Narrow-band Vegetation Indices Research Articles

Estimating aboveground biomass of urban trees based on ICESat-2 LiDAR and Zhuhai-1 hyperspectral data

Accurately and quickly estimating the aboveground biomass (AGB) of urban trees is crucial for assessing their ecological benefits and providing a scientific basis for city managers. The spaceborne LiDAR (ICESat-2) data offers vital vertical structure information of trees for precise AGB estimation. However, its discrete footprints limit spatially continuous AGB estimation. This study pioneers the integration of ICESat-2 and hyperspectral (Zhuhai-1) images for the estimation of AGB of trees in Shenzhen city, south of China. The study comprises three main steps. First, land cover classification based on GaoFen-6 images was conducted to extract the tree areas. Second, an AGB estimation model based on ICESat-2 photon features yields AGB values for all ICESat-2 footprints. Finally, a Random Forest (RF) regression model linking the AGB of footprints with narrowband vegetation indices from the Zhuhai-1 data was built to produce a wall-to-wall AGB map of Shenzhen. The results indicated that the 70th percentile of vegetation photon height was the optimal feature for estimating AGB within ICESat-2 footprints (R2: 0.62, RMSE: 4.45 kg/m2). For wall-to-wall AGB estimation based on RF regression, narrowband vegetation indices like SIPI, PSI, MNDVI, ARI2, CI2, and ACI2 were crucial, achieving an R2 of 0.55 and an RMSE of 3.88 kg/m2. This suggests that the combination of ICESat-2 and Zhuhai-1 data has a good ability to estimate the AGB of urban trees on a large scale and provides a new approach for forest resource studies in urban areas.

Inversion study of the meadow steppe above-ground biomass based on ground and airborne hyperspectral data

Above-ground biomass (AGB) is a critical criterion for assessing the ecological value and productivity of grasslands. Quickly and accurately estimating the AGB of grasslands has become a matter of great concern in grassland ecosystem research. By combining ground ASD hyperspectral data and airborne hyperspectral data, this study successfully extracted the sensitive bands of canopy reflectance and calculated the various vegetation indexes, thus providing information of more dimensions for above-ground biomass estimation. Biomass inversion-based yield modeling results reveal that the 377 nm raw band reflectance of the ASD data and the narrow-band vegetation index of the Resonon data exhibit outstanding performance in yield modeling. Comparative analysis indicates that the Resonon hyperspectral data demonstrate a distinct advantage in assessing meadow steppe yield, and the quadratic polynomial model based on its narrow-band SAVI achieves an R2 of 0.553 and an RMSE of 37.53.

Inversion study of the meadow steppe above-ground biomass based on ground and airborne hyperspectral data

Above-ground biomass (AGB) is a critical criterion for assessing the ecological value and productivity of grasslands. Quickly and accurately estimating the AGB of grasslands has become a matter of great concern in grassland ecosystem research. By combining ground ASD hyperspectral data and airborne hyperspectral data, this study successfully extracted the sensitive bands of canopy reflectance and calculated the various vegetation indexes, thus providing information of more dimensions for above-ground biomass estimation. Biomass inversion-based yield modeling results reveal that the 377 nm raw band reflectance of the ASD data and the narrow-band vegetation index of the Resonon data exhibit outstanding performance in yield modeling. Comparative analysis indicates that the Resonon hyperspectral data demonstrate a distinct advantage in assessing meadow steppe yield, and the quadratic polynomial model based on its narrow-band SAVI achieves an R2 of 0.553 and an RMSE of 37.53.

