MERIS Terrestrial Chlorophyll Index Research Articles

Developing a New Method to Rapidly Map Eucalyptus Distribution in Subtropical Regions Using Sentinel-2 Imagery

Eucalyptus plantations with fast growth and short rotation play an important role in improving economic conditions for local farmers and governments. It is necessary to map and update eucalyptus distribution in a timely manner, but to date, there is a lack of suitable approaches for quickly mapping its spatial distribution in a large area. This research aims to develop a uniform procedure to map eucalyptus distribution at a regional scale using the Sentinel-2 imagery on the Google Earth Engine (GEE) platform. Different seasonal Senstinel-2 images were first examined, and key vegetation indices from the selected seasonal images were identified using random forest and Pearson correlation analysis. The selected key vegetation indices were then normalized and summed to produce new indices for mapping eucalyptus distribution based on the calculated best cutoff values using the ROC (Receiver Operating Characteristic) curve. The uniform procedure was tested in both experimental and test sites and then applied to the entire Fujian Province. The results indicated that the best season to distinguish eucalyptus forests from other forest types was winter. The composite indices for eucalyptus–coniferous forest separation (CIEC) and for eucalyptus–broadleaf forest separation (CIEB), which were synthesized from the enhanced vegetation index (EVI), plant senescing reflectance index (PSRI), shortwave infrared water stress index (SIWSI), and MERIS terrestrial chlorophyll index (MTCI), can effectively differentiate eucalyptus from other forest types. The proposed procedure with the best cutoff values (0.58 for CIEC and 1.29 for CIEB) achieved accuracies of above 90% in all study sites. The eucalyptus classification accuracies in Fujian Province, with a producer’s accuracy of 91%, user’s accuracy of 97%, and overall accuracy of 94%, demonstrate the strong robustness and transferability of this proposed procedure. This research provided a new insight into quickly mapping eucalyptus distribution in subtropical regions. However, more research is still needed to explore the robustness and transferability of this proposed method in tropical regions or in other subtropical regions with different environmental conditions.

UAS‐based multispectral imaging for detecting iron chlorosis in grain sorghum

AbstractThis study uses a small unmanned aircraft system equipped with a multispectral sensor to assess various vegetation indices (VIs) for their potential to monitor iron deficiency chlorosis (IDC) in a grain sorghum (Sorghum bicolor L.) crop. IDC is a nutritional disorder that stunts a plants’ growth and causes its leaves to yellow due to an iron deficit. The objective of this project is to find the best VI to detect and monitor IDC. A series of flights were completed over the course of the growing season and processed using Structure‐from‐Motion photogrammetry to create orthorectified, multispectral reflectance maps in the red, green, red‐edge, and near‐infrared wavelengths. Ground data collection methods were used to analyze stress, chlorophyll levels, and grain yield, correlating them to the multispectral imagery for ground control and precise crop examination. The reflectance maps and soil‐removed reflectance maps were used to calculate 25 VIs whose separability was then calculated using a two‐class distance measure, determining which contained the largest separation between the pixels representing IDC and healthy vegetation. The field‐acquired data were used to conclude which VIs achieved the best results for the dataset as a whole and at each level of IDC (low, moderate, and severe). It was concluded that the MERIS terrestrial chlorophyll index, normalized difference red‐edge, and normalized green (NG) indices achieved the highest amount of separation between plants with IDC and healthy vegetation, with the NG reaching the highest levels of separability for both soil‐included and soil‐removed VIs.

Comparison of leaf chlorophyll content retrieval performance of citrus using FOD and CWT methods with field-based full-spectrum hyperspectral reflectance data

