Do flowers affect biomass estimate accuracy from NDVI and EVI?

The Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) are vegetation indices widely used in remote sensing of above-ground biomass. Because both indexes are based on spectral features of plant canopy, NDVI and EVI may suffer reduced accuracy in estimating above-ground biomass when flower signals are mixed in the plant canopy. This paper addresses how flowers influence the estimation of above-ground biomass using NDVI and EVI for an alpine meadow with mixed yellow flowers of Halerpestes tricuspis (Ranunculaceae). Field spectral measurements were used in combination with simulated reflectance spectra with precisely controlled flower coverage by applying a linear spectral mixture model. Using the reflectance spectrum for the in-situ canopy with H. tricuspis flowers, we found no significant correlation between above-ground biomass and EVI (p?=?0.17) or between above-ground biomass and NDVI (p?=?0.78). However, both NDVI and EVI showed very good prediction of above-ground biomass with low root mean square errors (RMSE?=?43 g m^?2 for NDVI and RMSE?=?43 g m^?2 for EVI, both p < 0.01) when all the flowers were removed from the canopies. Simulation analysis based on the in-situ measurements further indicated that high variation in flower coverage among different quadrats could produce more noise in the relationship between above-ground biomass and NDVI, or EVI, which results in an evident decline in the accuracy of above-ground biomass estimation. Therefore, the study suggests that attention should be paid both to the flower fraction and the heterogeneity of flower distribution in the above-ground biomass estimation via NDVI and EVI.     

Simple reflectance indices track heat and water stress-induced changes in steady-state chlorophyll fluorescence at the canopy scale

Non-invasive remote sensing techniques for monitoring plant stress and photosynthetic status have received much attention. The majority of published vegetation indices are not sensitive to rapid changes in plant photosynthetic status brought on by common environmental stressors such as diurnal fluxes in irradiance and heat. This is due to the fact that most vegetation indices have no direct link to photosynthetic functioning beyond their sensitivity to canopy structure and pigment concentration changes. In contrast, this study makes progress on a more direct link between passive reflectance measurements and plant physiological status through an understanding of photochemical quenching (qP) and non-photochemical quenching processes. This is accomplished through the characterization of steady-state fluorescence (Fs) and its influence on apparent reflectance in the red-edge spectral region. A series of experiments were conducted under controlled environmental conditions, linking passive reflectance measurements of a grapevine canopy (Vitis vinifera L. cv. Cabernet Sauvignon) to leaf level estimates of CO₂ assimilation (A), stomatal conductance (g), qP, and Fs. Plant stress was induced by imposing a diurnal heat stress and recovery event and by withholding water from the plant canopy over the course of the experiment. We outlined evidence for a link between Fs and photosynthetic status, identified the Fs signal in passive remote sensing reflectance data, and related reflectance-derived estimates of Fs to plant photosynthetic status. These results provide evidence that simple reflectance indices calculated in the red-edge spectral region can track temperature and water-induced changes in Fs and, consequently, provide a rapid assessment of plant stress that is directly linked to plant physiological processes.     

Post senescent grass canopy remote sensing

Analysis of in situ collected spectral reflectance data from a dormant or senescent grass canopy showed a direct relationship existed between spectral reflectance and biomass for the 0.50–0.80 μm spectral region. The data, collected four weeks after the end of the growing season, indicated that post senescent remote sensing of grass canopy biomass is possible and helps to elucidate the spectral contribution of recently dead vegetation in mixed live/dead canopy situations.     

Reflectance assessment of seasonal and annual changes in biomass and CO2 uptake of a Mediterranean shrubland submitted to experimental warming and drought

