Red-edge Wavelengths Research Articles

Estimating turbidity concentrations in highly dynamic rivers using Sentinel-2 imagery in Google Earth Engine: Case study of the Godavari River, India.

Turbidity is an essential biogeochemical parameter for water quality management because it shapes the physical landscape and regulates ecological systems. It varies spatially and temporally across large water bodies, but monitoring based on point-source field observations remains a difficult task in developing countries due to the need for logistics and costs. In this study, we present a novel semi-analytical approach for estimating turbidity from remote sensing reflectance in moderate to highly turbid waters in the lower part of the Godavari River (i.e., locations near Rajahmundry). The proposed method includes two sub-algorithms-Normalized Difference Turbidity Index (NDTI) and semi-empirical single-band turbidity ( ) algorithm-to retrieve spectral reflectance information corresponding to the study locations for turbidity modeling. Sentinel-2 Multi-Spectral Imager data have been used to quantify the turbidity in the Google Earth Engine (GEE) platform. The correlation analysis was observed between spectral reflectance values and in situ turbidity data using cubic polynomial regression equations. The results indicated that the , which uses the only red-edge wavelength, identified turbidity as the most accurate across all locations (highest R2 = 0.91, lowest RMSE = 0.003), followed by NDTI (highest R2 = 0.85, lowest RMSE = 0.05), respectively. The remote sensing data application provides a better way to monitor turbidity at large spatio-temporal scales in attaining the water quality standards of the Godavari River.

Monitoring turbidity in a highly variable estuary using Sentinel 2-A/B for ecosystem management applications

The Guadalquivir estuary (southern Spain) occasionally experiences medium to high turbidity, reaching above 700 Formazin Nephelometric Unit (FNU) during extreme events, thus negatively influencing its nursery function and the estuarine community structure. Although several turbidity algorithms are available to monitor water quality, they are mainly developed for mapping turbidity ranges of 0-100 FNU. Thus, their use in a highly turbid region may not give accurate results, which is crucial for estuarine ecosystem management. To fill this gap, we developed a multi-conditional turbidity algorithm that can retrieve turbidity from 0 to 600 FNU using the Sentinel-2 red and red-edge bands. Four major steps are implemented: atmospheric and sun glint correction of the Level-1C Sentinel-2 data, spectral analysis for different water turbidity levels, regression modelling between in situ turbidity and remote sensing reflectance (Rrs) for algorithm development, and validation of the best-suited model. When turbidity was < 85 FNU, the Rrs increased firstly in the red wavelength (665 nm), but it saturated beyond a certain turbidity threshold (> 250 FNU). At this time, Rrs started to increase in the red-edge wavelength (704 nm). Considering this spectral behavior, our algorithm is designed to automatically select the most sensitive turbidity vs. Rrs, thus avoiding the saturation effects of the red bands at high turbidity levels. The model showed good agreement between the satellite derived turbidity and the in situ measurements with a correlation coefficient of 0.97, RMSE of 15.93 FNU, and a bias of 13.34 FNU. Turbidity maps derived using this algorithm can be used for routine turbidity monitoring and assessment of potential anthropogenic actions (e.g., dredging activities), thus helping the decision-makers and relevant stakeholders to protect coastal resources and human health.

Leaf Spectral Analysis for Detection and Differentiation of Three Major Rice Diseases in the Philippines