Citrus is one of the most economically valuable fruit trees in the world, for which leaf chlorophyll content (LCC) serves as a crucial indicator for evaluating its growth and health status. However, the quantitative estimation of LCC using remote-sensing techniques is still challenging owing to unclear sensitive spectral ranges, baseline drift and overlapping spectrum peaks. To resolve these issues, we clarified the spectral response characteristics of citrus LCC using fractional-order derivatives (FOD) and continuous wavelet transform (CWT) methods to determine its sensitive spectral range with in situ full-spectrum leaf hyperspectral data. We proposed a novel method for estimating the LCC of citrus by combining an ensemble learning regression model based on Hyperopt optimization (H-ELR) with partial least squares regression (PLSR) using the ultra-high dimensional feature variables produced by dual- and tri-band combination strategies. We evaluated the retrieval performance of LCC between the FOD- and CWT-based optimal spectral feature variables. Besides, we further examined the feasibility of improving the estimation accuracy of LCC by the combination of their optimal feature variables. Finally, we evaluated the effect of the spectral curve trend changes and different dimensional spectral features on LCC estimation. The results showed that: (1) The FOD- and CWT-based methods improved the correlation between original spectral reflectance and LCC, with the correlation coefficient increasing by 0.046 and 0.054, respectively. We confirmed that 425–740 nm is the optimal spectral range for LCC estimation. (2) We found that the 0.9 order derivative Tri-band index (TBI2 (R571, R1697, R740)) constructed by combining the sensitive spectral bands of leaf water content achieved a high-precision LCC estimation (R2 = 0.876). In addition, the MERIS terrestrial chlorophyll index (MTCI) constructed based on the scale6 of CWT also gained good retrieval accuracy (R2 = 0.806). (3) We demonstrated that a combination of FOD-based and CWT-based sensitive spectral features improves estimation accuracies (R2 = 0.891) of LCC and revealed that the reflectance peaks and slope peaks at 550 and 750 nm are essential variables for predicting the citrus LCC. (4) The combination of PLSR and H-ELR model provided a good retrieval performance of citrus LCC with the kurtosis (γ = 3.2) and skewness (Sk = 0.066) of the residual prediction values. Our proposed method can provide a scientific basis for estimating LCC and other physiological parameters of citrus and other crop types, which is also important to optimize agricultural management practices and improve crop yield.

Yield Estimation of Wheat Using Cropland Masks from European Common Agrarian Policy: Comparing the Performance of Enhanced Vegetation Index 2, Normalized Difference Vegetation Index, and MERIS Terrestrial Chlorophyll Index in Spanish Nomenclature of Territorial Units for Statistics Level 2 Regions

Monitoring wheat yield and production is essential for ensuring global food security. Remote sensing can be used to achieve it due to its ability to provide global, comprehensive, synoptic, and repetitive information in near real-time. This study used the 2006–2016 Normalized Difference Vegetation Index (NVDI) and Enhanced Vegetation Index 2 (EVI2) time series at a 250 m spatial resolution and 2006–2011 MERIS Terrestrial Chlorophyll Index (MTCI) time series at a 300 m spatial resolution. The post-maximum period for pixels containing wheat was selected based on the EU’s Common Agrarian Policy (CAP) and Corine Land Cover (CLC) data. It was correlated with yield and production values from governmental statistics (GS) of the largest Nomenclature of Territorial Units for Statistics level 2 (NUTS-2) wheat producers in Spain and for Spain overall. The selection of wheat masks was crucial for the accuracy of the models, with CAP masks offering greater forecasting capability. Models using CLC produced R2 values between 0.45 and 0.7, while those using CAP outperformed the former with R2 values of 0.9 throughout Spain. Production models outperformed yield models, and MTCI was the vegetation index (VI) that provided the greatest R2 value of 0.94. However, model accuracy was heavily conditioned by the precision of input data, where anomalies were detected in some NUTS-2.

An integrative data-driven approach for monitoring corn biomass under irrigation water and nitrogen levels based on UAV-based imagery.

Unmanned aerial vehicle (UAV)-based remote sensing has been widely considered recently in field scale crop yield estimation. In this research, the capability of 13 spectral indices in the form of 5 groups was studied under different irrigation water and N fertilizer managements in terms of corn biomass monitoring and estimation. Farm experiments were conducted at Urmia University, Iran. The research was done using a randomized complete block design at three levels of 60, 80, and 100% of irrigation water and nitrogen requirements during four replications. The aerial imagery operations were performed using a fixed-wing UAV equipped with a Sequoia sensor during three plant growth stages including stem elongation, flowering, and silking. The effect of different irrigation water and nitrogen levels on vegetation indices and crop biomass was examined using variance decomposition analysis. Then, the correlation of the vegetation indices with corn biomass was evaluated by fitting linear regression models. Based on the obtained results, the indices based on near infrared (NIR) and red-edge spectral bands showed a better performance. Thus, the MERIS terrestrial chlorophyll index (MTCI) indicated the highest accuracy at estimating corn biomass during the growing season with the R2 and RMSE values of 0.92 and 8.27 ton/ha, respectively. Finally, some Bayesian model averaging (BMA) models were proposed to estimate corn biomass based on the selected indices and different spectral bands. Results of the BMA models revealed that the accuracy of biomass estimation models could be improved using the capabilities and advantages of different vegetation indices.

