@article {JRN55409, title = {Accuracy of regional-to-global soil maps for on-farm decision making: Are soil maps {\textquotedblleft}good enough{\textquotedblright}?}, journal = {Egusphere}, year = {2022}, month = {5/19/2022}, abstract = {A major obstacle to selecting the most appropriate crops and closing the yield gap in many areas of the world is a lack of site-specific soil information. Accurate information on soil properties is critical for identifying soil limitations and the management practices needed to improve crop yields. However, acquiring accurate soil information is often difficult due to the high spatial and temporal variability of soil properties at fine scales and the cost and inaccessibility of laboratory-based soil analyses. With recent advancements in predictive soil mapping, there is a growing expectation that soil map predictions can provide much of the information needed to inform soil management. Yet, it is unclear how accurate current soil map predictions are at scales relevant to management. The main objective of this study was to address this issue by evaluating the site-specific accuracy of regional-to-global soil maps, using Ghana as a test case. Four web-based soil maps of Ghana were evaluated using a dataset of 6,514 soil profile descriptions collected on smallholder farms using the LandPKS mobile application. Results from this study revealed that publicly available soil maps in Ghana lack the needed accuracy (i.e., correct identification of soil limitations) to reliably inform soil management decisions at the 1{\textendash}2 ha scale common to smallholders. Standard measures of map accuracy for soil texture class and rock fragment class showed that all soil maps had similar performance in estimating the correct property class, with overall accuracies ranging from 8{\textendash}39 \% for soil texture classes and 26{\textendash}33 \% for soil rock fragment classes. Furthermore, there were substantial differences in soil property predictions among the four maps, highlighting that soil map errors are not uniform between maps despite their similar overall accuracies. To better understand the functional implications of these soil property differences, we used a modified version of the FAO Global Agro-Ecological Zone (GAEZ) soil suitability modelling framework to derive soil suitability ratings for each soil data source. Using a low-input, rain-fed, maize production scenario, we evaluated the functional accuracy of map-based soil property estimates. This analysis showed that soil map data significantly overestimated crop suitability for over 65 \% of study sites, potentially leading to ineffective agronomic investments by farmers, including cash-constrained smallholders.}, keywords = {accuracy, decision making, on-farm, regional-to-global, soil maps}, doi = {10.5194/egusphere-2022-246}, url = {files/bibliography/22-021.pdf}, author = {Jonathan Maynard and Edward Yeboah and Stephen Owusu and M. Buenemann and J. Neff and Jeffrey E. Herrick} } @article {JRN55429, title = {LandPKS Toolbox: Open-source mobile app tools for sustainable land management}, journal = {Journal of Soil and Water Conservation}, volume = {77}, year = {2022}, month = {11/14/2022}, pages = {91A-97A}, abstract = {Land use planning and implementation of productive, sustainable land management practices requires careful matching of land use with its sustainable land potential at the field and even sub-field scale (FAO 2022). Sustainable land potential is defined as the potential of the land to generate ecosystem services for current and future generations (United Nations Environment Programme and International Resource Panel 2016). Appropriate matching of land use with land potential requires accurate, site-specific knowledge of land, including information on its long-term potential (based on inherent properties like texture, minerology, and slope), current condition (e.g., fertility, soil organic matter content, vegetation cover), and expected response to disturbances (e.g., management, climate). When land managers lack access to such information and knowledge, they are less likely to make informed management decisions that ensure long-term sustainability. To bridge this information gap, the Land Potential Knowledge System mobile application (LandPKS) was created with the goal of providing location-specific information on land potential that farmers and other land managers can use to make informed management decisions (Herrick et al. 2013, 2016). However, awareness of the availability of this free, open-source app with access to both US and global soil information remains low. The objective of this paper is to describe the app, with a focus on the recently released {\textquotedblleft}Toolbox{\textquotedblright} feature.}, keywords = {LandPKS Toolbox, mobile app, Open-source, planning, productive, soil texture, sustainable land management, tools}, doi = {10.2489/jswc.2022.0927A}, url = {files/bibliography/22-026.pdf}, author = {Jonathan Maynard and Sabina Maniak and Laura Hamrick and George Peacock and Sarah McCord and Jeffrey E. Herrick} } @proceedings {JRN55185, title = {Landpks Soilid: A Smartphone-Based Soil Identification Tool for Rangeland Management}, year = {2020}, month = {2/16/2020}, address = {Denver, CO}, abstract = {

