Journal Paper Digests 2017 #20
- Reconstructing daily clear-sky land surface temperature for cloudy regions from MODIS data
- A new method for geochemical anomaly separation based on the distribution patterns of singularity indices
- Assessment of biological activity in agrogenic and natural chernozems of Kabardino-Balkaria
- A fuzzy logic slope-form system for predictive soil mapping of a landscape-scale area with strong relief conditions
- Mapping mineral prospectivity by using one-class support vector machine to identify multivariate geological anomalies from digital geological survey data
- Soil carbon loss regulated by drought intensity and available substrate: A meta-analysis
- Is the rate of mineralization of soil organic carbon under microbiological control?
- Quantification of Soil Permanganate Oxidizable C (POXC) Using Infrared Spectroscopy
- Construction of Membership Functions for Soil Mapping using the Partial Dependence of Soil on Environmental Covariates Calculated by Random Forest
- Neighborhood Size of Training Data Influences Soil Map Disaggregation
Reconstructing daily clear-sky land surface temperature for cloudy regions from MODIS data
Authors: Sun, L; Chen, ZX; Gao, F; Anderson, M; Song, LS; Wang, LM; Hu, B; Yang, Y
Source: COMPUTERS & GEOSCIENCES, 105 10-20; AUG 2017
Abstract: Land surface temperature (LST) is a critical parameter in environmental studies and resource management. The MODIS LST data product has been widely used in various studies, such as drought monitoring, evapotranspiration mapping, soil moisture estimation and forest fire detection. However, cloud contamination affects thermal band observations and will lead to inconsistent LST results. In this study, we present a new Remotely Sensed DAily land Surface Temperature reconstruction (RSDAST) model that recovers clear sky LST for pixels covered by cloud using only clear-sky neighboring pixels from nearby dates. The reconstructed LST was validated using the original LST pixels. Model shows high accuracy for reconstructing one masked pixel with R-2 of 0.995, bias of -0.02 K and RMSE of 0.51 K. Extended spatial reconstruction results show a better accuracy for flat areas with R-2 of 0.72-0.89, bias of -0.02-0.21 K, and RMSE of 0.92-1.16 K, and for mountain areas with R-2 of 0.81-0.89, bias of 0.35-1.52 K, and RMSE of 1.42-2.24 K. The reconstructed areas show spatial and temporal patterns that are consistent with the clear neighbor areas. In the reconstructed LST and NDVI triangle feature space which is controlled by soil moisture, LST values distributed reasonably and correspond well to the real soil moisture conditions. Our approach shows great potential for reconstructing clear sky LST. under cloudy conditions and provides consistent daily LST which are critical for daily drought monitoring.
A new method for geochemical anomaly separation based on the distribution patterns of singularity indices
Authors: Liu, Y; Zhou, KF; Cheng, QM
Source: COMPUTERS & GEOSCIENCES, 105 139-147; AUG 2017
Abstract: Singularity analysis is one of the most important models in the fractal/multifractal family that has been demonstrated as an efficient tool for identifying hybrid distribution patterns of geochemical data, such as normal and multifractal distributions. However, the question of how to appropriately separate these patterns using reasonable thresholds has not been well answered. In the present study, a new method termed singularity-quantile (S-Q) analysis was proposed to separate multiple geochemical anomaly populations based on integrating singularity analysis and quantile-quantile plot (QQ-plot) analysis. The new method provides excellent abilities for characterizing frequency distribution patterns of singularity indices by plotting singularity index quantiles vs. standard normal quantiles. From a perspective of geochemical element enrichment processes, distribution patterns of singularity indices can be evidently separated into three groups by means of the new method, corresponding to element enrichment, element generality and element depletion, respectively. A case study for chromitite exploration based on geochemical data in the western Junggar region (China), was employed to examine the potential application of the new method. The results revealed that the proposed method was very sensitive to the changes of singularity indices with three segments when it was applied to characterize geochemical element enrichment processes. And hence, the S-Q method can be considered as an efficient and powerful tool for separating hybrid geochemical anomalies on the basis of statistical and inherent fractal/multifractal properties.
