Journal Paper Digests

Reading time ~8 minutes

Journal Paper Digests 2020 #8

  • VisNIR integrated multi-sensing penetrometer for in situ high-resolution vertical soil sensing
  • Classification of soil aggregates: A novel approach based on deep learning
  • Applications of SAR Interferometric Coherence Time Series: Spatiotemporal Dynamics of Geomorphic Transitions in the South-Central Andes.
  • Clay content and mineralogy, organic carbon and cation exchange capacity affect water vapour sorption hysteresis of soil
  • Simultaneous measurement of net nitrogen mineralization and denitrification rates in soil using nitrification inhibitor 3,5-dimethylpyrazole

VisNIR integrated multi-sensing penetrometer for in situ high-resolution vertical soil sensing

By: Wijewardane, Nuwan K.; Hetrick, Sarah; Ackerson, Jason; et al. SOIL & TILLAGE RESEARCH Volume: 199 Pages: 4604-4604 Published: MAY 2020 Context Sensitive Links Free Full Text from Publisher Close Abstract An in situ penetrometer system that can measure profile soil properties rapidly, cost-effectively, and at high vertical resolution would benefit the soil science and agriculture communities. A visible and near infrared (VisNIR) integrated multi-sensing penetrometer system was developed to automatically measure in situ soil VisNIR reflectance spectra, penetration resistance, and insertion depth along a soil profile. The system was tested in 11 agricultural fields in Nebraska, Illinois, Iowa, and South Dakota of the U.S. An independent soil VisNIR spectral library was used to build calibration models for soil property prediction. External Parameter Orthogonalization (EPO) was used to correct for the field intactness of in situ VisNIR spectra. The results showed that EPO was effective in correcting for the spectral disparity between in situ and dry-ground VisNIR spectra. The EPO correction showed an improvement of prediction accuracy of soil total carbon (R-2 and RMSE improved from 0.29 and 3.06 % to 0.5 and 0.79%, respectively) and total nitrogen (R-2 and RMSE improved from 0.51 and 0.36 % to 0.62 and 0.06 %, respectively). The system also predicted soil bulk density with an RMSE of 0.12 g cm(-3) and R-2 of 0.80. It is concluded that the VisNIR multi-sensing penetrometer, along with the use of external soil spectral libraries and the spectral correction algorithm EPO, can lead to a rapid, robust and cost-effective system for in situ high resolution vertical soil sensing.

Classification of soil aggregates: A novel approach based on deep learning

By: Azizi, Afshin; Gilandeh, Yousef Abbaspour; Mesri-Gundoshmian, Tarahom; et al. SOIL & TILLAGE RESEARCH Volume: 199 Pages: 4586-4586 Published: MAY 2020 Context Sensitive Links Close Abstract Having a powerful tool and the knowledge to classify soil aggregates, one of the most important factors in evaluating the performance of tillage implements, will result in quick and accurate classification of soil aggregates. By considering them as virtual sieve, a large part of the energy and workforce used in this sector can be reduced. In this regard, computational intelligence tools can play an important and optimal role in the evaluation of tillage quality and its real-time employment. The objective of the present study was to introduce a method known as deep learning to classify aggregates of any size in specific classes. Accordingly, stereo-pair images were used to provide multiple images simultaneously and the proper nutrition of the network. Since stereo-pair images are not dependent on changes in ambient light, imaging was done under conditions of the field with no lighting system. To train the deep models, the images of each lens were separated from each other and entered into the network. Without the extraction of the required features that is done manually in most image and vision-processing algorithms, the presented deep model began to learn to observe and could extract the required features from the lowest level to highly complex features automatically. Among the variety of neural network algorithms in deep learning, a convolutional neural network (CNN) was used in this study for its unique properties in working on images. To train the CNN, VggNet16, ResNet50, and Inception-v4 architects were used. Classification accuracy of these networks was above 95 %, but the highest accuracy achieved with ResNet50 (98.72 %). This accuracy, which was significantly different from previous studies, indicated the good performance of the deep learning method in the classification of aggregates. The results of the current study showed that the estimation of mean weight diameter (MWD) of aggregates without limitations in size and with great precision is completely practical and achievable.

Applications of SAR Interferometric Coherence Time Series: Spatiotemporal Dynamics of Geomorphic Transitions in the South-Central Andes

