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Plant trait data have been used in various studies related to ecosystem functioning, community ecology, and assessment of ecosystem services. Evidences are that plant scientists agree on a set of key plant traits, which are relatively easy to measure and have a stable and strong predictive response to ecosystem functions. However, the field measurements of plant trait data are still limited to small area, to a certain moment in time and to certain number of species only. Therefore, remote sensing (RS) offers potential to complement or even replace field measurements of some plant traits. It offers instantaneous spatially contiguous information, covers larger areas and in case of satellite observations profits from their revisit capacity. In this review, we first introduce RS concepts of light-vegetation interactions, RS instruments for vegetation studies, RS methods, and scaling between field and RS observations. Further we discuss in detail current achievements and challenges of optical RS for mapping of key plant traits. We concentrate our discussion on three categorical plant traits (plant growth and life forms, flammability properties and photosynthetic pathways and activity) and on five continuous plant traits (plant height, leaf phenology, leaf mass per area, nitrogen and phosphorous concentration or content). We review existing literature to determine the retrieval accuracy of the continuous plant traits. The relative estimation error using RS ranged between 10% and 45% of measured mean value, i.e. around 10% for plant height of tall canopies, 20% for plant height of short canopies, 15% for plant nitrogen, 25% for plant phosphorus content/concentration, and 45% for leaf mass per area estimates.The potential of RS to map plant traits is particularly high when traits are related to leaf biochemistry, photosynthetic processes and canopy structure. There are also other plant traits, i.e. leaf chlorophyll content, water content and leaf area index, which can be retrieved from optical RS well and can be of importance for plant scientists.We underline the need that future assessments of ecosystem functioning using RS should require comprehensive and integrated measurements of various plant traits together with leaf and canopy spectral properties. By doing so, the interplay between plant structural, physiological, biochemical, phenological and spectral properties can be better understood. Disciplines Medicine and Health Sciences | Social and Behavioral Sciences Publication DetailsHomolova, L., Malenovsky, Z., Clevers, J. G. P. W., Garcia-Santos, G. & Schaepman, M. E. (2013 ecology, and assessment of ecosystem services. Evidences are that plant scientists agree on a 22 set of key plant traits, which are relatively easy to measure and have a stable and strong 23 predictive response to ecosystem functions. However, the field measurements of plant trait 24 data are still limited to small area, to a certain moment in time and to certain number of 25 species only. Therefore, remote sensing (RS) offers potential...

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Historic land-cover/use change is important for studies on climate change, soil carbon, and biodiversity assessments. Available reconstructions focus on the net area difference between two time steps (net changes) instead of accounting for all area gains and losses (gross changes). This leads to a serious underestimation of land-cover/use dynamics with impacts on the biogeochemical and environmental assessments based on these reconstructions. In this study, we quantified to what extent land-cover/use reconstructions underestimate land-cover/use changes in Europe for the 1900-2010 period by accounting for net changes only. We empirically analyzed available historic land-change data, quantified their uncertainty, corrected for spatial-temporal effects and identified underlying processes causing differences between gross and net changes. Gross changes varied for different land classes (largest for forest and grassland) and led to two to four times the amount of net changes. We applied the empirical results of gross change quantities in a spatially explicit reconstruction of historic land change to reconstruct gross changes for the EU27 plus Switzerland at 1 km spatial resolution between 1950 and 2010. In addition, the reconstruction was extended back to 1900 to explore the effects of accounting for gross changes on longer time scales. We created a land-change reconstruction that only accounted for net changes for comparison. Our two model outputs were compared with five commonly used global reconstructions for the same period and area. In our reconstruction, gross changes led in total to a 56% area change (ca. 0.5% yr(-1)) between 1900 and 2010 and cover twice the area of net changes. All global reconstructions used for comparison estimated fewer changes than our gross change reconstruction. Main land-change processes were cropland/grassland dynamics and afforestation, and also deforestation and urbanization.

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Leaf area index (LAI) and chlorophyll content, at leaf and canopy level, are important variables for agricultural applications because of their crucial role in photosynthesis and in plant functioning. The goal of this study was to test the hypothesis that LAI, leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC) of a potato crop can be estimated by vegetation indices for the first time using Sentinel-2 satellite images. In 2016 ten plots of 30 × 30 m were designed in a potato field with different fertilization levels. During the growing season approximately 10 daily radiometric field measurements were used to determine LAI, LCC, and CCC. These radiometric determinations were extensively calibrated against LAI2000 and chlorophyll meter (SPAD, soil plant analysis development) measurements for potato crops grown in the years 2010-2014. Results for Sentinel-2 showed that the weighted difference vegetation index (WDVI) using bands at 10 m spatial resolution can be used for estimating the LAI (R 2 of 0.809; root mean square error of prediction (RMSEP) of 0.36). The ratio of the transformed chlorophyll in reflectance index and the optimized soil-adjusted vegetation index (TCARI/OSAVI) showed to be a good linear estimator of LCC at 20 m (R 2 of 0.696; RMSEP of 0.062 g·m −2 ). The performance of the chlorophyll vegetation index (CVI) at 10 m spatial resolution was slightly worse (R 2 of 0.656; RMSEP of 0.066 g·m −2 ) compared to TCARI/OSAVI. Finally, results showed that the green chlorophyll index (CI green ) was an accurate and linear estimator of CCC at 10 m (R 2 of 0.818; RMSEP of 0.29 g·m −2 ). Results for CI green were better than for the red-edge chlorophyll index (CI red-edge , R 2 of 0.576, RMSE of 0.43 g·m −2 ). Our results show that Sentinel-2 bands at 10 m spatial resolution are suitable for estimating LAI, LCC, and CCC, avoiding the need for red-edge bands that are only available at 20 m. This is an important finding for applying Sentinel-2 data in precision agriculture.

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