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Precise soil quality assessment is critical for designing sustainable agriculture policies, restoring degraded soils, carbon (C) modeling, and improving environmental quality. Although the consequences of soil quality reduction are generally recognized, the spatial extent of soil degradation is difficult to determine, because no universal equation or soil quality prediction model exists that fits all ecoregions. Furthermore, existing soil organic C (SOC) models generate estimates with uncertainties that may exceed 50%. Therefore it is possible that drastic changes in soil quality may be occurring in sites which are not identifiable on existing maps. Soil quality can either be directly inferred from SOC concentration, or through the assessment of the soil physical, chemical and biologic properties. Assessing the spatial distribution of SOC over large areas requires the calibration and development of models derived from laboratory or field based techniques. However, mapping SOC concentration in all soils is logistically challenging by using normal standard survey techniques. The availability of new generations of remotely sensed datasets and geographical information system (GIS) models (i.e. GEMS, RothC, and CENTURY) provides new opportunities for predicting soil properties and quality at different spatial scales. This article discusses the current approaches, identifies gaps and proposes improvements in techniques for measuring soil quality within agricultural fields.
