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Terrestrial LiDAR scanning (TLS) data can be used to efficiently estimate plant canopy structural variables including leaf area density (LAD). This is done by estimating the transmission probability of LiDAR beams through a canopy, which is directly related to the local LAD. Advancements in TLS technology have enabled instruments that can record multiple object intersection points along a single laser pulse path, but whether this additional information can improve LAD estimation from TLS is not fully understood. Several methods for incorporating multiple returns per beam into transmission probability and LAD estimations have been previously suggested, including the use of corrected relative intensity to weight multiple returns more precisely. Intensity-based weighting is complicated by unknown variation in surface reflectance and orientation. Synthetic multiple-return TLS simulations were performed for virtual homogeneous voxels and heterogeneous tree cases with known properties (LAD, leaf angle distribution, reflectivity) in order to evaluate LAD estimates using intensity-based weighting in comparison to first-hits and equal-weighting approaches. An idealized intensity-based weighting, where the fraction of beam energy hitting every surface is known, and an exact weighting, where the impact of partial misses is also accounted for were also analyzed. Intensity-based weighting of multiple-return TLS data did not necessarily improve transmission probability and LAD estimates compared with the more simple equal-weighting method. Both methods had relatively similar performance, with the intensity-weighting method tending towards slightly lower transmission and higher LAD. There could be significant errors for all methods, including when weighting of hit points was exact. The error in LAD caused by choice of weighting method was therefore sometimes overshadowed by a combination of other errors due to clumping, partial misses, voxel occlusion, and limitations of Beer's law for dense canopies, which could have substantial impacts. Given the small, but inconsistent potential improvements in accuracy of the intensity-based methods that were tested in this work, the equal-weighting method appears an acceptable choice. • Methods for weighting multiple-return terrestrial LiDAR data were tested. • Methods were evaluated with LiDAR simulations and known canopy geometry. • Weighting multiple returns equally or by intensity produced similar LAD estimates. • Partial misses and clumping may have a greater impact than return-weighting method. [ABSTRACT FROM AUTHOR]

Many plant species have dorsiventral leaves that have significant differences in optical properties from one side to the other. Several studies have revealed that ignoring this asymmetry induces significant errors in plant canopy reflectance, and current leaf models simulating leaf dorsiventrality are limited to the 0.4–2.5 μ m wavelength range. This article, partly based on two recently collected datasets in the 2.5–14 μ m wavelength range, demonstrates that ignoring leaf dorsiventrality induces significant errors in brightness temperature and effective emissivity at the canopy scale. The PROLIB model, which inherits from the PROSPECT-VISIR and LIBERTY models, is the first radiative transfer model to simulate the reflectance and transmittance of both leaf sides from 0.4 to 5.7 μ m. The palisade and spongy mesophylls are represented as plate and sphere layers, respectively, to account for the structural asymmetry of leaf cells. The sieve effect that explains the differences in transmittance between the adaxial and abaxial sides of the leaf is successfully incorporated into PROLIB. Evaluation of the model on several leaf datasets shows that: (1) It reproduces well the adaxial and abaxial optical properties of the leaves, with a root mean square error (RMSE) of 0.0109 for reflectance and transmittance. (2) It can be inversed to retrieve leaf traits, with RMSE values for leaf chlorophyll, carotenoid, anthocyanin, water, and dry matter content of 5.519 μ g/cm2, 2.344 μ g/cm2, 4.219 μ g/cm2, 0.0022 g/cm2, and 0.0017 g/cm2, respectively (corresponding normalized RMSE values of 22.0%, 34.0%, 49.4%, 19.6%, and 24.7%). However, better and more complete leaf datasets are needed for leaf dorsiventrality analysis and model calibration. • Adaxial and abaxial leaf surfaces have different spectral properties in 2.5–14 μ m. • A leaf model called PROLIB is developed to simulate leaf dorsiventrality. • PROLIB takes into account the sieve effect. • PROLIB reconstructs leaf spectra well from multiple datasets, with an RMSE of 0.0109. [ABSTRACT FROM AUTHOR]

The composition of the plant canopy is a key attribute of terrestrial ecosystems, influencing the fluxes of carbon, water, and energy between the land surface and the atmosphere. Terrestrial ecosystem and biosphere models, which are used to predict how ecosystems are expected to respond to changes in climate, atmospheric CO 2 , and land-use change, require accurate representations of plant canopy composition at large spatial scales. The ability to accurately specify plant canopy composition is important because it determines the physiological and ecological properties of plants (such as leaf photosynthetic capacity, patterns of plant carbon allocation and tissue turnover, and the resulting dynamics of plant demography) that govern the biophysical and biogeochemical functioning of ecosystems. Traditionally, plant canopy composition has been represented in a coarse-grained manner within terrestrial biosphere models, with ecosystems being comprised of a single plant functional type (PFT). However, models are increasingly seeking to represent fine-scale spatial variation in plant functional diversity. In this study, we show how imaging spectrometry measurements can provide spatially-comprehensive estimates of within-biome heterogeneity in PFT composition across a functionally diverse and topographically heterogeneous ~710 km2 area in the Southern Sierra Mountains of California. AVIRIS (Airborne Visible Infrared Imaging Spectrometer) data at 18 m resolution from the recent HyspIRI Preparatory Mission (Hyperspectral InfraRed Imager) were used to estimate the sub-pixel fractions of seven PFTs represented in the ED2 terrestrial biosphere model: Shrub, Oak, Western Hardwood, Western Pine, Cedar/Fir, and High-elevation Pine, plus a Grass/NPV (Non-Photosynthetic Vegetation) fraction using Multiple Endmember Spectral Mixture Analysis (MESMA). ED2 is an individual-based terrestrial biosphere model capable of representing fine-scale sub-pixel ecosystem heterogeneity. Our results show that this methodology captures important elevation-related shifts in canopy composition that occur within the study area that are not resolved by existing multi-spectral land-cover products. These estimates modestly improved when the putative PFT endmembers considered in the mixture analysis were constrained using available geospatial data about the presence and absence of the PFTs in particular areas: the average RMSEs (root-mean-square errors) with the geospatially-constrained versus conventional method were 11.3% and 11.9% respectively, with larger reductions in the bias (i.e. mean error) in the abundances of Oak, Cedar/Fir, and Western Hardwood PFTs (ranging from 2.0% to 7.8%). At the hectare scale around four flux towers in the Southern Sierra Mountains, the overall composition improved from an RMSE of 18.2% (5.0–24.2% for individual PFTs) to an RMSE of 9.5% (3.3–13.2% for individual PFTs). Downgrading AVIRIS to 30 m resolution resulted in a reduction in accuracy of the constrained method to an RMSE of 12.7% (0–23.7%) with <1% change in the bias for all tree and shrub PFTs. Our results demonstrate that imaging spectrometry measurements from planned satellite missions such as HyspIRI, EnMAP (Environmental Mapping and Analysis Program), and HISUI (Hyper-spectral Imager SUIte) can provide important and much-needed information about fine-scale heterogeneity in the composition of plant canopies for constraining and improving terrestrial ecosystem and biosphere model simulations of regional- and global-scale vegetation dynamics and function. • Imaging spectrometry used to estimate fine-scale terrestrial ecosystem composition • Analysis conducted over an ecologically diverse California landscape • Sub-pixel PFT abundances accurate to an average RMSE of 11.3% and Bias −2.4 to 3.1%. • Method provides a framework for improved terrestrial biosphere model simulations. [ABSTRACT FROM AUTHOR]

Abstract The thermal domain (TIR; 2.5–15 μm) delivers unique measurements of plant characteristics that are not possible in other parts of the electromagnetic spectrum. However, these TIR measurements have largely been restricted to laboratory leaf level or coarse spatial resolutions due to the lack of suitable data from airborne and spaceborne instruments. The airborne Hyperspectral Thermal Emission Spectrometer (HyTES) provides an opportunity to retrieve high spectral resolution emissivity and land surface temperature (LST) that can be exploited for canopy level vegetation research. This study is a high spatial resolution analysis of plant species' emissivity and LST using HyTES imagery acquired in the Huntington Botanical Gardens on 2014 July 5 and 2016 Jan 25. Leaf and canopy emissivity variation was identified among 24 plant species and used to determine leaf to canopy scaling capabilities. HyTES LST patterns among species and dates were quantified and correlated to LiDAR derived tree canopy attributes. At the leaf scale, one third of the species showed distinct spectral separation from other species. However, at the canopy scale most species were not spectrally separable. Random forest classification demonstrates the high level of confusion between species with overall accuracies <40%. LST data, derived from TIR measurements, showed that species exhibited significantly different distributions between dates and species. These distributions were largely explained by canopy structure (e.g. tree height and canopy density) and composition of neighboring pixels (e.g. presence of pavement versus trees). While species do not exhibit unique emissivity signatures at the canopy level, the LST variation among species provides a stronger understanding of LST variability in coarser resolution TIR imagery. This study represents the analysis of vegetation characteristics using the NASA's HyTES TIR sensor, opening the door for future remote sensing vegetation studies that include using the recently launched ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission. Highlights • Used HyTES to retrieve canopy high spectral emissivity and temperature. • One third of species showed distinct spectral separation with leaf emissivity. • The majority of species were not spectrally separable with canopy emissivity. • Canopy temperature was significantly different between dates and species. • Temperature patterns linked to canopy structure and neighbors derived from LiDAR. [ABSTRACT FROM AUTHOR]

Abstract In this study, we revised a two source energy balance model (G c -TSEB) that is based on canopy conductance and soil moisture in evapotranspiration (ET) estimation. We estimate soil evaporation (E) using the complementary concept and soil surface temperature, therefore, the revised G c -TSEB requires no soil moisture as input. We tested G c -TSEB at 10 flux sites under various land cover and climate conditions. The flux-calibrated G c -TSEB performed well in ET predictions, with determinant coefficients (R2) larger than 0.9 at most of the sites. The modeled transpiration was highly correlated with the Gross Primary Production, indicating the usefulness of the model in ET partitioning. More importantly, G c -TSEB can be calibrated against the land surface temperature (LST), which is operationally available using remote sensing technique. Overall, daily ET that was predicted by the LST-calibrated G c -TSEB generally matched the trends of the ET measurements at most of the sites, and R2 range from 0.63 to 0.93, with a median of 0.80, at all sites. Transpiration estimation was highly consistent with the simulations from the flux-calibrated model. Moreover, in vegetated surfaces, soil evaporation was estimated reasonably well using the LST-calibrated G c -TSEB. However, positive biases are prevalent when vegetated fraction is smaller than 0.2, especially in cold regions because of the dramatic differences in evaporation process between summer and winter. To improve model performances, calibrating G c -TSEB model in separated periods and using prior knowledge as constraints proved to be useful. Highlights • A two-source ET model is revised and evaluated under various climate conditions • Soil surface temperature instead of soil moisture is used to model soil evaporation • The model can be calibrated by remotely sensed LST [ABSTRACT FROM AUTHOR]

