Special Issue "Remote Sensing of Plant Functional Traits"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 1 August 2020.

Special Issue Editor

Dr. Oscar Perez-Priego
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Guest Editor
Department of Biological Science, Faculty of Science and Engineering, Macquarie University, 6 Wally's Walk, Room 162, New South Wales, Sydney, Australia
Interests: plant traits; plant functional ecology; vegetation modeling; hyperspectral data

Special Issue Information

Dear Colleagues,

Plant functional traits reflect key state variables of plant physiological performance. Recently, new generation vegetation models have used leaf traits to represent functional diversity across and within plant types and forms. Based on the radiation absorption and scattering properties of leaves, most functional relevant traits such as pigment contents (e.g., chlorophylls, carotenoids) and leaf nitrogen content can be retrieved from the spectral signal. With recent advancements in remote sensing technologies and data model approaches, a number of physiological attributes of vegetation can be successfully monitored at the appropriate temporal and spatial scales. For instance, the maximum carboxylation capacity (key trait related to photosynthesis) can be inferred from the reflectance via the inversion of the radiative transfer model. As a purely empirical approach, a range of statistical models (e.g., random forest regression, partial least square regression, among the most promising methods) provide an alternative means to squeezing the most relevant information out from the whole reflectance spectra, which offers a clear advantage against traditional multispectral approaches. With the constellation of remote sensing products, this emerges as a unique opportunity with extended applications in a range of research areas, including plant functional ecology.

In this Special Issue, we welcome studies linking spectral reflectance to plant functional traits. From a remote sensing application perspective, we particularly encourage studies detailing the functional convergency/divergency of specific plant traits across plant forms and environmental conditions. This includes scale studies from proximal sensing up to all the ways to satellite observations. In addition, we encourage studies covering the following topics:

  • Novel retrieval approaches of plant functional traits;
  • Uncertainties in measuring or modeling plant traits. Evaluating confounding factors such as the effects of leaf structure, plant architecture, as well as other properties at the community levels on the retrieval of plant functional traits;
  • Direct comparisons of empirical and statistical or model inversion approaches. Hyperspectral against multispectral approaches for the retrieval of plant traits;
  • Linkages between sun-induced chlorophyll fluorescence and plant functional traits.

Dr. Oscar Perez-Priego
Guest Editor

Manuscript Submission Information

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Published Papers (3 papers)