Assessment of biological activity in agrogenic and natural chernozems of Kabardino-Balkaria
Authors: Gorobtsova, ON; Uligova, TS; Tembotov, RK; Khakunova, EM
Source: EURASIAN SOIL SCIENCE, 50 (5):589-596; MAY 2017
Abstract: Parameters of biological activity (humus and microbial biomass reserves, potential intensity of the CO2 emission, and enzyme activity) have been determined in arable and natural chernozems on the plains of Kabardino-Balkaria as a part of the system for the ecological assessment of the state of the soil cover. Integral parameters of the eco-biological state of studied soils have been calculated on the basis of obtained data, and the level of changes in their total biological activity has been determined. A statistically significant decrease of the values of all the considered biological properties under the impact of tillage has been found. The data of two-way ANOVA suggest a stronger influence of agricultural management in comparison with genetic features of chernozems at the level of subtype. Differential approach is insufficient for evaluating the total level of soil biological activity, because there are many biological properties of soil, and the degrees of their changes in agrogenic soils are different. An integral approach has been used; it integrates the obtained data into a single integral assessment parameter. In arable soils, this integral parameter decreases by 39-46% and makes it possible to assess the degree of disturbance of the ecological functions of soils and their capacity for self-restoration.
A fuzzy logic slope-form system for predictive soil mapping of a landscape-scale area with strong relief conditions
Authors: Bui, LV; Stahr, K; Clemens, G
Source: CATENA, 155 135-146; AUG 2017
Abstract: We studied an improved slope form system using a fuzzy logic method to assess and map soil fertility of a mountain region in northern Vietnam that has strong relief conditions. The lack of good soil mapping techniques in Vietnam has brought about insufficient soil information, which often leads to false recommendations for land use and crop planning. The reviewed literature describes soil-mapping techniques using fuzzy logic method, but all of them are applied for mapping areas that have gentle relief conditions that are unlikely to be applicable to the mountainous soils in our study area. In this paper, we introduce a detailed slope form system that significantly describes the complexity of terrain characteristics of the area to be mapped, and provides more detail about the variability of the soil fertility of the area. Nine basic slopeforms were used to characterize for each of upper-, middle-, and foot slope positions, making the list 27 slopeforms. Together with crest and valley, the total unit number is 29. We investigated soils of the area and classified them into the major soil groups and calculated soil property indices for all of them. We identified four major environmental parameters affecting soil formation and soil quality: geology, elevation, slope inclination and land use. The findings indicated that soil fertility differs at slope positions. Soils located at upper slope positions, where agricultural activity only started recently, are more fertile than those found at middle slope positions. Soils located at foot slope positions, where eroded sediments accumulate, also have high levels of fertility compared to those on the middle slope. The improved slope form system then became an important additional environmental parameter for this soil mapping work. At a same comparable category, i.e. slope position, geology, soil group, elevation, slope gradient, straight slopeforms are an indicator for better soil fertility compared to convex and concave forms. Although the findings could not specify soil fertility variability for all 29 slopeforms, they did emphasize the major differences in soil fertility and soil formation based on three major forms of convex, straight and concave, with other factors taken into account, such as slope inclination, geology and elevation. We expect our results to be used by scientists and local authorities in deriving more effective land use and crop options for land use management strategies for the northern Vietnam’s mountain regions.
Mapping mineral prospectivity by using one-class support vector machine to identify multivariate geological anomalies from digital geological survey data
Authors: Chen, Y; Wu, W
Source: AUSTRALIAN JOURNAL OF EARTH SCIENCES, 64 (5):639-651; 2017
Abstract: Mineral targets are local geological anomalies. In a study area of a number of unit cells, mapping mineral prospectivity can be implemented by identifying anomaly cells from the unit cell population. One-class support vector machine (OCSVM) models can yield useful results in anomaly detection in high-dimensional data or without any assumptions on the distribution of the inlying data. The OCSVM model was applied to mapping gold prospectivity of the Laotudingzi-Xiaosiping district, an area with a complex geological background, in Jilin Province, China. The decision function value of each unit cell belonging to an anomaly was computed on the basis of the trained OCSVM model and used to express gold prospectivity of the cell. The receiver operating characteristic (ROC) curve, area under curve (AUC) and data-processing efficiency were used to compare the performance of the OCSVM model and a restricted Boltzmann machine (RBM) model in mapping gold prospectivity. The results show that the OCSVM model outperforms the RBM model in terms of ROC, AUC and data-processing efficiency. Gold targets were optimally delineated by using the Youden index to maximise the spatial association between the delineated gold targets and known gold deposits. The gold targets delineated by the OCSVM model occupy 11% of the study area and contain 88% of the known gold deposits; and the gold targets delineated by the RBM model occupy 10% of the study area and contain 81% of the known gold deposits. Therefore, the OCSVM model is a feasible mineral prospectivity mapping approach.