We aimed to evaluate how the remote sensing vegetation indices NDVI and PRI responded to seasonal and annual changes in an early successional stage Mediterranean coastal shrubland canopy that was submitted to experimental warming and drought simulating predicted climate change for the next decades. These conditions were obtained by using a new non-intrusive methodological approach that increases the temperature and prolongs the drought period by using roofs that automatically cover the vegetation after the sunset or when it rains. On average, warming increased air temperature by 0.7 °C and soil temperature by 1.6 °C, and the drought treatment reduced soil moisture by 22%. We measured spectral reflectance at the canopy level and at the individual plant level seasonally during 4 years. Shrubland NDVI tracked the community development and activity. In control and warming treatments, NDVI increased with the years while it did not change in the drought treatment. There was a good relationship between NDVI and both community and individual plant biomass. NDVI also decreased in summer seasons when some species dry or decolour. The NDVI of E. multiflora plant individuals was lower in autumn and winter than in the other seasons, likely because of flowering. Shrubland PRI decreased only in winter, similarly to the PRI of the most dominant species, G. alypum. At this community scale, NDVI was better related than PRI to photosynthetic activity, probably because photosynthetic fluxes followed canopy seasonal greening in this complex canopy, which includes brevideciduous, annual and evergreen species and variable morphologies and canopy coverage. PRI followed the seasonal variations in photosynthetic rates in E. multiflora and detected the decreased photosynthetic rates of drought treatment. However, PRI did not track the photosynthetic rates of G. alypum plants which have lower LAIs than E. multiflora. In this community, which is in its early successional stages, NDVI was able to track biomass, and indirectly, CO₂ uptake changes, likely because LAI values did not saturate NDVI. Thus, NDVI appears as a valid tool for remote tracking of this community development. PRI was less adequate for photosynthetic assessment of this community especially for its lower LAI canopies. PRI usefulness was also species-dependent and could also be affected by flowering. These results will help to improve the interpretation of remote sensing information on the structure and physiological status of these Mediterranean shrublands, and to gain better insight on ecological and environmental controls on their ecosystem carbon dioxide exchange. They also show the possibility of assessing the impacts of climate change on shrubland communities.     

Estimating natural grassland biomass by vegetation indices using Sentinel 2 remote sensing data

ABSTRACT

Estimation of natural grassland biomass was carried out in a region located in the Brazilian Pampa, using field and remote sensing data and statistical models. The study was conducted in a grassland with a rotational grazing system, with grazing rest interval based on accumulated thermal sums 375 and 750 Degrees Day (DD). One image of the MSI (MultiSpectral Instrument) sensor aboard the Sentinel-2 satellite was evaluated and calibrated by 57 sampled biomass units collected in the field. Initially, the image was preprocessed, with extraction of the reflectance values of the Sentinel-2 bands, re-sampling of the pixels to 20 metres and calculation of vegetation indices. Data statistical analyses indicated significant correlations between field and remote sensing data. Multiple linear regression analyses were applied at each grazing rest interval using the remote sensing variables as predictors (independent) of the biomass (dependent). Among the variables, it is important to highlight the significant correlation of the red-edge bands with the biomass. The equations for estimating green biomass-presented coefficients of determination (R²) of R² = 0.51 for the rest interval 375 DD and R² = 0.65 for the rest interval 750 DD, while the senescent and total biomass generated adjustments with R² ≤ 0.50 for the two rest intervals. Biomass estimates results were satisfactory, regardless of the interval evaluated. Sampling schemes at different seasons of the year and further spectral and field variables (spectral and biomass) are suggested to improve even more the accuracy of the estimates.     

Assessing photosynthetic light-use efficiency using a solar-induced chlorophyll fluorescence and photochemical reflectance index

Photosynthetic light-use efficiency (LUE) is an important indicator of plant photosynthesis, but assessment by remote sensing needs to be further explored. In this study, two protective mechanisms for photosynthesis, chlorophyll fluorescence (ChlF) and heat dissipation in the deep oxidation state of the xanthophyll cycle, were explored to estimate photosynthetic LUE from canopy radiance spectra. Four independent experiments were carried out on summer maize (C4 plant) on 5 July 2008, and winter wheat (C3 plant) on 18 April 2008, 16 April 2010, and 13 May 2010, by synchronously measuring daily canopy radiance spectra and photosynthetic LUE. The competitive relationship between ChlF and photochemical yield made it possible to estimate photosynthetic LUE. LUE–ChlF models were developed for ChlF at 688 nm (R ² = 0.72) and 760 nm (R ² = 0.59) based on the experimental data from 13 May 2010 at the Guantao flux site. The LUE–ChlF models were validated by three independent experiments, and the results showed that the LUE–ChlF relationship was weakened, possibly by variation in species, canopy density, and environmental conditions. As an easy, rapid, non-intrusive method, a photochemical reflectance index (PRI) provides an instantaneous assessment of dynamic photosynthetic LUE. The significant negative relationship between non-photochemical quenching and PRI was confirmed. Although there was a significantly positive relationship between LUE and PRI in all four independent experiments, this was evidently weakened by the canopy and environmental conditions. Difference in PRI (ΔPRI) from the minimum reference PRI around noontime can largely eliminate interference factors. The LUE–ΔPRI model was developed based on experimental data from 13 May 2010 at the Guantao flux site (with an R ² value of 0.85), and validated by the three other independent experiments. The validation result indicated that different species can markedly affect the precision of the PRI difference method.     

