PROSAIL Model Research Articles

Crop specific algorithms trained over ground measurements provide the best performance for GAI and fAPAR estimates from Landsat-8 observations

Estimation of Green Area Index (GAI) and fraction of Absorbed Photosynthetically Active Radiation (fAPAR) from decametric satellites was investigated in this study using a large database of ground measurements over croplands. It covers six main crop types including rice, corn, wheat and barley, sunflower, soybean and other types of crops. Ground measurements were completed using either digital hemispherical cameras, LAI-2000 or AccuPAR devices over sites representative of a decametric pixel. Sites were spread over the globe and the data collected at several growth stages concurrently to the acquisition of Landsat-8 images. Several machine learning techniques were investigated to retrieve GAI and fAPAR from the Landsat-8 top of canopy reflectance values, either using empirical or simulated calibration databases and generic or crop-type specific algorithms.Results show that using the six Landsat-8 bands together provided the best estimates of GAI and fAPAR. Machine learning techniques trained over a dataset simulated by the PROSAIL model provided less accurate estimates of GAI and fAPAR as compared to machine learning techniques trained over the ground data collected and applied over a similar dataset (best-case scenario). All machine learning techniques performed similarly when calibrated on PROSAIL simulations. The Gaussian process regression (GPR) was the best performing machine learning technique as compared to artificial neural networks (ANN), support vector machine regression (SVM) and as compared to NDVI simple model when calibrated over the ground dataset in the best-case scenario. However, the performance of the GPR trained over ground data is notably degraded when applied to crop types excluded from the calibration (worst-case scenario), and models based on simulations performs similarly of even better. Furthermore, training the GPR over specific crop types was performing slightly better than training over all the crop types together if at least 100 well distributed data points were available in the training dataset. Similar conclusions were obtained for the other machine learning techniques, with crop specific empirical models providing slightly better performances with a few exceptions. Finally, GAI was estimated by GPR with a RMSE varying between 0.45 and 1.19 and fAPAR with RMSE varying between 0.07 and 0.15 depending on the crop type.

Remote Sensing Energy Balance Model for the Assessment of Crop Evapotranspiration and Water Status in an Almond Rootstock Collection.

One of the objectives of many studies conducted by breeding programs is to characterize and select rootstocks well-adapted to drought conditions. In recent years, field high-throughput phenotyping methods have been developed to characterize plant traits and to identify the most water use efficient varieties and rootstocks. However, none of these studies have been able to quantify the behavior of crop evapotranspiration in almond rootstocks under different water regimes. In this study, remote sensing phenotyping methods were used to assess the evapotranspiration of almond cv. “Marinada” grafted onto a rootstock collection. In particular, the two-source energy balance and Shuttleworth and Wallace models were used to, respectively, estimate the actual and potential evapotranspiration of almonds grafted onto 10 rootstock under three different irrigation treatments. For this purpose, three flights were conducted during the 2018 and 2019 growing seasons with an aircraft equipped with a thermal and multispectral camera. Stem water potential (Ψstem) was also measured concomitant to image acquisition. Biophysical traits of the vegetation were firstly assessed through photogrammetry techniques, spectral vegetation indices and the radiative transfer model PROSAIL. The estimates of canopy height, leaf area index and daily fraction of intercepted radiation had root mean square errors of 0.57 m, 0.24 m m–1 and 0.07%, respectively. Findings of this study showed significant differences between rootstocks in all of the evaluated parameters. Cadaman® and Garnem® had the highest canopy vigor traits, evapotranspiration, Ψstem and kernel yield. In contrast, Rootpac® 20 and Rootpac® R had the lowest values of the same parameters, suggesting that this was due to an incompatibility between plum-almond species or to a lower water absorption capability of the rooting system. Among the rootstocks with medium canopy vigor, Adesoto and IRTA 1 had a lower evapotranspiration than Rootpac® 40 and Ishtara®. Water productivity (WP) (kg kernel/mm water evapotranspired) tended to decrease with Ψstem, mainly in 2018. Cadaman® and Garnem® had the highest WP, followed by INRA GF-677, IRTA 1, IRTA 2, and Rootpac® 40. Despite the low Ψstem of Rootpac® R, the WP of this rootstock was also high.

Analyzing the Effects of Hyperspectral ZhuHai-1 Band Combinations on LAI Estimation Based on the PROSAIL Model.