Soil carbon loss regulated by drought intensity and available substrate: A meta-analysis
Authors: Canarini, A; Kiaer, LP; Dijkstra, FA
Source: SOIL BIOLOGY & BIOCHEMISTRY, 112 90-99; SEP 2017
Abstract: Drought is one of the most important climate change factors, but its effects on ecosystems are little understood. While known to influence soil carbon (C) cycling, it remains unresolved if altered rainfall patterns induced by climate change will stimulate positive feedbacks of CO2 into the atmosphere. Using a meta-analysis frame-work including 1495 observations from 60 studies encompassing a variety of ecosystems and soil types, we investigated drought effects on respiration rates, cumulative respiration during drying-rewetting cycles, metabolic quotient (qCO(2)), dissolved organic C (DOC), microbial biomass and fungi to bacteria (F:B) ratios from laboratory and field experiments. We show that C-rich soils (>2% organic carbon) increase CO2 release into the atmosphere after intense droughts, but that C-poor soils show a net decline in C losses. We explain this self-reinforcing mechanism of climate change in C-rich soils by: (i) high substrate availability that magnify bursts of CO2 release after drought events and (ii) a shift in microbial community with increased loss of C per unit of biomass. These findings shed light on important responses of soil CO2 emissions to drought, which could either offset or facilitate positive feedbacks to global warming. Our results should be considered in global climate models, as even small changes in soil CO2 emission have large repercussions for global warming.
Is the rate of mineralization of soil organic carbon under microbiological control?
Authors: Brookes, PC; Chen, YF; Chen, L; Qiu, GY; Luo, Y; Xu, JM
Source: SOIL BIOLOGY & BIOCHEMISTRY, 112 127-139; SEP 2017
Abstract: A theory called the Regulatory Gate Hypothesis was previously proposed to considers that the rate limiting step in soil organic carbon (SOC) mineralization is independent of the size, community stricture or specific activity (mg CO2-C evolved g(-1) biomass C) of the soil microbial biomass. Here we report new experiments to test this hypothesis. In the first experiment, six different soils were perfused with CHCI3-saturated water to model SOC release and to stop microbial activity. Apart from one highly organic soil, they all released SOC at low and roughly constant rates, over sixty three days. In the second experiment, when the freeze-dried perfusates were returned to the parent soils, their % mineralization ranged from 17 to 35% over ten days, in contrast to bulk SOC (range 0.46-0.77%). In the third experiment, two soils were given three consecutive fumigations, each followed by 10 days aerobic incubation. The microbial biomass was decreased by > 90%, yet SOC mineralization proceeded at the same rate as in nonfumigated soil. In the fourth experiment, the six soils were subjected to various perturbations, including non perturbed controls, fumigation-incubation, air-drying rewetting, freeze-thaw (-20 degrees C and 80 degrees C) and sieving < 0.3 mm. After an initial flush due to the perturbations, the rates of mineralization became roughly equal in nearly all soil treatments and comparable to the control, despite significant declines in biomass. This shows that basal respiration was little affected by the perturbations. In Experiment five the effects of the perturbations on the microbial communities in the different soils and perturbations were determined. The bacterial community was significantly modified by both fumigation and air drying-rewetting, due mainly to increased Firmiculites and decreased Proteobacteria populations. Our findings suggest that mineralization of SOC is a two-stage process: firstly, non-bioavailable forms are converted abiologically to bioavailable forms (termed the Regulatory Gate), which, only then, undergo second process, biological mineralization. This finding has serious implications for theories of e.g. SOC dynamics, effects of global warming and soil nutrient cycling.