Correlation between soil apparent electroconductivity and plant hyperspectral reflectance in a managed wetland

The apparent electrical conductivity (σ_a) of soil is influenced by a complex combination of soil physical and chemical properties. For this reason, σ_a is proposed as an indicator of plant stress and potential community structure changes in an alkaline wetland setting. However, assessing soil σ_a is relatively laborious and difficult to accomplish over large wetland areas. This work examines the feasibility of using the hyperspectral reflectance of the vegetation canopy to characterize the σ_a of the underlying substrate in a study conducted in a Central California managed wetland. σ_a determined by electromagnetic (EM) inductance was tested for correlation with in-situ hyperspectral reflectance measurements, focusing on a key waterfowl forage species, swamp timothy (Crypsis schoenoides). Three typical hyperspectral indices, individual narrow-band reflectance, first-derivative reflectance and a narrow-band normalized difference spectral index (NDSI), were developed and related to soil σ_a using univariate regression models. The coefficient of determination (R ²) was used to determine optimal models for predicting σ_a, with the highest value of R ² at 2206 nm for the individual narrow bands (R ²?=?0.56), 462 nm for the first-derivative reflectance (R ²?=?0.59), and 1549 and 2205 nm for the narrow-band NDSI (R ²?=?0.57). The root mean squared error (RMSE) and relative root mean squared error (RRMSE) were computed using leave-one-out cross-validation (LOOCV) for accuracy assessment. The results demonstrate that the three indices tested are valid for estimating σ_a, with the first-derivative reflectance performing better (RMSE?=?30.3 mS m^?1, RRMSE?=?16.1%) than the individual narrow-band reflectance (RMSE?=?32.3 mS m^?1, RRMSE?=?17.1%) and the narrow-band NDSI (RMSE?=?31.5 mS m^?1, RRMSE?=?16.7%). The results presented in this paper demonstrate the feasibility of linking plant–soil σ_a interactions using hyperspectral indices based on in-situ spectral measurements.     

Separating physiologically and directionally induced changes in PRI using BRDF models

Monitoring of photosynthetic efficiency (ε) over space and time is a critical component of climate change research as it is a major determinant of the amount of carbon accumulated by terrestrial ecosystems. While the past decade has seen progress in the remote estimation of ε at the leaf, canopy and stand level using the photochemical reflectance index PRI (based on the normalized difference of reflectance at 531 and 570 nm), little is known about the temporal and spatial requirements for up-scaling PRI to landscape and global levels using satellite observations. One potential way to investigate these requirements is using automated tower-based remote sensing platforms, which observe stand level reflectance with high spatial, temporal, and spectral resolution. Prediction of ε from PRI diurnally or over a full year requires observations of canopy reflectance over multiple view and sun-angles. As a result, these observations are subject to directional reflectance effects which can be interpreted in terms of the bidirectional reflectance distribution function (BRDF) using semi-empirical kernel driven models. These semi-empirical models use a combination of physically based BRDF shapes and empirical observations to standardize multi-angular observations to a common viewing and illumination geometry. Directional reflectance effects are thereby modeled as a linear superposition of mathematical kernels, representing the bi-direction variation in reflectance from isotropic, geometric, and volumetric scattering components of the vegetation canopy. However, because variations in plant physiological conditions can also introduce bidirectional reflectance variations, we introduce an approach to separate bidirectional effects arising purely from plant physiological status from other effects by stratifying PRI observations into categories based on environmental conditions for which the expected physiological variability is low. Within each of these PRI strata, the derived physically based BRDF shapes were used to standardize multi-angular PRI measurements to a common viewing and illumination geometry. The method significantly enhanced the relationship found between PRI and ε (from r² = 0.38 for the directionally uncorrected case to r² = 0.82 for the directionally corrected case) from data measured continuously over the course of 1 year over an evergreen conifer forest using an automated platform. Results show that isotropic PRI scattering is highly correlated to changes in ε, while geometric scattering can be related to canopy level shading. Instrumentation and approaches such as the one demonstrated in this study may be integrated into current efforts aiming at predicting ε at global scales using satellite observations.     

