Fractional Snow-Cover Mapping Through Artificial Neural Network Analysis of MODIS Surface Reflectance.

Accurate areal measurements of snow-cover extent are important for hydrological and climate modeling. The traditional method of mapping snow cover is binary where a pixel is approximated to either snow-covered or snow-free. Fractional snow cover (FSC) mapping achieves a more precise estimate of areal snow-cover extent by determining the fraction of a pixel that is snow-covered. The two most common FSC methods using Moderate Resolution Imaging Spectroradiometer (MODIS) images are linear spectral unmixing and the empirical Normalized Difference Snow Index (NDSI) method. Machine learning is an alternative to these approaches for estimating FSC, as Artificial Neural Networks (ANNs) have been used for estimating the subpixel abundances of other surfaces. The advantages of ANNs over the other approaches are that they can easily incorporate auxiliary information such as land-cover type and are capable of learning nonlinear relationships between surface reflectance and snow fraction. ANNs are especially applicable to mapping snow-cover extent in forested areas where spatial mixing of surface components is nonlinear. This study developed an ANN approach to snow-fraction mapping. A feed-forward ANN was trained with backpropagation to estimate FSC from MODIS surface reflectance, NDSI, Normalized Difference Vegetation Index (NDVI) and land cover as inputs. The ANN was trained and validated with high spatial-resolution FSC derived from Landsat Enhanced Thematic Mapper Plus (ETM+) binary snow-cover maps. ANN achieved best result in terms of extent of snow-covered area over evergreen forests, where the extent of snow cover was slightly overestimated. Scatter plot graphs of the ANN and reference FSC showed that the neural network tended to underestimate snow fraction in high FSC and overestimate it in low FSC. The developed ANN compared favorably to the standard MODIS FSC product with the two methods estimating the same amount of total snow-covered area in the test scenes

Climate–Glacier Dynamics and Topographic Forcing in the Karakoram Himalaya: Concepts, Issues and Research Directions

Understanding climate-glacier dynamics in High Mountain Asia is of critical importance to address issues including water resources, sea-level rise, mountain geodynamics, natural hazards and ecosystem sustainability. The Karakoram Himalaya is arguably the least understood region, given its extreme topography, climate-system coupling, and advancing and surge-type glaciers that exhibit complex flow patterns. Glacier fluctuations in the Karakoram Himalaya are highly variable in space and time because of numerous controlling factors, including the westerlies, the Indian summer monsoon, various teleconnections, topographic effects, glacier debris-cover characteristics, glacier dynamics, and geological conditions. The influence of the integrative coupling of forcing factors, however, has not been adequately assessed for characterizing the glaciers in the Karakoram Himalaya. Given the scarcity of in-situ data and the difficulty of conducting fieldwork on these glaciers, recent research has focused on utilizing remote sensing, geospatial technologies, and scientific modeling to obtain baseline information about the state of glaciers in the region. This review summarizes our current knowledge of glaciers, climate-glacier interaction, and topographic forcing in the Karakoram Himalaya, and demonstrates the complexities in mountain geodynamics that influence climate-glacier dynamics. Innovative analysis is also presented in support of our review and discussion

Spatiotemporal Dynamics of Solar Radiation in the Himalaya: Topographic Forcing and Glacier Dynamics

Glaciers across the Himalaya exhibit significant spatial variations in morphology and dynamics. Climate, topography and debris cover variations are thought to significantly affect glacier fluctuations and glacier sensitivity to climate change, although the role of topography and solar radiation forcing have not been adequately characterized and related to glaciers. Analyzed are a set of glaciers in the Karakoram mountain range, where a clustering of surge type glaciers occurs. The objective of this works is to investigate topographic effects on glacier state, such as if a glacier is of surge type or not, and if a glaciers is retreating or advancing. Specifically, the focus of this work is the spatiotemporal effects of solar radiation on glaciers as modulated by the topography. A geomorphic assessment of the glaciers is also performed, so that solar radiation forcing could be studied in the appropriate context. A rigorous GIS-based solar radiation model that accounts for the direct and diffuse-skylight irradiance components was developed and applied for an ablation season over the study area. The model accounts for multiple topographic effects on the magnitude of surface irradiance. Enhanced ablation was determined to be a distinguishing characteristic of surge type glaciers as indicated by the positive relation between ablation-season surface irradiance and the probability of a glacier being of surge type, as well as by the positive relation between lesser topographic shielding and the probability of a glacier being of surge type. These results demonstrate the important role that local and regional topography plays in governing climate-glacier dynamics in the Himalaya

Spatiotemporal Dynamics of Solar Radiation in the Himalaya: Topographic Forcing and Glacier Dynamics

Glaciers across the Himalaya exhibit significant spatial variations in morphology and dynamics. Climate, topography and debris cover variations are thought to significantly affect glacier fluctuations and glacier sensitivity to climate change, although the role of topography and solar radiation forcing have not been adequately characterized and related to glaciers. Analyzed are a set of glaciers in the Karakoram mountain range, where a clustering of surge type glaciers occurs. The objective of this works is to investigate topographic effects on glacier state, such as if a glacier is of surge type or not, and if a glaciers is retreating or advancing. Specifically, the focus of this work is the spatiotemporal effects of solar radiation on glaciers as modulated by the topography. A geomorphic assessment of the glaciers is also performed, so that solar radiation forcing could be studied in the appropriate context. A rigorous GIS-based solar radiation model that accounts for the direct and diffuse-skylight irradiance components was developed and applied for an ablation season over the study area. The model accounts for multiple topographic effects on the magnitude of surface irradiance. Enhanced ablation was determined to be a distinguishing characteristic of surge type glaciers as indicated by the positive relation between ablation-season surface irradiance and the probability of a glacier being of surge type, as well as by the positive relation between lesser topographic shielding and the probability of a glacier being of surge type. These results demonstrate the important role that local and regional topography plays in governing climate-glacier dynamics in the Himalaya