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Research

Open AccessArticle
Upscaling from Instantaneous to Daily Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) for Satellite Products
Remote Sens. 2020, 12(13), 2083; https://doi.org/10.3390/rs12132083 - 29 Jun 2020
Abstract
The fraction of absorbed photosynthetically active radiation (FAPAR) is an essential climate variable (ECV) widely used for various ecological and climate models. However, all the current FAPAR satellite products correspond to instantaneous FAPAR values acquired at the satellite transit time only, which cannot [...] Read more.
The fraction of absorbed photosynthetically active radiation (FAPAR) is an essential climate variable (ECV) widely used for various ecological and climate models. However, all the current FAPAR satellite products correspond to instantaneous FAPAR values acquired at the satellite transit time only, which cannot represent the variations in photosynthetic processes over the diurnal period. Most studies have directly used the instantaneous FAPAR as a reasonable approximation of the daily integrated value. However, clearly, FAPAR varies a lot according to the weather conditions and amount of incoming radiation. In this paper, a temporal upscaling method based on the cosine of the solar zenith angle (SZA) at local noon ( c o s ( S Z A n o o n ) ) is proposed for converting instantaneous FAPAR to daily integrated FAPAR. First, the diurnal variations in FAPAR were investigated using PROSAIL (a model of Leaf Optical Properties Spectra (PROSPECT) integrating a canopy radiative transfer model (Scattering from Arbitrarily Inclined Leaves, SAIL)) simulations with different leaf area index (LAI) values corresponding to different latitudes. It was found that the instantaneous black sky FAPAR at 09:30 AM provided a good approximation for the daily integrated black sky FAPAR; this gave the highest correlation (R2 = 0.995) and lowest Root Mean Square Error (RMSE = 0.013) among the instantaneous black sky FAPAR values observed at different times. Secondly, the difference between the instantaneous black sky FAPAR values acquired at different times and the daily integrated black sky FAPAR was analyzed; this could be accurately modelled using the cosine value of solar zenith angle at local noon ( c o s ( S Z A n o o n ) ) for a given vegetation scene. Therefore, a temporal upscaling method for typical satellite products was proposed using a cos(SZA)-based upscaling model. Finally, the proposed cos(SZA)-based upscaling model was validated using both the PROSAIL simulated data and the field measurements. The validated results indicated that the upscaled daily black sky FAPAR was highly consistent with the daily integrated black sky FAPAR, giving very high mean R2 values (0.998, 0.972), low RMSEs (0.007, 0.014), and low rMAEs (0.596%, 1.378%) for the simulations and the field measurements, respectively. Consequently, the cos(SZA)-based method performs well for upscaling the instantaneous black sky FAPAR to its daily value, which is a simple but extremely important approach for satellite remote sensing applications related to FAPAR. Full article
(This article belongs to the Special Issue Remote Sensing of Plant Functional Traits)
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Open AccessArticle
Remote Sensing-Based Mapping of Senescent Leaf C:N Ratio in the Sundarbans Reserved Forest Using Machine Learning Techniques
Remote Sens. 2020, 12(9), 1375; https://doi.org/10.3390/rs12091375 - 27 Apr 2020
Abstract
Carbon to nitrogen ratio (C:N) of senescent leaf is a crucial functional trait and indicator of litter quality that affects belowground carbon and nitrogen cycles, especially soil decomposition. Although mapping the C:N ratio of fresh mature canopies has been attempted, few studies have [...] Read more.
Carbon to nitrogen ratio (C:N) of senescent leaf is a crucial functional trait and indicator of litter quality that affects belowground carbon and nitrogen cycles, especially soil decomposition. Although mapping the C:N ratio of fresh mature canopies has been attempted, few studies have attempted to map the C:N ratio of senescent leaves, particularly in mangroves. In this study, four machine learning models (Stochastic Gradient Boosting, SGB; Random Forest, RF; Support Vector Machine, SVM; and Partial Least Square Regression, PLSR) were compared for testing the predictability of using the Landsat TM 5 (LTM5) and Landsat 8 to map spatial and temporal distribution of C:N ratio of senescent leaves in Sundarbans Reserved Forest (SRF), Bangladesh. Surface reflectance of bands, texture metrics of bands and vegetation indices of LTM5 and Landsat 8 yearly composite images were extracted using Google Earth Engine for 2009–2010 and 2019. We found SGB, RF and SVM were significant different from PLSR based on MAE, RMSE, and R2 (p < 0.05). Our results indicate that remote sensing data, such as Landsat TM data, can be used to map the C:N ratio of senescent leaves in mangroves with reasonable accuracy. We also found that the mangroves had a high spatial variation of C:N ratio and the C:N ratio map developed in the current study can be used for improving the biogeochemical and ecosystem models in the mangroves. Full article
(This article belongs to the Special Issue Remote Sensing of Plant Functional Traits)
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Open AccessArticle
Validating the Predictive Power of Statistical Models in Retrieving Leaf Dry Matter Content of a Coastal Wetland from a Sentinel-2 Image
Remote Sens. 2019, 11(16), 1936; https://doi.org/10.3390/rs11161936 - 19 Aug 2019
Cited by 1
Abstract
Leaf dry matter content (LDMC), the ratio of leaf dry mass to its fresh mass, is a key plant trait, which is an indicator for many critical aspects of plant growth and survival. Accurate and fast detection of the spatiotemporal dynamics of LDMC [...] Read more.
Leaf dry matter content (LDMC), the ratio of leaf dry mass to its fresh mass, is a key plant trait, which is an indicator for many critical aspects of plant growth and survival. Accurate and fast detection of the spatiotemporal dynamics of LDMC would help understanding plants’ carbon assimilation and relative growth rate, and may then be used as an input for vegetation process models to monitor ecosystems. Satellite remote sensing is an effective tool for predicting such plant traits non-destructively. However, studies on the applicability of remote sensing for LDMC retrieval are scarce. Only a few studies have looked into the practicality of using remotely sensed data for the prediction of LDMC in a forest ecosystem. In this study, we assessed the performance of partial least squares regression (PLSR) plus 11 widely used vegetation indices (VIs), calculated based on different combinations of Sentinel-2 bands, in predicting LDMC in a coastal wetland. The accuracy of the selected methods was validated using LDMC, destructively measured in 50 randomly distributed sample plots at the study site in Schiermonnikoog, the Netherlands. The PLSR applied to canopy reflectance of Sentinel-2 bands resulted in accurate prediction of LDMC (coefficient of determination (R2) = 0.71, RMSE = 0.033). PLSR applied to the studied VIs provided an R2 of 0.70 and RMSE of 0.033. Four vegetation indices (enhanced vegetation index(EVI), specific leaf area vegetation index (SLAVI), simple ratio vegetation index (SRVI), and visible atmospherically resistant index (VARI)) computed using band 3 (green) and band 11 of the Sentinel-2 performed equally well and achieved a good measure of accuracy (R2 = 0.67, RMSE = 0.034). Our findings demonstrate the feasibility of using Sentinel-2 surface reflectance data to map LDMC in a coastal wetland. Full article
(This article belongs to the Special Issue Remote Sensing of Plant Functional Traits)
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