Leaf area index (LAI) is a key biophysical variable to characterize vegetation canopy. Accurate and quantitative LAI estimation is significant for monitoring vegetation growth status. ZhuHai-1 (ZH-1), which is a commercial remote sensing micro-nano satellite, provides a possibility for quantitative detection of vegetation with high spatial and spectral resolution. However, the band characteristics of ZH-1 are closely related to the accuracy of vegetation monitoring. In this study, a simulation dataset containing 32 bands of ZH-1 was generated by using the PROSAIL model, which was used to analyze the performance of 32 bands for LAI estimation by using the hybrid inversion method. Meanwhile, the effect of different band combinations on LAI estimation was discussed based on sensitivity analysis and the correlation between bands. Then, the optimal band combination from ZH-1 hyperspectral satellite data for LAI estimation was obtained. LAI estimation was performed based on the selected optimal band combination of ZH-1 satellite images in Xiantao city, Hubei province, and compared with the Sentinel-2 normalized difference vegetation index (NDVI) values and LAI product. The results demonstrated that the obtained LAI map based on the optimal band combination of ZH-1 was generally consistent with the overall distribution of Sentinel-2 NDVI and the LAI product, but had a moderate correlation with Sentinel-2 LAI (R = 0.60), which may not favorably indicate the validity of indirect validation. However, the method of this study on the analysis of hyperspectral data bands has application potential to provide a reference for selecting appropriate bands of hyperspectral satellite data to estimate LAI and improve the application of hyperspectral data such as ZH-1 in vegetation monitoring.

Investigating different versions of PROSPECT and PROSAIL for estimating spectral and biophysical properties of photosynthetic and non-photosynthetic vegetation in mixed grasslands

ABSTRACT The PROSPECT and PROSAIL family of radiative transfer models (RTMs) are among the most popular for simulating vegetation spectra and estimating vegetation properties at the leaf and canopy levels. However, the main limitation of the radiative transfer model approach is that model performance depends on the exhaustiveness of the calibration database(s). The PROSPECT model was calibrated mainly with photosynthetic leaves, and thus does not contain specific absorption coefficients of decay pigments responsible for the spectral behavior of non-photosynthetic vegetation. Hence, PROSPECT and PROSAIL may be ill-suited to mixed ecosystems (e.g., grasslands and wetlands), especially in the late growing season when the non-photosynthetic vegetation is likely to obfuscate the quantification of green vegetation. This study investigates the performance of different versions of PROSPECT/PROSAIL models for simulating spectra and estimating biophysical properties of photosynthetic and non-photosynthetic vegetation, and aims to better understand the limitations of these RTMs and identify possible ways to improve their performance. Results show that the PROSPECT-5 and PROSPECT-D had challenges in simulating spectra of non-photosynthetic leaves in a mixed grassland area, while a modified version (PROSPECT-5M) that considered the absorption effects of decay pigments achieved higher accuracy (e.g., mean Root Mean Square Error (RMSE) of 0.014 compared to 0.026 for the PROSPECT-5). In comparison, there is minimal difference in RMSE between models for simulating green photosynthetic leaves. At the canopy level, the original PROSAIL model simulated the spectra well for homogeneous green canopies (with mean RMSE around 0.012), while a modified PROSAIL model simulated the spectra more accurately for mixed canopies that have photosynthetic and non-photosynthetic leaves (e.g., with a mean RMSE of 0.010 compared to 0.020 of original PROSAIL). The original and modified PROSAIL were then inverted using helicopter-based high-spatial resolution hyperspectral imagery to estimate vegetation properties, and achieved higher accuracies for green and mixed canopies, respectively (e.g., estimating canopy chlorophyll with R2 values over 0.75). Overall, different versions of PROSPECT/PROSAIL models have a varied performance for photosynthetic and non-photosynthetic vegetation. Understanding the limitations of the models and adopting corresponding measures to improve their performance is critical for successful applications of RTMs in the estimation of vegetation spectral and biophysical properties.