Multi-stage fertilizer recommendation for spring maize at the field scale based on narrowband vegetation indices

Crop fertilizer recommendations that are site-specific and can be adjusted in multiple growth stages can result in improved nutrient management, fertilizer use efficiency, and grain yield. There are considerable challenges to collecting a large number of soil and crop samples using the traditional methods, so a modern approach to developing fertilizer recommendations is urgently needed. In this study, we used the topdressing recommendation method based on unmanned aerial vehicle-based hyperspectral images and conducted a long-term nitrogen–phosphorus–potassium (NPK) combined fertilizer experiment in a spring maize cropping system. The hyperspectral imagery was acquired at multiple critical stages (jointing, silking, and maturity) in the experimental field containing 120 microplots during 2019–2020. The leaf nitrogen content (LNC) and yield measurement data for each microplot were also collected in multiple stages. Ten narrowband vegetation indices (VIs) were derived from the hyperspectral imagery as effective indicators for the estimation of the crop N status and grain yield. To develop topdressing recommendations for multiple stages, first, the optimal NPK fertilizer amounts, which were applied as the total fertilizer requirement to achieve the highest yield, were obtained. Second, the thresholds of the narrowband VIs, which justify if topdressing N fertilizer is needed, were calculated using the regression model established based on the relative yield and VIs. Finally, the N fertilizer amounts already applied in the jointing and silking stages were obtained based on the response of the narrowband VIs to the N fertilizer amounts. The results reveal that the optimal amount of N fertilizer is 180 kg ha−1, and the grain yield can be improved by using a combination of P and K fertilizers. Five VIs were significantly correlated with both the LNC and yield in the jointing and silking stages, and the thresholds of these VIs, below which topdressing fertilizer is needed, were identified at 95% of relative yield. The five VIs all presented high fitting accuracies with the amount of N fertilizer applied. However, there were noticeable differences in the sensitivities of the VIs in the jointing and silking stages. Therefore, we developed a cost-effective approach and provide multi-stage fertilizer recommendations at the field scale for the spring maize cropping system. This method can also benefit other precision agriculture applications if incorporated with remote sensing techniques.

Phenotyping agronomic and physiological traits in peanut under mid‐season drought stress using UAV‐based hyperspectral imaging and machine learning

AbstractAgronomic and physiological traits in peanut (Arachis hypogaea) are important to breeders for selecting high‐yielding and resilient genotypes. However, direct measurement of these traits is labor‐intensive and time‐consuming. This study assessed the feasibility of using unmanned aerial vehicles (UAV)‐based hyperspectral imaging and machine learning (ML) techniques to predict three agronomic traits (biomass, pod count, and yield) and two physiological traits (photosynthesis and stomatal conductance) in peanut under drought stress. Two different approaches were evaluated. The first approach employed eighty narrowband vegetation indices as input features for an ensemble model that included K‐nearest neighbors, support vector regression, random forest, and multi‐layer perceptron (MLP). The second approach utilized mean and standard deviation of canopy spectral reflectance per band. The resultant 400 features were used to train a deep learning (DL) model consisting of one‐dimensional convolutional layers followed by an MLP regressor. Predictions of the agronomic traits obtained using feature learning and DL (R2 = 0.45–0.73; symmetric mean absolute percentage error [sMAPE] = 24%–51%) outperformed those obtained using feature engineering and conventional ML models (R2 = 0.44–0.61, sMAPE = 27%–59%). In contrast, the ensemble model had a slightly better performance in predicting physiological traits (R2 = 0.35–0.57; sMAPE = 37%–70%) compared to the results obtained from the DL model (R2 = 0.36–0.52; sMAPE = 47%–64%). The results showed that the combination of UAV‐based hyperspectral imaging and ML techniques have the potential to assist breeders in rapid screening of genotypes for improved yield and drought tolerance in peanut.