Quantification of Soil Permanganate Oxidizable C (POXC) Using Infrared Spectroscopy
Authors: Calderon, FJ; Culman, S; Six, J; Franzluebbers, AJ; Schipanski, M; Beniston, J; Grandy, S; Kong, AYY
Source: SOIL SCIENCE SOCIETY OF AMERICA JOURNAL, 81 (2):277-288; MAR-APR 2017
Abstract: Labile soil carbon is an important component of soil organic matter because it embodies the mineralizable material that is associated with short-term fertility. Permanganate-oxidizable C (POXC) is a widely used method for the study of labile C dynamics in soils. Rapid methods are needed to measure labile C, and better understand how this pool varies with soil C at regional scales. Infrared spectroscopy is an inexpensive way to quantify SOC and observe fluctuations in C functional groups. Using a sample set that encompassed several soil types and plant communities (seven different research projects, n = 496), soils were analyzed via diffuse reflectance Fourier transformed mid-infrared (MidIR, 4000-400 cm(-1)) and near-infrared (NIR, 10000-4000 cm(-1)) spectroscopy. Spectral data were used to develop calibrations for POXC, soil organic C (SOC), and total N (TN) using partial least squares (PLS) regression. The MidIR predicted POXC slightly better than the NIR, with calibration and/or validation R-2 values ranging from 0.77 to 0.81 depending on spectral pretreatments. Predictions for POXC were better than SOC and TN, but site variability influenced the calibration quality for SOC and TN. Using a selected MidIR region, which included bands correlated to POXC (3225-2270 cm(-1)), reduced the calibration quality, but still gave acceptable R-2 values of 0.76 to 0.77 for the calibration and validation sets. We show that POXC can be predicted using NIR and MidIR spectra. Selecting informative spectral bands offers an alternative to using full spectra for PLS regressions.
Construction of Membership Functions for Soil Mapping using the Partial Dependence of Soil on Environmental Covariates Calculated by Random Forest
Authors: Zeng, CY; Yang, L; Zhu, AX
Source: SOIL SCIENCE SOCIETY OF AMERICA JOURNAL, 81 (2):341-353; MAR-APR 2017
Abstract: Partial dependence plots generated by Random Forest (RF) imply an association between soil and environmental variables. This study develops a method to construct membership functions representing knowledge of soil-environment relationships from partial dependence. Key parameters were obtained from normalized partial dependence to define class limits and membership gradation. Seven environmental variables were selected on the basis of the variable’s importance within RF. Two cases were conducted to test the effectiveness of our method using different training samples. Case 1 used 33 representative locations as training samples and 50 locations as validations. Case 2 randomly split all 83 samples into training and validation subsets at a proportion of 2: 1; the splits were repeated seven times. For each case, the generated membership functions were used for mapping soil subgroups in Heshan, China, under the Soil Landscape Inference Model framework; RF was conducted for comparison. The results showed that mapping accuracy based on the membership functions (78%) was much higher than that of RF only (60%) in Case 1. In Case 2, the mapping accuracies using membership functions (an average of 67%, SD = 6.5%) were not always higher than those by RF (an average of 67%, SD = 8.0%). The constructed membership functions were impacted by the training samples. Use of representative training samples is recommended when applying the proposed method. However, training samples (including representative samples and other samples) with good coverage in the environmental feature space would allow RF to obtain more accurate soil maps than using representative samples.
Neighborhood Size of Training Data Influences Soil Map Disaggregation
Authors: Levi, MR
Source: SOIL SCIENCE SOCIETY OF AMERICA JOURNAL, 81 (2):354-368; MAR-APR 2017
Abstract: Soil class mapping relies on the ability of sample locations to represent portions of the landscape with similar soil types; however, most digital soil mapping (DSM) approaches intersect sample locations with one raster pixel per covariate layer regardless of pixel size. This approach does not take the variability of covariate information adjacent to the training data into account. The objective here was to disaggregate a soil map in a semiarid Arizona rangeland (78,569 ha) by exploring different neighborhood sizes for extracting covariate data to points. Eight machine learning algorithms were compared to assess the influence of summarizing covariate data in 0-, 15-, 30-, 60-, 90-, 120-, 150-, and 180-m circular neighborhoods and a multiscale model. K values of all models ranged between 0.24 and 0.44 and increased with neighborhood size up to 150 m. Support vector machine and random forest algorithms performed best across all scales. The radial support vector machine model using a 150-m neighborhood had the highest K and produced a more generalized map compared with the best multiscale model (random forest), which resulted in a mix of general and detailed soil features. Evaluating a range of neighborhood sizes for aggregating covariate data provides a method of accounting for multiscale processes that are important for predicting soil patterns without modifying the pixel size of the final maps. Incorporating concepts from traditional soil surveys with DSM approaches can strengthen ties between them and optimize the extraction of landscape information for predicting soil properties.