Accurately identifying soil class at a specific point-location or position within a landscape is critical for implementing sustainable soil management. Soil classes (e.g., soil components) are information carriers that allow land managers to infer a general range of soil behavior in response to management actions and disturbance effects. Recent advances in information technologies, in particular the global ubiquity of smart phones, has made it possible to create m.obile decision support tools that can inform rangeland management decisions. The Land Potential Knowledge System (LandPKS) is one such example, providing a complete mobile computing platform for assessing land potential and informing management activities. Here we present the development of a global soil identification modeling framework (SoillD) implemented within the LandPKS mobile app. SoillD leverages smartphone-based data acquisition and information delivery, with cloud-based computing to determine the most probable soil class at a user specified point. SoillD makes it possible for non-soil scientists to describe and identify soils in the field using limited, simple soil observations. Additionally, SoillD provides information on Ecological Sites based on the most probable soil class matched to the user{\textquoteright}s soil. Our presentation will describe the details of SoillD and its implementation in the LandPKS mobile app, and provide examples of its utility for rangeland management.

}, keywords = {rangeland management, Smartphone-Based Soil identification Tool}, author = {Jonathan Maynard and Jeffrey E. Herrick and S. W. Salley and Dylan, E. and D. Beaudette and A. O{\textquoteright}Green} } @article {JRN55233, title = {Numerical soil classification supports soil identification by citizen scientists using limited, simple soil observations}, journal = {Soil Science Society of America Journal}, year = {2020}, month = {09/2020}, pages = {1-18}, abstract = {Accurately identifying the soil map unit component at a specific point-location or position within a landscape is critical for implementing sustainable soil management. Recent developments in smartphone-based technologies for characterizing soil profiles, coupled with improved numerical soil classification algorithms, have made it more accessible for non-soil scientists to sample, characterize, and classify soil profiles. The main objective of this study was to evaluate an operational soil classification framework for identifying the soil component at a point-location based on its numerical similarity to the soil components mapped in that area. To implement this testing framework, we used a subset of the United States National Cooperative Soil Survey Soil Characterization Database (NCSS{\textendash}SCD) as our soil profile point dataset and the U.S. Soil Survey Geographic (SSURGO) database for querying profile data of mapped soil classes in the area surrounding each point. Numerical similarity was tested using soil property data representing different degrees of generalization, both in terms of geographic space (i.e., depth-wise variably) and feature space (e.g., texture class vs. clay percentage). Three soil property groups (i.e., Novice, Expert, Expert-Plus) representing different levels of experience required for their measurement and three types of depth-support (i.e., genetic horizon, depth intervals, and depth functions) were evaluated. Using a simple set of soil property inputs (i.e., Novice: soil texture class,rock fragment volume class, and soil color) resulted in nearly as high classification accuracy (46-53\%) as that achieved with an Expert (48-57\%) dataset that included more precise determinations (percent sand, silt, clay, and rock fragment volume), and virtually no further improvement with the addition of pH and organic matter in the Expert-Plus dataset (53-60\%). A measure of management relevant accuracy was assessed using correlated Ecological Sites (ESDs) and Land Capability Classifications (LCC) which followed similar trends for the Novice (ESD: 78-82\%; LCC: 70-76\%), Expert (ESD: 79-84\%; LCC: 71-77\%), and Expert-Plus (ESD: 83-85\%; LCC: 74-79\%) datasets. This study also showed minimal effect from the type of depth-support used to represent depth-wise variability.}, keywords = {characterizing soil profiles, landscape, numerical, smartphone-based technologies, soil classification algorithms, soil map unit component, specific point-location, sustainable soil management}, doi = {https://doi.org/10.1002/saj2.20119}, url = {files/bibliography/20-030.pdf}, author = {Jonathan Maynard and S. W. Salley and D. Beaudette and Jeffrey E. Herrick} } @article {JRN55131, title = {Digital mapping of ecological land units using a nationally scalable modeling framework}, journal = {Soil Science Society of America Journal}, volume = {83}, year = {2019}, month = {06/2019}, pages = {666-686}, chapter = {666}, abstract = {