Unmanned Aerial System-Based Wheat Biomass Estimation Using Multispectral, Structural and Meteorological Data

Rapid estimation of above-ground biomass (AGB) with high accuracy is essential for monitoring crop growth status and predicting crop yield. Recently, remote sensing techniques using unmanned aerial systems (UASs) have exhibited great potential in obtaining structural information about crops and identifying spatial heterogeneity. However, methods of data fusion of different factors still need to be explored in order to enhance the accuracy of their estimates. Therefore, the objective of this study was to investigate the combined metrics of different variables (spectral, structural and meteorological factors) for AGB estimation of wheat using UAS multispectral data. UAS images were captured on two selected growing dates at a typical reclaimed cropland in the North China Plain. The spectral response was determined using the highly correlated vegetation index (VI). A structural metric, the canopy height model (CHM), was produced using UAS-based multispectral images. The measure of growing degree days (GDD) was selected as a meteorological proxy. Subsequently, a structurally–meteorologically weighted canopy spectral response metric (SM-CSRM) was derived by the pixel-level fusion of CHM, GDD and VI. Both correlation coefficient analysis and simple function fitting were implemented to explore the highest correlation between the measured AGB and each proposed metric. The optimal regression model was built for AGB prediction using leave-one-out cross-validation. The results showed that the proposed SM-CSRM generally improved the correlation between wheat AGB and various VIs and can be used for estimating the wheat AGB. Specifically, the combination of MERIS terrestrial chlorophyll index (MTCI), vegetation-masked CHM (mCHM) and normalized GDD (nGDD) achieved an optimal accuracy (R2 = 0.8069, RMSE = 0.1667 kg/m2, nRMSE = 19.62%) through the polynomial regression method. This improved the nRMSE by 3.44% compared to the predictor using MTCI × mCHM. Moreover, the pixel-level fusion method slightly enhanced the nRMSE by ~0.3% for predicted accuracy compared to the feature-level fusion method. In conclusion, this paper demonstrated that an SM-CSRM using pixel-level fusion with canopy spectral, structural and meteorological factors can obtain a good level of accuracy for wheat biomass prediction. This finding could benefit the assessment of reclaimed cropland or the monitoring of crop growth and field management in precision agriculture.

Satellite-based assessment of water use and leaf area efficiencies of dryland conifer forests along an aridity gradient

Dryland forests worldwide are increasingly threatened by drought stress due to climate change. Understanding the relationships between forest structure and function is essential for managing dryland forests to adapt to these changes. We investigated the structure-function relationships in four dryland conifer forests distributed along a semiarid to subhumid climatic aridity gradient. Forest structure was represented by leaf area index (LAI) and function by gross primary productivity (GPP), evapotranspiration (ET), and the derived efficiencies of water use (WUE = GPP/ET) and leaf area (LAE = GPP/LAI). Estimates of GPP and ET were based on the observed relationships between high-resolution vegetation indices from VENμS and Sentinel-2A satellites and flux data from three eddy covariance towers in the study regions between November 2015 to October 2018. The red-edge-based MERIS Terrestrial Chlorophyll Index (MTCI) from VENμS and Sentinel-2A showed strong correlations to flux tower GPP and ET measurements for the three sites (R2cal > 0.91, R2val > 0.84). Using our approach, we showed that as LAI decreased with decreasing aridity index (AI) (i.e., dryer conditions), estimated GPP and ET decreased (R2 > 0.8 to LAI), while WUE (R2 = 0.68 to LAI) and LAE increased. The observed global-scale patterns are associated with a variety of forest vegetation characteristics, at the local scale, such as tree species composition and density. However, our results point towards a canopy-level mechanism, where the ecosystem-LAI and resultant proportion of sun-exposed vs. shaded leaves are primary determinants of WUE and LAE along the studied climatic aridity gradient. This work demonstrates the importance of high-resolution (spatially and spectrally) remote sensing data conjugated with flux tower data for monitoring dryland forests and understanding the intricate structure-function interactions in their response to drying conditions.

