Journal Paper Digests

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Journal Paper Digests 2022 #16

  • Effects of antecedent soil moisture on rill erodibility and critical shear stress
  • Using Nix color sensor and Munsell soil color variables to classify contrasting soil types and predict soil organic carbon in Eastern India
  • Statistically rigorous, model-based inferences from maps
  • Conterminous United States Landsat-8 top of atmosphere and surface reflectance tasseled cap transformation coefficients
  • Machine learning for cation exchange capacity prediction in different land uses
  • Pedogenetic processes operating at different intensities inferred by geophysical sensors and machine learning algorithms
  • Drought legacies and ecosystem responses to subsequent drought
  • Ecosystem health, ecosystem services, and the well-being of humans and the rest of nature

Ecosystem health, ecosystem services, and the well-being of humans and the rest of nature

An ecosystem is healthy if it is active, maintains its organization and autonomy over time, and is resilient to stress. Healthy ecosystems provide human well-being via ecosystem services, which are produced in interaction with human, social, and built capital. These services are affected by different ecosystem stewardship schemes. Therefore, society should be aiming for ecosystem health stewardship at all levels to maintain and improve ecosystem services. We review the relationship between ecosystem health and ecosystem services, based on a logic chain framework starting with (1) a development or conservation policy, (2) a management decision or origin of the driver of change, (3) the driver of change itself, (4) the change in ecosystem health, (5) the change in the provision of ecosystem services, and (6) the change in their value to humans. We review two case studies to demonstrate the application of this framework. We analyzed 6,131 records from the Ecosystem Services Valuation Database (ESVD) and found that in approximately 58% of the records data on ecosystem health were lacking. Finally, we describe how the United Nations’ System of Environmental-Economic Accounting (SEEA) incorporates ecosystem health as part of efforts to account for natural capital appreciation or depreciation at the national level. We also provide recommendations for improving this system.

Drought legacies and ecosystem responses to subsequent drought

Climate change is expected to increase the frequency and severity of droughts. These events, which can cause significant perturbations of terrestrial ecosystems and potentially long-term impacts on ecosystem structure and functioning after the drought has subsided are often called ‘drought legacies’. While the immediate effects of drought on ecosystems have been comparatively well characterized, our broader understanding of drought legacies is just emerging. Drought legacies can relate to all aspects of ecosystem structure and functioning, involving changes at the species and the community scale as well as alterations of soil properties. This has consequences for ecosystem responses to subsequent drought. Here, we synthesize current knowledge on drought legacies and the underlying mechanisms. We highlight the relevance of legacy duration to different ecosystem processes using examples of carbon cycling and community composition. We present hypotheses characterizing how intrinsic (i.e. biotic and abiotic properties and processes) and extrinsic (i.e. drought timing, severity, and frequency) factors could alter resilience trajectories under scenarios of recurrent drought events. We propose ways for improving our understanding of drought legacies and their implications for subsequent drought events, needed to assess the longer-term consequences of droughts on ecosystem structure and functioning.

Pedogenetic processes operating at different intensities inferred by geophysical sensors and machine learning algorithms

Pedogenetic processes such as ferralitization and argilluviation control various soil attributes. Understanding the intensities of pedogenesis can provide answers for several fields of environmental studies, including soil science and the geosciences. Recently, new geotechnologies such as geophysics applied to soil science and machine learning algorithms have proven to be a potential tool in pedosphere studies. In this research, we performed component principal analyses and determined the ideal number of clusters based on geophysical soil data and satellite images. Then, we used the ideal number of clusters, and ferralitization and argilluviation indices, as input data in five modeling (prediction and spatialization) algorithms to infer different ferralitization and argilluviation intensities in soils formed from the same soil parent material. The results showed that avNNet had the best model performance for modeling the clusters showing that the ideal number of clusters was three. The variables which contributed the most to the modeling were the solar diffuse radiation, topographic wetness index, and digital elevation model. The model’s specificity was greater than its sensitivity. The areas over diabase and Nitisols in the east of the study area presented greater ferralitization rates than diabase and Nitisols over western areas. On the other hand, the areas over siltite and Lixisols in the east presented greater argilluviation rates than metamorphosed siltite/siltite and Lixisols over western areas. The relief and topographic position strongly affected the evaluated pedogenetic processes, since they controlled the hydric dynamics in the area. The geophysical variables were related to soil attributes and pedogenesis, which contributed to modeling and clusterization processes.

