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 6514 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 1–2 ha scale common to smallholders. Standard measures of map accuracy for soil texture class and rock fragment class predictions showed that all soil maps had similar performance in estimating the correct property class. Overall soil texture class accuracies ranged from 8 %–14 % but could be as high as 38 %–64 % after accounting for uncertainty in the evaluation dataset. Soil rock fragment class accuracies ranged from 26 %–29 %. However, despite these similar overall accuracies, there were substantial differences in soil property predictions among the four maps, highlighting that soil map errors are not uniform between maps. 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 modeling 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.

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.

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, including high cost, long-term time investment and difficulties with data interpretation. Consequently, there is a critical need to develop rapid and repeatable approaches for quantifying changes in soil quality that land managers may use to assess the condition and trend of natural and managed ecosystems. Here we report on a rapid, high throughput approach using fingerprint Fourier transformed infrared (FTIR) spectroscopy and chemometric modeling. Fingerprint FTIR incorporates all information embedded within the FTIR spectrum, thus producing a biogeochemical or ecological “fingerprint” of the soil. This methodology was applied in a highly disturbed forest ecosystem over a 19-year sampling period to detect, via spectral analysis, changes in dynamic soil properties (e.g., soil organic matter and reactive mineralogy) that can indicate changes in soil quality. Two chemometric statistical techniques (i.e., hierarchical clustering analysis [HCA] and discriminate analysis of principal components [DAPC]) were evaluated for interpreting and quantifying similarities/dissimilarities between samples utilizing the entire FTIR spectra from each sample. We found that both statistical approaches provided a means for clearly discriminating between degraded soils, soils in recovery, and reference soils. DAPC analysis provided additional information on the spectral wavenumbers most important in differentiating between soil samples across our disturbance time series. Wavenumbers relating to stretching of phenolic (C-O), aromatic (C=C, C-C), carboxylic acids (C=O), and to a lesser extent aliphatic (C-H) bonds were important in discriminating between soil samples. These results confirmed our visual interpretation of FTIR spectra, where an increase in aliphatic (fats and lipids) and decrease in phenolic/aromatic (lignin) absorption occurred following intensive site disturbance, followed by a return to predisturbance absorption values 17 years later. Results from this study illustrate the potential of fingerprint FTIR and chemometrics as an efficient technique for quantifying changes in soil quality that may be used in the monitoring and assessment of soil landscape change. Additional research across a range of soil ecosystems and under different types of ecosystem disturbance is needed to further validate this approach and to demonstrate its wide applicability as a land management tool.