One of the most common approaches to reducing the environmental impact of nitrogen (N) fertilisation in intensive agrosystems is to adjust the N input of the crop requirement. This adjustment is frequently related to the nitrogen nutrition index (NNI) based on the concepts of the critical and actual N absorbed (kg/ha) in the crop canopy (respectively, N C and CNC). Accurate estimation of the N C and CNC at the field scale over large areas based on freely available satellite imagery is thus a key issue to address. Relying on a large dataset of farmers' fields, this study highlights the high correlation (R 2  = 0.90) between the wheat CNC and canopy chlorophyll content ( CCC ) retrieved from Sentinel-2 (S2) with an Artificial Neural Network (ANN). The estimation is related to errors of 4 and 21 kg/ha (depending on the growing stage), which is a promising result for evaluating the NNI. There are four major outcomes from this result: (i) the importance of working at the canopy level; (ii) the independence of the relationship to the considered cultivars; (iii) the dependence of the relationship on the growing stage; and (iv) the potential to use only the 10 m S2 bands, opening the way for precision agriculture. In parallel, estimation accuracies were investigated for the three biophysical variables (BV) related to the CNC and N C , i.e., the green area index ( GAI ), leaf chlorophyll content ( Cab ) and CCC . From this analysis, the added value of the red-edge bands for improving the estimation of the 3 BVs of interest was quantified as was the performance reduction related to the field heterogeneity. [ABSTRACT FROM AUTHOR]

A novel approach to characterize the physiological conditions of plants from hyperspectral remote sensing data through the numerical inversion of a light version of the SCOPE model is proposed. The combined retrieval of vegetation biochemical and biophysical parameters and Sun-induced chlorophyll fluorescence ( F ) was investigated exploiting high resolution spectral measurements in the visible and near-infrared spectral regions. First, the retrieval scheme was evaluated against a synthetic dataset. Then, it was applied to very high resolution (sub-nanometer) canopy level spectral measurements collected over a lawn treated with different doses of a herbicide (Chlorotoluron) known to instantaneously inhibit both Photochemical and Non-Photochemical Quenching (PQ and NPQ, respectively). For the first time the full spectrum of canopy F , the fluorescence quantum yield (Φ F ), as well as the main vegetation parameters that control light absorption and reabsorption, were retrieved concurrently using canopy-level high resolution apparent reflectance ( ρ * ) spectra. The effects of pigment content, leaf/canopy structural properties and physiology were effectively discriminated. Their combined observation over time led to the recognition of dynamic patterns of stress adaptation and stress recovery. As a reference, F values obtained with the model inversion were compared to those retrieved with state of the art Spectral Fitting Methods (SFM) and SpecFit retrieval algorithms applied on field data. Φ F retrieved from ρ * was eventually compared with an independent biophysical model of photosynthesis and fluorescence. These results foster the use of repeated hyperspectral remote sensing observations together with radiative transfer and biochemical models for plant status monitoring. [ABSTRACT FROM AUTHOR]

Clumping index (CI) is a measure of foliage aggregation relative to a random distribution of leaves in space. The CI can help with estimating fractions of sunlit and shaded leaves for a given leaf area index (LAI) value. Both the CI and LAI can be obtained from global Earth Observation data from sensors such as the Moderate Resolution Imaging Spectrometer (MODIS). Here, the synergy between a MODIS-based CI and a MODIS LAI product is examined using the theory of spectral invariants, also referred to as photon recollision probability (‘ p -theory’), along with raw LAI-2000/2200 Plant Canopy Analyzer data from 75 sites distributed across a range of plant functional types. The p- theory describes the probability ( p -value) that a photon, having intercepted an element in the canopy, will recollide with another canopy element rather than escape the canopy. We show that empirically-based CI maps can be integrated with the MODIS LAI product. Our results indicate that it is feasible to derive approximate p -values for any location solely from Earth Observation data. This approximation is relevant for future applications of the photon recollision probability concept for global and local monitoring of vegetation using Earth Observation data. [ABSTRACT FROM AUTHOR]

The temporal dynamics of optimum stomatal conductance ( g smax ), as well differences between C 3 and C 4 crops, have rarely been considered in previous remote sensing (RS)-based Jarvis-type canopy conductance ( G c ) models. To address this issue, a RS-based two-leaf Jarvis-type G c model, RST- G c , was optimized and validated for C 3 and C 4 crops using 19 crop flux sites across Europe, North America, and China. RST- G c included restrictive functions for air temperature, vapor pressure deficit, and soil water deficit, and it used satellite-retrieved NDVI to formulate the temporal variation of g smax defined at a photosynthetic photon flux density (PPFD) of 2000 μmol m −2 s −1 ( g sm, 2000 ). Results showed that the parameters of RST-G c differed between C 3 and C 4 crops. RST- G c successfully simulated variations in Penman–Monteith (PM)-derived daytime G c with R 2  = 0.57 for both C 3 and C 4 crops. RST-G c was incorporated into a revised evapotranspiration (ET) model and a new gross primary productivity (GPP) model. The two models were validated at 19 crop flux sites. Daily mean inputs were generally incorporated into a PM approach to model daily transpiration. This is inappropriate because available energy and stomatal conductance vary significantly on a diurnal basis, with both non-linearly regulating transpiration rate. The PM approach with daily mean inputs produced unreasonable transpiration rate estimates. Efforts were made in the revised ET model (denoted as RS-WBPM2), which was modified from the water balance based RS-PM (RS-WBPM) model of Bai et al. (2017), to address this issue by calculating transpiration using daytime inputs. The photosynthesis-based stomatal conductance model, developed by Ball et al. (1987a) and improved by Leuning (1995) (BBL model), was inverted to calculate GPP using canopy conductance; the inverted model was denoted as IBBL. Cross validation showed good agreement between flux tower measurements and modeled ET (R 2  = 0.79, RMSE (root mean standard error) = 20.66 W m −2 for daily ET and R 2  = 0.87, RMSE = 15.32 W m −2 for 16-day ET) and GPP (R 2  = 0.83, RMSE = 2.49 gC m −2 d −1 for daily GPP and R 2  = 0.86, RMSE = 1.96 gC m −2 d −1 for 16-day GPP) for the two models. Within-site validations demonstrated the successful performance of the two models at 18 sites (albeit with one outlier). Inter-site variations in ET and GPP were also successfully reproduced by the models. NDVI-derived g sm, 2000 outperformed the fixed g sm, 2000 in both ET and GPP estimates. The results imply that the RS-WBPM2 and IBBL models are useful tools for modeling regional and global ET and GPP. [ABSTRACT FROM AUTHOR]

Rugged terrain distorts optical remote sensing signals, and land-cover classification and biophysical parameter retrieval over mountainous regions must account for topographic effects. Therefore, topographic correction is a prerequisite for many remote sensing applications. In this study, we proposed a semi-physically based and simple topographic correction method for vegetation canopies based on path length correction (PLC). The PLC method was derived from the solution to the classic radiative transfer equation, and the influence of terrain on the radiative transfer process within the canopy is explicitly considered, making PLC physically sound. The radiative transfer equation was simplified to make PLC mathematically simple. Near-nadir observations derived from a Landsat 8 Operational Land Imager (OLI) image covering a mountainous region and wide field-of-view observations derived from simulation using a canopy reflectance model were combined to test the PLC correction method. Multi-criteria were used to provide objective evaluation results. The performances were compared to that of five other methods: CC, SCS + C, and SE, which are empirical parameter-based methods, and SCS and D-S, which are semi-physical methods without empirical parameter. All the six methods could significantly reduce the topographic effects. However, SCS showed obvious overcorrection for near-nadir observations. The correction results from D-S showed an obvious positive bias. For near-nadir observations, the performance of PLC was comparable to the well-validated parameter-based methods. For wide field-of-view observations, PLC obviously outperformed all other methods. Because of the physical soundness and mathematical simplicity, PLC provides an efficient approach to correct the terrain-induced canopy BRDF distortion and will facilitate the exploitation of multi-angular information for biophysical parameter retrieval over mountainous regions. [ABSTRACT FROM AUTHOR]

Monitoring grazing activities on grassland is crucial for ensuring sustainable grassland development and for protecting it from grazing-led degradation. The Leaf Area Index (LAI), which measures leaf coverage over a surface area, is commonly used as a proxy for grassland condition. However, current studies focus on the year-round or seasonal aggregated LAI change rather than the change that can be attributed explicitly to grazing, which is the important indicator for quantifying grassland grazing. This paper presents a new exponential growth function under grazing with an estimation algorithm, the purpose of which is to extract grazing-led LAI changes for every 8 days' satellite observations. All the analyses are based on the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD15A2H products. An improved MODIS LAI and an expected LAI are produced separately, considering both current and previous grazing-led LAI changes. The differences between expected LAI and improved LAI are then converted to the equivalent carbon mass of grazed material. This grazed carbon mass is then aggregated within the growing season, and compared with the expected carbon mass consumed by livestock (calculated from statistics yearbooks). In addition, Net Primary Productivity (NPP) is produced using the improved LAI, simulated by a Light Use Efficiency with Vegetation Photosynthesis Model (LUE-VPM). This is compared with the NPP produced by LUE-VPM based on original MODIS LAI, MODIS NPP products (MOD17A2H) and grassland monitoring stations' in situ measured data. Results show that the NPP calculated from the improved LAI is statistically the same as in situ converted NPP with a p-value equalling 0.998 (the RMSE between the two is 97.77 gC/m 2 ). Conversely, the p-value between converted in situ measured carbon mass and the MODIS NPP product is 0.011 (the RMSE between the two is 133.98 gC/m 2 ), indicating they are statistically different. The results detailed in this paper provide precise and almost real-time grassland grazing monitoring information for policy makers managing grassland. [ABSTRACT FROM AUTHOR]

The clumping index (CI) characterizes the grouping of foliage relative to a random spatial distribution of leaves and is an important structural parameter for plant canopies that can influence canopy radiation regimes. Consequently, the CI is very useful for ecological and meteorological models. One method used to retrieve the CIs of plant canopies is to construct a linear relationship between the CI and the normalized difference between hotspot and dark spot (NDHD) angular index. This method requires a particularly accurate reconstruction of hotspot signatures, which are difficult to measure. In this study, we propose a framework to retrieve CIs from Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF) parameters, which are generally based on linear CI-NDHD equations. The main algorithm is designed to retrieve CIs in the closed interval [0.33, 1.00]. This range is derived from the CI-NDHD equations and is thus called as the physical range here, although a modified lower boundary can be implemented in the future if necessary. If CIs are outside of this range, then a backup algorithm is designed to reprocess these so-called outlier CIs. The hotspot-adjusted version of the RossThick-LiSparseReciprocal (RTLSR) model (i.e., the RTCLSR model) is employed to reconstruct the hotspot signatures for the MODIS BRDF parameters. This method simplifies the hotspot reconstruction by using two hotspot parameters that are not distinctly scale-dependent particularly in the context of an inhomogeneous coarse spatial resolution. To evaluate this algorithm framework, we collect dozens of global field-measured CIs and calculate their determination coefficient ( R 2 ), root mean square error (RMSE) and bias relative to MODIS CIs derived using both the main algorithm and the backup algorithm. Our results show that this framework can derive MODIS CIs with a high accuracy (i.e., R 2  = 0.80 (0.72), RMSE = 0.07 (0.12), bias = −0.03 (−0.10)) using the main (backup) algorithms and that it shows promise for various ecological applications, especially in combination with the leaf area index (LAI). [ABSTRACT FROM AUTHOR]