Estimating forage quantity and quality using aerial hyperspectral imagery for northern mixed-grass prairie

Sustainable rangeland stewardship calls for synoptic estimates of rangeland biomass quantity (kg dry matter ha^− 1) and quality [carbon:nitrogen (C:N) ratio]. These data are needed to support estimates of rangeland crude protein in forage, either by percent (CP_c) or by mass (CP_m). Biomass derived from remote sensing data is often compromised by the presence of both photosynthetically active (PV) and non-photosynthetically active (NPV) vegetation. Here, we explicitly quantify PV and NPV biomass using HyMap hyperspectral imagery. Biomass quality, defined as plant C:N ratio, was also estimated using a previously published algorithm. These independent algorithms for forage quantity and quality (both PV and NPV) were evaluated in two northern mixed-grass prairie ecoregions, one in the Northwestern Glaciated Plains (NGGP) and one in the Northwestern Great Plains (NGP). Total biomass (kg ha^− 1) and C:N ratios were mapped with 18% and 8% relative error, respectively. Outputs from both models were combined to quantify crude protein (kg ha^− 1) on a pasture scale. Results suggest synoptic maps of rangeland vegetation mass (both PV and NPV) and quality may be derived from hyperspectral aerial imagery with greater than 80% accuracy.     

Estimating aboveground biomass of grassland having a high canopy cover: an exploratory analysis of in situ hyperspectral data

To improve the estimation of aboveground biomass of grassland having a high canopy cover based on remotely sensed data, we measured in situ hyperspectral reflectance and the aboveground green biomass of 42 quadrats in an alpine meadow ecosystem on the Qinghai–Tibetan Plateau. We examined the relationship between aboveground green biomass and the spectral features of original reflectance, first-order derivative reflectance (FDR), and band-depth indices by partial least squares (PLS) regression, as well as the relationship between the aboveground biomass and narrow-band vegetation indices by linear and nonlinear regression analyses. The major findings are as follows. (1) The effective portions of spectra for estimating aboveground biomass of a high-cover meadow were within the red-edge and near infrared (NIR) regions. (2) The band-depth ratio (BDR) feature, using NIR region bands (760–950 nm) in combination with the red-edge bands, yields the best predictive accuracy (RMSE?=?40.0 g m^?2) for estimating biomass among all the spectral features used as independent variables in the partial least squares regression method. (3) The ratio vegetation index (RVI2) and the normalized difference vegetation index (NDVI2) proposed by Mutanga and Skidmore (Mutanga, O. and Skidmore, A.K., 2004a Mutanga, O. and Skidmore, A. K. 2004a. Narrow band vegetation indices solve the saturation problem in biomass estimation. International Journal of Remote Sensing, 25: 1–16.  [Google Scholar], Narrow band vegetation indices solve the saturation problem in biomass estimation. International Journal of Remote Sensing, 25, pp. 1–6) are better correlated to the aboveground biomass than other VIs (R ²?=?0.27 for NDVI2 and 0.26 for RVI2), while RDVI, TVI and MTV1 predicted biomass with higher accuracy (RMSE?=?37.2 g m^?2, 39.9 g m^?2 and 39.8 g m^?2, respectively). Although all of the models developed in this study are probably acceptable, the models developed in this study still have low accuracy, indicating the urgent need for further efforts.     

Impact of understory vegetation on forest canopy reflectance and remotely sensed LAI estimates

Forest leaf area index (LAI), is an important variable in carbon balance models. However, understory vegetation is a recognized problem that limits the accuracy of satellite-estimated forest LAI. A canopy reflectance model was used to investigate the impact of the understory vegetation on LAI estimated from reflectance values estimated from satellite sensor data. Reflectance spectra were produced by the model using detailed field data as input, i.e. forest LAI, tree structural parameters, and the composition, distribution and reflectance of the forest floor. Common deciduous and coniferous forest types in southern Sweden were investigated. A negative linear relationship (r² = 0.6) was observed between field estimated LAI and the degree of understory vegetation, and the results indicated better agreement when coniferous and deciduous stands were analysed separately. The simulated spectra verified that the impact of the understory on the reflected signal from the top of the canopy is important; the reflectance values varying by up to ± 18% in the red and up to ± 10% in the near infra-red region of the spectra due to the understory. In order to predict the variation in LAI due to the understory vegetation, model inversions were performed where the input spectra were changed between the minimum, average and maximum reflectance values obtained from the forward runs. The resulting variation in LAI was found to be 1.6 units on average. The LAI of the understory could be predicted indirectly from simple stand data on forest characteristics, i.e. from allometric estimates, as an initial step in the process of estimating LAI. It is suggested here that compensation for the effect of the understory would improve the accuracy in the estimates of canopy LAI considerably.     