Inversion of Wheat Leaf Area Index by Multivariate Red-Edge Spectral Vegetation Index

Leaf area index (LAI) is an important parameter that determines the growth status of winter wheat and impacts the ecological and physical processes of plants in ecosystems. The problem of spectral saturation of winter wheat LAI at the booting stage was easily caused by the inversion of the univariate red-edge spectral vegetation index constructed by the red-edge band. In this paper, a new method that the univariate red-edge spectral vegetation index constructed in the red-edge band is used to invert the spectral saturation of the winter wheat LAI. The multivariable red-edge spectral vegetation index is used to invert the winter wheat LAI. This method can effectively delay the phenomenon of spectral saturation and improve the inversion precision. In this study, the Sentinel-2 data were used to invert the winter wheat LAI. An univariate and multivariate red-edge spectral vegetation index regression model was constructed based on the Red-edge Normalized Difference Spectral Indices 1 (NDSI1), Red-edge Normalized Difference Spectral Indices 2 (NDSI2), Red-edge Normalized Difference Spectral Indices 3 (NDSI3), Modified Chlorophyll Absorption Ratio Index (MCARI), MERIS Terrestrial Chlorophyll Index (MTCI), Transformed Chlorophyll Absorption in Reflectance Index (TCARI), and Transformed Chlorophyll Absorption in Reflectance Index/the optimized soil adjusted vegetation index (TCARI/OSAVI). Based on the correlation coefficient, the coefficient of determination (R2), the root mean square error (RMSE) and noise equivalent value (NE), the best model was selected and verified to generate an inverted map. The results showed that the multivariable red-edge spectral vegetation index of NDSI1 + NDSI2 + NDSI3 + TCARI/OSAVI + MCARI + MTCI + TCARI was the best model for inverting the winter wheat LAI. The R2, the RMSE and the NE values were all satisfied the requirements of the inversion precision (R2 = 0.8372/0.8818, RMSE = 0.2518/0.1985, NE = 5/5). In summary, this method can be used to judge the growth of winter wheat and provide an accurate basis for monitoring crop growth.

Using Sentinel-1 and Sentinel-2 imagery for estimating cotton crop coefficient, height, and Leaf Area Index

In cotton, an optimal balance between vegetative and reproductive growth will lead to high yields and water-use efficiency. Remote sensing estimations of vegetation variables such as crop coefficient (Kc), Leaf Area Index (LAI), and crop height during plant development can improve irrigation management. Optical and Synthetic Aperture Radar (SAR) satellite imagery can be a useful data source since they provide synoptic cover at fixed time intervals. Furthermore, they can better capture the spatial variability in the field compared to point measurements. Since clouds limit optical observations at times, the combination with SAR can provide information during cloudy periods. This study utilized optical imagery acquired by Sentinel-2 and SAR imagery acquired by Sentinel-1 over cotton fields in Israel. The Sentinel-2-based vegetation indices that are best suited for cotton monitoring were identified, and the most robust Sentinel-2 models for Kc, LAI, and height estimation achieved R2 = 0.879, RMSE= 0.0645 (MERIS Terrestrial Chlorophyll Index, MTCI); R2 = 0.9535, RMSE= 0.8 (MTCI); and R2 = 0.8883, RMSE= 10 cm (Enhanced Vegetation Index, EVI), respectively. Additionally, a model based on the output of the SNAP Biophysical Processor LAI estimation algorithm was superior to the empirical LAI models of the best-performing vegetation indices (R2 =0.9717, RMSE=0.6). The most robust Sentinel-1 models were obtained by applying an innovative local incidence angle normalization method with R2 = 0.7913, RMSE= 0.0925; R2 = 0.6699, RMSE= 2.3; R2 = 0.6586, RMSE= 18 cm for the Kc, LAI, and height estimation, respectively. This work paves the way for future studies to design decision support systems for better irrigation management in cotton, even at the sub-plot level, by monitoring the heterogeneous development of the crop from space and adapting the irrigation accordingly to reach the target development at different growth stages during the season.

A comparison of methods to estimate leaf area index using either crop-specific or generic proximal hyperspectral datasets

The leaf area index (LAI) is an important parameter indicating the crop growth status. Many vegetation indices and models have been developed to estimate LAI of different crops. However, the utility of and differences between crop-specific and generic algorithms for LAI estimation covering several crops, dates, and sites need to be compared. The main objectives of this study were to: (1) evaluate the suitability and robustness of different vegetation indices for LAI estimation of different crops; (2) compare the performance of crop-specific and generic algorithms; (3) evaluate the performance of statistical regression, machine learning regression, and the PROSAIL-D with different inversion strategies. Results showed that: (1) the simple ratio index (SR) performed best for cotton and winter wheat, the MERIS terrestrial chlorophyll index (MTCI) performed best for maize; (2) the models trained over specific crop types performed better than those trained over all crop types together; (3) for crop-specific models, artificial neural networks (ANN) performed better than support vector machine regression (SVR), partial least square regression (PLSR), and statistical regression methods, while the PROSAIL-D yielded similar or slightly better performance as compared with ANN method. Furthermore, the look-up table (LUT) strategy performed better than iterative optimization strategy (Shuffled Complex Evolution method developed at the University of Arizona, SCE-UA). Results indicated that there was not an optimal model that could be used to precisely estimate LAI of different crops since the relationship between the specific hyperspectral reflectance and the LAI of different crops were different. In addition, the PROSAIL-D with crop specific input parameters is a potential method for LAI estimation of different crops with hyperspectral reflectance.

