Remote estimation of gross primary production in maize and support for a new paradigm based on total crop chlorophyll content

The accurate quantification of gross primary production (GPP) in crops is important for regional and global studies of carbon budgets. Because of the observed close relationship between GPP and total canopy chlorophyll content in crops, vegetation indices related to chlorophyll can be used as a proxy of GPP. In this study, we justified the approach, tested the performance of several widely used chlorophyll-related vegetation indices in estimating total chlorophyll content and GPP in maize based on spectral data collected at a close range, 6 meters above the top of the canopy, over a period of eight years (2001 to 2008). The results show that GPP can be accurately estimated with chlorophyll-related indices that use near infra-red and either green or the red edge range of the spectrum. These indices provide the best approximation of the widely variable GPP in maize under both irrigated and rainfed conditions.     

Remote estimation of chlorophyll a in optically complex waters based on optical classification

Accurate assessment of phytoplankton chlorophyll a (Chla) concentration in turbid waters by means of remote sensing is challenging due to optically complexity and significant variability of case 2 waters, especially in inland waters with multiple optical types. In this study, a water optical classification algorithm is developed, and two semi-analytical algorithms (three- and four-band algorithm) for estimating Chla are calibrated and validated using four independent datasets collected from Taihu Lake, Chaohu Lake, and Three Gorges Reservoir. The optical classification algorithm is developed using the dataset collected in Taihu Lake from 2006 to 2009. This dataset is also used to calibrate the three- and four-band Chla estimation algorithms. The optical classification technique uses remote sensing reflectance at three bands: Rrs(G), Rrs(650), and Rrs(NIR), where G indicates the location of reflectance peak in the green region (around 560 nm), and NIR is the location of reflectance peak in the near-infrared region (around 700 nm). Optimal reference wavelengths of the three- and four-band algorithm are located through model tuning and accuracy optimization. The three- and four-band algorithm accuracy is further evaluated using other three independent datasets. The improvement of optical classification in Chla estimation is revealed by comparing the performance of the two algorithms for non-classified and classified waters.Using the slopes of the three reflectance bands, the 138 reflectance spectra samples in the calibration dataset are classified into three classes, each with a specific spectral shape character. The three- and four-band algorithm performs well for both non-classified and classified waters in estimating Chla. For non-classified waters, strong relationships are yielded between measured and predicted Chla, but the performance of the two algorithms is not satisfactory in low Chla conditions, especially for samples with Chla below 30 mg m^− 3. For classified waters, the class-specific algorithms perform better than for non-classified waters. Class-specific algorithms reduce considerable mean relative error from algorithms for non-classified waters in Chla predicting. Optical classification makes that there is no need to adjust the optimal position to estimate Chla for other waters using the class-specific algorithms. The findings in this study demonstrate that optical classification can greatly improve the accuracy of Chla estimation in optically complex waters.     

基于荧光光谱的空间高等植物叶绿素含量测量技术

开展高等植物光合作用过程参数的实时检测技术研究,对于深入开展空间生物学效应具有重要意义。作为光合作用重要参数的高等植物叶绿素含量与其荧光光谱具有较好的相关性,为此,可以通过探测分析植物荧光光谱,建立其与叶绿素的相关性模型,间接表征其中的叶绿素含量。介绍一种新的建模方法即支持向量机（SVM）,通过实验验证,方法预测叶绿素含量是可行的,相对于一般采用的线性回归法具有更好的预测效果和更高的测量精度。     

Non-destructive estimation of wheat leaf chlorophyll content from hyperspectral measurements through analytical model inversion