Characterizing vegetation indices derived from active and passive sensors

New ‘active’ sensors containing their own light source may provide consistent measures of plant and soil characteristics under varying illumination without calibration to reflectance. In 2006, an active sensor (Crop Circle) and various passive sensors were compared in a wheat (Triticum aestivum L., c.v. Chara) experiment in Horsham, VIC, Australia. The normalized difference vegetation index (NDVI) and soil-adjusted vegetation index (SAVI) were calculated from plot data with a range of canopy cover, leaf area and biomass. The active sensor NDVI and SAVI data were slightly less effective than corresponding passive sensor data at estimating green cover (r ²?=?0.80?0.90 vs. ～0.95). Passive sensor measurements showed strong non-linearity for estimating dry biomass and green leaf area index (GLAI), whereas SAVI calculated from the active sensor was linear (r ²?=?0.86 and 0.90). Scaling effects were not apparent when point, transect and plot areas were compared at the given level of spatial variation. Sensor height above the target confounded SAVI data probably due to differential irradiance from the light sources and the unbalanced effect of the ‘L’ factor within the algorithm. The active sensor was insensitive to light conditions (r ²?=?0.99 for cloudy vs. clear skies) and had no requirement for optical calibration.     

Atmospheric optical depth effects on angular anisotropy of plant canopy reflectance

Abstract

A field experiment was conducted to determine whether changes in atmospheric aerosol optical depth would effect changes in bi-directional reflectance distributions of vegetation canopies. Measurements were made of the directionally reflected radiance distributions of two pasture grass canopies (same species, different growth forms) and one soya bean plant canopy under different sky irradiance distributions, which resulted from a variation in aerosol optical depth. The reflected radiance data were analysed in the solar principal plane in two narrow spectral bands, one visible (662 nm) and one infrared (826 nm). The observed changes in reflectance for both wavelengths from irradiance distribution variation is interpreted to be due largely to changes in the percentage of shadowed area viewed by the sensor for the incomplete canopies (pasture grass). For the complete coverage vegetation canopy (soya bean) studied, the effects of specular reflection and the increased diffuse irradiance penetration into the canopy are concluded to be primary physical mechanisms responsible for reflectance changes. Observed reflectivities were found to be lower on a hazy day (higher optical depth with a greater diffuse fraction) than on a clear day, with solar zenith angles at about 58^° on both days, for full-coverage soya bean canopies. The reduced reflectance most likely results from a diminished specular reflection and a greater diffuse radiation penetration into the canopy, which effects an increased energy absorption at large solar zenith angles. The opposite was true for fractional coverage grass canopies at solar zenith angles of about 56^° since the shadowing was less on the hazy day and, therefore, the soil/litter background was more fully illuminated. In the near-infrared waveband the changes in reflectance are much less than in the visible and, therefore, normalized difference vegetation index values differ substantially under clear and hazy sky conditions for the same vegetation canopy conditions. Thus, the influence of atmospheric optical depth must be considered for accurate remote sensing and in situ data interpretation.     

Identification and quantification of aquatic vegetation with hyperspectral remote sensing in western Nevada rivers,USA

This study used a portable spectrometer to assess the feasibility of using airborne hyperspectral imagery to map the dominant types and amounts of aquatic vegetation in the Carson and Truckee Rivers of western Nevada. Spectral reflectance data were acquired for a number of periphyton and macrophyte types, and corresponding vegetation samples were processed in the laboratory to quantify chlorophyll a (chla) and ash-free dry mass. The dominant periphyton and macrophyte communities encountered in the field could be identified with an overall accuracy of greater than 95%. The lowest individual class accuracy was 82% for one community type, primarily green filamentous algae (GF), which was brown in colour and mixed with diatoms and sediments. Separate stepwise regression models were developed for chla and biomass of each type of vegetation. Regression models had r ²s greater than 0.92, except for the aforementioned brown-coloured community of mixed algae that had r ²s of just over 0.5 for both laboratory measurements. This study suggests good prospects for airborne hyperspectral surveys of aquatic vegetation for water quality studies, assuming a sensor with a high signal-to-noise ratio, high spatial resolution and good environmental conditions at the time of image acquisition.     