Sensitivity of hyperspectral vegetation indices to rainfall seasonality in the Brazilian savannahs: an analysis using PRISMA data

ABSTRACT We evaluated the sensitivity of 14 narrowband vegetation indices (VIs) to rainfall seasonality over the Brazilian savannahs. Five images obtained in 2020 by the PRecursore IperSpettrale della Missione Applicativa (PRISMA) tracked the transition from the rainy (11 May) to the dry season (8 July, 17 August, and 4 September), and towards the beginning of a new seasonal cycle on 3 October. We considered two scenarios in the data analysis. First, we kept the PRISMA image from 11 May as a reference to evaluate the VI sensitivity with increasing water deficit from 8 July (49 days without any precipitation) to 4 September (102 days). Second, we changed the reference image to 4 September to evaluate the largest VI responses on 3 October after the first rainfall. The first three VIs (ranked by F-values) having significant changes with increasing water deficit over grasslands were the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Moisture Stress Index (MSI). The Vogelmann red edge index (VOG) and Red-Edge Normalized Difference Vegetation Index (RENDVI) presented the largest F-values over woodlands. In the second scenario, Red-edge Vegetation Stress Index (RVSI) and RENDVI were among the first five ranked VIs by t-values over both areas. The largest changes in VIs were generally observed over savannah grassland, which is the most sensitive physiognomy to water deficit. The lowest modifications were noted over riparian forests, which have access to waters from rivers. The vegetation-type dependence of the VI changes was also observed after the occurrence of rainfall. Results suggest the potential use of different VIs to obtain phenological metrics for more accurate savannah mapping in Brazil.

Hyperspectral Vegetation Indices to Assess Water and Nitrogen Status of Sweet Maize Crop

The deployment of novel technologies in the field of precision farming has risen to the top of global agendas in response to the impact of climate change and the possible shortage of resources such as water and fertilizers. The present research addresses the performance of water and nitrogen-sensitive narrow-band vegetation indices to evaluate the response of sweet maize (Zea mays var. saccharata L.) to different irrigation and nitrogen regimes. The experiment was carried out in Valenzano, Bari (Southern Italy), during the 2020 growing season. Three irrigation regimes (full irrigation, deficit irrigation, and rainfed) and two nitrogen levels (300 and 50 kg ha−1) were tested. During the growing season, a Field Spec Handheld 2 spectroradiometer operating in the range of 325–1075 nm was utilized to capture spectral data regularly. In addition, soil water content, biometric parameters, and physiological parameters were measured. The DATT index, based on near-infrared and red-edge wavelengths, performed better than other indices in explaining the variation in chlorophyll content, whereas the double difference index (DD) showed the greatest correlation with the leaf–gas exchange. The modified normalized difference vegetation index (NNDVI) and the ratio of water band index to normalized difference vegetation index (WBI/NDVI) showed the highest capacity to distinguish the interaction of irrigation x nitrogen, while the best discriminating capability of these indices was under a low nitrogen level. Moreover, red-edge-based indices had higher sensitivity to nitrogen levels compared to the structural and water band indices. Our study highlighted that it is critical to choose proper narrow-band vegetation indices to monitor the plant eco-physiological response to water and nitrogen stresses.

Application of an innovative narrow band vegetation index to corn leaves to explore the characteristics of corn spectra under Cu2+ stress

ABSTRACT In this study, an innovative narrow band vegetation index (INBVI) was proposed to explore the sensitive regions of corn leaves spectra under copper stress. In 2016, the spectra and Cu2+ contents of corn leaves under different copper stress concentrations were measured by designing corn stress experiments with different copper gradients. Firstly, we divided the spectra of corn leaves into 11 subintervals, and chose the spectral reflectance which corresponded to the middle wavelength of each subinterval to build INBVI. Then, the Pearson correlation coefficient (r) and Root Mean Square Error (RMSE) were calculated, and we analysed correlation of the INBVI and Cu2+ contents in corn leaves. What’s more, we compared it with the three conventional vegetation indexes of Water Band Index (WBI), Modified Chlorophyll Absorption Ratio Index (MCARI) and Normalized Difference Water Index (NDWI). Finally, the spectra of corn leaves obtained under the same experimental conditions in other year were used to verify the stability and effectiveness of INBVI. The results showed that the four subintervals of Green peak, Red edge, Near valley and Near peak A were sensitive intervals, because the absolute value of r exceeded .9, and the RMSE values were lower than other subintervals and the three conventional vegetation indexes. We verified the above results and found the sensitive intervals were Green peak, Red edge, Near valley and Near peak A, which were consistent with the sensitive subintervals obtained from the experiment data in 2016, and confirmed the effectiveness and stability of INBVI. The INBVI proposed in this paper can be used as a method to monitor copper pollution in corn leaves, and provide some theoretical support for the research of heavy metal pollution in other crops.

