Hyperspectral remote sensing is increasingly being recognized as a powerful tool to map ecosystem properties and functions through time and space. However, general information on the accuracy of this technology to assess the vegetation's biophysical and-chemical trait composition, and on the variables which are mediating this accuracy, is often lacking so far. Here, we addressed this knowledge gap for grass-and shrubland ecosystems and applied novel three-level meta-analytical regression equations to 77 studies that validated hyperspectral remote sensing data with field observations. Our results showed that the accuracy of hyperspectral sensors is generally high, but strongly depends on the trait being studied (leaf area index: R² = 0.79 and nRMSE = 0.19, chlorophyll: R² = 0.77 and nRMSE = 0.21, carotenoids: R² = 0.80 and nRMSE = 0.29, phosphorus: R² = 0.76 and nRMSE = 0.14, nitrogen: R² = 0.74 and nRMSE =0.09, water: R² = 0.69 and nRMSE = 0.13, and lignin content: R² = 0.64 and nRMSE = 0.26). Moreover, they indicated that the use of multivariate signal processing techniques could improve these estimation accuracies (adjusted p < 0.06 for LAI, chlorophyll and nitrogen). Finally, estimations from air-and spaceborne imaging spectrometers, allowing for functional mapping at broader spatial scales, were found to be as accurate as estimations from ground-based spectral measurements. Despite these promising findings, we revealed that leaf morphological properties (e.g. specific leaf area and leaf dry matter content) and biochemical traits which are not growth-related (e.g. lignin and cellulose) remain underexplored in grass-and shrublands. Moreover there was a strong publication bias towards R² for assessing model performance. Our findings foster and direct further methodological and technological developments for a more accurate and complete functional characterization of these ecosystems worldwide.

Aims Mapping gradual transitions in plant species composition via a combination of ordination and regression from remote sensing data is becoming an established approach. However, straightforward analysis of areas with high species turnover rates may result in a loss of information since a high level of generalization is required. In this study, we investigate whether analysis of more homogeneous subsets, in contrast to processing of the complete dataset, is a viable approach to mapping multiple floristic gradients. Location The coastal nature reserve “Zwin” (Belgium). Methods The measured dataset is partitioned into more homogeneous subsets based upon species composition using hierarchical classification. The dataset and subsets are then processed separately. First, ordination is performed to extract floristic gradients in plant species composition; second, these gradients are related to airborne hyperspectral remote sensing data through regression models and mapped by projecting these models on image data. Regression validation and Mantel tests are used to compare the results within the study and to other studies. Results Hierarchical classification resulted in two homogeneous vegetation subsets. Ordination yielded four gradients in the area and all regression models compared favorably to similar studies in other areas with R² values ranging from 0.47 to 0.74. The Mantel test showed that by dividing the dataset into subsets, higher resemblance to the original vegetation data can be achieved. Conclusion We showed that mapping gradual transitions in plant species composition across multiple subsets sampled from one measured vegetation dataset is a promising approach for retrospective analysis of areas with high species turnover rates. In addition to potential improvements in performance, this complementary analysis enables mapping of additional gradients, suggesting that all conventionally predicted maps remain available, valuable, and necessary for thorough understanding of plant species composition.

Storage of soil organic carbon (SOC) is an essential function of ecosystems underpinning the delivery of multiple services to society. Regional SOC stock estimates often rely on data collected during land‐use‐specific inventory schemes with varying sampling depth and density. Using such data requires techniques that can deal with the associated heterogeneity. As the resulting SOC assessments are not calibrated for the local scale, they could suffer from oversimplification of landscape processes and heterogeneity. This might especially be the case for sandy regions where typical historical land use practices and soil development processes determine SOC storage. The aims of this study were (a) to combine four land‐use‐specific SOC stock assessments to estimate the total stock in Flanders, Belgium, and (b) to evaluate the applicability of this regional‐scale estimate at the local scale. We estimated the SOC stock in the upper 100 cm of the unsealed area in Flanders (887,745 ha) to be 111.67 Mt OC, or 12.6 ± 5.65 kg OC m−2 on average. In general, soils under (semi‐) natural land‐use types, for example forests, store on average more organic carbon than under agriculture. However, overall agricultural soils store the largest amounts of SOC due to their vast spatial extent. Zooming in on a sandy location study (13.55 km2) revealed the poor performance of the regional estimates, especially where Histosols occurred. Our findings show that a greater spatial sampling density is required when SOC stock estimates are needed to inform carbon‐aware land management rather than to provide for regional reporting.