping sand, silt, soil organic matter (SOM), and pH over a 12,000‐km2 low‐relief area of Jiangsu Province, China, using a random forest model trained on 144 soil observations. The model was built and evaluated for 13 single days. For the non‐winter days with relatively high solar radiation, the covariates were strongly correlated with the soil properties, so that their use in predictive mapping gives good results for soil texture and useful results for SOM and pH. We also present plausible physical interpretations for the action of the covariates based on thermal properties of soils. We conclude that, despite the coarse resolution of the MODIS‐derived covariates, the proposed approach is a feasible solution for predictive soil mapping over large areas of low relief and should be investigated at finer resolutions. [ABSTRACT FROM AUTHOR]

But this is far from the full value that could be extracted from a dataset, if the ingenuity of other researchers, perhaps combined with other datasets, could be applied to the dataset. Data articles should give sufficient information to enable other scientists, whether soil scientists or those in related disciplines, to properly use the datasets in their own research. These datasets are sometimes re-used or integrated with other datasets by the original researchers or selected collaborators. [Extracted from the article]

Conductive sapwood (xylem) area (Ax) of all trees in a given forested area is the main factor contributing to spatial tree transpiration. One hundred ninety-five trees of 9 species in the Kalahari region of Botswana were felled, stained, cut into discs, and measured to develop allometric equations predicting Ax from estimates of stem (As) and canopy (Ac) areas. Stem discs were also subjected to laboratory-based computed tomography, which well detected wood density contrasts but was not diagnostic with regard to delineation of Ax. The staining experiment, along with the help of visual and computed tomography analysis, allowed the definition of 4, tree-species categories of Ax, C1-C4. In C1 ( Acacia erioloba, Terminalia sericea, and Burke Africana), the staining and visual delineation of Ax matched the natural color difference between sapwood and heartwood; in C2 ( Dichrostachys cinerea and Ochna pulchra), sapwood was divided into external conductive and internal nonconductive annuli; in C3 ( Acacia fleckii and Acacia luederitzii), sapwood had sharp staining boundary between external highly conductive and internal low-conductive annuli; and in C4 ( Lonchocarpus nelsii and Boscia albitrunca), stems had no heartwood. Per-species 0-intercept linear regression models, Ax = slope .As (slope = 0.392 ÷ 0.794; R2 = 96.7 ÷ 99.8%) and Ax = slope .Ac (slope = 1.477 ÷ 17.044; R2 = 82.1 ÷ 92.2%) yielded excellent to good predictive allometric equations. The first equation is suitable for Ax scaling of small-size Kalahari areas, where the As of all trees can be estimated on the ground, whereas the second, as contribution to automated tree transpiration mapping of large-size Kalahari areas, where the Ac of trees can be derived through remote sensing interpretation of high-resolution images. [ABSTRACT FROM AUTHOR]

In temperate climates, a significant fraction of annual emissions of N2O from agricultural land can occur during soil thaw in late winter and early spring. The objective of this study was to determine the impact of land use change from long-term fallow grassland to managed perennial grass crops on these thaw-related N2O emissions and to identify field-scale drivers that influence emissions. Using static chambers, we monitored mid-afternoon N2O fluxes during the 2013 spring thaw from 27 March to 7 April, observing fallow grassland and second-year reed canarygrass (Phalaris arundinaceae L., 'Bellevue') across a short topographical gradient. Soil temperature, soil moisture, and residual aboveground biomass were also observed, as were hourly air temperature and precipitation. Fluxes of N2O were generally low (-9.8 to 21.3 mg N2O-N m-2 h-1) except for one observation of 77.6 mg N2O-N m-2 h-1. Hot-moment analysis, non-parametric statistical tests, and ANOVA results showed that downslope positions converted to managed and fertilized grass had significantly higher N2O emissions than the fallow and upslope positions. We found that these downslope managed grass sites had mean soil moisture of 75.0% water-filled pore space and less insulating residual aboveground biomass than the fallow grassland. Our results suggest that converting fallow grassland to managed perennial grass cropping systems for bioenergy or other uses could increase spring-thaw N2O emissions in wetness- prone areas. [ABSTRACT FROM AUTHOR]

The accuracy and precision of predictions of soil texture based on visible- near-infrared (Vis-NIR) spectra are affected by the soil moisture content at the time of measurement. This study aimed to quantify these effects. We also developed a method to improve the accuracy of soil texture prediction when the difference in moisture content is large and unknown, as is usually the case in field measurements. Reflection spectra (380-2400 nm) of 89 soil samples were obtained in the laboratory under nine moisture conditions. Prediction models for each moisture condition were built separately using partial least-squares (PLS) regression. Each model was applied to independent validation sets under the same moisture condition as the calibration sets, and then the model based on air-dried soils (dry model) was applied to the other eight moisture conditions to quantify the effect of soil moisture on the prediction accuracy. Finally, the perpendicular drought index (PDI) was used as an indicator of soil moisture, and the nine moisture conditions were regrouped to four groups using the PDI. Prediction models for each group with the same PDI were built and evaluated. The results show that Vis-NIR spectra can be directly applied to predict the soil texture of soils in different known moisture states. When the moisture state is unknown, the models based on PDI grouping markedly improve prediction accuracy. The RMSEp of prediction from PDI-grouped models of prediction of the content of clay and sand separates were between 1 and 2% and between 8 and11%, respectively, for the different moisture classes. This method has potential for direct application in the field with on-the-go sensors. [ABSTRACT FROM AUTHOR]