Ecological site descriptions (ESDs) and associated state-and-transition models (STMs) provide a nationally consistent classification and information system for defining ecological land units for management applications in the U.S. Current spatial representations of ESDs, however, occur via soil mapping and are therefore confined to the spatial resolution used to map soils within a survey area. \ Land management decisions occur across a range of spatial scales and therefore require ecological information that spans similar scales. Digital mapping provides an approach for optimizing the spatial scale of modeling products to best serve decision makers and have the greatest impact in addressing land management concerns. Here we present a spatial modeling framework for mapping ecological sites using machine learning algorithms, soil survey field observations, soil survey geographic databases, ecological site data, and a suite of remote sensing-based spatial covariates (e.g., hyper-temporal remote sensing, terrain attributes, climate data, land-cover, lithology). Based upon the theoretical association between ecological sites and landscape biophysical properties, we hypothesized that the spatial distribution of ecological sites could be predicted using readily available geospatial data. This modeling approach was tested at two study areas within the western U.S., representing 6.1 million ha on the Colorado Plateau and 7.5 million ha within the Chihuahuan Desert. Results show our approach was effective in mapping grouped ecological site classes (ESGs), with a 70\% correct classification based on 1,405 point observations across 8 expertly-defined ESG classes in the Colorado Plateau and a 79\% correct classification based on 2,589 point observations across 9 expertly-defined ESG classes in the Chihuahuan Desert. National coverage of the training and covariate data used in this study provides opportunities for a consistent national-scale mapping effort of ecological sites.

}, doi = {10.2136/sssaj2018.09.0346}, url = {files/bibliography/19-012.pdf}, author = {Jonathan Maynard and Travis Nauman and S. W. Salley and Brandon T Bestelmeyer and Danielle Duni and Curtis Talbot and Joel R. Brown} } @proceedings {JRN54870, title = {Science with a selfie stick: Plant biomass estimation using smartphone based {\textquoteleft}Structure From Motion{\textquoteright} photogrammetry}, volume = {January 28-February 2, 2018}, year = {2018}, month = {01/28/2018}, publisher = {Society for Range Management Meeting Proceedings}, address = {Sparks, NV}, abstract = {

Significant advancements in photogrammetric Structure-from-Motion (SfM) software, coupled with improvements in the quality and resolution of smartphone cameras, has made it possible to create ultra-fine resolution three-dimensional models of physical objects using an ordinary smartphone. Here we present an open-source modeling framework for creating three-dimensional models of vegetation structure using smartphone video of a user-defined sampling space (e.g., 1 m2). Our main objective was to evaluate the accuracy of our SfM sampling method in predicting above-ground biomass relative to traditional estimation techniques. A series of permanent 1-m2 quadrat sampling sites at the Jornada Experimental Range in southern New Mexico were used to develop the SfM sampling method, with each quadrat differing in its proportions of shrub vs. herbaceous vegetation composition. \ A smartphone mounted on a selfie stick was used to capture video of each 1-m2 quadrat by circumnavigating it while moving the smartphone up and down to capture a range of image angles. Images were extracted from video frames at sampling rate of 4 frames per second and used to create densely reconstructed point clouds using open-source SfM software. Point clouds were georeferenced using a set of 3 control points located on the ends of a 0.25-m3 PVC corner frame placed at each site prior to imaging. Canopy volume was calculated for each point cloud using a foliar canopy approach, and used to model above ground biomass using published linear regression models. These results were compared to traditional nondestructive biomass estimates that were calculated by measuring the dimensions (cover and height) of individual plants or plant parts within each quadrat and converted to aboveground biomass using allometric equations. Preliminary results show a strong correspondence between measured and SfM modeled biomass. Our presentation will describe the details of this method and discuss its potential utility in increasing the accuracy and repeatability of field-based biomass estimation.