Monitoring the plant’s health and early detection of disease are essential to facilitate effective management, decrease disease spread, and minimize yield loss. Spectroscopic techniques in remote sensing offer less laborious methods and high spatiotemporal scale to monitor diseases in crops. Spectral measurements during the development of disease infection may reveal differences among diseases and determine the stage it can be effectively detected. In this study, spectral analysis was performed over the visible and near-infrared (400–850 nm) portions of the spectrum to detect and differentiate three major rice diseases in the Philippines, namely tungro, BLB, and blast disease. Reflectance of infected rice leaves was recorded repeatedly from inoculation to the late stage of each disease. Results show that spectral reflectance is characteristically affected by each disease, resulting in different spectral, signature sensitivity, and first-order derivatives. Red and red-edge wavelength ranges are the most sensitive to the three diseases. Near-infrared wavelengths decreased as tungro and blast diseases progressed. In addition, the spectral reflectance was resampled to common reflectance sensitivity bands of optical sensors and used in the cluster analysis. It showed that BLB and blast can be detected in the early disease stage on the IRRI Standard Evaluation System (SES) scale of 1 and 3, respectively. Alternatively, tungro was detected in its later stage, with an 11–30% height reduction and no distinct yellow to yellow-orange discoloration (5 SES scale). Three regression techniques, Partial Least Square, Random Forest, and Support Vector Regression were performed separately on each disease to develop models predicting its severity. The validation results of the PLSR and SVR models in tungro and blast show accuracy levels that are promising to be used in estimating the severity of the disease in leaves while RFR shows the best results for BLB. Early disease detection and regression models from spectral measurements and analysis for disease severity estimation can help in disease monitoring and proper disease management implementation.

Effect of the Red-Edge Band from Drone Altum Multispectral Camera in Mapping the Canopy Cover of Winter Wheat, Chickweed, and Hairy Buttercup

The detection and mapping of winter wheat and the canopy cover of associated weeds, such as chickweed and hairy buttercup, are essential for crop and weed management. With emerging drone technologies, the use of a multispectral camera with the red-edge band, such as Altum, is commonly used for crop and weed mapping. However, little is understood about the contribution of the red-edge band in mapping. The aim of this study was to examine the addition of the red-edge band from a drone with an Altum multispectral camera in improving the detection and mapping of the canopy cover of winter wheat, chickweed, and hairy buttercup. The canopy cover of winter wheat, chickweed, and hairy buttercup were classified and mapped with the red-edge band inclusively and exclusively using a random forest classification algorithm. Results showed that the addition of the red-edge band increased the overall mapping accuracy of about 7%. Furthermore, the red-edge wavelength was found to better detect winter wheat relative to chickweed and hairy buttercup. This study demonstrated the usefulness of the red-edge band in improving the detection and mapping of winter wheat and associated weeds (chickweed and hairy buttercup) in agricultural fields.

Dry Matter Yield and Nitrogen Content Estimation in Grassland Using Hyperspectral Sensor

Estimation of Dry Matter Yield (DMY) and Nitrogen Content (NC) in forage is a big concern for growers. In this study, an estimation model of DMY and NC using Visible and Near Infrared (V-NIR) spectroscopy was developed. An adequate number of grass samples (5078) of perennial ryegrass (Lolium perenne), collected from Dutch grassland in 2019 and 2020 were sensed with a hyperspectral sensor, while grass height was recorded in situ by an ultrasonic sensor mounted on a tractor. The samples were treated with Artificial Intelligence (AI) techniques. PCA based feature selection was applied first, revealing that visible green wavelength (around 500 nm) and red edge wavelength (around 700 nm) were enough to express the overall variability of the dataset. Then, Feature Importance analysis of Random Forest Regressor showed that NIR wavelengths (around 910, 960 and 990nm) were the most sensitive in DMY estimation, while red edge (around 710 nm) and visible orange wavelengths (around 610 nm) were the most related to NC estimation. Finally, SHAP (SHapley Additive exPlanations) analysis was applied to the Random Forest estimation models, resulting in the visualization of wavelength selection, thus assisting in the interpretation of the results and the intermediate processes. Overall, this method can lead to the reduction of the number of wavelengths to be measured in the field and thus, to the possible development of a low cost hyperspectral sensor for the above purposes.

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.