Correlated change in normalized difference vegetation index and the seasonal trajectory of photosynthetic capacity in a conifer stand

Seasonal changes in canopy photosynthetic activity play an important role in carbon assimilation. However, few simulation models for estimating carbon balances have included them due to scarcity in quality data. This paper investigates some important aspects of the relationship between the seasonal trajectory of photosynthetic capacity and the time series of a common vegetation index (normalized difference vegetation index, NDVI), which was derived from on site micrometeorological measurements or smoothed and downscaled from satellite‐borne NDVI sensors. A parameter indicating the seasonality of canopy physiological activity, P _E, was retrieved through fitting a half‐hour step process model, PROXEL_NEE, to gross primary production (GPP) estimates by inversion for carboxylation and light utilization efficiencies. The relative maximum rate of carboxylation (V _rm), a parameter that indicates the seasonality of CO₂ uptake potential under prevailing temperature, was then calculated from P _E and daily average air temperature. Statistical analysis revealed that there were obvious exponential relationships between NDVI and the seasonal courses for both canopy physiological activities P _E and V _rm. Among them, the on‐site broadband NDVI provided a robust and consistent relationship with canopy physiological activities (R ² = 0.84). The relationships between satellite‐borne NDVI time series with instantaneous canopy physiological activities at the time of satellite passing were also checked. The results indicate that daily step NDVI time series (data downscaled from composite temporal resolution NDVI) better represent the daily average activity of the canopy. These findings may enable us to retrieve the seasonal course of canopy physiological activity from widely available NDVI data series and, thus, to include it into carbon assimilation models. However, both smoothing methods for satellite‐borne NDVI time series may generate incorrect estimates and must be treated with care.     

Leaf and stand-level carbon uptake of a Mediterranean forest estimated using the satellite-derived reflectance indices EVI and PRI

Various aspects of global environmental change affect plant photosynthesis, the primary carbon input in ecosystems. Thus, accurate methods of measuring plant photosynthesis are important. Remotely sensed spectral indices can monitor in detail the green biomass of ecosystems, which provides a measure of potential photosynthetic capacity. In evergreen vegetation types, however, such as Mediterranean forests, the amount of green biomass changes little during the growing season and, therefore, changes in green biomass are not responsible for changes in photosynthetic rates in those forests. This study examined the net photosynthetic rates and the diametric increment of stems in a Mediterranean forest dominated by Quercus ilex using three spectral indices (normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and photochemical reflectance index (PRI)) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Average annual EVI accounted for 83% of the variability of the diametric increment of Q. ilex stems over a 10 year period. NDVI was marginally correlated with the diametric increment of stems. This study was the first to identify a significant correlation between net photosynthetic rates and radiation use efficiency at the leaf level using PRI derived from satellite data analysed at the ecosystem level. These results suggest that each spectral index provided different and complementary information about ecosystem carbon uptake in a Mediterranean Q. ilex forest.     

Modelling light use efficiency using vegetation index and land surface temperature from MODIS in Harvard Forest

Light use efficiency (LUE) is of great importance for carbon cycle and climate change research. This study presents a new LUE model incorporation of vegetation indices (VIs) and land surface temperature (LST) derived from the Moderate-Resolution Imaging Spectroradiometer (MODIS) in Harvard Forest. Three indices, including the normalized difference vegetation index (NDVI), the two-band enhanced vegetation index (EVI2) and the soil-adjusted vegetation index (SAVI), were selected as indicators of forest canopy greenness. A single VI provided moderate estimates of LUE with coefficients of determination (R ²) 0.6219, 0.7094 and 0.7502 for NDVI, EVI2 and SAVI, respectively. Our results demonstrated that canopy LUE was related both to the canopy photosynthesis efficiency and air temperature (R ²?=?0.5634). Therefore, the MODIS LST product was incorporated as a surrogate for monitoring of environmental stresses as the observed relationship between LST and both air temperature (R ²?=?0.8828) and vapour pressure deficit (VPD) (R ²?=?0.6887). The new model in terms of (VI)?×?(Scaled (LST)) provided improved estimates of LUE estimation with R ² of 0.7349, 0.7561 and 0.7879 for NDVI, EVI2 and SAVI, respectively. The results will be useful for the development of future LUE models based entirely on remote-sensing observations.     

