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Vegetation health and vigour may be affected by oil leakage or pollution. This effect can alter a plant's behaviour and may be used as evidence for detecting oil pollution in the environment. Satellite remote sensing has been shown to be an effective tool and approach to detect and monitor vegetation health and status in polluted areas. Previous research has used vegetation indices derived from remotely sensed satellite data to monitor vegetation health. This study investigated the potential for using broadband multispectral vegetation indices to detect impacts of oil pollution on vegetation conditions. Twenty indices were explored and evaluated in this study. The indices use data acquired at the visible, near infrared and shortwave infrared wavelengths. Comparative index values from the 37 oil polluted and non-polluted (control) sites show that 12 Broadband multispectral vegetation indices (BMVIs) indicated significant differences (p-value < 0.05) between pre-and postspill observations. The 12 BMVI values at the polluted sites before and after the spill are significantly different with the ones obtained on the spill event date. The result at the nonpolluted (control) sites shows that 11 of the 20 BMVI values did not indicate significant change and remained statistically invariant before and after the spill date (p-value > 0.05). Therefore, it can be stated that, in this study, oil spills seem to result in biophysical and biochemical alteration of the vegetation, leading to changes in reflectance signature detected by these indices. Five spectral indices (normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), adjusted resistant vegetation index (ARVI2), green near infrared (G/NIR) and green shortwave infrared (G/SWIR)) were found to be consistently sensitive to the effects of oil pollution on vegetation and hence could be used to map and monitor oil pollution in vegetated areas.

Estimates of biophysical and biochemical variables such as leaf area index (LAI) and canopy chlorophyll content (CCC) are a fundamental requirement for effectively monitoring and managing forest environments. With its red-edge bands and high spatial resolution, the Multispectral Instrument (MSI) on board the Sentinel-2 missions is particularly well-suited to LAI and CCC retrieval. Using field data collected throughout the growing season at a deciduous broadleaf forest site in Southern England, we evaluated the performance of two hybrid retrieval algorithms for estimating LAI and CCC from MSI data: the Scattering by Arbitrarily Inclined Leaves (SAIL)-based L2B retrieval algorithm made available to users in the Sentinel Application Platform (SNAP), and an alternative retrieval algorithm optimised for forest environments, trained using the Invertible Forest Reflectance Model (INFORM). Moderate performance was associated with the SNAP L2B retrieval algorithm for both LAI (r2 = 0.54, RMSE = 1.55, NRMSE = 43%) and CCC (r2 = 0.52, RMSE = 0.79 g m−2, NRMSE = 45%), while improvements were obtained using the INFORM-based retrieval algorithm, particularly in the case of LAI (r2 = 0.79, RMSE = 0.47, NRMSE = 13%), but also in the case of CCC (r2 = 0.69, RMSE = 0.52 g m−2, NRMSE = 29%). Forward modelling experiments confirmed INFORM was better able to reproduce observed MSI spectra than SAIL. Based on our results, for forest-related applications using MSI data, we recommend users seek retrieval algorithms optimised for forest environments.

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Over the last two decades, satellite-derived estimates of biophysical variables have been increasingly used in operational services, requiring quantification of their accuracy and uncertainty. Evaluating satellitederived vegetation products is challenging due to their moderate spatial resolution, the heterogeneity of the terrestrial landscape, and difficulties in adequately characterising spatial and temporal vegetation dynamics. In recent years, near-surface remote sensing has emerged as a potential source of data against which satellite-derived vegetation products can be evaluated. Several studies have focussed on the evaluation of satellite-derived phenological transition dates, however in most cases the shape and magnitude of the underlying time-series are neglected. In this paper, we investigated the relationship between the green chromatic coordinate (GCC) derived using near-surface remote sensing and a range of vegetation products derived from the Medium Resolution Imaging Spectrometer (MERIS) throughout the growing season. Moderate to strong relationships between the GCC and vegetation products derived from MERIS were observed at deciduous forest sites. Weak relationships were observed over evergreen forest sites as a result of their subtle seasonality, which is likely masked by atmospheric, bidirectional reflectance distribution function (BRDF), and shadowing effects. Temporal inconsistencies were attributed to the oblique viewing geometry of the digital cameras and differences in the incorporated spectral bands. In addition, the commonly observed summer decline in GCC values was found to be primarily associated with seasonal variations in brown pigment concentration, and to a lesser extent illumination geometry. At deciduous sites, increased sensitivity to initial increases in canopy greenness was demonstrated by the GCC, making it particularly well-suited to identifying the start of season when compared to satellite-derived vegetation products. Nevertheless, in some cases, the relationship between the GCC and vegetation products derived from MERIS was found to saturate asymptotically. This limits the potential of the approach for the evaluation of the underlying satellite-derived vegetation products, and for the continuous monitoring of vegetation during the growing season, particularly at medium to high biomass study sites.