The upcoming Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) mission will offer prospects for mapping and monitoring biomass and carbon of terrestrial ecosystems over large areas using photon counting LiDAR data. In this paper, we aim to develop a methodology to derive terrain elevation and vegetation canopy height from test-bed sensor data and further pre-validate the capacity of the mission to meet its science objectives for the ecosystem community. We investigated a novel methodological framework with two essential steps for characterizing terrain and canopy height using Multiple Altimeter Beam Experimental LiDAR (MABEL) data and simulated ICESat-2 data with various vegetation conditions. Our algorithm first implements a multi-level noise filtering approach to minimize noise photons and subsequently classifies the remaining photons into ground and top of canopy using an overlapping moving window method and cubic spline interpolation. Results of noise filtering show that the design of the multi-level filtering process is effective to identify background noise and preserve signal photons in the raw data. Moreover, calibration results using MABEL and simulated ICESat-2 data share similar trends with the retrieved terrain being more accurate than the retrieved canopy height, and the nighttime results being better than corresponding daytime results. Compared to the results of simulated ICESat-2 data, MABEL data achieve lower accuracy for ground and canopy heights in terms of root mean square error (RMSE), which may partly result from the inconsistency between MABEL and reference data. Specifically, simulated ICESat-2 data using 115 various nighttime and daytime scenarios, yield average RMSE values of 1.83 m and 2.80 m for estimated ground elevation, and 2.70 m and 3.59 m for estimated canopy height. Additionally, the accuracy assessment of percentile heights of simulated ICESat-2 data further substantiates the robustness of the methodology from different perspectives. The methodology developed in this study illustrates plausible ways of processing the data that are structurally similar to expected ICESat-2 data and holds the potential to be a benchmark for further method adjustment once genuine ICESat-2 are available. [ABSTRACT FROM AUTHOR]

With the increasing availability of spectral sensors and consumer-grade data processing software, a democratization of imaging spectroscopy is taking place. In particular, novel lightweight 2D spectral imagers in combination with UAVs are increasingly being adapted for imaging spectroscopy. In contrast to traditional line-scanners, these sensors capture spectral information as a 2D image within every exposure. With computer vision algorithms embedded in consumer grade software packages, these data can be processed to hyperspectral digital surface models that hold spectral and 3D spatial information in very high resolution. To understand the spectral signal, however, one must comprehend the complexity of the capturing and data processing process in imaging spectroscopy with 2D imagers. This study establishes the theoretical background to comprehend the properties of spectral data acquired with 2D imagers and investigates how different data processing schemes influence the data. To improve the interpretability of a spectral signal derived for an area of interest (AOI), the specific field of view is introduced as a concept to understand the composition of pixels and their angular properties used to characterize a specific AOI within a remote sensing scene. These considerations are applied to a multi-temporal field study carried out under different illumination conditions in a barley field phenotyping experiment. It is shown that data processing significantly affects the angular properties of the spectral data and influences the apparent spectral signature. The largest differences are found in the red domain, where the signal differs by approximately 10% relative to a single nadir image. Even larger differences of approximately 14% are found in comparison with ground-based non-imaging field spectrometer measurements. The differences are explained by investigating the interaction between the angular properties of the data and canopy anisotropy, which are wavelength and growth stage dependent. Additionally, it is shown that common vegetation indices cannot normalize the differences and that the retrieval of chlorophyll is affected. In conclusion, this study helps to understand the process of imaging spectroscopy with 2D imagers and provides recommendations for future missions. [ABSTRACT FROM AUTHOR]

Large-scale information on several vegetation properties ('plant traits') is critical to assess ecosystem functioning, functional diversity and their role in the Earth system. Hyperspectral remote sensing of plant canopies offers a key tool to map multiple plant traits. However, we are still lacking generalized methods to translate hyperspectral reflectance into a suite of relevant plant traits across biomes, land cover and sensor types. The absence of globally representative data sets and the gap between the available reflectance data with corresponding in-situ measurements have hampered such approaches. In recent years, the scientific community acquired multiple data sets encompassing canopy hyperspectral reflectance and plant traits from different plant types and sensors. To combine these heterogeneous data sets, we propose three multi-trait modeling approaches based on Convolutional Neural Networks (CNNs) to simultaneously infer a broad set of 20 structural and chemical traits (e.g. leaf mass per area, leaf area index, pigments, nitrogen). The performance of these multi-trait CNN models predicting these traits was compared against single-trait CNN as well as single-trait partial least squares regression (PLSR). We found that the multi-trait CNNs performances significantly increased from single-trait CNNs (nRMSE = 0.027–19.61%) and the state-of-the-art PLSR models (nRMSE = 1.94–40.07%) across a broad range of vegetation types (crops, forest, tundra, grassland, shrubland) and sensor types. Thus, providing a single model for multiple traits not only proved to be computationally more efficient, but also more accurate, since it enabled the model to incorporate traits' co-variation. Despite the data heterogeneity of the merged data set, our models performances' were comparable or exceeded those of previous studies. Overall, this study highlights the potential of weakly supervised approaches to overcome the scarcity of in-situ measurements and take a step forward in creating efficient predictive models of multiple biochemical and biophysical vegetation properties. [Display omitted] • We retrieve a set of 20 plant traits with a CNN model from canopy spectra. • The multi-trait model covers various ecosystems, vegetation types and sensors. • The multi-trait model outperforms partial least squares regression approaches. • Despite sparsity, data heterogeneity facilitated high retrieval performance. • Data sharing and collaboration advances the development of transferable models. [ABSTRACT FROM AUTHOR]

The vertical heterogeneity of leaf biophysical and biochemical properties may have a large effect on the bidirectional reflectance and fluorescence of vegetation canopies. This has implications for the interpretation of remote sensing data. We developed a model for light interaction and energy balance in vegetation canopies in which leaf biophysical and biochemical properties vary in the vertical. The model mSCOPE is an extension of the Soil-Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model, which simulates spectral and bidirectional reflectance, fluorescence, and photosynthesis of vertically heterogeneous vegetation canopies. The modelling of radiative transfer in mSCOPE is based on the classical SAIL theory. A solution to the radiative transfer equation for multi-layer canopies is given, which allows calculating top-of-canopy (TOC) reflectance and the flux profile. The latter is used for the simulation of fluorescence emission and photosynthesis of every leaf through the leaf radiative transfer model Fluspect and a biochemical model. The radiative transfer of fluorescence in multi-layer canopies is solved numerically in mSCOPE to obtain TOC bidirectional fluorescence. The significant effect of vertical heterogeneity of leaf properties on TOC reflectance, fluorescence and photosynthesis is demonstrated by different scenarios with customized vertical profiles of leaf chlorophyll content and leaf water content, and also with measured vertical profiles of leaf chlorophyll content in corn canopies. A preliminary validation of the reflectance calculating routine of mSCOPE is conducted by comparing measured and simulated TOC reflectance spectra of the corn canopies. We conclude that it is important to consider the vertical heterogeneity of leaf properties for the prediction of reflectance, fluorescence and photosynthesis. The model mSCOPE could serve as a tool to better understand vertically heterogeneous vegetation canopies. [ABSTRACT FROM AUTHOR]

The forest above-ground biomass (AGB) and spatial distribution of vegetation elements have profound effects on the productivity and biodiversity of terrestrial ecosystems. In this paper, we evaluated biomass estimation from L-band Synthetic Aperture Radar (SAR) data acquired by National Aeronautics and Space Administration (NASA) Uninhabited Aerial Vehicle SAR (UAVSAR) and the improvement of accuracy by adding canopy height information derived from stereo imagery acquired by Japan Aerospace Exploration Agency (JAXA) Panchromatic Remote Sensing Instrument for Stereo Mapping (PRISM) on-board the Advanced Land Observing Satellite (ALOS). Various models for prediction of forest biomass from UAVSAR data were investigated at pixel sizes of 1/4 ha (50 m × 50 m) and 1 ha. The variance inflation factor (VIF) was calculated for each of the explanatory variables in multivariable regression models to assess the multi-collinearity between explanatory variables. In addition, the t - and p -values were used to interpret the significance of the coefficients of each explanatory variables. The R 2 , Root Mean Square Error (RMSE), bias and Akaike information criterion (AIC), and leave-one-out cross-validation (LOOCV) and bootstrapping were used to validate models. At 1/4-ha scale, the R 2 and RMSE of biomass estimation from a model using a single track of polarimetric UAVSAR data were 0.59 and 52.08 Mg/ha. With canopy height from PRISM as additional independent variable, R 2 increased to 0.76 and RMSE decreased to 39.74 Mg/ha (28.24%). At 1-ha scale, the RMSE of biomass estimation based on UAVSAR data of a single track was 39.42 Mg/ha with a R 2 of 0.77. With the canopy height from PRISM, R 2 increased to 0.86 and RMSE decreased to 29.47 Mg/ha (20.18%). The models using UAVSAR data alone underestimated biomass at levels above ~ 150 Mg/ha showing the saturation phenomenon. Adding canopy height from PRISM stereo imagery significantly improved the biomass estimation and elevated the saturation level in estimating biomass. Combined use of UAVSAR data acquired from opposite directions (odd and even tracks) slightly improved the biomass estimation. Combined use of UAVSAR data acquired from opposite directions (odd and even tracks) slightly improved the biomass estimation at 1/4-ha scale, R 2 increased from 0.59 to 0.66 and RMSE reduced from 52.08 to 48.57 Mg/ha. Averaging multiple acquisitions of UAVSAR data from the same look azimuth direction did not improve biomass estimation. A biomass map derived from NASA's LVIS (Laser Vegetation Imaging System) waveform data was used as a reference for evaluation of the biomass maps from these models. The study has also shown that the errors decreased when deciduous, evergreen, and mixed forests were modeled separately but the improvement was not significant. [ABSTRACT FROM AUTHOR]