Linking physical remote sensing models with crop growth simulation models,applied for sugar beet

Abstract

In recent years, remote sensing and crop growth simulation models have become increasingly recognized as potential tools for growth monitoring and yield estimation of agricultural crops. In this paper, a methodology is developed to link remote sensing data with a crop growth model for monitoring crop growth and development. The Cloud equations for radar backscattering and the optical canopy radiation model EXTRAD were linked to the crop growth simulation model SUCROS: SUCROS-Cloud-EXTRAD. This combined model was initialized and re-parameterized to fit simulated X-band radar backscattering and/or optical reflectance values, to measured values. The developed methodology was applied for sugar beet. The simulated canopy biomass after initialization and re-parameterization was compared with simulated canopy biomass with SUCROS using standard input, and with measured biomass in the field, for 11 fields in different years and different locations. The seasonal-average error in simulated canopy biomass was smaller with the initialized and re-parameterized model (225-475 kg ha^?1), than with SUCROS using standard input (390-700 kg ha^?1), with ‘end-of-season’ canopy biomass values between 5500 and 7000kgha^?1. X-band radar backscattering and optical reflectance data were very effective in the initialization of SUCROS. The radar backscattering data further adjusted SUCROS only during early crop growth (exponential growth), whereas optical data still adjusted SUCROS until late in the growing season (at high levels of leaf area index (LAI), 3-5).     

Responses of the reflectance indices PRI and NDVI to experimental warming and drought in European shrublands along a north-south climatic gradient

The aim of this study was to evaluate the use of ground-based canopy reflectance measurements to detect changes in physiology and structure of vegetation in response to experimental warming and drought treatment at six European shrublands located along a North-South climatic gradient. We measured canopy reflectance, effective green leaf area index (green LAIe) and chlorophyll fluorescence of dominant species. The treatment effects on green LAIe varied among sites. We calculated three reflectance indices: photochemical reflectance index PRI [531 nm; 570 nm], normalized difference vegetation index NDVI₆₈₀ [780 nm; 680 nm] using red spectral region, and NDVI₅₇₀ [780 nm; 570 nm] using the same green spectral region as PRI. All three reflectance indices were significantly related to green LAIe and were able to detect changes in shrubland vegetation among treatments. In general warming treatment increased PRI and drought treatment reduced NDVI values. The significant treatment effect on photochemical efficiency of plants detected with PRI could not be detected by fluorescence measurements. However, we found canopy level measured PRI to be very sensitive to soil reflectance properties especially in vegetation areas with low green LAIe. As both soil reflectance and LAI varied between northern and southern sites it is problematic to draw universal conclusions of climate-derived changes in all vegetation types based merely on PRI measurements. We propose that canopy level PRI measurements can be more useful in areas of dense vegetation and dark soils.     

Using wavelet analysis of hyperspectral remote-sensing data to estimate canopy chlorophyll content of winter wheat under stripe rust stress

Chlorophyll content can be used as an indicator to monitor crop diseases. In this article, an experiment on winter wheat stressed by stripe rust was carried out. The canopy reflectance spectra were collected when visible symptoms of stripe rust in wheat leaves were seen, and canopy chlorophyll content was measured simultaneously in laboratory. Continuous wavelet transform (CWT) was applied to process the smoothed spectral and derivative spectral data of winter wheat, and the wavelet coefficient features obtained by CWT were regarded as the independent variable to establish estimation models of chlorophyll content. The hyperspectral vegetation indices were also regarded as the independent variable to build estimation models. Then, two types of models above-mentioned were compared to ascertain which type of model is better. The cross-validation method was used to determine the model accuracies. The results indicated that the estimation model of chlorophyll content, which is a multivariate linear model constructed using wavelet coefficient features extracted by Mexican Hat wavelet function processing the smoothed spectrum (WSMH1 and WSMH2), is the best model. It has the highest estimation accuracy with modelled coefficient of determination (R²) of 0.905, validated R² of 0.913, and root mean square error (RMSE) of 0.288 mg fg^?1. The univariate linear model built by wavelet coefficient feature of WSMH1 is secondary and the modelled R² is 0.797, validated R² is 0.795, and RMSE is 0.397 mg fg^?1. Both estimation models are better than those of all hyperspectral vegetation indices. The research shows that the feature information of canopy chlorophyll content of winter wheat can be captured by wavelet coefficient features which are extracted by the method of CWT processing canopy reflectance spectrum data. Therefore, it could provide theoretical support on detecting diseases of crop by remote sensing quantitatively estimating chlorophyll content.