Toward Automated Machine Learning-Based Hyperspectral Image Analysis in Crop Yield and Biomass Estimation

The incorporation of autonomous computation and artificial intelligence (AI) technologies into smart agriculture concepts is becoming an expected scientific procedure. The airborne hyperspectral system with its vast area coverage, high spectral resolution, and varied narrow-band selection is an excellent tool for crop physiological characteristics and yield prediction. However, the extensive and redundant three-dimensional (3D) cube data processing and computation have made the popularization of this tool a challenging task. This research integrated two important open-sourced systems (R and Python) combined with automated hyperspectral narrowband vegetation index calculation and the state-of-the-art AI-based automated machine learning (AutoML) technology to estimate yield and biomass, based on three crop categories (spring wheat, pea and oat mixture, and spring barley with red clover) with multifunctional cultivation practices in northern Europe and Estonia. Our study showed the estimated capacity of the empirical AutoML regression model was significant. The best coefficient of determination (R2) and normalized root mean square error (NRMSE) for single variety planting wheat were 0.96 and 0.12 respectively; for mixed peas and oats, they were 0.76 and 0.18 in the booting to heading stage, while for mixed legumes and spring barley, they were 0.88 and 0.16 in the reproductive growth stages. In terms of straw mass estimation, R2 was 0.96, 0.83, and 0.86, and NRMSE was 0.12, 0.24, and 0.33 respectively. This research contributes to, and confirms, the use of the AutoML framework in hyperspectral image analysis to increase implementation flexibility and reduce learning costs under a variety of agricultural resource conditions. It delivers expert yield and straw mass valuation two months in advance before harvest time for decision-makers. This study also highlights that the hyperspectral system provides economic and environmental benefits and will play a critical role in the construction of sustainable and intelligent agriculture techniques in the upcoming years.

Assessment of cotton and sorghum stand establishment using UAV‐based multispectral and DSLR‐based RGB imagery

AbstractPlant density and canopy cover are key agronomic traits for cotton (Gossypium hirsutum L.) and sorghum [Sorghum bicolor (L.) Moench] phenotypic evaluation. The objective of this study was to evaluate utility of broadband red–green–blue (RGB) and narrowband green, red, red‐edge, and near‐infrared spectral data taken by an unmanned aerial vehicle (UAV), and RGB taken by a digital single‐lens reflex camera for assessing the cotton and sorghum stands. Support Vector Machine was used to analyze UAV images, whereas ImageJ was used for RGB images. Fifteen vegetation indices (VIs) were evaluated for their accuracy, predictability, and residual yield. All VIs had Cohen's k > .65, F score > .63, and User and Producer accuracy of more than 71 and 69%, respectively. Soil‐adjusted vegetation indices (SAVIs) among narrowband VIs and excess green minus excess red (ExG–ExR) among broadband VIs provided more agreeable estimates of cotton and sorghum density than the remaining VIs with R2 and index of agreement (IoA) up to .79 and .92, respectively. The estimated canopy cover explained up to 83 and 82% variability in leaf area index (LAI) of cotton and sorghum, respectively. The ImageJ produced R2 from .79 to .90 and .83 to .86 and IoA .89 to .97 and ∼.91 between estimated and observed cotton and sorghum density, respectively. ImageJ explained up to 82 and 79% variability in cotton and sorghum LAI, respectively. Although ImageJ can give close estimates of crop density and cover, UAV‐based narrowband VIs still can provide an agreeable, reliable, and time‐efficient estimate of these attributes.

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.