}, author = {Jonathan Maynard and Jason W. Karl} } @proceedings {JRN54679, title = {An automated approach to mapping ecological sites using hyper-temporal remote sensing and SVM classification}, volume = {Jan 01 - Feb 02, 2017}, year = {2017}, month = {01/2017}, publisher = {Society for Range Management}, address = {Saint George, Utah}, abstract = {

The development of ecological sites as management units has emerged as a highly effective land management framework, but its utility has been limited by spatial ambiguity of ecological site locations in the U.S., lack of ecological site concepts in many other parts of the world, and the inability to accurately assess the state of ecological sites or monitor changes in state through time. Here we present a modeling framework for high-resolution mapping of ecological sites in a semi-arid ecosystem using hyper-temporal remote sensing (i.e., hundreds of images) and support vector machine (SVM) classification. Results from this study show that SVM classification was effective in modeling ecological sites using a 28-year time series of normalized difference vegetation index (NDVI), with a 62\% correct classification. Results were compared to Soil Survey Geographic database and expert delineated maps of ecological sites which had a 51 and 89\% correct classification, respectively. The hyper-temporal remote sensing framework is effective in modeling the spatial distribution of ecological sites through its ability to characterize the soil-vegetation relationship and its response to climatic variability (i.e., drought or elevated rainfall). Additionally, the proposed hyper-temporal remote sensing technique may provide an objective framework to evaluate and test ecological site concepts through examining differences in vegetation dynamics in response to climatic variability, as well as the ability to assess and monitor changes in ecological state due to other change drivers. Given limited financial and human resources, an improved understanding of ecosystem potential is needed to maximize ecosystem services, promote the recovery of degraded lands, and adapt to and mitigate the impacts of climate change. The hyper-temporal remote sensing approach presented here has potential to greatly improve the efficiency of high-resolution ecological site mapping, and highlights its utility in terms of reduced cost and time investment relative to traditional manual mapping approaches.

}, author = {Jonathan Maynard and Jason W. Karl} } @article {JRN54661, title = {Hyper-temporal remote sensing for digital soil mapping: Characterizing soil-vegetation response to climatic variability}, journal = {Geoderma}, volume = {285:94-109}, year = {2017}, month = {01/2017}, abstract = {
Indices derived from remotely-sensed imagery are commonly used to predict soil properties with digital soil mapping (DSM) techniques. The use of images from single dates or a small number of dates is most common for DSM; however, selection of the appropriate images is complicated by temporal variability in land surface spectral properties. We argue that hyper-temporal remote sensing (RS) (i.e., hundreds of images) can provide novel insights into soil spatial variability by quantifying the temporal response of land surface spectral properties. This temporal response provides a spectral {\textquoteleft}fingerprint{\textquoteright} of the soil-vegetation relationship which is directly related to a range of soil properties. To evaluate the hyper-temporal RS approach, this study first reviewed and synthesized, within the context of temporal variability, previous research that has used RS imagery for DSM. From this analysis we developed a conceptual framework for understanding the soil-vegetation-climate relationship discernable with hyper-temporal RS, which encapsulates both intra- and inter-annual variability. Finally, we demonstrate the utility of this approach by presenting a case study in a semiarid landscape of southeastern Arizona, USA; where surface soil texture and coarse fragment classes were predicted using a 29 year time series of normalized difference vegetation index (NDVI) from Landsat TM data and modeled using support vector machine (SVM) classification. Results from the case study show that SVM classification using hyper-temporal RS imagery was effective in modeling both soil texture and coarse fragment classes, where both variables were 60\% percent correctly classified (PCC). In this semiarid ecosystem, variability in precipitation at short time scales (i.e., \<6 weeks) was the dominant driver of vegetation spectral variability, with maximum variability corresponding to short-term transitions between climatic states. These short-term transitions between climatic states corresponded to the RS scenes with the highest variable importance in our SMV models, confirming the importance of spectral variability in predicting soil texture and coarse fragment classes. Results from the case study demonstrate the efficacy of the hyper-temporal RS approach in predicting soil properties and highlights how hyper-temporal RS can improve current methods of soil mapping efforts through its ability to characterize subtle changes in RS spectra relating to variation in soil properties.
}, doi = {dx.doi.org/10.1016/j.geoderma.2016.09.024}, url = {files/bibliography/17-001.pdf}, author = {Jonathan Maynard and Matthew Levi} } @article {JRN54599, title = {Effect of spatial image support in detecting long-term vegetation change from satellite time-series}, journal = {Landscape Ecology}, year = {2016}, month = {05/2016}, pages = {2045-2062}, type = {Peer Reviewed}, chapter = {2045}, abstract = {Context: Arid rangelands have been severely degraded over the past century. Multi-temporal remote sensing techniques are ideally suited to detect significant changes in ecosystem state; however, considerable uncertainty exists regarding the effects of changing image resolution on their ability to detect ecologically meaningful change from satellite time-series. Objectives: (1) Assess the effects of image resolution in detecting landscape spatial heterogeneity. (2) Compare and evaluate the efficacy of coarse (MODIS) and moderate (Landsat) resolution satellite time-series for detecting ecosystem change. Methods: Using long-term vegetation monitoring data from grassland and scrubland sites in southern New Mexico, USA, we evaluated the effects of changing image support using MODIS (250-m) and Landsat (30-m) time-series in modeling and detecting significant changes in vegetative biomass using time-series decomposition techniques. Results: Within our study ecosystem, landscape-scale (>20-m) spatial heterogeneity was low, resulting in a similar ability to detect vegetation changes across both satellite sensors and levels of spatial image support. While both Landsat and MODIS imagery were effective in modeling temporal dynamics in vegetation structure and composition, MODIS was more strongly correlated to biomass due to its cleaner (i.e., fewer artifacts/data gaps) 16-day temporal signal. Conclusions: The optimization of spatial/temporal scale is critical in ensuring adequate detection of change. While the results presented in this study are likely specific to arid shrub-grassland ecosystems, the approach presented here is generally applicable. Future analysis is needed in other ecosystems to assess how scaling relationships will change under different vegetation communities that range in their degree of landscape heterogeneity. }, doi = {10.1007/s10980-016-0381}, url = {/files/bibliography/16-026.pdf}, author = {Jonathan Maynard and Jason W. Karl and Dawn M. Browning} } @conference {JRN54404, title = {Detecting long-term vegetation change in an arid rangeland ecosystem: Investigating effects of spatial image support within satellite time-series}, booktitle = {68th Annual Meeting of the Society for Range Management}, year = {2015}, month = {02/2015}, publisher = {Sociiety for Range Management}, organization = {Sociiety for Range Management}, address = {Sacramento, CA}, abstract = {