Machine learning for cation exchange capacity prediction in different land uses

Cation exchange capacity (CEC) is a major indicator of soil quality and nutrient retention capacity. Despite the considerable progress in CEC prediction using various models, studies to develop CEC pedotransfer functions (PTFs) using machine learning algorithms precisely, such as support vector regression (SVR) and random forest (RF), have not yet been performed in various land uses globally. This study aims to develop, evaluate, and compare the effectiveness of RF and SVR algorithms in determining CEC in different land uses that included agriculture, plantations, grasslands, forests, fallow land and deserts in five countries (Sudan, India, Italy, Iran, and Senegal). A total of 2418 soil samples were fully analyzed and clay, silt, sand, pH, and soil organic carbon (SOC) were the selected covariates for modelling. Both RF and SVR were calibrated with a training dataset (70%, 1693 samples) and validated by the remaining data (30%, 725 samples). The performance and accuracy of both models were evaluated using the Lin’s concordance correlation coefficient (LCCC), root mean square error (RMSE), and normalized root mean square error (NRMSE). The accuracy of the modeling predictions was further analyzed via the Taylor diagram. The findings revealed that clay content showed a positive significant correlation with CEC in all land uses, with highest correlation in desert land use (r = 0.94; p < 0.05). Conversely, CEC was significantly and negatively correlated with sand in all land uses, with highest negative correlation obtained in desert land use (r = -0.84; p < 0.05). The RF algorithm was able to predict the CEC better than SVR in nearly 67% of the validated land use datasets precisely in desert (RMSE = 2.68 cmol(c) kg(-1), NRMSE = 29.9%, and LCCC = 0.94), fallow land (RMSE = 5.12 cmol(c) kg(-1), NRMSE = 55.6%, and LCCC = 0.82), forest (RMSE = 4.78 cmol(c) kg(-1), NRMSE = 78.2%, and LCCC = 0.59), and grassland (RMSE = 8.39 cmol(c) kg(-1), NRMSE = 50.5%, and LCCC = 0.84). Conversely, SVR better predicted CEC in agriculture (RMSE = 5.82 cmol(c) kg(-1), NRMSE = 57.9%, and LCCC = 0.78) and plantation (RMSE = 4.64 cmol(c) kg(-1), NRMSE = 57.9%, and LCCC = 0.74). Therefore, RF represents a promising technique to estimate soil CEC and can be used to derive effective CEC-PTFs in case of limited data availability, due to the lack of time and financial resources when the few basic soil properties are available. The findings reported in this study can be used to verify the suggested CEC-PTFs and/or their improvement. We recommend that further similar studies based on RF and SVR algorithms should consider including land use type in the Whole dataset and clay minerals in the modelling, and then compare the performance of both algorithms considering the climatic regions of the different studied countries.

Conterminous United States Landsat-8 top of atmosphere and surface reflectance tasseled cap transformation coefficients

The tasseled cap transformation (TCT) has been widely used to decompose satellite multi-spectral information into “brightness”, “greenness”, and “wetness” components. Published TCT coefficients for the Landsat sensor series have mainly been derived using top of atmosphere (TOA) reflectance and sparse data sets. Studies to derive TCT coefficients for Landsat surface reflectance (SR) are lacking. In this study, the TCT coefficients were derived independently for Landsat-8 Operational Land Imager (OLI) SR and TOA reflectance using the Gram-Schmidt orthogonalization (GSO) method. To ensure that the derived TCT coefficients are robust and broadly applicable, representative samples of soil, vegetation, and water were selected from summer and autumn Landsat-8 OLI Analysis Ready Data (ARD) sampled from 40.4 million 30 m pixel locations across the conterminous United States (CONUS). Given that the blue band is susceptible to atmospheric contamination due to its shorter wavelength, two groups of TCT coefficients were derived: one from 6 bands (Blue, Green, Red, NIR, SWIR1, SWIR2) and one from 5 bands without the blue band. As TCT results cannot be validated in a formal way, the TCT components for CONUS summer TOA and SR composites were generated and compared with National Land Cover Database (NLCD) land cover classes to provide a synoptic assessment and provide confidence in the results. In addition, three ARD tiles selected to encompass a mix of land cover types, predominantly, desert in Nevada, wetland and urban in Florida, and agriculture in North Dakota, were used to analyze the seasonal variation of the TCT components. The results demonstrate that the derived Landsat-8 TCT coefficients can effectively characterize brightness, greenness, and wetness components across the CONUS, and show good consistency for discrimination of land cover types and track seasonal surface variations. There was no significant difference between each TCT component derived using the 6-band and 5-band TCT coefficients considering a large sample of CONUS pixels. Therefore, the 5-band TCT coefficients provided in this study are recommended for use, as the blue band is atmospherically sensitive and difficult to atmospherically correct reliably.