Reflectance spectroscopy to characterize the response of Corymbia calophylla to Phytophthora root rot and waterlogging stress

Abstract The health of Corymbia calophylla (marri), a keystone tree species in the native forests of southwest Western Australia, has been in decline for the past few decades. Phytophthora root disease and waterlogging have often been cited as contributing to this decline. Traditional methods (i.e., field surveys and sampling) of mapping Phytophthora root infection in the field are time-consuming and expensive; thus, the potential of reflectance spectroscopy to characterize marri response to Phytophthora and waterlogging stress was investigated. Twelve-month old marri plants were infected with either P. cinnamomi or P. multivora in two glasshouse trials and waterlogged for 24 h each fortnight. Spectral measurements with a portable high-resolution spectroradiometer were taken weekly. Plant biophysical measurements were taken at harvest time. Normalized difference spectral index (NDSI) was calculated for every combination of reflectance values between 400 and 2500 nm for all time points, correlated with the treatment effects and displayed as heat maps. Narrowband vegetation indices (VIs), utilizing different wavelengths of the electromagnetic spectrum, were also calculated from the spectral data. The Phytophthora treatments did not cause significant differences with the biophysical measurements in both trials. In the second trial, the waterlogging treatment significantly lowered plant top dry weight (P = 0.016) and diameter (P = 0.044). Reflectance values plotted against wavelength displayed differences between treatments as well as a seasonal trend. The NDSI heat maps indicated that the Phytophthora and waterlogging treatment effects were strongest correlated with bandwidths in the visible and near-infrared portions of the electromagnetic spectrum (538–558 nm and 701–709 nm). Six of the VIs (normalized difference nitrogen index 2, anthocyanin reflectance index 1, photochemical reflectance index, Carter index 1, Vogelman index 3 and water band index) were able to track the biochemical changes in the leaves over the 10 weeks, confirming the seasonal trend. The interaction effect between P. cinnamomi, waterlogging and elapsed time in the first trial was significant for water band index (P = 0.010). This study demonstrates that reflectance spectroscopy holds promise for characterizing marri response but more work needs to be done to identify the optimum wavelengths for identifying Phytophthora and waterlogging stress with marri.

Aboveground biomass estimates over Brazilian savannas using hyperspectral metrics and machine learning models: experiences with Hyperion/EO-1

ABSTRACT We investigated the potential of hyperspectral remote sensing to estimate aboveground biomass (AGB) over the Brazilian savannas (Cerrado), the second-largest source of carbon emissions in Brazil. For this purpose, a Hyperion/Earth Observing-1 (EO-1) image was collected in the dry season at the Ecological Station of Águas Emendadas (ESAE). In order to estimate the AGB, we evaluated the performance of five machine learning models (Classification and Regression Trees – CART; Cubist – CB, Partial Least Squares Regression – PLS; Random Forest – RF; and Support Vector Machine – SVM) and four sets of metrics (reflectance, narrowband vegetation indices – VIs; absorption band parameters; and the combination of these attributes). The lowest root mean square error (RMSE) was obtained for RF using VIs (29%) and a combination of metrics (28%). For VIs, RF differed from CUB, PLS and SVM at 5% significance level. From cross-validation results, the RMSE was 26.36% for grasslands, 35.04% for open savannas, and 24.85% for dense savannas. The RF model with VIs had the most stable predictive performance across the models, as indicated by small variations in RMSE from CART to SVM. The five most important ranked VIs in the RF model were the Normalized Difference Vegetation Index (NDVI), Pigment Specific Simple Ratio (PSSR), Enhanced Vegetation Index (EVI), Red Edge Normalized Difference Vegetation Index (RENDVI) and Structure Insensitive Pigment Index (SIPI). Most of their relationships with AGB were non-linear. The resultant AGB estimates showed consistent results with a vegetation cover map of the ESAE. Areas of the ESAE with AGB lower than 10 Mg.ha−1 were coincident with the occurrence of grassland physiognomies (savanna grasslands and shrub savannas), while areas with AGB higher than 25 Mg.ha−1 matched the occurrence of dense savanna physiognomies (woodland savanna and dense woodland savanna). Grassland areas showed larger values of coefficient of variation (CV) than areas of dense savannas. These first-hand results set a baseline of models and metrics for AGB modeling of savannas during the future transition from current sampling-type hyperspectral missions (< 10 km of swath) to large-coverage hyperspectral satellites (> 100 km of swath).