Arid rangelands within the southwestern United States have been severely degraded over the past century due to intensive land-use practices (e.g., livestock overgrazing, recreation) and the increasing effects of drought and climate change. Consequently, there is a critical need to develop monitoring approaches that can detect significant changes in vegetation health and distribution across vast spatial extents. Multi-temporal remote sensing techniques are ideally suited to address these challenges; however, considerable uncertainty exists regarding the effects of changing image resolution on the ability to detect ecologically meaningful change from satellite time-series. The main objective of this study was to explicitly test the effects of changing image resolution on the ability of NDVI time-series to detect observed long-term changes in plant biomass.\  Satellite time series of NDVI were compiled for the period between 2000 and 2013 from the Landsat (30-meter, ~16-day resolution) and MODIS (250-meter, 16-day resolution) sensors at the Jornada Experimental Range (JER) in southwestern New Mexico. Each time-series image was coarsened using mean resampling to yield a range of spatial resolutions (i.e., Landsat: 30, 90, 240, 990 meters; MODIS: 250, 500, 1000, 3000 meters).\  Long-term plant biomass data from 15 sites at the JER, representing both grassland and shrubland ecosystems, were used to examine these scaling effects. Each time-series was decomposed into seasonal and long-term trend components using the Breaks For Additive Seasonal and Trend (BFAST) model and significant deviations from modeled trends were identified. Plant biomass data was then regressed against the decomposed time-series, with model results used to identify optimal spatial scales. Preliminary results indicate that finer resolution imagery produces stronger correlations with temporal shifts in plant biomass. However, when considering the use of finer resolution imagery, any potential improvement in identifying shifts in vegetation dynamics must be weighed against the higher computational requirements involved with its use.

}, author = {Jonathan Maynard and Jason W. Karl and Dawn M. Browning} } @conference {JRN54406, title = {A hyper-temporal remote sensing protocol for detecting ecosystem disturbance, classifying ecological state, and assessing soil resilience}, booktitle = { 2015 Soil Science Society of America Annual Meeting}, year = {2015}, month = {11/2015}, publisher = {SSSA}, organization = {SSSA}, address = {Minneapolis, MN}, abstract = {