Accurate estimation of light use efficiency (LUE) of plant canopies is essential for calculating gross primary productivity (GPP) using LUE models and is also useful for calibrating process-based models for regional and global applications. A promising method for estimating LUE is through remote sensing of the photochemical reflectance index (PRI). However, there are internal (e.g. pigment concentrations) and external factors (e.g. environmental conditions and sun-target-view geometry) that affect PRI signals. Considering the reflectance difference between sunlit and shaded leaves, the ratio of observed canopy reflectance to leaf reflectance is used to represent the observed fraction of sunlit leaves, and the observed fraction of shaded leaves is calculated with a geometrical optical model. Thus, a canopy-level PRI observation is separated into sunlit and shaded PRI values, and a two-leaf canopy PRI (PRI t ) is calculated as sum of these two values weighted by their respective sunlit and shaded leaf area indices. The usefulness of PRI t in assessing the canopy-level LUE is evaluated with automated multi-angle PRI observations acquired on a flux tower from April to September 2013 over a sub-tropical coniferous forest in southern China. In each 15-minute observation cycle, PRI is observed at four view zenith angles fixed at (37°, 47°, 57°) or (42°, 52°, 62°) and the instantaneous solar zenith angle in the azimuth angle range from 45° to 325° (from the geodetic north). In both the half-hourly and daily time steps, PRI t can effectively improve (> 50% and > 35% increases in R 2 , respectively) the ability as a proxy of LUE derived from the tower flux measurements over the big-leaf PRI taken as the arithmetic average of the multi-angle measurements in a given time interval. In the dry season from July to September, correlations of PRI with LUE at daily time steps are much stronger in the two-leaf case than in the big-leaf case. The correlation between PRI t and LUE is the strongest ( R 2 = 0.785, p < 0.001) in July. PRI t is very effective in detecting the light and low-moderate drought stress on LUE at half-hourly time steps, while ineffective in detecting severe atmospheric water and heat stress, which is probably due to alternative radiative energy sink, i.e. photorespiration. Overall, the two-leaf approach well overcomes some external effects (e.g. sun-target-view geometry) that interfere with PRI signals. [ABSTRACT FROM AUTHOR]

Accurate three-dimensional information on canopy structure contributes to better understanding of radiation fluxes within the canopy and the physiological processes associated with them. Small-footprint airborne laser scanning (ALS) data proved valuable for characterising the three-dimensional structure of forest canopies and the retrieval of biophysical parameters such as plant and leaf area index (PAI and LAI), fractional cover or canopy layering. Nevertheless, few studies analysed combined occluded and observed canopy elements in dense vegetation as a result of airborne laser scanning geometries. The occluded space contains a substantial amount of vegetation elements (i.e. leaf, needle and wood material), which are missing in the analysis of the three-dimensional canopy structure. Consequently, this will lead to erroneous retrieval of biophysical parameters. In this study, we introduce a voxel traversal algorithm to characterise ALS observation patterns inside a voxel grid. We analyse the dependence of occluded and unobserved canopy volume on pulse density, flight strip overlap and season of overflight in a temperate mixed forest. ALS measurements under leaf-on and leaf-off conditions were used. For cross-comparison purposes, terrestrial laser scanning (TLS) measurements on a 50 ×50 m 2 subplot under leaf-on conditions were used. TLS acquisitions were able to depict the three-dimensional structure of the forest plot in high detail, ranging up to the top-most canopy layer. Our results at 1 m voxel size show that even with the highest average pulse density of 11 pulses/m 2 , at least 25% of the forest canopy volume remains occluded in the ALS acquisition under leaf-on conditions. Comparison with TLS acquisitions further showed that roughly 28% of the vegetation elements detected by the TLS acquisitions were not detected by the ALS system due to occlusion effects. By combining leaf-on and leaf-off acquisitions, we were able to recover roughly 7% of the occluded vegetation elements from the leaf-on acquisition. We find that larger flight strip overlap can significantly increase the amount of observed canopy volume due to the added observation angles and increased pulse density. [ABSTRACT FROM AUTHOR]

Remote sensing of plant carbon uptake, or gross primary production (GPP), in a repeatable and consistent manner remains a key element of a comprehensive understanding of the role of vegetation within the global carbon cycle. To further this understanding at a landscape level or global scale, accurate remote sensing of photosynthetic light-use efficiency (LUE) is required to understand photosynthetic down-regulation and environmental constraints to plant photosynthesis. The past decade has seen advances in detecting both leaf- and canopy-level physiological stress behaviours using the photochemical reflectance index (PRI), a narrow-waveband normalized difference index that relates LUE to a xanthophyll-induced absorption feature at 531 nm. To date, however, much of this research has occurred using top of canopy measurements, while our understanding of the vertical distribution of LUE within the crown is limited. In this study, we demonstrate an approach which could be used to scale photosynthetic behaviour of vegetation vertically and horizontally using estimates of vertical canopy structure obtained from terrestrial Light Detection and Ranging (LiDAR) data to predict proportions of shaded and sunlit canopy which are then linked to predictions of LUE. We apply the approach over a mature Aspen study site located in central Saskatchewan, Canada utilising full-waveform LiDAR data provided by the ground-based laser scanner system and canopy spectra obtained by the AMSPEC II spectro-radiometer. Agreement between predictions of Gross Primary Productivity (GPP) using the developed approach compared to independent observations was highly significant at hourly intervals (R 2 = 0.80, p < 0.01) under clear sky conditions. A range of LUE vertical profiles for different stand structures across the growing season were developed providing estimations of how crown structure can impact LUE vertically in the crown. We conclude with a recommendation for ongoing research to verify these types of trends using concurrently acquired, independently derived leaf LUE from photosynthesis light-response curves, and forest structure variation from LiDAR, to provide a more exact quantification of these patterns. [ABSTRACT FROM AUTHOR]

Empirical and theoretical evidence has shown that the scattering and absorptance of a homogeneous leaf canopy with black soil can be modeled using simple algebraic combinations of two spectrally invariant parameters (the recollision probability and the canopy interceptance) and one spectrally dependent quantity (the single scattering albedo of an average phytoelement). This study generalizes such results for vegetation canopies composed of one or more types of phytoelements and with a reflective background surface also composed of one or more materials, all of which are treated as endmembers of a nonlinear spectral mixture. The conservative radiation field of a canopy is represented by a finite state Markov chain, such that for a vegetation-surface medium composed of m endmembers, the model is thereby parameterized in terms of an m -length vector of endmember interceptances and an m th-order square matrix containing probabilities of photon recollision amongst the endmembers. Comparisons of some instances of the model with radiative transfer based approximations showed how the latter may overestimate canopy reflectance and transmittance. The two-endmembers model was also compared with Monte Carlo simulations for spherically-oriented leaf canopies with reflective soil. Results indicated that the analytical model accurately describes the radiation field for a wide range of soil reflectance and LAI values. Simulations also showed that while the zenith angle of the illumination source has a small influence on the recollision probabilities, the LAI value mainly determines these through distinct functional relationships. The application of the model to estimate the canopy radiation field of two coniferous species located on the Mexico City Conservation Land is also demonstrated using airborne hyperspectral measurements. [ABSTRACT FROM AUTHOR]

Clumping index (CI) describes the spatial distribution pattern of foliage, and is a critical parameter to characterize the terrestrial ecosystem and model land-surface processes. Global and regional scale CI maps have been generated from POLDER, MODIS, and MISR sensors based on an empirical relationship with the normalized difference between hotspot and darkspot (NDHD) index by previous studies. However, the hotspot, darkspot, and CI values can be considerably different from different bidirectional reflectance distribution function (BRDF) models and solar zenith angles (SZA). In this study, we evaluated the effects of different configurations of BRDF models and SZA values on CI estimation using the NDHD method. CI maps derived from MISR and MODIS were compared with reference data at the VALERI sites. Results show that for moderate to least clumped vegetation (CI > 0.5), CI values retrieved with the observational SZA agree well with field measurements, while SZA = 0° underestimates, and SZA = 60° overestimates. For highly clumped (CI < 0.5) and sparsely vegetated areas (FCOVER < 25%), the Ross-Li model with 60° SZA is recommended for CI estimation. The best NDHD configuration was used to estimate a 15-year time series CI from MODIS BRDF data. The time series CI shows a reasonable seasonal trajectory, and varies consistently with the MODIS leaf area index (LAI). This study enables better usage of the NDHD method for CI estimation, and can be a useful reference for research on CI validation. [ABSTRACT FROM AUTHOR]

A model to simulate radiative transfer (RT) of sun-induced chlorophyll fluorescence (SIF) of three-dimensional (3-D) canopy, FluorWPS (Fluorescence model with Weighted Photon Spread method), was proposed and evaluated. The inclusion of fluorescence excitation was implemented with the ‘weight reduction’ and ‘photon spread’ concepts based on Monte Carlo ray-tracing technique. The radiative transfer of SIF in a 3-D canopy was simulated in a physically-based and rigorous way so that various contributions from the radiative process can be accurately quantified. The physical mechanism behind the spectral and angular distributions of canopy SIF was analyzed based on FluorWPS. SIF anisotropy is an intrinsic property of the vegetative surface and it can be significantly influenced by the canopy structure. The performance of the model was evaluated with field measurements and systematically compared with an established RT model of canopy SIF. Especially the detailed comparison with the RT model for four canopy scenes demonstrates that FluorWPS is capable of faithfully reproducing the spectral and angular distributions of SIF, with the coefficient of determination (R 2 ) and root mean square error (RMSE) being higher than 0.92 and lower than 0.066 W·m − 2 ·sr − 1 ·μm − 1 , respectively, for the red peak, and higher than 0.92 and lower than 0.16 W·m − 2 ·sr − 1 ·μm − 1 for the far-red peak. The independent nature of the model's canopy realization from the radiative processes is promising for its wide applications for scientific investigations. [ABSTRACT FROM AUTHOR]

Marsh canopy structure was mapped yearly from 2009 to 2012 in the Barataria Bay, Louisiana coastal region that was impacted by the 2010 Deepwater Horizon (DWH) oil spill. Based on the previously demonstrated capability of NASA's UAVSAR polarimetric synthetic aperture radar (PolSAR) image data to map Spartina alterniflora marsh canopy structure, structure maps combining the leaf area index (LAI) and leaf angle distribution (LAD, orientation) were constructed for yearly intervals that were directly relatable to the 2010 LAI-LAD classification. The yearly LAI-LAD and LAI difference maps were used to investigate causes for the previously revealed dramatic change in marsh structure from prespill (2009) to postspill (2010, spill cessation), and the occurrence of structure features that exhibited abnormal spatial and temporal patterns. Water level and salinity records showed that freshwater releases used to keep the oil offshore did not cause the rapid growth from 2009 to 2010 in marsh surrounding the inner Bay. Photointerpretation of optical image data determined that interior marsh patches exhibiting rapid change were caused by burns and burn recovery, and that the pattern of 2010 to 2011 LAI decreases in backshore marsh and extending along some tidal channels into the interior marsh were not associated with burns. Instead, the majority of 2010 to 2011 shoreline features aligned with vectors displaying the severity of 2010 shoreline oiling from the DWH spill. Although the association is not conclusive of a causal oil impact, the coexistent pattern is a significant discovery. PolSAR marsh structure mapping provided a unique perspective of marsh biophysical status that enhanced detection of change and monitoring of trends important to management effectiveness. [ABSTRACT FROM AUTHOR]

