Errors in soil maps: The need for better on-site estimates and soil map predictions.

High-quality soil maps are urgently needed by diverse stakeholders, but errors in existing soil maps are often unknown, particularly in countries with limited soil surveys. To address this issue, we used field soil data to assess the accuracy of seven spatial soil databases (Digital Soil Map of the World, Namibian Soil and Terrain Digital Database, Soil and Terrain Database for Southern Africa, Harmonized World Soil Database, SoilGrids1km, SoilGrids250m, and World Inventory of Soil Property Estimates) using topsoil texture as an example soil property and Namibia as a case study area. In addition, we visually compared topsoil texture maps derived from these databases. We found that the maps showed the correct topsoil texture in only 13% to 42% of all test sites, with substantial confusion occurring among all texture categories, not just those in close proximity in the soil texture triangle. Visual comparisons of the maps moreover showed that the maps differ greatly with respect to the number, types, and spatial distribution of texture classes. The topsoil texture information provided by the maps is thus sufficiently inaccurate that it would result in significant errors in a number of applications, including irrigation system design and predictions of potential forage and crop productivity, water runoff, and soil erosion. Clearly, the use of these existing maps for policy- and decision-making is highly questionable and there is a critical need for better on-site estimates and soil map predictions. We propose that mobile apps, citizen science, and crowdsourcing can help meet this need.

Applying fingerprint FTIR spectroscopy and chemometrics to assess soil ecosystem disturbance and recovery.

The assessment and monitoring of soil disturbance and its effect on soil quality (i.e., ability to support a range of ecosystem services) has been hindered due to the shortcomings of many traditional analytical techniques (e.g., soil enzyme activities, microbial incubations), including: high cost, long-term time investment and difficulties with data interpretation. Consequently, there is a critical need to develop a rapid and repeatable approach for quantifying changes in soil quality that will provide an assessment of the current status, condition and trend of natural and managed ecosystems. Here we report on a rapid, high-throughput approach to develop an ecological 'fingerprint' of a soil using Fourier transformed infrared (FTIR) spectroscopy and chemometric modeling, and its application to assess soil ecosystem status and trend. This methodology was applied in a highly disturbed forest ecosystem over a 19-year sampling period to detect changes in soil quality (detected via changes in spectral properties), resulting from changes in dynamic soil properties (e.g., soil organic matter, reactive mineralogy). Two chemometric statistical techniques (i.e., hierarchical clustering analysis and discriminate analysis of principal components) were evaluated for interpreting and quantifying similarities/dissimilarities between samples utilizing the entire FTIR spectra (i.e., fingerprint) from each sample. We found that this approach provided a means for clearly discriminating between degraded soils, soils in recovery and reference soils. Results from fingerprint FTIR analysis illustrate its power and potential for the monitoring and assessment of soil quality and soil landscape change.

A hyper-temporal remote sensing protocol for high-resolution mapping of ecological sites.

Ecological site classification has emerged as a highly effective land management framework, but its utility at a regional scale has been limited due to the spatial ambiguity of ecological site locations in the U.S. or the absence of ecological site maps in other regions of the world. In response to these shortcomings, this study evaluated the use of hyper-temporal remote sensing (i.e., hundreds of images) for high spatial resolution mapping of ecological sites. We posit that hyper-temporal remote sensing can provide novel insights into the spatial variability of ecological sites by quantifying the temporal response of land surface spectral properties. This temporal response provides a spectral 'fingerprint' of the soil-vegetation-climate relationship which is central to the concept of ecological sites. Consequently, the main objective of this study was to predict the spatial distribution of ecological sites in a semi-arid rangeland using a 28-year time series of normalized difference vegetation index from Landsat TM 5 data and modeled using support vector machine classification. Results from this study show that support vector machine classification using hyper-temporal remote sensing imagery was effective in modeling ecological site classes, with a 62% correct classification. These results were compared to Gridded Soil Survey Geographic database and expert delineated maps of ecological sites which had a 51 and 89% correct classification, respectively. An analysis of the effects of ecological state on ecological site misclassifications revealed that sites in degraded states (e.g., shrub-dominated/shrubland and bare/annuals) had a higher rate of misclassification due to their close spectral similarity with other ecological sites. This study identified three important factors that need to be addressed to improve future model predictions: 1) sampling designs need to fully represent the range of both within class (i.e., states) and between class (i.e., ecological sites) spectral variability through time, 2) field sampling protocols that accurately characterize key soil properties (e.g., texture, depth) need to be adopted, and 3) additional environmental covariates (e.g. terrain attributes) need to be evaluated that may help further differentiate sites with similar spectral signals. Finally, the proposed hyper-temporal remote sensing framework may provide a standardized approach to evaluate and test our ecological site concepts through examining differences in vegetation dynamics in response to climatic variability and other drivers of land-use change. Results from this study demonstrate the efficacy of the hyper-temporal remote sensing approach for high resolution mapping of ecological sites, and highlights its utility in terms of reduced cost and time investment relative to traditional manual mapping approaches.