Rapid prediction of winter wheat yield and nitrogen use efficiency using consumer-grade unmanned aerial vehicles multispectral imagery.

Rapid and accurate assessment of yield and nitrogen use efficiency (NUE) is essential for growth monitoring, efficient utilization of fertilizer and precision management. This study explored the potential of a consumer-grade DJI Phantom 4 Multispectral (P4M) camera for yield or NUE assessment in winter wheat by using the universal vegetation indices independent of growth period. Three vegetation indices having a strong correlation with yield or NUE during the entire growth season were determined through Pearson's correlational analysis, while multiple linear regression (MLR), stepwise MLR (SMLR), and partial least-squares regression (PLSR) methods based on the aforementioned vegetation indices were adopted during different growth periods. The cumulative results showed that the reciprocal ratio vegetation index (repRVI) had a high potential for yield assessment throughout the growing season, and the late grain-filling stage was deemed as the optimal single stage with R2, root mean square error (RMSE), and mean absolute error (MAE) of 0.85, 793.96 kg/ha, and 656.31 kg/ha, respectively. MERIS terrestrial chlorophyll index (MTCI) performed better in the vegetative period and provided the best prediction results for the N partial factor productivity (NPFP) at the jointing stage, with R2, RMSE, and MAE of 0.65, 10.53kg yield/kg N, and 8.90kg yield/kg N, respectively. At the same time, the modified normalized difference blue index (mNDblue) was more accurate during the reproductive period, providing the best accuracy for agronomical NUE (aNUE) assessment at the late grain-filling stage, with R2, RMSE, and MAE of 0.61, 7.48kg yield/kg N, and 6.05kg yield/kg N, respectively. Furthermore, the findings indicated that model accuracy cannot be improved by increasing the number of input features. Overall, these results indicate that the consumer-grade P4M camera is suitable for early and efficient monitoring of important crop traits, providing a cost-effective choice for the development of the precision agricultural system.

Assessing a soil-removed semi-empirical model for estimating leaf chlorophyll content

Leaf chlorophyll content (LCC) is an important indicator of foliar nitrogen status and photosynthetic capacity. Compared to physical models, the generality of empirical models based on vegetation indices is often questioned when they are used to estimate LCC due to the influence from canopy structure, such as leaf area index (LAI). A recent study developed the LAI-insensitive chlorophyll index (LICI) and established a semi-empirical LICI-based LCC quantification model, which inherits both the robustness of physical models and the simplicity of empirical models. However, it is unclear whether such a simple model is as accurate and generic as physical models. Here, we adopted an innovative approach to disentangle the confounding effects of LAI and LCC on LICI and found that LICI was strongly correlated to LCC but only marginally sensitive to LAI. Moreover, we also found that LICI was sensitive to the soil background and thus proposed a spectral separation of soil and vegetation (3SV) algorithm, which is automatic and does not require prior information of soil background. After implementing the 3SV algorithm to remove the contributed reflectance of soil, we then obtained the contributed reflectance of vegetation (CRv). Model simulations showed that the soil background effect on the CRv-derived LICI was largely eliminated and hence this index was viewed to be soil-removed. As a result, the accuracy and generality of the soil-removed LICI-based model for LCC estimation was evaluated using comprehensive datasets from multiple vegetation types, years, sites, and observation platforms and compared to that of a MatrixVI-based physical model and a MERIS terrestrial chlorophyll index (MTCI)-based semi-empirical model. The root-mean-square error (RMSE) for LCC estimated by the soil-removed LICI-based model was 6.22–6.87 μg/cm2 for the crop datasets and 10.68 μg/cm2 for the multi-ecosystem dataset when the equivalent wet soil fraction was <0.7. Although further efforts are required to mitigate the effects of soil on the LICI-based model over sparse vegetation, this research is highly beneficial for extending its potential applications to the globe and advancing the development of an operational LCC monitoring system in the emerging satellite hyperspectral era.