Quantification of Soil Organic Carbon in Biochar-Amended Soil Using Ground Penetrating Radar (GPR)

The application of biochar amendments to soil has been proposed as a strategy for mitigating global carbon (C) emissions and soil organic carbon (SOC) loss. Biochar can provide additional agronomic benefits to cropping systems, including improved crop yield, soil water holding capacity, seed germination, cation exchange capacity (CEC), and soil pH. To maximize the beneficial effects of biochar amendments towards the inventory, increase, and management of SOC pools, nondestructive analytical methods such as ground penetrating radar (GPR) are needed to identify and quantify belowground C. The use of GPR has been well characterized across geological, archaeological, engineering, and military applications. While GPR has been predominantly utilized to detect relatively large objects such as rocks, tree roots, land mines, and peat soils, the objective of this study was to quantify comparatively smaller, particulate sources of SOC. This research used three materials as C sources: biochar, graphite, and activated C. The C sources were mixed with sand—12 treatments in total—and scanned under three moisture levels: 0%, 10%, and 20% to simulate different soil conditions. GPR attribute analyses and Naïve Bayes predictive models were utilized in lieu of visualization methods because of the minute size of the C particles. Significant correlations between GPR attributes and both C content and moisture levels were detected. The accuracy of two predictive models using a Naïve Bayes classifier for C content was trivial but the accuracy for C structure was 56%. The analyses confirmed the ability of GPR to identify differences in both C content and C structure. Beneficial future applications could focus on applying GPR across more diverse soil conditions

Prediction of Root Biomass in Cassava Based on Ground Penetrating Radar Phenomics

Cassava as a world food security crop still suffers from an inadequate means to measure early storage root bulking (ESRB), a trait that describes early maturity and a key characteristic of improved cassava varieties. The objective of this study is to evaluate the capability of ground penetrating radar (GPR) for non-destructive assessment of cassava root biomass. GPR was evaluated for this purpose in a field trial conducted in Ibadan, Nigeria. Different methods of processing the GPR radargram were tested, which included time slicing the radargram below the antenna surface in order to reduce ground clutter; to remove coherent sub-horizontal reflected energy; and having the diffracted energy tail collapsed into representative point of origin. GPR features were then extracted using Discrete Fourier Transformation (DFT), and Bayesian Ridge Regression (BRR) models were developed considering one, two and three-way interactions. Prediction accuracies based on Pearson correlation coefficient (r) and coefficient of determination (R2) were estimated by the linear regression of the predicted and observed root biomass. A simple model without interaction produced the best prediction accuracy of r = 0.64 and R2 = 0.41. Our results demonstrate that root biomass can be predicted using GPR and it is expected that the technology will be adopted by cassava breeding programs for selecting early stage root bulking during the crop growth season as a novel method to dramatically increase crop yield

Prediction of Root Biomass in Cassava Based on Ground Penetrating Radar Phenomics

Cassava as a world food security crop still suffers from an inadequate means to measure early storage root bulking (ESRB), a trait that describes early maturity and a key characteristic of improved cassava varieties. The objective of this study is to evaluate the capability of ground penetrating radar (GPR) for non-destructive assessment of cassava root biomass. GPR was evaluated for this purpose in a field trial conducted in Ibadan, Nigeria. Different methods of processing the GPR radargram were tested, which included time slicing the radargram below the antenna surface in order to reduce ground clutter; to remove coherent sub-horizontal reflected energy; and having the diffracted energy tail collapsed into representative point of origin. GPR features were then extracted using Discrete Fourier Transformation (DFT), and Bayesian Ridge Regression (BRR) models were developed considering one, two and three-way interactions. Prediction accuracies based on Pearson correlation coefficient (r) and coefficient of determination (R2) were estimated by the linear regression of the predicted and observed root biomass. A simple model without interaction produced the best prediction accuracy of r = 0.64 and R2 = 0.41. Our results demonstrate that root biomass can be predicted using GPR and it is expected that the technology will be adopted by cassava breeding programs for selecting early stage root bulking during the crop growth season as a novel method to dramatically increase crop yield

Thresholding Analysis and Feature Extraction from 3D Ground Penetrating Radar Data for Noninvasive Assessment of Peanut Yield

This study explores the efficacy of utilizing a novel ground penetrating radar (GPR) acquisition platform and data analysis methods to quantify peanut yield for breeding selection, agronomic research, and producer management and harvest applications. Sixty plots comprising different peanut market types were scanned with a multichannel, air-launched GPR antenna. Image thresholding analysis was performed on 3D GPR data from four of the channels to extract features that were correlated to peanut yield with the objective of developing a noninvasive high-throughput peanut phenotyping and yield-monitoring methodology. Plot-level GPR data were summarized using mean, standard deviation, sum, and the number of nonzero values (counts) below or above different percentile threshold values. Best results were obtained for data below the percentile threshold for mean, standard deviation and sum. Data both below and above the percentile threshold generated good correlations for count. Correlating individual GPR features to yield generated correlations of up to 39% explained variability, while combining GPR features in multiple linear regression models generated up to 51% explained variability. The correlations increased when regression models were developed separately for each peanut type. This research demonstrates that a systematic search of thresholding range, analysis window size, and data summary statistics is necessary for successful application of this type of analysis. The results also establish that thresholding analysis of GPR data is an appropriate methodology for noninvasive assessment of peanut yield, which could be further developed for high-throughput phenotyping and yield-monitoring, adding a new sensor and new capabilities to the growing set of digital agriculture technologies