Estimating the maize biomass by crop height and narrowband vegetation indices derived from UAV-based hyperspectral images

Monitoring the aboveground biomass (AGB) of maize is essential for improving site-specific nutrient management and predicting yield to ensure food safety. A low-altitude unmanned aerial vehicle (UAV) was employed to acquire hyperspectral imagery of the maize canopy at three growth stages (V6, R1, R3) to estimate the maize AGB. Five maize nitrogen (N) rate experiments were conducted in Lishu County, Jilin Province, Northeastern China, to create different biomass conditions. Combined with crop height data obtained from the field measurements, 30 narrowband vegetation indices (VIs) were extracted using surface reflectance data from the hyperspectral imagery. Stepwise regression, random forest (RF) regression and XGBoost regression models were used to predict the fresh and dry AGB of the 2019 growth season. The study revealed that (1) crop height explained the most variability (60–70%) in the maize dry and fresh AGB estimation of the V6 growth stage, while different VIs exhibited various importance for AGB estimation at other maize growth stages; (2) XGBoost regression models demonstrated high prediction accuracy in both fresh and dry AGB estimation, compared with stepwise regression and RF models. (3) XGBoost models also presented high prediction accuracy at each single-growth stage and the whole-growth stage, with the highest accuracy for the dry AGB at V6 growth stage (R2 = 0.81, RMSE = 0.27 t/ha). This study demonstrated the capability of UAV-based hyperspectral imagery for estimating maize AGB at the field scale, which can be used to assist precision agriculture.

Identification of Rice Sheath Blight through Spectral Responses Using Hyperspectral Images.

Sheath blight (ShB), caused by Rhizoctonia solani AG1-I, is one of the most important diseases in rice worldwide. The symptoms of ShB primarily develop on leaf sheaths and leaf blades. Hyperspectral remote sensing technology has the potential of rapid, efficient and accurate detection and monitoring of the occurrence and development of rice ShB and other crop diseases. This study evaluated the spectral responses of leaf blade fractions with different development stages of ShB symptoms to construct the spectral feature library of rice ShB based on “three-edge” parameters and narrow-band vegetation indices to identify the disease on the leaves. The spectral curves of leaf blade lesions have significant changes in the blue edge, green peak, yellow edge, red valley, red edge and near-infrared regions. The variables of the normalized index between green peak amplitude and red valley amplitude (Rg − Ro)/(Rg + Ro), the normalized index between the yellow edge area and blue edge area (SDy − SDb)/(SDy + SDb), the ratio index of green peak amplitude and red valley amplitude (Rg/Ro) and the nitrogen reflectance index (NRI) had high relevance to the disease. At the leaf scale, the importance weights of all attributes decreased with the effect of non-infected areas in a leaf by the ReliefF algorithm, with Rg/Ro being the indicator having the highest importance weight. Estimation rate of 95.5% was achieved in the decision tree classifier with the parameter of Rg/Ro. In addition, it was found that the variety degree of absorptive valley, reflection peak and reflecting steep slope was different in the blue edge, green and red edge regions, although there were similar spectral curve shapes between leaf sheath lesions and leaf blade lesions. The significant difference characteristic was the ratio index of the red edge area and green peak area (SDr/SDg) between them. These results can provide the basis for the development of a specific sensor or sensors system for detecting the ShB disease in rice.