Spectral discrimination using infinite leaf reflectance and simulated canopy reflectance

ABSTRACT An important application of hyperspectral remote sensing is monitoring the diversity of plant species and functional types, which correlate with differences of leaf chemical constituents. Diffuse reflectance of an infinitely thick leaf ( R ∞) is directly related to chemical absorbance and may be calculated from leaf spectral reflectance and transmittance based on Kubelka-Munk theory or other radiative transfer models. The PROSPECT-D leaf optics model was used to create leaf optical properties of nine pseudo-species, and R ∞ were calculated using Stokes’, Lillesaeter’s, Goudriaan’s, and Hapke’s equations. Conceptually, R ∞ is assumed to equal canopy reflectance (canopy ρ ) at maximum leaf area index. For each pseudo-species, canopy ρ at a leaf area index of 10 was obtained using the PROSAIL model, but the simulated canopy ρ did not match R ∞. Similarity between R ∞ and canopy ρ for each pseudo-species was calculated using five spectral information metrics: Euclidean distance (ED), an adjusted spectral correlation measure (SCM), spectral angle mapper (SAM), spectral information divergence (SID), and the product of SID × sin(SAM). Differences using ED between canopy ρ and R ∞ were large, especially using Stokes’ equation. However, the SCM, SAM, SID, and SID × sin(SAM) metrics showed R ∞ from the Stokes’ equation was the most similar to canopy ρ , because these metrics are insensitive to differences in magnitude. The combined SID × sin(SAM) metric had the greatest similarity between R ∞ and canopy ρ compared to the other spectral information metrics. While R ∞ was not a good substitute for canopy ρ, R ∞ may be important for assessing differences of leaf chemical composition among species, thereby avoiding effects of taxon-independent factors on canopy ρ .

Estimation of water content for short vegetation based on PROSAIL model and vegetation water indices

Estimation of water content for short vegetation based on PROSAIL model and vegetation water indices

Estimation of leaf area index and leaf chlorophyll content in Sporobolus densiflorus using hyperspectral measurements and PROSAIL model simulations

ABSTRACT Previous studies have shown the potential of remote-sensing tools to monitor coastal wetlands at a landscape scale. Several biophysical parameters are typically used to evaluate ecosystem conditions such as the chlorophyll content, an indicator of photosynthesis activity. In natural environments characterized by a large fraction of standing litter – such as the marshes on the Atlantic coast of the Buenos Aires Province – spectral indices designed for green vegetation change their typical response and, hence, their biophysical interpretation requires more attention. In this work, a theoretical study was performed to determine if it is possible to detect and eventually quantify the abrupt reductions of leaf chlorophyll content () in Sporobolus densiflorus – the dominant vegetation in these marshes – using hyperspectral data. To achieve this, in situ radiometric measurements in the VIS-NIR-SWIR (Visible, Near-Infrared and Short Wave Infrared) spectral region and biological data, acquired over S. densiflorus specimens in several campaigns, were used to set up an inversion procedure based on the radiative transfer model PROSAIL. By applying this model, simulated reflectances that fit the measured reflectances were obtained and by means of this inversion a theoretical canopy reflectance data set for S. densiflorus was modelled using the PROSAIL parameters. The performance of several vegetation indices typically used to estimate chlorophyll content was studied using the simulated and modelled reflectances, among which MTCI (MERIS Terrestrial Chlorophyll Index), Macc and MCARI/OSAVI (Modified Chlorophyll Absorption in Reflectance Index/Optimized Soil-Adjusted Index) indices showed significant correlation with . By means of addressing the performance of these indices together with BLRs (Baseline Residuals), a two-step VI-LUT (Vegetation Index – Look-Up Table) inversion model was proposed to retrieve , which first corrects the effect of variable leaf area index (LAI) using a BLR index (using bands at 800, 1100 and 1300 nm), and then retrieves by either using MCARI/OSAVI or another BLR (using bands at 657, 672 and 700 nm). Even though both procedures perform similarly well in the estimation of (coefficient of determination about 0.6) the two steps BLR-based approach is preferred, given that these indices are a priori less sensitive to undesired atmospheric effects.

RADIOMETRIC CHARACTERIZATION OF A MARSH SITE AT THE ARGENTINIAN PAMPAS IN THE CONTEXT OF HYPERNETS PROJECT (A NEW AUTONOMOUS HYPERSPECTRAL RADIOMETER)