Breaks in MODIS time series portend vegetation change: verification using long-term data in an arid grassland ecosystem.

Frequency and severity of extreme climatic events are forecast to increase in the 21st century. Predicting how managed ecosystems may respond to climatic extremes is intensified by uncertainty associated with knowing when, where, and how long effects of extreme events will be manifest in an ecosystem. In water-limited ecosystems with high inter-annual variability in rainfall, it is important to be able to distinguish responses that result from seasonal fluctuations in rainfall from long-term directional increases or decreases in precipitation. A tool that successfully distinguishes seasonal from directional biomass responses would allow land managers to make informed decisions about prioritizing mitigation strategies, allocating human resource monitoring efforts, and mobilizing resources to withstand extreme climatic events. We leveraged long-term observations (2000-2013) of quadrat-level plant biomass at multiple locations across a semiarid landscape in southern New Mexico to verify the use of Normalized Difference Vegetation Index (NDVI) time series derived from 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) data as a proxy for changes in aboveground productivity. This period encompassed years of sustained drought (2000-2003) and record-breaking high rainfall (2006 and 2008) followed by subsequent drought years (2011 through 2013) that resulted in a restructuring of plant community composition in some locations. Our objective was to decompose vegetation patterns derived from MODIS NDVI over this period into contributions from (1) the long-term trend, (2) seasonal cycle, and (3) unexplained variance using the Breaks for Additive Season and Trend (BFAST) model. BFAST breakpoints in NDVI trend and seasonal components were verified with field-estimated biomass at 15 sites that differed in species richness, vegetation cover, and soil properties. We found that 34 of 45 breaks in NDVI trend reflected large changes in mean biomass and 16 of 19 seasonal breaks accompanied changes in the contribution to biomass by perennial and/or annual grasses. The BFAST method using satellite imagery proved useful for detecting previously reported ground-based changes in vegetation in this arid ecosystem. We demonstrate that time series analysis of NDVI data holds potential for monitoring landscape condition in arid ecosystems at the large spatial scales needed to differentiate responses to a changing climate from responses to seasonal variability in rainfall.

Bioavailability and fate of phosphorus in constructed wetlands receiving agricultural runoff in the San Joaquin Valley, California.

Elevated nutrient concentrations in agricultural runoff contribute to seasonal eutrophication and hypoxia in the lower portion of the San Joaquin River, California. Interception and filtration of agricultural runoff by constructed wetlands may improve water quality of return flows ultimately destined for major water bodies. This study evaluated the efficacy of two small flow-through wetlands (2.3 and 7.3 ha; hydraulic residence time = 11 and 31 h) for attenuating various forms of P from irrigation tailwaters during the 2005 irrigation season (May to September). Our goal was to examine transformations and removal efficiencies for bioavailable P in constructed wetlands. Inflow and outflow water volumes were monitored continuously and weekly water samples were collected to measure total P (TP), dissolved-reactive P (DRP), and bioavailable P (BAP). Suspended sediment was characterized and fractionated into five operationally-defined P fractions (i.e., NH4Cl, bicarbonate-dithionite, NaOH, HCl, residual) to evaluate particulate P (PP) transformations. DRP was the major source of BAP with the particulate fraction contributing from 11 to 26%. On a seasonal basis, wetlands removed 55 to 65% of PP, 61 to 63% of DRP, 57 to 62% of BAP, and 88 to 91% of TSS. Sequential fractionation indicated that the bioavailable fraction of PP was largely associated with clay-sized particles that remain in suspension, while less labile P forms preferentially settle with coarser sediment. Thus, removal of potentially bioavailable PP is dependent on factors that promote particle settling and allow for the removal of colloids. This study suggests that treatment of tailwaters in small, flow-through wetlands can effectively remove BAP. Wetland design and management strategies that enhance sedimentation of colloids can improve BAP retention efficiency.