Investigation of Vegetation Changes in Different Mining Areas in Liaoning Province, China, Using Multisource Remote Sensing Data

Mining can provide necessary mineral resources for humans. However, mining activities may cause damage to the surrounding ecology and environment. Vegetation change analysis is a key tool for evaluating damage to ecology and the environment. Liaoning is one of the major mining provinces in China, with rich mineral resources and long-term, high-intensity mining activities. Taking Liaoning Province as an example, vegetation change in six mining areas was investigated using multisource remote sensing data to evaluate ecological and environmental changes. Based on MODIS NDVI series data from 2000 to 2019, change trends of vegetation were evaluated using linear regression. According to the results, there are large highly degraded vegetation areas in the Anshan, Benxi, and Yingkou mining areas, which indicates that mining activities have seriously damaged the vegetation in these areas. In contrast, there are considerable areas with improved vegetation in the Anshan, Fushun, and Fuxin mining areas, which indicates that ecological reclamation has played a positive role in these areas. Based on Sentinel-2A data, leaf chlorophyll content was inferred by using the vegetation index MERIS Terrestrial Chlorophyll Index (MTCI) after measurement of leaf spectra and chlorophyll content were carried out on the ground to validate the performance of MTCI. According to the results, the leaf chlorophyll content in the mines is generally lower than in adjacent areas in these mining areas with individual differences. In the Yingkou mining area, the chlorophyll content in adjacent areas is close to the magnesite mines, which means the spillover effect of environmental pollution in mines should be considerable. In the Anshan, Benxi, and Diaobingshan mining areas, the environmental stress on adjacent areas is slight. All in all, iron and magnesite open-pit mines should be monitored closely for vegetation destruction and stress due to the high intensity of mining activities and serious pollution. In contrast, the disturbance to vegetation is limited in resource-exhausted open-pit coal mines and underground coal mines. It is suggested that land reclamation should be enhanced to improve the vegetation in active open-pit mining areas, such as the Anshan, Benxi, and Yingkou mining areas. Additionally, environmental protection measures should be enhanced to relieve vegetation stress in the Yingkou mining area.

Applicability of multiple remotely sensed vegetation indices for extracting key phenological metrics of Tamarix chinensis shrubs based on CO2 flux observation and Sentinel-2 data

We analyzed the relationship between gross primary productivity (GPP) and environmental factors at Sidaoqiao Superstation of the Ejina Oasis in China's Gobi Desert, by combining eddy flux and meteorological data from 2018 to 2019 and Sentinel-2 remote sensing images from 2017 to 2020. We evaluated the applicability of 12 remote sensing vegetation indices to simulate the growth of Tamarix chinensis and extract key phenological metrics. A seven-parameter double-logistic function (DL-7) + global model function (GMF) was used to fit the growth curves of GPP and vegetation indices. Three key phenological metrics, i.e., the start of the growing season (SOS), the peak of the growing season (POS), and the end of the growing season (EOS), were extracted for each year. Growing season degree days (GDD) and soil water content were the main environmental factors affecting the phenological dynamics of T. chinensis. Compared with 2018, the lower temperatures in 2019 resulted in slower accumulation rate of accumulated temperature before the SOS. T. chinensis required longer heat accumulation to enter growing season, which might cause later SOS in 2019. The hydrothermal conditions between SOS and POS were similar for 2018 and 2019. Howe-ver, the POS in 2019 was 8 days later than that in 2018, because of the late SOS in 2019. Following the POS in 2019, high GDD and low soil water content caused the T. chinensis to suffer from water stress, resulting in a shortened late growing season. The linear regression between the standardized Sentinel-2 vegetation index and the average value of GPP between 10:00 and 14:00 indicated that the enhanced vegetation index of the broadband vegetation index and the chlorophyll red edge index, inverted red edge chlorophyll index, and red-edge normalized difference vegetation index (NDVI705) of the narrowband vegetation index were highly consistent with the GPP of T. chinensis. Remote sensing extraction of SOS and POS of T. chinensis suggested that the Sentinel-2 narrowband vegetation index was more accurate than the broadband vegetation index. The modified chlorophyll absorption in reflectance index provided the most accurate extraction of SOS, while the MERIS terrestrial chlorophyll index provided the most accurate extraction of EOS. Conversely, the Sentinel-2 broadband vegetation index was the most accurate for extracting POS, especially the 2-band enhanced vegetation index and the near-infrared reflectance of vegetation. Overall, NDVI705 was the best index to estimate phenological metrics.