Abstract. In the context of HYPERNETS project, which is developing a relatively low cost hyperspectral radiometer (and associated pointing system and embedded calibration device for automated measurement of water and land bidirectional reflectance), the tidal coastal marsh in the Mar Chiquita (Argentina) lagoon is being characterized as a test site for validation of radiometric variables. High quality in situ measurements will be available at all spectral bands at this site (and other sites over land and water around the world) for the validation of the surface reflectance data issued from all earth observation missions. This site, dominanted by Sporobolus densiflorus vegetation, is a coastal habitat that provides ecosystem services essential to people and the environment. There is evidence that growth and photosynthetic apparatus of S. densiflorus is negatively affected by the herbicide glyphosate, which is extensively used in the Argentinian agricultural production. As a way to monitor this risk, in this work a theoretical study was performed to establish if it is possible to estimate the chlorophyll content (Ca+b in S. densiflorus), which concentrations are known to be affected by the herbicide, using hyperspectral reflectance. Signatures recorded in situ plus other parameters obtained from a biochemical characterization of the plant were used to obtain a simulated reflectance with the radiative transfer model PROSAIL. Then, a BaseLine Residual approach, based on close band triplets, was proposed to retrieve Ca+b. As a result, we found that it is possible to distinguish between two levels of Ca+b.

Estimating Crop LAI Using Spectral Feature Extraction and the Hybrid Inversion Method

The leaf area index (LAI) is an essential indicator used in crop growth monitoring. In the study, a hybrid inversion method, which combined a physical model with a statistical method, was proposed to estimate the crop LAI. The simulated compact high-resolution imaging spectrometer (CHRIS) canopy spectral crop reflectance datasets were generated using the PROSAIL model (the coupling of PROSPECT leaf optical properties model and Scattering by Arbitrarily Inclined Leaves model) and the CHRIS band response function. Partial least squares (PLS) was then used to reduce the dimension of the simulated spectral data. Using the principal components (PCs) of PLS as the model inputs, the hybrid inversion models were built using various modeling algorithms, including the backpropagation artificial neural network (BP-ANN), least squares support vector regression (LS-SVR), and random forest regression (RFR). Finally, remote sensing mapping of the CHRIS data was achieved with the hybrid model to test the inversion accuracy of LAI estimates. The validation result yielded an accuracy of R2 = 0.939 and normalized root-mean-square error (NRMSE) = 6.474% for the PLS_RFR model, which indicated that the crops LAI could be estimated accurately by using spectral feature extraction and a hybrid inversion strategy. The results showed that the model based on principal components extracted by PLS had a good estimation accuracy and noise immunity and was the preferred method for LAI estimation. Furthermore, the comparative analysis results of various datasets showed that prior knowledge could improve the precision of the retrieved LAI, and using this information to constrain parameters (e.g., chlorophyll content or LAI), which make important contributions to the spectra, is the key to this improvement. In addition, among the PLS, BP-ANN, LS-SVR, and RFR methods, RFR was the optimal modeling algorithm in the paper, as indicated by the high R2 and low NRMSE in various datasets.

Atmospherically resistant vegetation water indices using the 970-nm water absorption feature

Atmospheric correction can introduce errors in surface spectral reflectance, and hence induces errors in plant water estimation from remote sensing water indices. We intend to develop water indices that are less impacted by atmospheric effects for plant water content estimation based on the 970-nm water absorption feature. A simulation study using the PROSAIL and 6S models showed that uncertainty in atmospheric water vapor (WV) content can induce large variation in existing 970-nm water indices, such as WI, NWI-1, and NWI-3. An attempt was made to incorporate atmospheric WV absorption at 940 nm to correct for the perturbation due to atmospheric WV variability, leading to the development of improved indices, named as ARWI, NARWI-1, and NARWI-3. The performance of these indices was evaluated using the simulated and field spectral reflectance data, as well as Hyperion and GF5 satellite data. Results showed that the new indices were resistant to uncertainty of WV and could be used to deliver improved estimation of canopy water content, with a smaller root-mean-square-error (ARWI: 7.4 mg/cm2, NARWI-1: 8.3 mg/cm2, and NARWI-3: 8.8 mg/cm2) compared to that obtained using the traditional water indices (WI: 8.9 mg/cm2, NWI-1: 9.4 mg/cm2, and NWI-3: 16.6 mg/cm2). The water indices developed in this study, although needing further assessment in wide application scenarios, have great potential for monitoring of vegetation water status using satellite hyperspectral data with reflectance measurement around 970 nm.