By: Olen, Stephanie; Bookhagen, Bodo JOURNAL OF GEOPHYSICAL RESEARCH-EARTH SURFACE Volume: 125 Issue: 3 Pages: 5141-5141 Published: MAR 2020 Context Sensitive Links Close Abstract Sediment transport domains in mountain landscapes are characterized by fundamentally different processes and rates depending on several factors, including geology, climate, and biota. Accurately identifying where transitions between transport domains occur is an important step to quantify the past, present, and future contribution of varying erosion and sedimentation processes and enhance our predictive capabilities. We propose a new methodology based on time series of synthetic aperture radar (SAR) interferometric coherence images to map sediment transport regimes across arid and semiarid landscapes. Using 4 years of Sentinel-1 data, we analyze sediment transport regimes for the south-central Andes in northwestern Argentina characterized by steep topographic and climatic gradients. We observe seasonally low coherence during the regional wet season, particularly on hillslopes and in alluvial channels. The spatial distribution of coherence is compared to drainage areas extracted from digital topography to identify two distinct transitions within watersheds: (a) a hillslope-to-fluvial and (b) a fluvial-to-alluvial transition. While transitions within a given basin can be well-constrained, the relative role of each sediment transport domain varies widely over the climatic and topographic gradients. In semiarid regions, we observe larger relative contributions from hillslopes compared to arid regions. Across regional gradients, the range of coherence within basins positively correlates to previously published millennial catchment-wide erosion rates and to topographic metrics used to indicate long-term uplift. Our study suggests that a dense time series of interferometric coherence can be used as a proxy for surface sediment movement and landscape stability in vegetation-free settings at event to decadal timescales.

Clay content and mineralogy, organic carbon and cation exchange capacity affect water vapour sorption hysteresis of soil

By: Arthur, Emmanuel; Tuller, Markus; Moldrup, Per; et al. EUROPEAN JOURNAL OF SOIL SCIENCE Volume: 71 Issue: 2 Pages: 204-214 Published: MAR 2020 Context Sensitive Links Full Text from Publisher Close Abstract The hysteretic behaviour of the dry region (<-1.5 MPa) of the soil water characteristic, which is of the essence for accurate characterization and modelling of bio-physicochemical soil processes under dry conditions, is well documented. However, knowledge about how to best quantify water vapour sorption hysteresis and about the effects of soil properties on dry-region hysteretic behaviour is limited. To overcome this knowledge gap, we proposed a new method for quantifying sorption hysteresis and evaluated its applicability based on measured sorption isotherms of four source clay minerals and 147 soil samples. Furthermore, the effects of clay mineralogy, clay content, soil organic carbon (SOC) and cation exchange capacity (CEC) on the magnitude of sorption hysteresis were investigated. For the clay minerals, kaolinite did not exhibit hysteretic behaviour, illite showed some hysteresis, whereas Na- and Ca-smectite exhibited strong hysteretic behaviour. The average hysteresis, corrected for clay and SOC contents, was strongly reflective of the dominant clay mineralogy of the soil samples. For the soil samples with low SOC content, the average hysteresis significantly increased with increasing clay content (R-2 = 0.92), except for the kaolinite-rich samples (R-2 = 0.35). The SOC-rich samples that exhibited illitic clay mineralogy and similar soil texture showed a significant increase in average hysteresis with increasing organic carbon content (R-2 = 0.93). For all soil samples combined, the CEC was the strongest indicator for the magnitude of water vapour sorption hysteresis. Highlights A new index for quantification of soil vapour sorption hysteresis was proposed Large SOC and clay content increased sorption hysteresis For soil samples, dominant clay mineralogy controlled the magnitude of hysteresis Cation exchange capacity was the best predictor of hysteresis for all soil types

Simultaneous measurement of net nitrogen mineralization and denitrification rates in soil using nitrification inhibitor 3,5-dimethylpyrazole

By: Zebarth, B. J.; Burton, D. L.; Spence, J.; et al. CANADIAN JOURNAL OF SOIL SCIENCE Volume: 100 Issue: 1 Pages: 1-10 Published: MAR 2020 Context Sensitive Links Free Full Text from Publisher Close Abstract A practical means to quantify the response of the rates of net N mineralization and denitrification over a wide range of soil water contents is generally lacking. This study examined the potential to use a nitrification inhibitor (NI) assay system to simultaneously estimate the rates of net N mineralization and denitrification, and applied the NI assay to assess the effect of water content on net N mineralization and denitrification rates in two soils with contrasting soil texture. The compound 3,5-dimethylpyrazole (DMP) applied at a rate of 200 mg kg(-1) was found to provide essentially complete inhibition of nitrification over the duration of the soil incubation for two soils with contrasting soil texture (clay loam vs. sandy loam) and over a range of soil water contents (35%, 55%, and 85% water-filled pore space). This allowed net N mineralization to be estimated as the accumulation of soil ammonium (NHthorn 4) and of denitrification as the disappearance of added nitrate (NO3-). Addition of DMP resulted in a small increase in soil respiration rate but did not appear to influence the rate of net soil N mineralization. The NI assay provides a practical means to quantify the rates of net N mineralization and denitrification simultaneously over a wide range of soil water contents. The assay can be readily scaled up to routinely test multiple soils in an efficient manner, has limited material costs, and is also relatively simple to perform.

Journal Paper Digests

## Journal Paper Digests 2020 #15* Characterising the biophysical, economic and social impacts of soil carbon sequestration as a greenhou...… Continue reading

Journal Paper Digests

Published on August 06, 2020

Journal Paper Digests

Published on July 30, 2020