The screening of sensitive spectral indictors is essential for quantitative diagnosis of ozone (O 3 )-induced stress in plants. Four wheat ( Triticum aestivum L.) cultivars with different degrees of O 3 -tolerance were grown under an elevated O 3 concentration (E-O 3 ) in fully open-air field conditions for two consecutive growth seasons from 2012 to 2013. The aim was to find sensitive hyperspectral indictors for real-time detection of O 3 effects. The results showed that E-O 3 caused a significant decrease in leaf thickness and pigment concentrations, resulting in a change in leaf reflectance. The effects of E-O 3 on both physiological variables and reflectance characteristics were wheat cultivar-specific, with a greater and earlier O 3 effect found in the O 3 -sensitive wheat cultivars than in the O 3 -tolerant cultivars. Spectral indices that were previously developed to detect leaf biological variables were examined, and highly correlated relations were found between the chlorophyll content and the three spectral parameters ND 705 , mND 705 and R 550 , with Pearson r values of 0.896, 0.892 and − 0.872, respectively. When independently determined data from 2013 were used to test the derived equations, the coefficients (R 2 ) of correlation between the measured and estimated chlorophyll were 0.817 ( ND 705 ), 0.833 ( mND 705 ) and 0.776 ( R 550 ); the root mean square errors (RMSE) were 0.227 ( ND 705 ), 0.228 ( mND 705 ) and 0.254 ( R 550 ) (mg kg − 1 ); and the mean relative errors (RE) were 8.7% ( ND 705 ), 8.1% ( mND 705 ) and 9.1% ( R 550 ). Furthermore, O 3 -induced changes in the three optical parameters were in accordance with the leaf chlorophyll responses in wheat. Our study suggested that the reflectance indices mND 705 , ND 705 and R 550 , especially the former two spectral indices that contained information from several bands, could help to support the diagnosis and real-time monitoring of O 3 -induced damage in wheat. To estimate the wheat yield accurately using the selected spectral indices, the filling stage was found to be the best time for measuring canopy reflectance. [ABSTRACT FROM AUTHOR]

Accurate monitoring of climate-driven expansion of low-stature shrubs in Arctic tundra requires high-resolution maps of shrub biomass that can accurately quantify the current baseline over relevant spatial and temporal extents. In this study, our goal was to use airborne lidar and imagery to build accurate high-resolution shrub biomass maps for an important research landscape in the American Arctic. In a leave-one-out cross-validation analysis, optimized lidar-derived canopy volume was a good single predictor of harvested shrub biomass (R 2 = 0.62; RMSD = 219 g m − 2 ; slope = 1.08). However, model accuracy was improved by incorporating additional lidar-derived canopy metrics and airborne spectral metrics in a Random Forest regression approach (pseudo R 2 = 0.71; RMSD = 197 g m − 2 ; slope = 1.02). The best Random Forest model was used to map shrub biomass at 0.80 m resolution across three lidar collection footprints (~ 12.5 km 2 total) near Toolik Field Station on Alaska's North Slope. We characterized model uncertainty by creating corresponding maps of the coefficient of variation in Random Forest shrub biomass estimates. We also explore potential benefits of incorporating lidar-derived topographic metrics, and consider tradeoffs inherent in employing different data sources for high-resolution vegetation mapping efforts. This study yielded maps that provide valuable, high-resolution spatial estimates of aboveground shrub biomass and canopy volume in a rapidly changing tundra ecosystem. [ABSTRACT FROM AUTHOR]

There is ongoing interest to develop remote sensing methods for mapping and monitoring the spatial distribution and biomass of mangroves. In this study, we develop a suite of methods to evaluate the combination of Landsat-8, ALOS PALSAR, and SRTM data for mapping spatial distribution of mangrove composition, canopy height, and aboveground biomass in the wide intertidal zones and coastal plains of Mimika district, Papua, Indonesia. Image segmentation followed by visual interpretation of composite PALSAR images was used to delineate mangrove areas, whereas a flexible statistical rule based classification of spectral signatures from Landsat-8 images was used to classify mangrove associations. The overall accuracy of land cover classification was 94.38% with a kappa coefficient of 0.94 when validated with field inventory data and Google Earth images. Mangrove height and aboveground biomass were mapped using the SRTM DEM, which were calibrated with field-measured data via quantile regression models. There was a strong correlation between the SRTM DEM and the 0.98 quantile of field canopy heights (H .98 ), which was used to represent the tallest trees in each of 196 10 m radius subplots (r = 0.84 and R 2 = 0.804). Model performance was evaluated through 10,000 bootstrapped simulations, producing a mean absolute error (MAE) of 3.0 m for canopy height estimation over 30 m pixels of SRTM data. Quantile regression revealed a relatively strong non-linear relationship between the SRTM derived canopy height model and aboveground biomass measured in 0.5 ha mangrove inventory plots (n = 33, R 2 = 0.46). The model results produced estimates of mean standing biomass of 237.52 ± 98.2 Mg/ha in short canopy ( Avicennia / Sonneratia ) stands to 353.52 ± 98.43 Mg/ha in mature tall canopy ( Rhizophora ) dominated forest. The model estimates of mangrove biomass were within 90% confidence intervals of area-weighted biomass derived from field measurements. When validated at the landscape scale, the difference between modeled and measured aboveground mangrove biomass was 3.48% with MAE of 105.75 Mg/ha. These results indicate that the approaches developed here are reliable for mapping and monitoring mangrove composition, height, and biomass over large areas of Indonesia. [ABSTRACT FROM AUTHOR]

The remote monitoring of plant canopies is critically needed for understanding of terrestrial ecosystem mechanics and biodiversity as well as capturing the short- to long-term responses of vegetation to disturbance and climate change. A variety of orbital, sub-orbital, and field instruments have been used to retrieve optical spectral signals and to study different vegetation properties such as plant biochemistry, nutrient cycling, physiology, water status, and stress. Radiative transfer models (RTMs) provide a mechanistic link between vegetation properties and observed spectral features, and RTM spectral inversion is a useful framework for estimating these properties from spectral data. However, existing approaches to RTM spectral inversion are typically limited by the inability to characterize uncertainty in parameter estimates. Here, we introduce a Bayesian algorithm for the spectral inversion of the PROSPECT 5 leaf RTM that is distinct from past approaches in two important ways: First, the algorithm only uses reflectance and does not require transmittance observations, which have been plagued by a variety of measurement and equipment challenges. Second, the output is not a point estimate for each parameter but rather the joint probability distribution that includes estimates of parameter uncertainties and covariance structure. We validated our inversion approach using a database of leaf spectra together with measurements of equivalent water thickness (EWT) and leaf dry mass per unit area (LMA). The parameters estimated by our inversion were able to accurately reproduce the observed reflectance (RMSE VIS = 0.0063, RMSE NIR-SWIR = 0.0098) and transmittance (RMSE VIS = 0.0404, RMSE NIR-SWIR = 0.0551) for both broadleaved and conifer species. Inversion estimates of EWT and LMA for broadleaved species agreed well with direct measurements (CV EWT = 18.8%, CV LMA = 24.5%), while estimates for conifer species were less accurate (CV EWT = 53.2%, CV LMA = 63.3%). To examine the influence of spectral resolution on parameter uncertainty, we simulated leaf reflectance as observed by ten common remote sensing platforms with varying spectral configurations and performed a Bayesian inversion on the resulting spectra. We found that full-range hyperspectral platforms were able to retrieve all parameters accurately and precisely, while the parameter estimates of multispectral platforms were much less precise and prone to bias at high and low values. We also observed that variations in the width and location of spectral bands influenced the shape of the covariance structure of parameter estimates. Our Bayesian spectral inversion provides a powerful and versatile framework for future RTM development and single- and multi-instrumental remote sensing of vegetation. [ABSTRACT FROM AUTHOR]

Nearly two decades ago, the idea of the ‘spectral invariants theory’ was put forth as a new tool to model the shortwave radiation absorbed or scattered by vegetation. The theory states that the amount of radiation absorbed by a canopy should to a great accuracy depend only on the wavelength and a wavelength-independent parameter describing canopy structure. The revolutionary idea behind this theory was that it would be possible to approximate vegetation canopy absorptance, transmittance and reflectance based on only the optical properties of foliage elements and the spectrally invariant parameter(s). This paper explains how this so-called spectral invariant is related to photon recollision probability and to canopy structural variables. Other spectral invariants were later introduced to quantify the directionality of canopy scattering. Moreover, the paper reviews the advances in the theoretical development of the photon recollision probability ( p ) concept and demonstrates some of its applications in global and local monitoring of vegetation using remote sensing data. [ABSTRACT FROM AUTHOR]

There is a well-established need within the remote sensing community for improved estimation and understanding of canopy structure and its influence on the retrieval of leaf biochemical properties. The main goal of this research was to assess the potential of optical spectral information from NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) to discriminate different canopy structural types. In the first phase, we assessed the relationships between optical metrics and canopy structural parameters obtained from LiDAR in terms of different canopy structural attributes (biomass (i.e., area under Vegetation Vertical Profile, VVP int ), canopy height and vegetation complexity). Secondly, we identified and classified different “canopy structural types” by integrating several structural traits using Random Forests (RF). The study area is a heterogeneous forest in Sierra National Forest in California (USA). AVIRIS optical properties were analyzed by means of several sets of variables, including single narrow band reflectance and 1st derivative, sub-pixel cover fractions, narrow-band indices, spectral absorption features, optimized normalized difference indices and Principal Component Analysis (PCA) components. Our results demonstrate that optical data contain structural information that can be retrieved. The first principal component, used as a proxy for albedo, was the most strongly correlated optical metric with vegetation complexity, and it also correlated well with biomass (VVP int ) and height. In conifer forests, the shade fraction was especially correlated to vegetation complexity, while water-sensitive optical metrics had high correlations with biomass (VVP int ). Single spectral band analysis results showed that correlations differ in magnitude and in direction, across the spectrum and by vegetation type and structural variable. This research illustrates the potential of AVIRIS to analyze canopy structure and to distinguish several structural types in a heterogeneous forest. Furthermore, RF using optical metrics derived from AVIRIS proved to be a powerful technique to generate maps of structural attributes. The results emphasize the importance of using the whole optical spectrum, since all spectral regions contributed to canopy structure assessment. [ABSTRACT FROM AUTHOR]

Sun-induced chlorophyll fluorescence (SIF) is known to relate directly to leaf and canopy scale photosynthesis. Retrieving SIF from space can thus provide an indication on the temporal and spatial patterns of the terrestrial gross primary productivity (GPP). Recent studies have successfully demonstrated the serendipitous retrieval of SIF from satellite remote sensing instruments originally destined to atmospheric studies. However, the finest spatial resolution achieved by these products is 0.5°, which remains too coarse for many applications, including the early detection of drought impacts on vegetation and the integration with ground GPP measurements from flux-towers. This paper proposes a methodology to spatially disaggregate the information contained within each coarse SIF pixels by using a non-linear model based on the concept of light use efficiency (LUE). The strategy involves the aggregation of high-resolution (0.05°) remote sensing biophysical variables to calibrate the downscaling model locally and independently at each time step, which can then be applied to non-aggregated data to create a new layer, denoted SIF*, with a spatial resolution of 0.05°. A global SIF* dataset is generated by applying this methodology globally to 7 years of monthly GOME-2 SIF data. SIF* is shown to be a better proxy for GPP than the original coarse spatial resolution product according to flux-tower eddy covariance measurements. Its performance is comparable to dedicated GPP products despite that (unlike SIF*) these are calibrated based on the same flux towers, driven by meteorological data and not hampered by the large noise caused by the SIF retrieval. To further illustrate the added-value of the global SIF* product, this paper also presents: (1) an ecosystem level assessment showing a considerable reduction of noise with respect to the original SIF; (2) a spatio-temporal inter-comparison with existing GPP products; and (3) estimations of global terrestrial productivity per selected vegetation types based on SIF*. [ABSTRACT FROM AUTHOR]