Leaf Area Index Estimation Algorithm for GF-5 Hyperspectral Data Based on Different Feature Selection and Machine Learning Methods

Leaf area index (LAI) is an essential vegetation parameter that represents the light energy utilization and vegetation canopy structure. As the only in-operation hyperspectral satellite launched by China, GF-5 is potentially useful for accurate LAI estimation. However, there is no research focus on evaluating GF-5 data for LAI estimation. Hyperspectral remote sensing data contains abundant information about the reflective characteristics of vegetation canopies, but these abound data also easily result in a dimensionality curse. Therefore, feature selection (FS) is necessary to reduce data redundancy to achieve more reliable estimations. Currently, machine learning (ML) algorithms have been widely used for FS. Moreover, the same ML algorithm is usually conducted for both FS and regression in LAI estimation. However, no evidence suggests that this is the optimal solution. Therefore, this study focuses on evaluating the capacity of GF-5 spectral reflectance for estimating LAI and the performances of different combination of FS and ML algorithms. Firstly, the PROSAIL model, which coupled leaf optical properties model PROSPECT and the scattering by arbitrarily inclined leaves (SAIL) model, was used to generate simulated GF-5 reflectance data under different vegetation and soil conditions, and then three FS methods, including random forest (RF), K-means clustering (K-means) and mean impact value (MIV), and three ML algorithms, including random forest regression (RFR), back propagation neural network (BPNN) and K-nearest neighbor (KNN) were used to develop nine LAI estimation models. The FS process was conducted twice using different strategies: Firstly, three FS methods were conducted to search the lowest dimension number, which maintained the estimation accuracy of all bands. Then, the sequential backward selection (SBS) method was used to eliminate the bands having minimal impact on LAI estimation accuracy. Finally, three best estimation models were selected and evaluated using reference LAI. The results showed that although the RF_RFR model (RF used for feature selection and RFR used for regression) achieved reliable LAI estimates (coefficient of determination (R2) = 0.828, root mean square error (RMSE) = 0.839), the poor performance (R2 = 0.763, RMSE = 0.987) of the MIV_BPNN model (MIV used for feature selection and BPNN used for regression) suggested using feature selection and regression conducted by the same ML algorithm could not always ensure an optimal estimation. Moreover, RF selection preserved the most informative bands for LAI estimation so that each ML regression method could achieve satisfactory estimation results. Finally, the results indicated that the RF_KNN model (RF used as feature selection and KNN used for regression) with seven GF-5 spectral band reflectance achieved the better estimation results than others when validated by simulated data (R2 = 0.834, RMSE = 0.824) and actual reference LAI (R2 = 0.659, RMSE = 0.697).

Estimación de variables esenciales de la vegetación en un ecosistema de dehesa utilizando factores de reflectividad simulados estacionalmente

<p>Mixed vegetation systems such as wood pastures and shrubby pastures are vital for extensive and sustainable livestock production as well as for the conservation of biodiversity and provision of ecosystem services, and are mostly located in areas that are expected to be more strongly affected by climate change. However, the structural characteristics, phenology, and the optical properties of the vegetation in these mixed -ecosystems such as savanna-like ecosystems in the Iberian Peninsula which combines herbaceous and/or shrubby understory with a low density tree cover, constitute a serious challenge for the remote sensing studies. This work combines physical and empirical methods to improve the estimation of essential vegetation variables: leaf area index (<em>LAI</em>, m<sup>2</sup> / m<sup>2</sup> ), leaf (C<sub>ab,leaf</sub>, μg / cm<sup>2</sup> ) and canopy(C<sub>ab,canopy</sub>, g / m<sup>2 </sup>) chlorophyll content, and leaf (C<sub>m, leaf</sub>, g / cm<sup>2</sup> ) and canopy (C<sub>m,canopy</sub>, g / m<sup>2</sup> ) dry matter content in a dehesa ecosystem. For this purpose, a spectral simulated database for the four main phenological stages of the highly dynamic herbaceous layer (summer senescence, autumn regrowth, greenness peak and beginning of senescence), was built by coupling PROSAIL and FLIGHT radiative transfer models. This database was used to calibrate different predictive models based on vegetation indices (VI) proposed in the literature which combine different spectral bands; as well as Partial Least Squares Regression (PLSR) using all bands in the simulated spectral range (400-2500 nm). PLSR models offered greater predictive power (<em>R<sup>2</sup></em> ≥ 0.93, <em>RRMSE</em> ≤ 10.77 %) both for the leaf and canopy- level variables. The results suggest that directional and geometric effects control the relationships between simulated reflectance factors and the foliar parameters. High seasonal variability is observed in the relationship between biophysical variables and IVs, especially for <em>LAI</em> and <em>C<sub>ab</sub></em>, which is confirmed in the PLSR analysis. The models developed need to be validated with spectral data obtained either with proximal or remote sensors.</p>