Assessment of Regression Models for Predicting Rice Yield and Protein Content Using Unmanned Aerial Vehicle-Based Multispectral Imagery

Unmanned aerial vehicle-based multispectral imagery including five spectral bands (blue, green, red, red-edge, and near-infrared) for a rice field in the ripening stage was used to develop regression models for predicting the rice yield and protein content and to select the most suitable regression analysis method for the year-invariant model: partial least squares regression, ridge regression, and artificial neural network (ANN). The regression models developed with six vegetation indices (green normalization difference vegetation index (GNDVI), normalization difference red-edge index (NDRE), chlorophyll index red edge (CIrededge), difference NIR/Green green difference vegetation index (GDVI), green-red NDVI (GRNDVI), and medium resolution imaging spectrometer terrestrial chlorophyll index (MTCI)), calculated from the spectral bands, were applied to single years (2018, 2019, and 2020) and multiple years (2018 + 2019, 2018 + 2020, 2019 + 2020, and all years). The regression models were cross-validated through mutual prediction against the vegetation indices in nonoverlapping years, and the prediction errors were evaluated via root mean squared error of prediction (RMSEP). The ANN model was reproducible, with low and sustained prediction errors of 24.2 kg/1000 m2 ≤ RMSEP ≤ 59.1 kg/1000 m2 in rice yield and 0.14% ≤ RMSEP ≤ 0.28% in rice-protein content in all single-year and multiple-year analyses. When the importance of each vegetation index of the regression models was evaluated, only the ANN model showed the same ranking in the vegetation index of the first (MTCI in both rice yield and protein content) and second importance (CIrededge in rice yield and GRNDVI in rice-protein content). Overall, this means that the ANN model has the highest potential for developing a year-invariant model with stable RMSEP and consistent variable ranking.

Sequential forward selection and support vector regression in comparison to LASSO regression for spring wheat yield prediction based on UAV imagery

Traditional plant breeding based on selection for grain yield is time-consuming and costly; therefore, new innovative methods are in high demand to reduce costs and accelerate genetic gains. Remote sensing-based platforms such as unmanned aerial vehicles (UAV) show promise to predict different traits including grain yield. Attention is currently being devoted to machine learning methods in order to extract the most meaningful information from the massive amounts of data generated by UAV images. These methods have shown a promising capability to come up with nonlinearity and explore patterns beyond the human ability. This study investigates the application of two different machine learning based regressor methods to predict wheat grain yield using extracted vegetation indices from UAV images. The goal of the study was to investigate the strength of Support Vector Regression (SVR) in combination with Sequential Forward Selection (SFS) for grain yield prediction and compare the results with LASSO regressor with an internal feature selector. Models were tested on grain yield data from 600 plots of spring wheat planted in South-Eastern Norway in 2018. Five spectral bands along with three different vegetation indices; the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and MERIS Terrestrial Chlorophyll Index (MTCI) were extracted from multispectral images at three dates between heading and maturity of the plants. These features for each field trial plot at each date were used as input data for the SVR model. The best model hyperparameters were estimated using grid search. Based on feature selection results from both methods, NDVI showed the highest prediction ability for grain yield at all dates and its explanatory power increased toward maturity, while adding MTCI and EVI at earlier stages of grain filling improved model performance. Combined models based on all indices and dates explained up to 90% of the variation in grain yield on the test set. Inclusion of individual bands added collinearity to the models and did not improve the predictions. Although both regression methods showed a good capability for grain yield prediction, LASSO regressor proved to be more affordable and economical in terms of time.

Improving the Retrieval of Crop Canopy Chlorophyll Content Using Vegetation Index Combinations

Estimates of crop canopy chlorophyll content (CCC) can be used to monitor vegetation productivity, manage crop resources, and control disease and pests. However, making these estimates using conventional ground-based methods is time-consuming and resource-intensive when deployed over large areas. Although vegetation indices (VIs), derived from satellite sensor data, have been used to estimate CCC, they suffer from problems related to spectral saturation, soil background, and canopy structure. A new method was, therefore, proposed for combining the Medium Resolution Imaging Spectrometer (MERIS) terrestrial chlorophyll index (MTCI) and LAI-related vegetation indices (LAI-VIs) to increase the accuracy of CCC estimates for wheat and soybeans. The PROSAIL-D canopy reflectance model was used to simulate canopy spectra that were resampled to match the spectral response functions of the MERIS carried on the ENVISAT satellite. Combinations of the MTCI and LAI-VIs were then used to estimate CCC via univariate linear regression, binary linear regression and random forest regression. The accuracy using the field spectra and MERIS data was determined based on field CCC measurements. All the MTCI and LAI-VI combinations for the selected regression techniques resulted in more accurate estimates of CCC than the use of the MTCI alone (field spectra data for soybeans and wheat: R2 = 0.62 and RMSE = 77.10 μg cm−2; MERIS satellite data for soybeans: R2 = 0.24 and RMSE = 136.54 μg cm−2). The random forest regression resulted in better accuracy than the other two linear regression models. The combination resulting in the best accuracy was the MTCI and MTVI2 and random forest regression, with R2 = 0.65 and RMSE = 37.76 μg cm−2 (field spectra data) and R2 = 0.78 and RMSE = 47.96 μg cm−2 (MERIS satellite data). Combining the MTCI and a LAI-VI represents a further step towards improving the accuracy of estimation CCC based on multispectral satellite sensor data.