Mapping Canopy Chlorophyll Content in a Temperate Forest Using Airborne Hyperspectral Data

Chlorophyll content, as the primary pigment driving photosynthesis, is directly affected by many natural and anthropogenic disturbances and stressors. Accurate and timely estimation of canopy chlorophyll content (CCC) is essential for effective ecosystem monitoring to allow for successful management interventions to occur. Hyperspectral remote sensing offers the possibility to accurately estimate and map canopy chlorophyll content. In the past, research has predominantly focused on the use of hyperspectral data on canopy chlorophyll content retrieval of crops and grassland ecosystems. Therefore, in this study, a temperate mixed forest, the Bavarian Forest National Park in Germany, was chosen as the study site. We compared different statistical models (narrowband vegetation indices (VIs), partial least squares regression (PLSR) and random forest (RF)) in their accuracy to predict CCC using airborne hyperspectral data. The airborne hyperspectral imagery was acquired by the AisaFenix sensor (623 bands; 3.5 nm spectral resolution in the visible near-infrared (VNIR) region, and 12 nm spectral resolution in the shortwave infrared (SWIR) region; 3 m spatial resolution) on July 6, 2017. In situ leaf chlorophyll content and leaf area index measurements were sampled from the upper canopy of coniferous, mixed, and deciduous forest stands in July and August 2017. The study yielded the highest retrieval accuracies with PLSR (root mean square error (RMSE) = 0.25 g/m2, R2 = 0.66). It further indicated specific spectral regions within the visible (390–400 nm and 470–540 nm), red edge (680–780 nm), near-infrared (1050–1100 nm) and shortwave infrared regions (2000–2270 nm) that were important for CCC retrieval. The results showed that forest CCC can be mapped with relatively high accuracies using image spectroscopy.

Mapping leaf area index in a mixed temperate forest using Fenix airborne hyperspectral data and Gaussian processes regression

Machine learning algorithms, in particular, kernel-based machine learning methods such as Gaussian processes regression (GPR) have shown to be promising alternatives to traditional empirical methods for retrieving vegetation parameters from remotely sensed data. However, the performance of GPR in predicting forest biophysical parameters has hardly been examined using full-spectrum airborne hyperspectral data. The main objective of this study was to evaluate the potential of GPR to estimate forest leaf area index (LAI) using airborne hyperspectral data. To achieve this, field measurements of LAI were collected in the Bavarian Forest National Park (BFNP), Germany, concurrent with the acquisition of the Fenix airborne hyperspectral images (400−2500 nm) in July 2017. The performance of GPR was further compared with three commonly used empirical methods (i.e., narrowband vegetation indices (VIs), partial least square regression (PLSR), and artificial neural network (ANN)). The cross-validated coefficient of determination (Rcv2) and root mean square error (RMSEcv) between the retrieved and field-measured LAI were used to examine the accuracy of the respective methods. Our results showed that using the entire spectral data (400−2500 nm), GPR yielded the most accurate LAI estimation (Rcv2 = 0.67, RMSEcv = 0.53 m2 m−2) compared to the best performing narrowband VIs SAVI2 (Rcv2 = 0.54, RMSEcv = 0.63 m2 m−2), PLSR (Rcv2 = 0.74, RMSEcv = 0.73 m2 m−2) and ANN (Rcv2 = 0.68, RMSEcv = 0.54 m2 m−2). Consequently, when a spectral subset obtained from the analysis of VIs was used as model input, the predictive accuracies were generally improved (GPR RMSEcv = 0.52 m2 m−2; ANN RMSEcv = 0.55 m2 m−2; PLSR RMSEcv = 0.69 m2 m−2), indicating that extracting the most useful information from vast hyperspectral bands is crucial for improving model performance. In general, there was an agreement between measured and estimated LAI using different approaches (p > 0.05). The generated LAI map for BFNP using GPR and the spectral subset endorsed the LAI spatial distribution across the dominant forest classes (e.g., deciduous stands were generally associated with higher LAI values). The accompanying LAI uncertainty map generated by GPR shows that higher uncertainties were observed mainly in the regions with low LAI values (low vegetation cover) and forest areas which were not well represented in the collected sample plots. This study demonstrated the potential of GPR for estimating LAI in forest stands using airborne hyperspectral data. Owing to its capability to generate accurate predictions and associated uncertainty estimates, GPR is evaluated as a promising candidate for operational retrieval applications of vegetation traits.