Towards a semi-automated mapping of Australia native invasive alien Acacia trees using Sentinel-2 and radiative transfer models in South Africa

Invasive alien plants (IAPs) threaten biodiversity and critical ecosystem services worldwide.There is, therefore, an urgent need to develop intervention measures to control the spread of IAPs. Efforts to control and monitor the spread of IAPs would require their current and detailed distribution over a large geographic area. Recently launched multispectral instrument on-board Sentinel-2 provides free data with good spatiotemporal and spectral resolution, compared to Landsat datasets. The Sentinel-2 dataset, therefore, can be a useful source of the IAPs spatial information required for detection and monitoring purposes. We combined Sentinel-2 data with a radiative transfer model to discriminate IAPs (Acacia mearnsii and Acacia dealbata) from surrounding native tree species in Van Reenen, KwaZulu-Natal, South Africa. The forward mode of combined PROSPECT leaf optical properties model and SAIL canopy bidirectional reflectance model, also referred to as PROSAIL was used to simulate reflectance corresponding to bands of Sentinel-MSI, while the PROSAIL model inversion retrieved leaf area index (LAI) and canopy chlorophyll contents (CCC) of the IAPs and native species. Both reflectance and retrieved properties were used to map the distribution of the species within thestudy area. Our results showed that A. mearnsii and A. dealbata could be accurately discriminatedfrom the surrounding native trees using integrated PROSAIL Sentinel-2 based model. We found that CCC– and LAI-based (% accuracy = 92.8%, 91.4% for CCC and LAI, respectively) modelling produced a higher classification accuracy than field sampling-based modelling (Accuracy = 90.2% (IAP), 82.2% (NAT) and kappa coefficient = 0.84 (IAP), 0.78 (NAT)). Simulated bands corresponding to Sentinel-2 data, on the other hand, produced species maps comparable to field sampling-based maps. Overall, the integrated PROSAIL Sentinel-2 inversion approach proved suitable for detecting and mapping IAPs over a large area. Due to the high spatiotemporal coverage of Sentinel-2, satellite images, the model developed showed the potentialto contribute to the IAPs monitoring systems.

Estimation of Seasonal Sun-Induced Fluorescence Dynamics of Indian Tropical Deciduous Forests using SCOPE and Sentinel-2 MSI

This paper presents a new approach to estimate spatial Sun-Induced Fluorescence (SIF) using the empirical relationship between simulated Canopy Chlorophyll Concentration (CCC) and simulated SIF. PROSAIL model [PROpriétésSPECTrales (PROSPECT) and Scattering by Arbitrarily Inclined Leaves (SAIL) models] was used to simulate CCC. CCC maps were generated through an Automated Radiative Transfer Model Operator (ARTMO) using the PROSAIL model and Sentinel-2 Multi-Spectral Imager (MSI) imagery. The Soil Canopy Observation, Photochemistry, and Energy fluxes (SCOPE) model was used to simulate SIF emitted at 740 nm (SIF740), at 760 nm (SIF760), and top of canopy (SIFTOC) (640-850 nm). The SCOPE model, configured with the specification of the Sentinel-2 sensor, simulates SIF within the spectrum range of 640-850 nm. A non-linear logarithmic relationship (R2>0.9, p < 0.05) was observed between simulated SIF and simulated CCC. Simulated CCC was linearly related to observed CCC with R2 0.88, 0.92 and 0.89 and RMSE = 0.04, 0.17 and 0.09 gm/m2 at p < 0.05 for summer, post-monsoon and early winter respectively. Whereas, the simulated CCC did not capture the full range of CCC variability for the post-monsoon season. Simulated SIF (SIF760) was well correlated with SIF from Orbiting Carbon Observatory-2 (OCO-2) satellite with R2 0.68, 0.73 and 0.73 (RMSE = <1 W/m2/sr/μm, p < 0.05) for the month of summer (April), pre-monsoon (May) and early winter season (November) respectively. Temporal SIFTOC effectively captured the seasonal variability associated with the phenology of deciduous tree species. Among various Sentinel-2 MSI derived VIs, Red Edge NDVI (RENDVI) exhibited maximum sensitivity with SIF (highest monthly average R2> 0.6, p < 0.05). The spatial SIF would serve as an useful link between airborne /satellite derived SIF and in-situ fluorescence measurements to understand multiscale SIF variability of terrestrial vegetation.