Optimizing nitrogen (N) fertilization in crop production by in-season measurements of crop N status may improve fertilizer N use efficiency. Hyperspectral measurements may be used to assess crop N status indirectly by estimating leaf and canopy chlorophyll content. This study evaluated the ability of the PROSAIL canopy-level reflectance model to predict leaf chlorophyll content of spring wheat (Triticum aestivum L.) during the growth stages between pre-tillering (Zadoks Growth Stage (ZGS 15)) to booting (ZGS50). Spring wheat was grown under different N fertility rates (0–200 kg N ha^?1) in 2002. Canopy reflectance, leaf chlorophyll content, N content and leaf area index (LAI) values were measured. There was a weakly significant trend for the PROSAIL model to over-estimate LAI and under-estimate leaf chlorophyll content. To compensate for this interdependency by the model, a canopy chlorophyll content parameter (the product of leaf chlorophyll content and LAI) was calculated. The estimation accuracy for canopy chlorophyll content was generally low earlier in the growing season. This failure of the PROSAIL model to estimate leaf and canopy variables could be attributed to model sensitivity to canopy architecture. Earlier in the growing season, full canopy closure was not yet achieved, resulting in a non-homogenous canopy and strong soil background interference. The canopy chlorophyll content parameter was predicted more accurately than leaf chlorophyll content alone at booting (ZGS 45). A strong relationship between canopy chlorophyll content and canopy N content at ZGS 45 indicates that the PROSAIL model may be used as a tool to predict wheat N status from canopy reflectance measurements at booting or later.     

Simultaneous measurements of plant structure and chlorophyll content in broadleaf saplings with a terrestrial laser scanner

Plant structure and chlorophyll content strongly affect rates of photosynthesis. Rapid, objective, and repeatable methods are needed to measure these vegetative parameters to advance our understanding and modeling of plant ecophysiological processes. Terrestrial laser scanners (TLS) can be used to measure structural and potentially chemical properties of objects by quantifying the x,y,z coordinates and intensity of laser light, respectively, returned from an object's surface. The objective of this study was to determine the potential usefulness of TLS with a green (532 nm) laser to simultaneously measure the spatial distribution of chlorophyll a and b content (Chl_ab), leaf area (LA), and leaf angle (LAN). The TLS measurements were obtained from saplings of two tree species (Quercus macrocarpa and Acer saccharum) and from an angle-adjustable cardboard surface. The green laser return intensity value was strongly correlated with wet-chemically determined Chl_ab (r² = 0.77). Strong agreement was shown between measured and TLS-derived LA (r² = 0.95, intercept = − 1.43, slope = 0.97). The TLS derived LANs of both species followed a plagiophile LAN distribution, and the measured angles of the cardboard surface allowed us to quantify that these LAN values were strongly correlated with TLS derived angles (r² = 1.0, intercept and slope = 0.98). Our results show that terrestrial laser scanners are feasible for simultaneous measurement of LA, LAN, and Chl_ab in simple canopies of small broadleaved plants. Further research is needed in more complex and larger canopies.     

Needle chlorophyll content estimation through model inversion using hyperspectral data from boreal conifer forest canopies

Leaf chlorophyll content in coniferous forest canopies, a measure of stand condition, is the target of studies and models linking leaf reflectance and transmittance and canopy hyperspectral reflectance imagery. The viability of estimation of needle chlorophyll content from airborne hyperspectral optical data through inversion of linked leaf level and canopy level radiative transfer models is discussed in this paper. This study is focused on five sites of Jack Pine (Pinus banksiana Lamb.) in the Algoma Region (Canada), where field, laboratory and airborne data were collected in 1998 and 1999 campaigns. Airborne hyperspectral CASI data of 72 bands in the visible and near-infrared region and 2 m spatial resolution were collected from 20×20 m study sites of Jack Pine in 2 consecutive years. It was found that needle chlorophyll content could be estimated at the leaf level (r²=0.4) by inversion of the PROSPECT leaf model from needle reflectance and transmittance spectra collected with a special needle carrier apparatus coupled to the Li-Cor 1800 integrating sphere. The Jack Pine forest stands used for this study with LAI>2, and the high spatial resolution hyperspectral reflectance collected, allowed the use of the SPRINT canopy reflectance model coupled to PROSPECT for needle chlorophyll content estimation by model inversion. The optical index R750/R710 was used as the merit function in the numerical inversion to minimize the effect of shadows and LAI variation in the mean canopy reflectance from the 20×20 m plots. Estimates of needle pigment content from airborne hyperspectral reflectance using this linked leaf-canopy model inversion methodology showed an r²=0.4 and RMSE=8.1 μg/cm² when targeting sunlit crown pixels in Jack Pine sites with pigment content ranging between 26.8 and 56.8 μg/cm² (1570-3320 μg/g).     