Detection of Eucalyptus Leaf Disease with UAV Multispectral Imagery

Forest disease is one of the most important factors affecting tree growth and product quality, reducing economic values of forest ecosystem goods and services. In order to prevent and control forest diseases, accurate detection in a timely manner is essential. Unmanned aerial vehicles (UAVs) are becoming an important tool for acquiring multispectral imagery, but have not been extensively used for detection of forest diseases. This research project selected a eucalyptus forest as a case study to explore the performance of leaf disease detection using high spatial resolution multispectral imagery that had been acquired by UAVs. The key variables sensitive to eucalyptus leaf diseases, including spectral bands and vegetation indices, were identified by using a mutual information–based feature selection method, then distinguishing disease levels using random forest and spectral angle mapper approaches. The results show that green, red edge, and near-infrared wavelengths, nitrogen reflectance index, and greenness index are sensitive to forest diseases. The random forest classifier, based on a combination of sensitive spectral bands (green, red edge, and near-infrared wavelengths) and a nitrogen reflectance index, provided the best differentiation results for healthy and three disease severity levels (mild, moderate, and severe) with overall accuracy of 90.1% and kappa coefficient of 0.87. This research provides a new way to detect eucalyptus leaf diseases, and the proposed method may be suitable for other forest types.

The Impact of NPV on the Spectral Parameters in the Yellow-Edge, Red-Edge and NIR Shoulder Wavelength Regions in Grasslands

Even though research has shown that the spectral parameters of yellow-edge, red-edge and NIR (near-infrared) shoulder wavelength regions are able to estimate green cover and leaf area index (LAI), a large amount of dead materials in grasslands challenges the accuracy of their estimation using hyperspectral remote sensing. However, the exact impact of dead vegetation cover on these spectral parameters remains unclear. Therefore, we evaluated the influences of dead materials on the spectral parameters in the wavelength regions of yellow-edge, red-edge and NIR shoulder by comparing normalized difference vegetation indices (NDVI) including the common red valley at 670 nm and NDVI using the red valley extracted by a new statistical method. This method, based on the concept of segmented linear regression, was developed to extract the spectral parameters and calculate NDVI automatically from the hyper-spectra. To fully understand the impact of dead cover on the spectral parameters (i.e., consider full coverage combinations of green vegetation, dead materials and bare soil), both in situ measured and simulated hyper-spectra were analyzed. The impact of dead cover on LAI estimation by those spectral parameters and NDVI were also evaluated. The results show that: (i) without considering the influence of bare soil, dead materials decreases the slope of red-edge, the slope of NIR shoulder and NDVI, while dead materials increases the slope of yellow-edge; (ii) the spectral characteristics of red valley disappear when dead cover exceeds 67%; (iii) large amount of dead materials also result in a blue shift of the red-edge position; (iv) accurate extraction of the red valley position enhances LAI estimation and reduces the influences of dead materials using hyperspectral NDVI; (v) the accuracy of LAI estimation using the slope of yellow-edge, the slope of red-edge, red-edge position and NDVI significantly drops when dead cover exceeds 72.3–74.5% (variation among indices).

Mapping algal bloom dynamics in small reservoirs using Sentinel-2 imagery in Google Earth Engine

Freshwater algal blooms have caused ecological damage and public health concerns throughout the world. Monitoring such blooms via in situ sampling is both costly and time-consuming, and satellite imagery provides a rapid and relatively inexpensive way to supplement these techniques. Sentinel-2 MultiSpectral Imager data have effectively detected chlorophyll-a, a proxy for algal biomass, in large bodies of water, but few studies have shown the applicability in small (<10 km2) reservoirs, which are critically important for aquatic species, drinking water, irrigation, cultural activities, and recreation. This study provides a test of the use of Sentinel-2 imagery in Google Earth Engine for algal bloom detection in two small freshwater reservoirs in northern California, USA, from October 2015 to December 2020. Google Earth Engine’s cloud computing allows for the analysis of extensive datasets and time series, expanding the capacity to analyze the spatial and temporal heterogeneity of floating algal blooms. Here we analyzed four spectral indices - Normalized Difference Vegetation Index (NDVI), Normalized Difference Chlorophyll Index (NDCI), B8AB4, and B3B2 - to retrieve chlorophyll-a data for algal bloom identification in two highly dynamic freshwater systems. We assessed the relationship between spectral indices and monthly in situ water samples that were collected at three sites within the reservoirs using cubic polynomial regression equations. NDCI, which leverages the red-edge wavelength, most accurately identified chlorophyll-a across all study sites (highest adjusted R2 = 0.84, lowest RMSE = 0.02 µg/l), followed by NDVI. We demonstrate that Sentinel-2 imagery can capture greater spatial and temporal heterogeneity of algal blooms than typical in situ sampling. This suggests that remote sensing may be an increasingly important tool in monitoring algal bloom dynamics in small reservoirs and other aquatic environments.