Statistically rigorous, model-based inferences from maps

Statistically rigorous inferences in the form of confidence intervals for map-based estimates require model-based inferential methods. Model-based mean square errors (MSE) incorporate estimates of both residual variability and sampling variability, of which the latter includes population unit variance estimates and pairwise population unit covariance estimates. Bootstrapping, which can be used with any prediction technique, provides a means of estimating the required variances and covariances. The objectives of the study were to to demonstrate a method for estimating the sampling variability, (Var) over cirucumflex(sam)((mu) over cirucumflex) that can be used with all prediction techniques, to develop an efficient method that map makers can use to disseminate metadata that facilitates calculation of (Var) over cirucumflex(sam)((mu) over cirucumflex) for arbitrary subregions of maps, and to estimate the individual contributions of sampling variability and residual variability to the overall standard error of the prediction of the population mean. The primary results were fourfold: (i) map makers must provide metadata that facilitate estimation of population unit variances and covariances for arbitrary map subregions, (ii) bootstrapping was demonstrated as an effective means of estimating the variances and covariances, (iii) the very large matrix of pairwise population unit covariances can be aggregated into a much smaller matrix that can be readily communicated by map makers to map users, and (iv) MSEs that include only estimates of residual variability and/or estimates of population unit variances, but not estimates of the pairwise population unit covariances, grossly under-estimate the actual MSEs.

Using Nix color sensor and Munsell soil color variables to classify contrasting soil types and predict soil organic carbon in Eastern India

Optimal soil management depends on rapid and frequent monitoring of key soil properties, which are conventionally measured in the laboratory using laborious wet-chemistry protocols. The Nix color sensor has recently exhibited promise for predicting several soil properties using soil color. This study evaluated the relationship between the Munsell Soil Color Chart (MSCC) color values of dry and ground surface soil samples to those re-ported by the Nix color sensor with (Nix(STD)) and without MSCC standardization (Nix(NON-STD)) to classify 371 samples collected from three contrasting soil types, collected from three agroclimatic zones (coastal saline zone, red and laterite zone, and Gangetic alluvial zone) and to predict soil organic carbon (OC) using different multivariate data mining algorithms. Comparing the CIELab* color values reported by the MSCC and the Nix(STD), an acceptable mean color difference (delta E(ab)) value of 5.20 was obtained, indicating the potential accuracy of the Nix sensor. Principal component analysis efficiently clustered the soil types using the RGB variables extracted from the MSCC color chips in tandem with the Nix(STD)/Nix(NON-STD) data. Both classification tree and linear support vector machine algorithms perfectly classified all three contrasting soil types using Nix(NON-STD) data alone. Besides, the combination of the MSCC and the Nix(NON-STD) datasets produced the best OC prediction (R2 = 0.66) via random forest (RF) algorithm and indicated the potential of Nix in digital soil morphometrics. In most of the RF models, redness (a), yellowness (b*), and yellow (Y) variables appeared influential, presumably because of their negative correlation with OC in red and laterite soils. More research is warranted to measure the impacts of variable soil moisture and other confounding soil morphological features on the soil classification and OC prediction performance to extend the approach for classifying soil types and predicting OC in-situ.

Effects of antecedent soil moisture on rill erodibility and critical shear stress

Antecedent soil moisture is known to exert a complex, perhaps controversial, effect on rill erodibility and critical shear stress. To understand their dynamic nature as a function of antecedent soil moisture, the rill erodibility and critical shear stress for sandy loam and silty loam soil, representing coarse-grained and fine-grained soil, respectively, were measured using a hydraulic flume under six antecedent soil moisture contents, i.e., 3, 6, 9, 12, 15, and 18%. The results show that antecedent soil moisture had a different effect on critical shear stress and rill erodibility for the two different soil textures. As antecedent soil moisture increased, rill erodibility for the finegrained soil first increased and then decreased, whereas for coarse-grained soil, rill erodibility exhibited a decreasing pattern. Conversely, as the antecedent soil moisture increased, the critical shear stress decreased first and then increased for the fine-grained soil, but for coarse-grained soil, the critical shear stress decreased after a slight increase. These different patterns can be interpreted by aggregate slaking, capillary force, soil cementation, and water separation. However, when the patterns of critical shear stress and rill erodibility were neglected, the differences in critical shear stress and rill erodibility between the coarse- and fine-grained soils were not statistically significant (P < 0.05). Therefore, indiscriminate treatments of critical shear stress and rill erodibility for different textural soils under changing antecedent soil moisture regimes may cause errors in soil erosion modeling. The relationship between rill erodibility and critical shear stress for coarse- and fine-grained soils could be fitted by a power and a polynomial function, with R-2 value of 0.94 and 0.78, respectively. Future work should include additional soil textures to study the influence of soil physical characteristics in combination with antecedent soil moisture on soil erosion.

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