A novel two-step method for winter wheat-leaf chlorophyll content estimation using a hyperspectral vegetation index

Remote sensing estimation of leaf chlorophyll content is of importance to crop nutrition diagnosis and yield assessment, yet the feasibility and stability of such estimation has not been assessed thoroughly for mixed pixels. This study analyses the influence of spectral mixing on leaf chlorophyll content estimation using canopy spectra simulated by the PROSAIL model and the spectral linear mixture concept. It is observed that the accuracy of leaf chlorophyll content estimation would be degraded for mixed pixels using the well-accepted approach of the combination of transformed chlorophyll absorption index (TCARI) and optimized soil-adjusted vegetation index (OSAVI). A two-step method was thus developed for winter wheat chlorophyll content estimation by taking into consideration the fractional vegetation cover using a look-up-table approach. The two methods were validated using ground spectra, airborne hyperspectral data and leaf chlorophyll content measured the same time over experimental winter wheat fields. Using the two-step method, the leaf chlorophyll content of the open canopy was estimated from the airborne hyperspectral imagery with a root mean square error of 5.18 μg cm^?2, which is an improvement of about 8.9% relative to the accuracy obtained using the TCARI/OSAVI ratio directly. This implies that the method proposed in this study has great potential for hyperspectral applications in agricultural management, particularly for applications before crop canopy closure.     

Effects of adaxial and abaxial surface on the estimation of leaf chlorophyll content using hyperspectral vegetation indices

Vegetation indices are frequently used for the non-destructive assessment of leaf chemistry, especially chlorophyll content. However, most vegetation indices were developed based on the statistical relationship between the spectral reflectance of the adaxial leaf surface and chlorophyll content, even though abaxial leaf surfaces may influence reflectance spectra because of canopy structure or the inclination of leaves. In the present study, reflectance spectra from both adaxial and abaxial leaf surfaces of Populus alba and Ulmus pumila var. pendula were measured. The results showed that structural differences of the two leaf surfaces may result in differences in reflectance and hyperspectral vegetation indices. Among 30 vegetation indices tested, R₆₇₂/(R₅₅₀ × R₇₀₈) had the smallest difference (4.66% for P. alba, 2.30% for U. pumila var. pendula) between the two blade surfaces of the same leaf in both species. However, linear regression analysis showed that several vegetation indices (R₈₅₀ ? R₇₁₀)/(R₈₅₀ ? R₆₈₀), VOG₂, D₇₃₀, and D₇₄₀, had high coefficients of determination (R² > 0.8) and varied little between the two leaf surfaces of the plants we sampled. This demonstrated that these four vegetation indices had relatively stable accuracy for estimating leaf chlorophyll content. The coefficients of determination (R²) for the calibration of P. alba leaves were 0.92, 0.98, 0.93, and 0.95 on the adaxial surfaces, and 0.88, 0.87, 0.88, and 0.92 on the abaxial surfaces. The coefficients of determination (R²) for the calibration of U. pumila var. pendula leaves were 0.85, 0.91, 0.86, and 0.90 on adaxial surface, and 0.80, 0.80, 0.84, and 0.88 on abaxial surface. These four vegetation indices were readily available and were little influenced by the differences in the two leaf surfaces during the estimation of leaf chlorophyll content.     

The red edge position and shape as indicators of plant chlorophyll content,biomass and hydric status.