Predicting the Chlorophyll Content of Maize over Phenotyping as a Proxy for Crop Health in Smallholder Farming Systems

Smallholder farmers depend on healthy and productive crop yields to sustain their socio-economic status and ensure livelihood security. Advances in South African precision agriculture in the form of unmanned aerial vehicles (UAVs) provide spatially explicit near-real-time information that can be used to assess crop dynamics and inform smallholder farmers. The use of UAVs with remote-sensing techniques allows for the acquisition of high spatial resolution data at various spatio-temporal planes, which is particularly useful at the scale of fields and farms. Specifically, crop chlorophyll content is assessed as it is one of the best known and reliable indicators of crop health, due to its biophysical pigment and biochemical processes that indicate plant productivity. In this regard, the study evaluated the utility of multispectral UAV imagery using the random forest machine learning algorithm to estimate the chlorophyll content of maize through the various growth stages. The results showed that the near-infrared and red-edge wavelength bands and vegetation indices derived from these wavelengths were essential for estimating chlorophyll content during the phenotyping of maize. Furthermore, the random forest model optimally estimated the chlorophyll content of maize over the various phenological stages. Particularly, maize chlorophyll was best predicted during the early reproductive, late vegetative, and early vegetative growth stages to RMSE accuracies of 40.4 µmol/m−2, 39 µmol/m−2, and 61.6 µmol/m−2, respectively. The least accurate chlorophyll content results were predicted during the mid-reproductive and late reproductive growth stages to RMSE accuracies of 66.6 µmol/m−2 and 69.6 µmol/m−2, respectively, as a consequence of a hailstorm. A resultant chlorophyll variation map of the maize growth stages captured the spatial heterogeneity of chlorophyll within the maize field. Therefore, the study’s findings demonstrate that the use of remotely sensed UAV imagery with a robust machine algorithm is a critical tool to support the decision-making and management in smallholder farms.

Determining leaf nutrient concentrations in citrus trees using UAV imagery and machine learning

Nutrient assessment of plants, a key aspect of agricultural crop management and varietal development programs, traditionally is time demanding and labor-intensive. This study proposes a novel methodology to determine leaf nutrient concentrations of citrus trees by using unmanned aerial vehicle (UAV) multispectral imagery and artificial intelligence (AI). The study was conducted in four different citrus field trials, located in Highlands County and in Polk County, Florida, USA. In each location, trials contained either ‘Hamlin’ or ‘Valencia’ sweet orange scion grafted on more than 30 different rootstocks. Leaves were collected and analyzed in the laboratory to determine macro- and micronutrient concentration using traditional chemical methods. Spectral data from tree canopies were obtained in five different bands (red, green, blue, red edge and near-infrared wavelengths) using a UAV equipped with a multispectral camera. The estimation model was developed using a gradient boosting regression tree and evaluated using several metrics including mean absolute percentage error (MAPE), root mean square error, MAPE-coefficient of variance (CV) ratio and difference plot. This novel model determined macronutrients (nitrogen, phosphorus, potassium, magnesium, calcium and sulfur) with high precision (less than 9% and 17% average error for the ‘Hamlin’ and ‘Valencia’ trials, respectively) and micro-nutrients with moderate precision (less than 16% and 30% average error for ‘Hamlin’ and ‘Valencia’ trials, respectively). Overall, this UAV- and AI-based methodology was efficient to determine nutrient concentrations and generate nutrient maps in commercial citrus orchards and could be applied to other crop species.