New spectral model for estimating leaf area index based on gene expression programming

The accurate evaluation of leaf area index (LAI) in paddy rice is of great importance in predicting the production and assessing food security. Traditional LAI estimation based on the PROSAIL model requires a large number of parameters. To solve this problem, this paper proposes a new estimation model between LAI and spectral reflectance for paddy rice canopies based on gene expression programming. And, we suggest a new spectral index for maximizing the response to LAI and weakening the sensitivity to chlorophyll concentration in the proposed LAI estimation model. Experimental results show that compared with existing indices, the proposed index (VI-R2) maximized the response to green LAI and was less sensitive to chlorophyll content variations, and in the validation with measured LAI values, VI-R2 shows the excellent fitting performance with coefficient of determination (r2) of 0.954. Meanwhile, based on a comparison of the estimated LAI values among existing indices using hyperspectral image, the results indirectly demonstrate that VI-R2 is usable for estimating LAI at the remote sensing pixel scale.

Application conditions and impact factors for various vegetation indices in constructing the LAI seasonal trajectory over different vegetation types

Abstract Leaf area index (LAI) is a required input for various ecological and crop models. To investigate the application conditions of various vegetation indices (VIs), especially the VIs constructed by red-edge band (VIRE) for estimating LAI, six VIs derived from Medium Resolution Imaging Spectrometer (MERIS) data were used to construct LAI seasonal trajectory for different vegetation types at 15 sites. The PROSAIL model combined with the Extended Fourier Amplitude Sensitivity Test (EFAST) method was adopted to explore the influences and physical basis of canopy biophysical and non-canopy variables on the construction of LAI seasonal trajectory using VIs. For deciduous forests, the normalized difference vegetation index (NDVI) had the highest sensitivity to LAI when LAI 2. For evergreen forests, there were no obvious differences among the sensitivities of six VIs to LAI when LAI 5. For crops, all the VIs had the similar sensitivities at LAI 3. For all three types of vegetation, the VIRE maintained relatively high sensitivity to LAI over the whole range of LAI, especially at high LAI values. The VIs were most affected by chlorophyll content (Cab) and average leaf inclination angle (ALA); their total contribution was about 85%. However, the influence of ALA on VIRE was relatively weak, implying that the VIRE had the potential to establish a universal model for LAI estimation among different vegetation types. Therefore, the optimal VIs over different ranges of LAI were suggested to estimate LAI. In addition, the VIRE should be a preferred choice for estimating LAI to reduce the simulation errors of seasonal LAI, if the RE band is available.

Influence of Different Bandwidths on LAI Estimation Using Vegetation Indices

Leaf area index (LAI) is a valuable indicator used in vegetation growth monitoring. To optimize the index selection according to the type of remote sensing data and to improve the inversion accuracy of LAI, this article analyzes the influence of different bandwidths on the accuracy of the inversion model based on vegetation indices. First, the simulation dataset is generated by the PROSAIL model, and on this basis, 15 vegetation indices with high correlation coefficients with LAI. Then, by analyzing the sensitivity of these 15 indices to the variations in bandwidth, and to the coefficient of determination (R2) of the LAI inversion model with the variations of bandwidth, the influence of different bandwidths on the accuracy of LAI estimation by each index is determined. The results show that bandwidth is one of the most important factors in determining the accuracy of LAI inversion, and the influence on different vegetation indices can be divided into the following three categories. First, narrowband vegetation index, the accuracy of inversion models built by vegetation indices decreases with the increase of bandwidth, including SR[800,680], OSAVI, MTVI2, SR[752,690], RDVI, NDCI, and NVI. Second, middleband vegetation index, the accuracy first increases and then decreases with the increase of bandwidth, including SR[700,670], Carte5, and SR[675,700]. Third, broadband vegetation index, the accuracy increases with the increase of bandwidth, including SPVI, Carte2, OSAVI2, MTVI1, and NDVI705. The study provides a scientific basis for vegetation index optimization in the process of LAI inversion.