Studying Optical Characteristics of Diffused Light Reflecting from Naturally Senescing Leaves of Deciduous Trees

Study object included leaves of deciduous trees in the Krasnodar Territory at different stages of senescing visually manifested in their color alteration. Study subject was the optical characteristics of light diffused reflection from green, yellow-green and yellow leaves of deciduous trees in the Krasnodar Territory during the autumn season. Work objective lies in identifying the possibility to establish differences between green leaves of deciduous trees, and yellow-green and yellow leaves of deciduous trees using the terrain multispectral and hyperspectral sounding methods, as well as collecting information on spectral characteristics of the diffused light being reflected from various biological objects. Results of quantitative and qualitative analysis of data obtained through the diffused light reflectance spectroscopy from leaves of deciduous trees are presented. Narrow-band vegetation indices mNDVI705, mSR705, CRI1, SIPI and PSRI were used in quantitative analysis of data on the diffused light reflection spectra obtained from green, yellow-green and yellow leaves of deciduous trees. It was revealed that the use of narrow-band vegetation indices in the remote sensing algorithms using multi- and hyperspectral cameras makes it possible to rather accurately distinguish leaves at different stages of senescing. Optical characteristics of diffused light reflection from green, yellow-green and yellow leaves of deciduous trees, which are typical species of trees in urban and rural plantings in the Krasnodar Territory, are described for the first time

Alfalfa Yield Prediction Using UAV-Based Hyperspectral Imagery and Ensemble Learning

Alfalfa is a valuable and intensively produced forage crop in the United States, and the timely estimation of its yield can inform precision management decisions. However, traditional yield assessment approaches are laborious and time-consuming, and thus hinder the acquisition of timely information at the field scale. Recently, unmanned aerial vehicles (UAVs) have gained significant attention in precision agriculture due to their efficiency in data acquisition. In addition, compared with other imaging modalities, hyperspectral data can offer higher spectral fidelity for constructing narrow-band vegetation indices which are of great importance in yield modeling. In this study, we performed an in-season alfalfa yield prediction using UAV-based hyperspectral images. Specifically, we firstly extracted a large number of hyperspectral indices from the original data and performed a feature selection to reduce the data dimensionality. Then, an ensemble machine learning model was developed by combining three widely used base learners including random forest (RF), support vector regression (SVR) and K-nearest neighbors (KNN). The model performance was evaluated on experimental fields in Wisconsin. Our results showed that the ensemble model outperformed all the base learners and a coefficient of determination (R2) of 0.874 was achieved when using the selected features. In addition, we also evaluated the model adaptability on different machinery compaction treatments, and the results further demonstrate the efficacy of the proposed ensemble model.

Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging

Rapid and accurate biomass and yield estimation facilitates efficient plant phenotyping and site-specific crop management. A low altitude unmanned aerial vehicle (UAV) was used to acquire RGB and hyperspectral imaging data for a potato crop canopy at two growth stages to estimate the above-ground biomass and predict crop yield. Field experiments included six cultivars and multiple treatments of nitrogen, potassium, and mixed compound fertilisers. Crop height was estimated using the difference between digital surface model and digital elevation models derived from RGB imagery. Combining with two narrow-band vegetation indices selected by the RReliefF feature selection algorithm. Random Forest regression models demonstrated high prediction accuracy for both fresh and dry above-ground biomass, with a coefficient of determination (r2) > 0.90. Crop yield was predicted using four narrow-band vegetation indices and crop height (r2 = 0.63) with imagery data obtained 90 days after planting. A Partial Least Squares regression model based on the full wavelength spectra demonstrated improved yield prediction (r2 = 0.81). This study demonstrated the merits of UAV-based RGB and hyperspectral imaging for estimating the above-ground biomass and yield of potato crops, which can be used to assist in site-specific crop management.