SummaryThe fraction of absorbed photosynthetically active radiation (FAPAR) is a key vegetation biophysical variable in most production efficiency models (PEMs). Operational FAPAR products derived from satellite data do not distinguish between the fraction of photosynthetically active radiation (PAR) absorbed by nonphotosynthetic and photosynthetic components of vegetation canopy, which would result in errors in representation of the exact absorbed PAR utilized in photosynthesis.The possibility of deriving only the fraction of PAR absorbed by photosynthetic elements of the canopy (i.e. FAPAR ps ) was investigated.The approach adopted involved inversion of net ecosystem exchange data from eddy covariance measurements to calculate FAPAR ps . The derived FAPAR ps was then related to three vegetation indices (i.e. Normalized Difference Vegetation Index (NDVI), Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) and Enhanced Vegetation Index (EVI)) in an attempt to determine their potential as surrogates for FAPAR ps . Finally, the FAPAR ps was evaluated against two operational satellite data-derived FAPAR products (i.e. MODIS and CYCLOPES products).The maximum FAPAR ps from the inversion approach ranged between 0.6 and 0.8. The inversion approach also predicted site-specific Q 10 -modelled daytime respiration successfully (R 2 > 0.8). The vegetation indices were positively correlated (R 2 = 0.67-0.88) to the FAPAR ps .Finally, the two operational FAPAR products overestimated the FAPAR ps . This was attributed to the two products deriving FAPAR for the whole canopy rather than for only photosynthetic elements in the canopy.

Since the launch of the first Landsat satellite in the early 1970s, the field of space-borne optical remote sensing has made significant progress. Advances have been made in all aspects of optical remote sensing data, including improved spatial, temporal, spectral and radiometric resolutions, which have increased the uptake of these data by wider scientific communities. Flagship satellite missions such as NASA’s Terra and Aqua and ESA’s Envisat with their high temporal (<3days) and spectral (15–36 bands) resolutions opened new opportunities for routine monitoring of various aspects of terrestrial ecosystems at the global scale and have provided greater understanding of critical biophysical processes in the terrestrial ecosystem. The launch of new satellite sensors such as Landsat 8 and the European Space Agency’s Copernicus Sentinel missions (e.g. Sentinel 2 with improved spatial resolution (10–60 m) and potential revisit time of five days) is set to revolutionise the availability and use of remote sensing data in global terrestrial ecosystem monitoring. Furthermore, the recent move towards use of constellations of nanosatellites (e.g. the Flock missions by Planet Labs) to collect on-demand high spatial and temporal resolution optical remote sensing data would enable uptake of these data for operational monitoring. As a result of increase in data availability, optical remote sensing data are now increasingly used to support a number of operational services (e.g. land monitoring, atmosphere monitoring and climate change studies). However, many challenges still remain in exploiting the growing volume of optical remote sensing data to monitor global terrestrial ecosystems. These challenges include ensuring the highest data quality both in terms of the sensitivity of sensors and the derived biophysical products, affordability and availability of the data and continuity of data acquisition. This review provides an overview of the developments in space-borne optical remote sensing in the past decade and discusses a selection of aspects of global terrestrial ecosystems where the data are currently used. It concludes by highlighting some of the challenges and opportunities of using optical remote sensing data in monitoring global terrestrial ecosystems.