Photochemical reflectance index (PRI) is one of the best proxies to estimate the light use efficiency and photosynthetic activity of vegetation from remote sensing observations, especially if diurnal variations can be monitored. The calculation of PRI from leaf-level spectral reflectance measurements is unambiguous. Interpretation of the value of this index is more complicated, as it is affected by leaf structure and its carotenoid and chlorophyll content. Generally, a change in leaf-level PRI indicates a change in its photosynthetic capacity. At the scales of the canopy and beyond, various non-physiological factors modulate the leaf-level PRI signal inducing large angular and spatial variations in PRI. Specifically, previous studies have shown that within-canopy illumination variations and shadowing effects directly affect the PRI of a canopy. When observing a forest with a resolution finer than the size of a tree crown, large areas of shaded and sunlit foliage become visible. The spectral distribution of irradiance in these canopy regions is different from the average top-of-canopy irradiance used in the calculation of the canopy PRI. Thus, the leaf and canopy PRI can become decoupled. To date, no thorough analytical and empirical analysis of how the spectrally variable within-canopy light conditions cause apparent, non-physiological variation in canopy PRI has been published. In this study, we propose a new method to assess these PRI variations in structured vegetation from high spatial resolution (pixel size smaller than 1 m) imaging spectroscopy data. We used airborne imaging spectroscopy of boreal forest stands to evaluate the spectral irradiance in different locations inside the canopy and calculated a correction term for the canopy PRI estimates defined using top-of-canopy irradiance. We determined the maximum value of the correction term by sampling the most sunlit and shaded road surface locations adjacent to tree crowns. Results indicated that under the particular illumination-view geometry, irradiance variations decreased the canopy PRI by as much as 0.06 (relative change > 100%). The correction depended only slightly on atmospheric correction parameters. Finally, we reduced the illumination-related apparent variation in canopy PRI using the two-leaf canopy photosynthesis modeling scheme, canopy shadow fraction and the maximum correction term. In a test scene, the average illumination-corrected PRI was 0.027 smaller than non-corrected canopy PRI and showed no correlation with the shadow fraction, indicating a lack of down-regulation at the time of measurement. In theory, approach can be applied to all canopy level PRI measurements from towers, aircrafts and satellites under any observation geometry. However, further validation, preferably using in situ leaf reflectance data from different biomes, would be required before the algorithm can be routinely applied. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a good indicator of the land surface state. The measurement of LST is however prone to directional anisotropy which may severely affect the interpretation of the measurements if it is not corrected. This study aims at determining and describing the impact of various factors on anisotropy of continuous crops at mid-latitudes. The SCOPE (Soil Canopy Observation, Photochemistry and Energy fluxes) model is used as a data generator of directional anisotropy since it enables exploring a very large range of meteorological, biochemical and geometrical conditions. An original indicator, the standard deviation of anisotropy in principal plane, is used in order to investigate the impact of the tested variables and parameters. We found that anisotropy is, at first order, related to seasonal trends, in relation to the amount of incident radiation and the solar zenith angle. Then the geometrical structure of the canopy modifies the anisotropy (LAI, LADF, hot spot parameter) followed by the coupling between the water status of the soil and the stress of canopy. Wind speed which is known for having a significant impact on temperature level has a very limited influence on anisotropy. An analysis of the amplitude of anisotropy in the principal and perpendicular planes (from − 50° to 50° zenith) showed that anisotropy can reach up to 11 °C and ~ 3.5 °C respectively. The impact of satellite orbit on anisotropy is also discussed and it is found that, given the latitudes and the season, the anisotropy can severely affect measurements. This is particularly true when the satellite measurements are acquired in a configuration close to the solar principal plane, which often occur at low latitude. These results are of great help in the context of developing simple methods which could then be integrated into satellite data processing algorithms. [ABSTRACT FROM AUTHOR]

Radiative transfer models have long been used to characterize the foliar content at the leaf and canopy levels. However, they still do not apply well to close-range imaging spectroscopy, especially because directional effects are usually not taken into account. For this purpose, we introduce a physical approach to describe and simulate the variation in leaf reflectance observed at this scale. Two parameters are thus introduced to represent (1) specular reflection at the leaf surface and (2) local leaf orientation. The model, called COSINE (ClOse-range Spectral ImagiNg of lEaves), can be coupled with a directional–hemispherical reflectance model of leaf optical properties to relate the measured reflectance to the foliar content. In this study, we show that, when combining COSINE with the PROSPECT model, the overall PROCOSINE model allows for a robust submillimeter retrieval of foliar content based on numerical inversion and pseudo-bidirectional reflectance factor hyperspectral measurements. The relevance of the added parameters is first shown through a sensitivity analysis performed in the visible and near-infrared (VNIR) and shortwave infrared (SWIR) ranges. PROCOSINE is then validated based on VNIR and SWIR hyperspectral images of various leaf species exhibiting different surface properties. Introducing these two parameters within the inversion allows us to obtain accurate maps of PROSPECT parameters, e.g., the chlorophyll content in the VNIR range, and the equivalent water thickness and leaf mass per area in the SWIR range. Through the estimation of light incident angle, the PROCOSINE inversion also provides information on leaf orientation, which is a critical parameter in vegetation remote sensing. [ABSTRACT FROM AUTHOR]

Increasing size and abundance of lianas relative to trees are pervasive changes in Neotropical forests that may lead to reduced forest carbon stocks. Yet the liana growth form is chronically understudied in large-scale tropical forest censuses, resulting in few data on the scale, cause, and impact of increasing lianas. Satellite and airborne remote sensing provide potential tools to map and monitor lianas at much larger spatial and rapid temporal scales than are possible with plot-based forest censuses. We combined high-resolution airborne imaging spectroscopy and a ground-based tree canopy census to investigate whether tree canopies supporting lianas could be discriminated from tree canopies with no liana coverage. Using support vector machine algorithms, we achieved accuracies of nearly 90% in discriminating the presence–absence of lianas, and low error (15.7% RMSE) when predicting liana percent canopy cover. When applied to the full image of the study site, our model had a 4.1% false-positive error rate as validated against an independent plot-level dataset of liana canopy cover. Using the derived liana cover classification map, we show that 6.1%–10.2% of the 1823 ha study site has high-to-severe (50–100%) liana canopy cover. Given that levels of liana infestation are increasing in Neotropical forests and can result in high tree mortality, the extent of high-to-severe liana canopy cover across the landscape may have broad implications for ecosystem function and forest carbon storage. The ability to accurately map landscape-scale liana infestation is crucial to quantifying their effects on forest function and uncovering the mechanisms underlying their increase. [ABSTRACT FROM AUTHOR]

Progress in imaging spectroscopy technology and data processing can enable derivation of the complete sun-induced chlorophyll fluorescence (SIF) emission spectrum. This opens up opportunities to fully exploit the use of the SIF spectrum as an indicator of photosynthetic activity. Simulations performed with the coupled fluorescence–photosynthesis model SCOPE were used to determine how strongly canopy-leaving SIF can be related to net photosynthesis of the canopy (NPC) for various canopy configurations. Regression analysis between SIF retrievals and NPC values produced the following general findings: (1) individual SIF bands that were most sensitive to NPC were located around the first emission peak (SIF red ) for heterogeneous canopy configurations (i.e., varying biochemistry, leaf, canopy variables); (2) using two SIF retrieval bands, e.g. O 2 -B at 687 nm and O 2 -A at 760 nm, or the red and NIR emission peaks at 685 nm and 740 nm, led to stronger correlations than using only one band; (3) using the O 2 -B and the O 2 -A SIF retrieval bands was at least as effective as using the two emission peaks; (4) superior correlations were achieved by using the four main SIF retrieval bands (Hα, O 2 -B, water vapor, O 2 -A); and (5) further improvements may be obtained by exploiting the full SIF profile and by using an adaptive, nonlinear regression algorithm such as Gaussian processes regression (GPR). Relationships can be due to variation in photosynthetic capacity (V cmo ), but also from variation in leaf optical and canopy structural variables such as chlorophyll content and leaf area index. Overall, modeling results suggest that sampling the SIF profile in at least both O 2 -B and O 2 -A bands enables quantification photosynthetic activity of vegetation with high accuracy. [ABSTRACT FROM AUTHOR]

We proposed a new area-based approach to process Lidar point clouds and develop new sets of metrics to improve models dedicated to predict forest parameters. First, we introduced point normalization based on penetration depth below the outer canopy layer to avoid biases introduced by ground normalization and canopy surface heterogeneity during metric computation. Second, we proposed computation of area and volume metrics from canopy surface models computed from both first and last returns to better characterize the 3D plot heterogeneity. The set of proposed metrics were combined with traditional ones, based on point height above ground level, to measure their contribution to models of basal area (BA) and aboveground volume (AGV). The modeling framework included a wide range of forest types, canopy structures and Lidar characteristics. Models were developed for all sites grouped together or separately. In each case, the set of metrics was submitted to a hierarchical clustering process to select the best variables to be included in the models that were further established using a best-subset method. Overall, the introduction of the proposed metrics allowed a reduction in models root mean squared error from − 0.06% to 19.58% according to forest types and target forest parameters. Best improvements were achieved for broadleaved forests, showing the potential of the proposed metrics to efficiently characterize the structure of such porous forest canopies. [ABSTRACT FROM AUTHOR]

Among the forestry-related applications for which airborne laser scanning (ALS) data have been shown to be beneficial, forest inventory has been investigated as much if not more than other applications. Metrics extracted from ALS data for spatial units such as plots and grid cells are typically of two forms: echo-based metrics derived directly from the three-dimensional distribution of the point cloud data and metrics derived from a canopy height model (CHM). For both cases, a large number of metrics can be calculated and used to construct parametric and non-parametric models to predict forest variables. We compared model-assisted estimates of total forest aboveground biomass (AGB) obtained using echo-based and CHM-based height metrics with two prediction methods: (i) a parametric linear model, and (ii) the non-parametric k-Nearest Neighbors (k-NN) technique. Model-assisted (MA) estimators were used with sample data obtained using a two-phase, tessellation stratified sampling (TSS) framework to estimate population parameters. The study was conducted in Molise Region in central Italy. For the four combinations of metrics and prediction techniques, estimates of total biomass were similar, in the range 1.96–2.1 million t, with standard error estimates that were also similar, in the range 0.20–0.21 t. Thus, the CHM-based metrics produced AGB estimates that were similar to and as accurate as those for the echo-based metrics, regardless of whether the parametric or the non-parametric prediction method was used. Additionally, the proposed MA estimator was more accurate than the estimator that did not use auxiliary data. [ABSTRACT FROM AUTHOR]