yper-temporal remote sensing is capable of detecting detailed information on vegetation dynamics relating to plant functional types (PFT), a useful proxy for estimating soil physical and chemical properties. A central concept of PFT is that plant morphological and physiological adaptations are linked in predictable ways by resource limitation, responses to disturbance, and edaphic factors. Consequently, the main objectives of this study were to evaluate the ability of hyper-temporal remotely sensed imagery to (i) classify PFT relating to different ecological states, (ii) quantify the magnitude and timing of significant ecosystem changes, and (iii) assess the dominant soil properties that impart ecosystem resilience within a semi-arid rangeland ecosystem. The modified soil adjusted vegetation index (MSAVI), derived from Landsat imagery, was compiled for the period between 2000 and 2012 (16-day frequency), resulting in 269 observations per 30-m pixel. Hierarchical cluster analysis was performed on the hyper-temporal image stack, delineating areas with similar ecological states based on temporal variability within each time-series. The average temporal signal from each cluster was then decomposed into seasonal and long-term trend components using the Breaks For Additive Seasonal and Trend (BFAST) model, which further identified the direction, magnitude and timing of significant deviations from modeled trends. Significant breaks, in combination with ancillary environmental data, were used to evaluate the resistance and resilience of each ecological state. Soil properties maps (0-50 cm), created from gridded SSURGO data, provided explanatory variables in an ordination and variance partitioning analysis to determine which key soil properties correspond to identified patterns of ecosystem resilience. Results from this study showed that physical soil properties (e.g., texture, water holding capacity) were the most significant in explaining resilience patterns. Furthermore, this study demonstrates the power and utility of this approach in delineating ecological states, interpreting temporal drivers of change, and identifying edaphic controls on ecosystem resilience.

}, author = {Jonathan Maynard and Jason W. Karl} } @conference {JRN54407, title = {On quantifying active soil carbon using mid-infrared spectroscopy}, booktitle = {5th International Symosium on Soil Organic Matter Dynamics: Land USe, Management and Global Change}, year = {2015}, month = {09/2015}, address = {Gottingen, Germany}, abstract = {

Soil organic matter (SOM) is derived from plant or animal residues deposited on or in soil and is in various stages of decomposition and mineralization.\  Total SOM is a common measure of soil quality, although due to its heterogeneous composition SOM can vary dramatically in terms of its biochemical properties and residence times, which ultimately affects soil heath and function.\ \ \  One operationally defined SOM fraction is {\textquotedblleft}active soil carbon{\textquotedblright} (ASC) which is thought to consist of readily oxidizable SOM that is responsive to management practices and may provide one measure of {\textquotedblleft}soil health{\textquotedblright} closely associated with soil biological activity.\  ASC can be a useful indicator to assist farmers and land managers in their selection of soil management practices to maintain ASC or to build total SOM.\  ASC has generally been measured using permangate oxidation, a costly and time-intensive procedure.\  Chemometric modeling using mid-infrared spectroscopy (MIR) has been successfully used to estimate a range of soil properties, including total organic carbon (TOC) and particulate organic carbon (POC).\  Consequently, we hypothesized that we could use MIR to estimate ASC.\  Here we report on a method that uses MIR and chemometric signal processing to quantify TOC and ASC on a variety of soils collected serially and seasonally from 76 locations across the United States.\  TOC was measured using high temperature oxidation and ASC was measured as permanganate-oxidizable carbon.\  These data were used to calibrate and validate MIR-based partial least squares (PLS) regression models that were used to estimate TOC and ASC using MIR alone.\  Signal processing included 1st or 2nd derivatives of the spectra with various gaps in the data and continuum removal.\  We used a goodness of fit statistic (R2) and the lowest root mean square error of prediction (RMSEP) to select the most robust models.\  The best predictive model for TOC was built on spectral data following continuum removal with 9 components, and had an R2 of 0.95 and RMSEP of 0.18.\  For ASC, the model was built on a 1st derivative of the spectral data with a gap of 8, and had an R2 of 0.86 and RMSEP of 0.22.\  We conclude that ASC can be reasonably estimated with MIR.\  When combined simultaneously to estimate ASC and other soil properties (e.g., TOC, POC), MIR becomes a cost effective means of characterizing large numbers of samples and could easily provide repeatable measures of the status and condition of\  soil carbon due to land management practices.\  Our presentation will describe the details of developing these models and the utility of ASC as one metric for quantifying {\textquotedblleft}soil health{\textquotedblright}.

}, author = {Jonathan Maynard and Mark Johnson} }