Inversion of the Fraction of Absorbed Photosynthetically Active Radiation (FPAR) from FY-3C MERSI Data

An accurate inversion of the fraction of absorbed photosynthetically active radiation (FPAR) based on remote sensing data is particularly important for understanding global climate change. At present, there are relatively few studies focusing on the inversion of FPAR using Chinese autonomous satellites. This work intends to investigate the inversion of the FPAR obtained from the FengYun-3C (FY-3C) data of domestic satellites by using the PROSAIL model and the look-up table (LUT) algorithm for different vegetation types from various places in China. After analyzing the applicability of existing models using FY-3C data and MOD09GA data, an inversion strategy for FY-3C data is implemented. This strategy is applied to areas with various types of vegetation, such as grasslands, croplands, shrubs, broadleaf forests, and needleleaf forests, and produces FPAR products, which are cross-validated against the FPAR products from the Moderate Resolution Imaging Spectro Radiometer (MODIS), Geoland Version 1 (GEOV1), and Global Land Surface Satellite (GLASS). Accordingly, the results show that the FPAR retrieved from the FY-3C data has good spatial and temporal consistency and correlation with the three FPAR products. However, this technique does not favor all types of vegetation equally; the FY-FPAR is relatively more suitable for the inversion of grasslands and croplands during the lush period than for others. Therefore, the inversion strategy provides the potential to generate large-area and long-term sequence FPAR products from FY-3C data.

Estimation of leaf area index using PROSAIL based LUT inversion, MLRA-GPR and empirical models: Case study of tropical deciduous forest plantation, North India

Forests play a vital role in biological cycles and environmental regulation. To understand the key processes of forest canopies (e.g., photosynthesis, respiration and transpiration), reliable and accurate information on spatial variability of Leaf Area Index (LAI), and its seasonal dynamics is essential. In the present study, we assessed the performance of biophysical parameter (LAI) retrieval methods viz. Look-Up Table (LUT)-inversion, MLRA-GPR (Machine Learning Regression Algorithm- Gaussian Processes Regression) and empirical models, for estimating the LAI of tropical deciduous plantation using ARTMO (Automated Radiative Transfer Models Operator) tool and Sentinel-2 satellite images. The study was conducted in Central Tarai Forest Division, Haldwani, located in the Uttarakhand state, India. A total of 49 ESUs (Elementary Sampling Unit) of 30 m × 30 m size were established based on variability in composition and age of plantation stands. In-situ LAI was recorded using plant canopy imager during the leaf growing, peak and senescence seasons. The PROSAIL model was calibrated with site-specific biophysical and biochemical parameters before used to the predicted LAI. The plantation LAI was also predicted by an empirical approach using optimally chosen Sentinel-2 vegetation indices. In addition, Sentinel-2 and MODIS LAI products were evaluated with respect to LAI measurements. MLRA-GPR offered best results for predicting LAI of leaf growing (R2 = 0.9, RMSE = 0.14), peak (R2 = 0.87, RMSE = 0.21) and senescence (R2 = 0.86, RMSE = 0.31) seasons while LUT inverted model outperformed VI’s based parametric regression model. Vegetation indices (VIs) derived from 740 nm, 783 nm and 2190 nm band combinations of Sentinel-2 offered the best prediction of LAI.

Multi-Scale Validation of MODIS LAI Products Based on Crop Growth Period

Leaf area index (LAI) is one of the most important canopy structure parameters utilized in process-based models of climate, hydrology, and biogeochemistry. In order to determine the reliability and applicability of satellite LAI products, it is critical to validate satellite LAI products. Due to surface heterogeneity and scale effects, it is difficult to validate the accuracy of LAI products. In order to improve the spatio-temporal accuracy of satellite LAI products, we propose a new multi-scale LAI product validation method based on a crop growth cycle. In this method, we used the PROSAIL model to derive Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LAI data and Gaofen-1 (GF-1) for the study area. The Empirical Bayes Kriging (EBK) interpolation method was used to perform a spatial multi-scale transformation of Moderate Resolution Imaging Spectroradiometer (MODIS) LAI products, GF-1 LAI data, and ASTER LAI data. Finally, MODIS LAI satellite products were compared with field measured LAI data, GF-1 LAI data, and ASTER LAI data during the growing season of crop field. This study was conducted in the agricultural oasis area of the middle reaches of the Heihe River Basin in northwestern China and the Conghua District of Guangzhou in Guangdong Province. The results suggest that the validation accuracy of the multi-scale MODIS LAI products validated by ASTER LAI data were higher than those of the GF-1 LAI data and the reference field measured LAI data, showing a R2 of 0.758 and relative mean square error (RRMSE) of 28.73% for 15 m ASTER LAI and a R2 of 0.703 and RRMSE of 30.80% for 500 m ASTER LAI, which imply that the 15 m MODIS LAI product generated by the EBK method was more accurate than the 500 m and 8 m products. This study provides a new validation method for satellite remotely sensed products.