The fundamental basis for applying remote sensing methods to crop assessments is the interrelationships between crop features and canopy spectral characteristics. Thus, a thorough understanding of the differences in the distributions of crop variables among different vertical layers and modules, as well as the effects of these layers and modules on canopy spectral characteristics and derived vegetation indices, is vital to improving the performance of crop monitoring by remote sensing. The main goal of this work was to provide insight into the above issues based on field measurements of winter wheat under different treatments during anthesis. The results demonstrated that the leaf area index (LAI), leaf chlorophyll a and b content (Chl a + b ), nitrogen (N) content per unit leaf area (N L ), N concentration on a mass basis (N M ), dry matter accumulation (DM), N accumulation (NA), and water content (W c ) in leaves and stems generally exhibited great vertical differences within wheat canopies during anthesis. There were also significant differences in N M , DM, NA, and W c among the different plant modules (i.e., leaves, stems, and spikes) and the entire canopy. Moreover, the removal of wheat spikes and different vertical leaf layers had evident effects on the canopy spectral reflectance, and there were differences in the types and degrees of these effects at different wavelengths and treatments. In most cases, the vegetation indices decreased after removing wheat spikes and different leaf layers, although a few indices increased. There were also some vegetation indices that responded weakly to removal events. In comparison, the vegetation indices mostly showed stronger responses to the removal of the top 1st leaves than the spikes. To a certain extent, the responses to the removal of the top 2nd leaves were comparable to those of the top 1st leaves. The responses of most vegetation indices to the top 3rd leaves' removal were less than the responses to the top 2nd leaves' removal. In the case of the removal of the top 4th leaves and below, many of the R V (i.e., relative variation rate) absolute values of vegetation indices were greater than those of the top 3rd leaves' removal but smaller than or close to those of the top 2nd leaves' removal; this may partly be because all the withered and dead leaves covering the soil surface were also removed in this case. The above findings indicated that crop features may not be well characterized when they are based solely on leaves, especially only the upper leaves, as the lower leaves, stems, and spikes also have important effects. Moreover, wheat spikes, not just leaves, and even bottom-layer leaves, could have evident effects on canopy reflectance characteristics and derived vegetation indices. More effort is needed in future research to obtain a thorough understanding of the effects of canopy heterogeneity on canopy spectral characteristics and the relationships between crop variables and spectral vegetation indices. [ABSTRACT FROM AUTHOR]

The RAdiative transfer Model Intercomparison (RAMI) activity focuses on the benchmarking of canopy radiative transfer (RT) models. For the current fourth phase of RAMI, six highly realistic virtual plant environments were constructed on the basis of intensive field data collected from (both deciduous and coniferous) forest stands as well as test sites in Europe and South Africa. Twelve RT modelling groups provided simulations of canopy scale (directional and hemispherically integrated) radiative quantities, as well as a series of binary hemispherical photographs acquired from different locations within the virtual canopies. The simulation results showed much greater variance than those recently analysed for the abstract canopy scenarios of RAMI-IV. Canopy complexity is among the most likely drivers behind operator induced errors that gave rise to the discrepancies. Conformity testing was introduced to separate the simulation results into acceptable and non-acceptable contributions. More specifically, a shared risk approach is used to evaluate the compliance of RT model simulations on the basis of reference data generated with the weighted ensemble averaging technique from ISO-13528. However, using concepts from legal metrology, the uncertainty of this reference solution will be shown to prevent a confident assessment of model performance with respect to the selected tolerance intervals. As an alternative, guarded risk decision rules will be presented to account explicitly for the uncertainty associated with the reference and candidate methods. Both guarded acceptance and guarded rejection approaches are used to make confident statements about the acceptance and/or rejection of RT model simulations with respect to the predefined tolerance intervals. [ABSTRACT FROM AUTHOR]

To date, the utility of ecosystem and Earth system models (EESMs) has been limited by poor spatial and temporal representation of critical input parameters. For example, EESMs often rely on leaf-scale or literature-derived estimates for a key determinant of canopy photosynthesis, the maximum velocity of RuBP carboxylation ( V cmax , μmol m − 2 s − 1 ). Our recent work (Ainsworth et al., 2014; Serbin et al., 2012) showed that reflectance spectroscopy could be used to estimate V cmax at the leaf level. Here, we present evidence that imaging spectroscopy data can be used to simultaneously predict V cmax and its sensitivity to temperature ( E V ) at the canopy scale. In 2013 and 2014, high-altitude Airborne Visible/Infrared Imaging Spectroscopy (AVIRIS) imagery and contemporaneous ground-based assessments of canopy structure and leaf photosynthesis were acquired across an array of monospecific agroecosystems in central and southern California, USA. A partial least-squares regression (PLSR) modeling approach was employed to characterize the pixel-level variation in canopy V cmax (at a standardized canopy temperature of 30 °C) and E V , based on visible and shortwave infrared AVIRIS spectra (414–2447 nm). Our approach yielded parsimonious models with strong predictive capability for V cmax (at 30 °C) and E V (R 2 of withheld data = 0.94 and 0.92, respectively), both of which varied substantially in the field (≥ 1.7 fold) across the sampled crop types. The models were applied to additional AVIRIS imagery to generate maps of V cmax and E V , as well as their uncertainties, for agricultural landscapes in California. The spatial patterns exhibited in the maps were consistent with our in-situ observations. These findings highlight the considerable promise of airborne and, by implication, space-borne imaging spectroscopy, such as the proposed HyspIRI mission, to map spatial and temporal variation in key drivers of photosynthetic metabolism in terrestrial vegetation. [ABSTRACT FROM AUTHOR]

This study provides insight into the key variables that drive sun-induced chlorophyll fluorescence (SIF) emanating from vegetation canopies, based on a global sensitivity analysis (GSA) of the Soil-Canopy Observation of Photosynthesis and Energy (SCOPE) balance model. An updated version of the SCOPE model was used here (v1.53) which contains novel leaf physiological modules for determination of the steady state fluorescence yield: a photosynthesis model coupled with (a) submodels having empirically derived relationships, identified as TB12 for unstressed and TB12-D for drought conditions and (b) a mechanistic (MD12) submodel based on theoretical relationships. By inspecting Sobol's total order (main effect and all the interactions) sensitivity index ( S Ti ) rankings, the influential and non-influential variables were determined. Two experiments were conducted for the different leaf physiology modules in SCOPE considering (1) only vegetation variables, and (2) all SCOPE variables, i.e., including micrometeorological, aerodynamic and geometry variables. Considering TB12-D S Ti results using only vegetation input variables, the canopy-leaving broadband (641–800 nm) SIF variability was determined mainly by leaf optical properties and canopy structural variables. The most important variables were (with decreasing importance) leaf chlorophyll content (Cab), leaf inclination (LIDFa) and leaf area index (LAI). These three variables alone determined 77.9% of the SIF variability. V cmo , the variable related to photosynthetic capacity, determined 11.4% of overall SIF variability, and its importance declined considerably when moving from the first emission peak (SIF red ; with maximal relevance of 17.9% at 676 nm) to the second emission peak (SIF NIR ; e.g., 9.6% at 740 nm). Stronger relationships with V cmo were obtained when retrieving the full broadband SIF flux and calculating total fluorescence yield (F yield , determined as the integral of the hemispherical broadband SIF flux divided by the total absorbed PAR), of which 35% of the variability was influenced by V cmo . Using the TB12 submodel, the major drivers of SIF flux were similar to TB12-D except that V cmo accounted for very little (< 2%) variability. The MD12 submodel identified the components of long-term PSII photoprotection and photodamage as the dominant factors for SIF variability: these two variables alone accounted for 51.4% of the variability of SIF flux and 61% of F yield , whereas V cmo explained only 9.7% and 10.9% of variability in SIF flux and F yield , respectively. Analysis of the relative importance of all SCOPE variables revealed that in addition to the key vegetation variables, micrometeorological variables were important in driving SIF variability, especially incoming shortwave radiation (Rin) and to a lesser extent air temperature (Ta), atmospheric vapor pressure (ea) and atmospheric CO 2 concentration (Ca). Their impact further reduced the relative importance of V cmo . The GSA experiments led to the following conclusions: (1) explicit knowledge of key variables driving the SIF flux is essential in order to achieve unbiased SIF interpretation related to photosynthetic activity at local and global scales; (2) information related to photosynthetic activity is found more in the first emission peak (SIF red ) than in the second peak (SIF NIR ), and more in the full broadband SIF emission, which allows calculation of F yield , than in individual wavebands. [ABSTRACT FROM AUTHOR]

Photosynthetically active radiation (PAR) is an important parameter in ecosystem and land surface models. PAR represents the amount of solar radiation in the spectral range of 400–700 nm that travels through the atmosphere to the top of the vegetation canopy. In recent years, various methods using different input data to estimate PAR and produce different PAR products have been developed. However, most of the algorithms used in these state-of-the-art studies have not fully compensated for the low spatial and temporal resolution of the data, which affects the accuracy of the PAR estimates. In this study, we have developed a method for estimating hourly PAR based on a combination of geostationary and polar-orbiting satellite data. The Multifunctional Transport Satellite (MTSAT) was selected to retrieve cloud optical depth (COD) with a higher spatial resolution, and the polar orbit satellite data of the Moderate Resolution Imaging Spectroradiometer (MODIS) products were used to derive surface parameters based on multispectral characteristics. A look-up table was established by the Second Simulation of a Satellite Signal in the Solar Spectrum-Vector (6SV) model and the Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model consisting the following parameters: solar zenith angle, total water vapor, total ozone column, aerosol optical depth (AOD), COD, surface elevation, surface albedo and PAR. The instantaneous PAR was linearly interpolated from the input data for the selected parameters and the look-up table. The root mean square error (RMSE) between the estimated and observed instantaneous PAR at the Huailai station was 45.72 W/m 2 for all sky conditions. The RMSE between the estimated and observed daily PAR at the meteorological stations was 17% in the eastern regions of China. The mean bias error (MBE) was between − 2.83 and 32.43 W/m 2 for the Tibetan Plateau. These results indicated that the proposed method can significantly improve the accuracy of PAR estimates and can be used to produce PAR products with high spatial and temporal resolution. However, the method requires further improvement, especially with respect to cloudy conditions. [ABSTRACT FROM AUTHOR]

In this paper we present novel automated field spectroscopy systems for collecting unattended, continuous and long-term measurements of plant canopies and, more in general, of Earth's ecosystems. These systems simultaneously collect high and ultra-high resolution spectra in the visible to near-infrared (VNIR) domain employing two spectrometers: i) the first covers the spectral range 400–1000 nm with a 1.0 nm spectral resolution; ii) the second provides a sub-nanometer spectral resolution within the 700–800 nm spectral range. The data collected by the first spectrometer allow retrieval of VNIR reflectance, while the higher spectral resolution data from the second device permit estimation of vegetation Sun-Induced Fluorescence (SIF) in the O 2 –A band. The instruments are constructed by assembling commercial and on-the-shelf optoelectronic devices to facilitate reproduction of the instrument for promoting measurements over different ecosystems. The instrument's optical design, data collection and processing, laboratory and in-field calibration methods are reported and discussed. The high spectral resolution and the rigorous calibration methods enable accurate estimation of SIF in physical units by exploiting almost the same retrieval concept as that of the European Space Agency FLuorescence EXplorer mission. The instruments have been operated in several field campaigns with the aim to show: i) the possibility of continuous and seasonal monitoring of plant growth and activity of an agricultural crop; and ii) the diverse and specific daily course patterns of different types of canopy. The datasets of canopy reflectance, vegetation indices and SIF collected are shown and discussed. [ABSTRACT FROM AUTHOR]