Some red edge parameters in the first derivative reflectance curve (wavelength, amplitude and area of the red edge peak) were studied to evaluate plant chlorophyll content, biomassand RelativeWater Content (RWC).Plants of Capsicum annuum and Phaseolus vulgaris under different nitrogen and water availabilities, and plants of Gerbera jamesonii with different hydric status were studied. A high correlation was found between chlorophyll content and the wavelength of the red edge peak (λ_re ), and between LAI (leaf area index)and the amplitude of the red edge peak (dr_{r e} ), but the area of the red edge peak (σ_680–780 nm) was the best estimator of LAI. Thus, red edge was found valuable for assessment of plant chlorophyll concentration and LAI, and therefore nutritional status. Water stress also affected dr_re, but only when the stress was well developed.     

Remote sensing based estimation of evapotranspiration rates

A remote sensing based method is presented for calculating evapotranspiration rates (λE) using standard meteorological field data and radiometric surface temperature recorded for bare soil, maize and wheat canopies in Denmark. The estimation of λE is achieved using three equations to solve three unknowns; the atmospheric resistance (r_ae ), the surface resistance (r_s ) and the vapour pressure at the surface (e_s ) where the latter is assessed using an empirical expression. The method is applicable, without modification, to dense vegetation and moist soil, but for a dry bare soil, where the effective source of water vapour is below the surface, the temperature of the evaporating front (T_s *) can not be represented by the measured surface temperature (T_s ). In this case (T_s -T_s *) is assessed as a linear function of the difference between surface temperature and air temperature. The calculated λE is comparable to latent heat fluxes recorded by the eddy covariance technique.     

Utility of an image-based canopy reflectance modeling tool for remote estimation of LAI and leaf chlorophyll content at the field scale

This paper presents a physically-based approach for estimating critical variables describing land surface vegetation canopies, relying on remotely sensed data that can be acquired from operational satellite sensors. The REGularized canopy reFLECtance (REGFLEC) modeling tool couples leaf optics (PROSPECT), canopy reflectance (ACRM), and atmospheric radiative transfer (6SV1) model components, facilitating the direct use of at-sensor radiances in green, red and near-infrared wavelengths for the inverse retrieval of leaf chlorophyll content (C_ab) and total one-sided leaf area per unit ground area (LAI). The inversion of the canopy reflectance model is constrained by assuming limited variability of leaf structure, vegetation clumping, and leaf inclination angle within a given crop field and by exploiting the added radiometric information content of pixels belonging to the same field. A look-up-table with a suite of pre-computed spectral reflectance relationships, each a function of canopy characteristics, soil background effects and external conditions, is accessed for fast pixel-wise biophysical parameter retrievals. Using 1 m resolution aircraft and 10 m resolution SPOT-5 imagery, REGFLEC effectuated robust biophysical parameter retrievals for a corn field characterized by a wide range in leaf chlorophyll levels and intermixed green and senescent leaf material. Validation against in-situ observations yielded relative root-mean-square deviations (RMSD) on the order of 10% for the 1 m resolution LAI (RMSD = 0.25) and C_ab (RMSD = 4.4 μg cm^− 2) estimates, due in part to an efficient correction for background influences. LAI and C_ab retrieval accuracies at the SPOT 10 m resolution were characterized by relative RMSDs of 13% (0.3) and 17% (7.1 μg cm^− 2), respectively, and the overall intra-field pattern in LAI and C_ab was well established at this resolution. The developed method has utility in agricultural fields characterized by widely varying distributions of model variables and holds promise as a valuable operational tool for precision crop management. Work is currently in progress to extend REGFLEC to regional scales.     

Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops

An investigation of the estimation of leaf biochemistry in open tree crop canopies using high-spatial hyperspectral remote sensing imagery is presented. Hyperspectral optical indices related to leaf chlorophyll content were used to test different radiative transfer modelling assumptions in open canopies where crown, soil and shadow components were separately targeted using 1 m spatial resolution ROSIS hyperspectral imagery. Methods for scaling-up of hyperspectral single-ratio indices such as R₇₅₀/R₇₁₀ and combined indices such as MCARI, TCARI and OSAVI were studied to investigate the effects of scene components on indices calculated from pure crown pixels and from aggregated soil, shadow and crown reflectance. Methods were tested on 1-m resolution hyperspectral ROSIS datasets acquired over two olive groves in southern Spain during the HySens 2002 campaign conducted by the German Aerospace Center (DLR). Leaf-level biochemical estimation using 1-m ROSIS data when targeting pure olive tree crowns employed PROSPECT-SAILH radiative transfer simulation. At lower spatial resolution, therefore with significant effects of soil and shadow scene components on the aggregated pixels, a canopy model to account for such scene components had to be used for a more appropriate estimation approach for leaf biochemical concentration. The linked models PROSPECT-SAILH-FLIM improved the estimates of chlorophyll concentration from these open tree canopies, demonstrating that crown-derived relationships between hyperspectral indices and biochemical constituents cannot be readily applied to hyperspectral imagery of lower spatial resolutions due to large soil and shadow effects. Predictive equations built on a MCARI/OSAVI scaled-up index through radiative transfer simulation minimized soil background variations in these open canopies, demonstrating superior performance compared to other single-ratio indices previously shown as good indicators of chlorophyll concentration in closed canopies. The MCARI/OSAVI index was demonstrated to be less affected than TCARI/OSAVI by soil background variations when calculated from the pure crown component even at the typically low LAI orchard and grove canopies.     

Remote sensing of solar-induced chlorophyll fluorescence: Review of methods and applications

Interest in remote sensing (RS) of solar-induced chlorophyll fluorescence (F) by terrestrial vegetation is motivated by the link of F to photosynthetic efficiency which could be exploited for large scale monitoring of plant status and functioning. Today, passive RS of F is feasible with different prototypes and commercial ground-based, airborne, and even spaceborne instruments under certain conditions. This interest is generating an increasing number of research projects linking F and RS, such as the development of new F remote retrieval techniques, the understanding of the link between the F signal and vegetation physiology and the feasibility of a satellite mission specifically designed for F monitoring. This paper reviews the main issues to be addressed for estimating F from RS observations. Scattered information about F estimation exists in the literature. Here, more than 40 scientific papers dealing with F estimation are reviewed and major differences are found in approaches, instruments and experimental setups. Different approaches are grouped into major categories according to RS data requirements (i.e. radiance or reflectance, multispectral or hyperspectral) and techniques used to extract F from the remote signal. Theoretical assumptions, advantages and drawbacks of each method are outlined and provide perspectives for future research. Finally, applications of the measured F signal at the three scales of observation (ground, aircraft and satellite) are presented and discussed to provide the state of the art in F estimation.     

Leaf chlorophyll content retrieval from airborne hyperspectral remote sensing imagery