Improving the Retrieval of Forest Canopy Chlorophyll Content From MERIS Dataset by Introducing the Vegetation Clumping Index

The accurate retrieval of canopy chlorophyll content (CCC) is essential to the effective monitoring of forest productivity, and environmental stress. However, the clumping index (CI), a vital canopy structural parameter, affects inaccurate remote sensing of forest CCC. In this article, we proposed a concept of effective CCC (CCC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> ) and an integrated CI approach to retrieving forest CCC using empirical regression and random forest regression. First, the PROSPECT-D and four-scale models were used to simulate forest canopy spectra, and the forest CCC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> including CI was found more feasible to be remotely sensed than the CCC. Then, an empirical regression model and random forest model trained using different combinations of the medium resolution imaging spectrometer (MERIS) terrestrial chlorophyll index (MTCI), reflectance, and CI values were used to estimate the CCC. Finally, the proposed approach was tested using satellite-based CI and MERIS product. Using the empirical regression model, the results showed that the retrieval of forest CCC using the MTCI was greatly improved by the inclusion of the CI (RMSE from 63.64 to 36.51 μg cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> for broadleaf; RMSE from 96.02 to 58.49 μg cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> for coniferous). Using the random forest approach and the model trained using the reflectance in red and red-edge bands, MTCI, and CI performed best, with RMSE = 27.95 μg cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> for broadleaf and RMSE = 34.83 μg cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> for coniferous. Overall, it is concluded that include the CI, particularly the approach using forest random regression, have the potential for satellite-based forest CCC mapping at regional and global scales.

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.

The Sentinel-3 OLCI Terrestrial Chlorophyll Index (OTCI): Algorithm Improvements, Spatiotemporal Consistency and Continuity with the MERIS Archive

The Ocean and Land Colour Instrument (OLCI) on-board Sentinel-3 (2016–present) was designed with similar mechanical and optical characteristics to the Envisat Medium Resolution Imaging Spectrometer (MERIS) (2002–2012) to ensure continuity with a number of land and marine biophysical products. The Sentinel-3 OLCI Terrestrial Chlorophyll Index (OTCI) is an indicator of canopy chlorophyll content and is intended to continue the legacy of the Envisat MERIS Terrestrial Chlorophyll Index (MTCI). Despite spectral similarities, validation and verification of consistency is essential to inform the user community about the product’s accuracy, uncertainty, and fitness for purpose. This paper aims to: (i) describe the theoretical basis of the Sentinel-3 OTCI and (ii) evaluate the spatiotemporal consistency between the Sentinel-3 OTCI and the Envisat MTCI. Two approaches were used to conduct the evaluation. Firstly, agreement between the Sentinel-3 OTCI and the Envisat MTCI archive was assessed over the Committee for Earth Observation Satellites (CEOS) Land Product Validation (LPV) core validation sites, enabling the temporal consistency of the two products to be investigated. Secondly, intercomparison of monthly Level-3 Sentinel-3 OTCI and Envisat MTCI composites was carried out to evaluate the spatial distribution of differences across the globe. In both cases, the agreement was quantified with statistical metrics (R2, NRMSD, bias) using an Envisat MTCI climatology based on the MERIS archive as the reference. Our results demonstrate strong agreement between the products. Specifically, high 1:1 correspondence (R2 &gt;0.88), low global mean percentage difference (−1.86 to 0.61), low absolute bias (&lt;0.1), and minimal error (NRMSD ~0.1) are observed. The temporal profiles reveal consistency in the expected range of values, amplitudes, and seasonal trajectories. Biases and discrepancies may be attributed to changes in land management practices, land cover change, and extreme climatic events occurred during the time gap between the missions; however, this requires further investigation. This research confirms that Sentinel-3 OTCI dataset can be used along with the Envisat MTCI to provide a data coverage over the last 20 years.