ABSTRACT In this study we develop an empirical method to estimate the volume threshold for predicting coseismic landslide dam formation using landscape parameters obtained from digital elevation models (DEMs). We hypothesize that the potential runout and volume of landslides, together with river features, determine the likelihood of the formation of a landslide dam. To develop this method, a database was created by randomly selecting 140 damming and 200 non-damming landslides from 501 landslide dams and > 60 000 landslides induced by the Mw 7.9 2008 Wenchuan earthquake in China. We used this database to parameterize empirical runout models by stepwise multivariate regression. We find that factors controlling landslide runout are landslide initiation volume, landslide type, internal relief ( H) and the H/ L ratio (between H and landslide horizontal distance to river, L). In order to obtain a first volume threshold for a landslide to reach a river, the runout regression equations were converted into inverse volume equations by taking the runout to be the distance to river. A second volume threshold above which a landslide is predicted to block a river was determined by the correlation between river width and landslide volume of the known damming landslides. The larger of these two thresholds was taken as the final damming threshold. This method was applied to several landslide types over a fine geographic grid of assumed initiation points in a selected catchment. The overall prediction accuracy was 97.4% and 86.0% for non-damming and damming landslides, respectively. The model was further tested by predicting the damming landslides over the whole region, with promising results. We conclude that our method is robust and reliable for the Wenchuan event. In combination with pre-event landslide susceptibility and frequency-size assessments, it can be used to predict likely damming locations of future coseismic landslides, thereby helping to plan emergency response. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Walker and Syers' model of gradual P depletion and decreasing bioavailability during pedogenesis is well accepted for natural ecosystems but untested beyond decadal scales in agricultural ecosystems. We investigated changes in P speciation and distribution using sequential extraction in soil profiles from two contrasting paddy chronosequences developed on calcareous marine sediments at Cixi and acidic quaternary red clays at Jinxian, respectively, in subtropical China. Our results demonstrate shifts in P abundance and speciation with depth during paddy soil development. Total P (PT) accumulated in surface and subsurface horizons in the young paddy soils (<300 yr) at both sites due to P additions by paddy cultivation. However, in the old paddy soils (700 and 1000 yr) at Cixi, we observed depletion of PT, despite continuous P additions, which we attribute to the decline in soil P sorption capacity coincident with the near complete removal of CaCO3 and substantial loss of Fe oxides. Compared to the systematic decline of PT below the plow pan in the lowland Cixi chronosequence, PT increased in the upland Jinxian subsoils, suggesting that terraced paddy cultivation resulted in significant translocation of P to the lower horizons. In calcareous paddy soils, 80% of changes in PT can be attributed to Ca-bound P while for acid soils 43% was explained by Fe- and Al-associated P. Our study demonstrates distinct patterns of P transformation in agroecosystems with human activities altering both the rate and trajectory of P transformations during the early stage of paddy soil development, after which P becomes rapidly depleted and less biological available. [ABSTRACT FROM AUTHOR]

A method to enhance manual landform delineation using photointerpretation to map a larger area is described. Conventional aerial photo-interpretation (API) maps using a geo-pedological legend of 21 classes were prepared for six sample areas totaling 111 km² in the Baranja region, eastern Croatia. Nine terrain parameters extracted from a digital elevation model (DEM) (ground water depth, slope, plan curvature, profile curvature, viewshed, accumulation flow, wetness index, sediment transport index, and the distance to nearest watercourse) were used to extrapolate photo-interpretation over the entire survey area (1062 km²). The classification accuracy was assessed using the error matrix, calculated by comparing both the whole API maps and point samples, with the results of classification. The first results, using a maximum-likelihood classifier, were 58.2% (hill land), 39.1% (plain), and 45.3% (entire area) reproducibility of the training set. Six classes in the plain were responsible for a large proportion of the misclassifications, due to an insufficiently detailed DEM and the complex nature of landforms (point bar complexes, levees, active channel banks), which cannot be explained with the terrain parameters only. Reproducibility for a simplified legend of 15 classes over the study area was improved to 65.8% (plain), 58.2% (hill land), and 63.4% (entire area) using the whole-API training set. After the simplification of legend (15) and with the iterative (3) selection of pointsample training set, classification was able to reproduce 97.6% (hill land), 86.7% (plain), and 90.2% (entire area) of the training set. The supervised classification showed fine details not achieved by photointerpretation. The number of manual photo-interpretations that had to be prepared was reduced from 84 to 6. The methodology can be applied by soil survey teams to edit and update current maps and to enhance or replace APl for new surveys. [ABSTRACT FROM AUTHOR]