Hyperspectral remote sensing has great potential for accurate retrieval of forest biochemical parameters. In this paper, a hyperspectral remote sensing algorithm is developed to retrieve total leaf chlorophyll content for both open spruce and closed forests, and tested for open forest canopies. Ten black spruce (Picea mariana (Mill.)) stands near Sudbury, Ontario, Canada, were selected as study sites, where extensive field and laboratory measurements were carried out to collect forest structural parameters, needle and forest background optical properties, and needle biophysical parameters and biochemical contents chlorophyll a and b. Airborne hyperspectral remote sensing imagery was acquired, within one week of ground measurements, by the Compact Airborne Spectrographic Imager (CASI) in a hyperspectral mode, with 72 bands and half bandwidth 4.25-4.36 nm in the visible and near-infrared region and a 2 m spatial resolution. The geometrical-optical model 4-Scale and the modified leaf optical model PROSPECT were combined to estimate leaf chlorophyll content from the CASI imagery. Forest canopy reflectance was first estimated with the measured leaf reflectance and transmittance spectra, forest background reflectance, CASI acquisition parameters, and a set of stand parameters as inputs to 4-Scale. The estimated canopy reflectance agrees well with the CASI measured reflectance in the chlorophyll absorption sensitive regions, with discrepancies of 0.06%-1.07% and 0.36%-1.63%, respectively, in the average reflectances of the red and red-edge region. A look-up-table approach was developed to provide the probabilities of viewing the sunlit foliage and background, and to determine a spectral multiple scattering factor as functions of leaf area index, view zenith angle, and solar zenith angle. With the look-up tables, the 4-Scale model was inverted to estimate leaf reflectance spectra from hyperspectral remote sensing imagery. Good agreements were obtained between the inverted and measured leaf reflectance spectra across the visible and near-infrared region, with R² = 0.89 to R² = 0.97 and discrepancies of 0.02%-3.63% and 0.24%-7.88% in the average red and red-edge reflectances, respectively. Leaf chlorophyll content was estimated from the retrieved leaf reflectance spectra using the modified PROSPECT inversion model, with R² = 0.47, RMSE = 4.34 μg/cm², and jackknifed RMSE of 5.69 μg/cm² for needle chlorophyll content ranging from 24.9 μg/cm² to 37.6 μg/cm². The estimates were also assessed at leaf and canopy scales using chlorophyll spectral indices TCARI/OSAVI and MTCI. An empirical relationship of simple ratio derived from the CASI imagery to the ground-measured leaf area index was developed (R² = 0.88) to map leaf area index. Canopy chlorophyll content per unit ground surface area was then estimated, based on the spatial distributions of leaf chlorophyll content per unit leaf area and the leaf area index.     

Remote sensing of leaf water content in the near infrared

A stochastic leaf radiation model was used to predict leaf spectral reflectance as a function of leaf water content for a dicot leaf. Simulated spectral reflectances were analyzed to quantify reflectance differences between different equivalent water thicknesses. Simulated results coupled with consideration of atmoshperic transmission properties and the incident solar spectral irradiance at the earth's surface resulted in the conclusion that the 1.55–1.75 μm region was the best-suited wavelength interval for satellite—platform remote sensing of plant canopy water status in the 0.7–2.5 μm region of the spectrum.     

Comparison between back propagation neural network and regression models for the estimation of pigment content in rice leaves and panicles using hyperspectral data

Two nitrogen experiments on rice were conducted in 2002, and the reflectances (350 to 2500 nm) and pigment contents (chlorophylls a and b, total chlorophylls and carotenoids) for leaf and panicle samples at different growth stages were measured in the laboratory. After performing an outlier analysis, the number of samples were 843 for leaves and 188 for panicles. Absorption features at 430, 460, 470, 640 and 660 nm for different pigments, and the relative reflectance of the green peak around 550 nm calculated by the continuum‐removed method, as well as the red edge position (REP) of rice leaves and panicles were selected as the independent variables, and measured pigment contents were selected as the dependent variables. Then, back propagation neural network (BPN) models, a kind of artificial neuron network (ANN), and multivariate linear regression models (MLR) were trained and tested. The main objective of this study was to compare the predictive ability of the ANN models to that of the MLR models in estimating the content of pigments in rice leaves and panicles. Results showed that all BPN models gave higher coefficients of determination (R²) and lower absolute errors (ABSEs) and root mean squared errors (RMSEs) than the corresponding MLR models, in both calibration and validation tests. Further significance tests by paired t tests and bootstrapping algorithms indicated that most of the BPN models outperformed the MLR models. When trained by combination data that did not meet the assumption of normal distribution, the BPN models appeared to not only have a better learning ability, but also had a more accurate predictive power than the MLR models. The estimation of leaf pigments was more accurate than that of panicle pigments, independent of which model was used.     