Aquatic vegetation in shallow waters can absorb nitrogen and phosphorus from eutrophic waters, and effectively control water bloom. However, our knowledge of the interaction between solar radiation and aquatic vegetation is limited. This restricts the protection of aquatic vegetation. This paper proposes a radiative transfer approach for homogeneous aquatic vegetation canopy, based on the theories for terrestrial vegetation and Case II water. The effects of rough water surface and bottom reflection are also taken into consideration. Validations against an independent Monte Carlo model and field experiment data exhibit well consistencies on the bidirectional reflectance of homogeneous aquatic vegetation canopy. As expected, the results for sparse canopy degenerate. This model can be used for canopy modeling of emergent, floating or submerged aquatic vegetation. Potential applications include retrieval of biophysical or biochemical parameters based on hyperspectral data and physical process. [ABSTRACT FROM AUTHOR]

Climate and physiological controls of vegetation gross primary production (GPP) vary in space and time. In many ecosystems, GPP is primary limited by absorbed photosynthetically-active radiation; in others by canopy conductance. These controls further vary in importance over daily to seasonal time scales. We propose a simple but effective conceptual model that estimates GPP as the lesser of a conductance-limited ( F c ) and radiation-limited ( Fr ) assimilation rate. F c is estimated from canopy conductance while Fr is estimated using a light use efficiency model. Both can be related to vegetation properties observed by optical remote sensing. The model has only two fitting parameters: maximum light use efficiency, and the minimum achieved ratio of internal to external CO 2 concentration. The two parameters were estimated using data from 16 eddy covariance flux towers for six major biomes including both energy- and water-limited ecosystems. Evaluation of model estimates with flux tower-derived GPP compared favourably to that of more complex models, for fluxes averaged; per day (r 2 = 0.72, root mean square error, RMSE = 2.48 μmol C m 2 s − 1 , relative percentage error, RPE = − 11%), over 8-day periods (r 2 = 0.78 RMSE = 2.09 μmol C m 2 s − 1 ,RPE = − 10%), over months (r 2 = 0.79, RMSE = 1.93 μmol C m 2 s − 1 , RPE = − 9%) and over years (r 2 = 0.54, RMSE = 1.62 μmol C m 2 s − 1 , RPE = − 9%). Using the model we estimated global GPP of 107 Pg C y − 1 for 2000–2011. This value is within the range reported by other GPP models and the spatial and inter-annual patterns compared favourably. The main advantages of the proposed model are its simplicity, avoiding the use of uncertain biome- or land-cover class mapping, and inclusion of explicit coupling between GPP and plant transpiration. [ABSTRACT FROM AUTHOR]

The Suomi NPP VIIRS Vegetation Index (VI) Environment Data Record (EDR) includes the Top Of the Atmosphere Normalized Difference Vegetation Index (TOA NDVI) and the Top Of the Canopy Enhanced Vegetation Index (TOC EVI). TOC NDVI will be included in the VI EDR suite for the upcoming JPSS-1 and JPSS-2 missions. Currently, the VIIRS VI suite is undergoing extensive Calibration and Validation (Cal/Val) activities to quantify product performance and to provide guidance for algorithm improvement. In this study we utilized AErosol RObotic NETwork (AERONET) based Surface Reflectance (SR) match-up data sets. Match-ups are pairs of VIIRS SR and SR derived from atmospheric correction of VIIRS TOA reflectances using AERONET ground measurements (aerosol and water vapor parameters) and ancillary data. Atmospheric correction is performed with the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer code, the same code used in the operational VIIRS atmospheric correction. Daily time series January 1, 2013–March 31, 2014 of 101 × 101 pixel window match-up subsets at 375 m VIIRS Imagery resolution, centered at the selected AERONET site locations were used. The overall objective of the study was to characterize the performance of the VIIRS TOC VIs (TOC EVI and TOC NDVI) over AERONET sites as a function of accuracy of inputs of atmospheric correction algorithm (aerosol, water vapor and others) and quality control screening (cloud and snow masks). We performed three types of analyses: (1) analysis of overall performance of VI product over all sites, (2) analysis of time series over select sites representative of vegetation types, and (3) sensitivity analysis of VI to cloud, aerosols and snow contamination. Over the period of time series average Accuracy, Precision and Uncertainty statistics were 0.009, 0.035, and 0.038, respectively, for TOC NDVI and − 0.004, 0.015, and 0.016, respectively, for TOC EVI. Those statistics were derived based on screening to retain only confidently clear, snow free, non-urban pixels. The reason for the substantial difference in the performance of VIs is a different sensitivity of VIs to residual atmospheric contamination. According to our study VIIRS cloud mask performed reasonably. However, Aerosol Optical Thickness (AOT) was substantially overestimated in the vicinity of clouds, which resulted in overcorrection of VIIRS visible channels and ultimately overestimation of TOC NDVI. TOC EVI, on the other hand, remained largely unaffected, as an atmospherically resistant index. TOC EVI was also found to be less sensitive to residual snow contamination. Results of this study indicate the need to develop VI-specific quality control, which effectively screens data based on index sensitivity to atmospheric contamination. Monitoring VIIRS VIs over AERONET sites has been automated with a web-based STAR JPSS VI Monitor. [ABSTRACT FROM AUTHOR]

It is well established that a natural surface exhibits anisotropic reflectance properties that depend on the characteristics of the surface. Spectral measurements of the bidirectional reflectance factor (BRF) at ground level provide us a method to capture the directional characteristics of the observed surface. Various spectro-radiometers with different field of views (FOVs) were used under different mounting conditions to measure crop reflectance. The impact and uncertainty of sensor FOV and distance from the target have rarely been considered. The issue can be compounded with the characteristic reflectance of heterogeneous row crops. Because of the difficulty of accurately obtaining field measurements of crop reflectance under natural environments, a method of computer simulation was proposed to study the impact of sensor FOV and distance on field measured BRFs. A Monte Carlo model was built to combine the photon spread method and the weight reduction concept to develop the weighted photon spread (WPS) model to simulate radiation transfer in architecturally realistic canopies. Comparisons of the Monte Carlo model with both field BRF measurements and the RAMI Online Model Checker (ROMC) showed good agreement. BRFs were then simulated for a range of sensor FOV and distance combinations and compared with the reference values (distance at infinity) for two typical row canopy scenes. Sensors with a finite FOV and distance from the target approximate the reflectance anisotropy and yield average values over FOV. Moreover, the perspective projection of the sensor causes a proportional distortion in the sensor FOV from the ideal directional observations. Though such factors inducing the measurement error exist, it was found that the BRF can be obtained with a tolerable bias on ground level with a proper combination of sensor FOV and distance, except for the hotspot direction and the directions around it. Recommendations for the choice of sensor FOV and distance are also made to reduce the bias from the real angular signatures in field BRF measurement for row crops. [ABSTRACT FROM AUTHOR]

Accurate leaf area index (LAI) measurements are important for understanding and evaluating global mass and energy cycle. Foliage clumping serves an important function in traditional indirect LAI measurement methods. The clumping index has been used to modify effective LAI for decades. However, the change in path length within canopies is often the most uncertain factor in indirect LAI estimation using Beer's law. A simple clumping index is incapable of describing the heterogeneity of a canopy and may cause large errors in calculating true LAI values. We proposed a new LAI estimation method by using path length distribution functions in optical measurement. Both simulation and field measurements show that the path length-based method can effectively characterize the LAI values of heterogeneous canopies. Deviation is less than 10% for all the validations. One of the advantages of path length distribution theory is that it can characterize and handle crown shape-induced non-randomness within canopies. Such non-randomness, which may cause underestimation of up to 25%, has not been well addressed by existing algorithms. Path length theory is expected to improve the indirect measurement accuracy of LAI significantly with the use of current optical instruments. [ABSTRACT FROM AUTHOR]

As the availability of satellite and airborne thermal infrared remote sensing (TIR-RS) data increases and their spatial, temporal, and spectral resolutions improve, researchers are finding diverse applications for TIR-RS measurements. TIR-RS is now commonly applied in regional- and continental-scale analyses, such as those focused on fire and surface energy balance. However, its application lags in plant physiology and ecology, for which a finer-scale understanding of plant canopy temperatures would be useful to elucidate plant water dynamics, for example. In particular, while methods to disaggregate TIR-RS pixels in horizontal space have advanced, possible vertical stratification of plant canopy temperature and its implications for understanding the correspondence between TIR-RS and finer-scale, field-based thermal measurements (e.g. made with a thermal camera) remain unexplored. Here, we use data from a thermal camera deployed concurrently with the recent ECOSTRESS mission to quantify vertical temperature gradients within tree canopies and temperatures of over- vs. under-story plants in a Mediterranean woodland savanna. We then leverage diverse ancillary data to maximize the geometric comparability of ECOSTRESS and thermal camera measurements, in order to assess the extent to which the two forms of thermal measurements correspond. Specifically, we ask: (1) What are the patterns of intra-canopy and over- vs. under-story vertical temperature in a Mediterranean woodland savanna?, and (2) How can vertically-resolved, but spatially-limited field-based temperature measurements be reconciled with spatially-extensive, but surface-only, temperature measurements of a space-borne remote sensor? We found consistent patterns of vertical thermal heterogeneity both within tree canopies and between ecosystem over- and under-stories. The daytime difference between the top and bottom thirds of blue oak canopies was, on average, 0.48 ° C – and sometimes several times larger. Notably, canopy tops are cooler, likely associated with the under-story grass reaching daytime temperatures often exceeding over-story temperatures by 10° C. Given the consistency of the intra-canopy temperature gradients, we expected the ECOSTRESS sensor would be in better agreement with camera measurements of canopy tops than bulk canopies or canopy bottoms. However, within-canopy gradients were overwhelmed by other sources of disagreement between the measurements, in part associated with upscaling camera measurements across space. Overall, thermal camera and ECOSTRESS measurements were largely in agreement at night (pixel RMSE = 1.1°C), but they were more divergent during times of low (but >0 W/m2) and high incoming solar radiation (daytime pixel RMSE = 3.5°C). • Field-based thermal remote sensing can resolve vertical tree crown temperatures. • In a woodland savanna, canopy tops are cooler than canopy bottoms at midday. • Satellite (ECOSTRESS) and field-based (thermal camera) measurements agree at night. • During the day, ECOSTRESS and camera temperatures diverge considerably. • ECOSTRESS/camera mismatch is more related to light than to crown thermal gradients. [ABSTRACT FROM AUTHOR]