Remote sensing image fusion based on Bayesian linear estimation

A new remote sensing image fusion method based on statistical parameter estimation is proposed in this paper. More specially, Bayesian linear estimation (BLE) is applied to observation models between remote sensing images with different spa- tial and spectral resolutions. The proposed method only estimates the mean vector and covariance matrix of the high-resolution multispectral (MS) images, instead of assuming the joint distribution between the panchromatic (PAN) image and low-resolution multispectral image. Furthermore, the proposed method can enhance the spatial resolution of several principal components of MS images, while the traditional Principal Component Analysis (PCA) method is limited to enhance only the first principal component. Experimental results with real MS images and PAN image of Landsat ETM demonstrate that the proposed method performs better than traditional methods based on statistical parameter estimation, PCA-based method and wavelet-based method.     

Control relevant estimation of plant and disturbance dynamics

Estimating models for both plant and disturbance dynamics is important in control design applications that focus on disturbance rejection. Several methods for low-order approximate model estimation on the basis of closed-loop data exist in the literature, but fail to address the simultaneous estimation of low-order approximate models of both plant and disturbance dynamics. In this paper a new extended two-stage methodology is proposed that allows for low-order approximate disturbance model estimation. In the proposed extended two-stage method the first stage is used to estimate high-order models for filtering purposes. In the second stage, filtered signals are used to provide the means for low-order approximate model estimation of both plant and disturbance dynamics.     

Linear estimation in the presence of errors in assumed plant dynamics

This paper is concerned with certain practical aspects of linear estimation theory. Several questions, which arise during most applications of the theory, are partially answered by considering the following problem: Obtain the linear minimum variance estimate of the systemx_{n+1} = (Phi_{n+1.n} + deltaPhi_{n+1.n})x_{n} + w_{n}y_{n} = M_{n}x_{n}when the assumed model is given byx_{n+1} = Phi_{n+1.n}x_{n} + w_{n}y_{n} = M_{n}x_{n}     

Effects of chlorophyll fluorescence on the estimation of microphytobenthos biomass using spectral reflectance indices

The communities of benthic microalgae that form dense biofilms at the surface of aquatic sediments, or microphytobenthos, are important primary producers in estuarine intertidal flats and shallow coastal waters. The microalgal biomass present in the photic zone of the sediment is a key parameter for ecological and photophysiological studies on microphytobenthos, and has been routinely estimated using hyperspectral reflectance indices based on the chlorophyll (Chl) a red absorption peak at 675 nm, usually the Normalised Difference Vegetation Index (NDVI). This study reports that red region-based biomass indices measured on microphytobenthos biofilms can be significantly affected by the enrichment of reflected light with solar-induced Chl fluorescence emitted by the microalgae. Chl fluorescence emission peaks at 683 nm, counterbalancing the decrease in reflectance centered at 675 nm, thus causing the underestimation of NDVI. The interference of Chl fluorescence was found to be easily identified by a conspicuous double-peak feature in the 670-700 nm region of the second-derivative reflectance spectra. The fluorescence-induced NDVI underestimation was shown to be most pronounced for high surface biomass levels and low incident solar irradiance. Particular aspects of microphytobenthos biofilms, such as the increase in surface Chl fluorescence due the contribution of emission by subsurface layers, and vertical migratory responses by motile microalgae to changes in ambient light, further complicate the effects on biomass estimation using NDVI-like indices. By comparing NDVI with a fluorescence-independent biomass index for a wide range of natural light conditions, it was found that Chl fluorescence interference may cause the underestimation of microalgal biomass to reach over 25%, with errors above 10% being expected for more than half of the measuring occasions. These results indicate that the use of NDVI may compromise the correct assessment of important aspects of microphytobenthos ecology, such as the characterisation of migratory behaviour or the determination of biomass-specific productivity rates, and call for the use of alternative biomass indices, not based on the Chl a red absorption peak.