Evaluating the Spectral Response and Yield of Soybean Following Exposure to Sublethal Rates of 2,4-D and Dicamba at Vegetative and Reproductive Growth Stages

The commercialization of synthetic auxin-resistant crops and the commensurate increase in post-emergent auxin-mimic herbicide applications has resulted in millions of hectares of injury to sensitive soybeans in the United States since 2016. Visual yield loss estimations following auxin injury can be difficult. The goal of this research was to determine if spectral variations following auxin injury to soybean allow for more precise yield loss estimations. Identical field experiments were performed in 2018, 2019, and 2020 in Columbia, Missouri to compare the ability of established vegetative indices to differentiate between exposure levels of 2,4-D and dicamba in soybean and predict yield loss. Soybeans were planted at three timings for growth stage separation and were exposed to sublethal rates of 2,4-D and dicamba at the R2, R1, and V3 growth stages. A UAV-mounted multispectral sensor was flown over the trial 14 days after the herbicide treatments. The results of this research found that vegetative indices incorporating the red-edge wavelength were more consistent in estimating yield loss than indices comprised of only visible or NIR wavelengths. Yield loss estimations became difficult when soybean injury occurred during later reproductive stages when soybean biomass was increased. This research also determined that when injury occurs to soybean in vegetative growth stages late in the growing season there is a greater likelihood for yield loss to occur due to decreased time for recovery. The results of this research could provide direction for more objective and accurate evaluations of yield loss following synthetic auxin injury than what is currently available.

Remote sensing retrieval of winter wheat leaf area index and canopy chlorophyll density at different growth stages

ABSTRACT Leaf area index (LAI) and canopy chlorophyll density (CCD) are key indicators of crop growth status. In this study, we compared several vegetation indices and their red-edge modified counterparts to evaluate the optimal red-edge bands and the best vegetation index at different growth stages. The indices were calculated with Sentinel-2 MSI data and hyperspectral data. Their performances were validated against ground measurements using R2, RMSE, and bias. The results suggest that indices computed with hyperspectral data exhibited higher R2 than multispectral data at the late jointing stage, head emergence stage, and filling stage. Furthermore, red-edge modified indices outperformed the traditional indices for both data genres. Inversion models indicated that the indices with short red-edge wavelengths showed better estimation at the early jointing and milk development stage, while indices with long red-edge wavelength estimate the sought variables better at the middle three stages. The results were consistent with the red-edge inflection point shift at different growth stages. The best indices for Sentinel-2 LAI retrieval, Sentinel-2 CCD retrieval, hyperspectral LAI retrieval, and hyperspectral CCD retrieval at five growth stages were determined in the research. These results are beneficial to crop trait monitoring by providing references for crop biophysical and biochemical parameters retrieval.

Potential use of hyperspectral data to monitor sugarcane nitrogen status

Nitrogen management in crops is a key activity for agricultural production. Methods that can determine the levels of this element in plants in a quick and non-invasive way are extremely important for improving production systems. Within several fronts of study on this subject, proximal and remote sensing methods are promising techniques. In this regard, this research sought to demonstrate the relationships between variations in leaf nitrogen content (LNC) and sugarcane spectral behaviour. The work was carried out in three experimental areas in São Paulo State, Brazil, with different soils, varieties and nitrogen rates during the 2012/13 and 2013/14 seasons. A significant correlation was observed between the LNC and variations in the sugarcane spectra. The green and red-edge spectral bands were the most consistent and stable predictors of LNC among the evaluated harvests. Stepwise multiple linear regression analysis (MSLR) generated better models for LNC estimation when calibrated with experimental area, independent of the variety. The present research demonstrates that specific wavelengths are associated with the variation in LNC in sugarcane, and these are reported in the green region (near 550 nm) and in the red-edge wavelengths (680 to 720 nm). These results may help in future research on the direct in situ application of nitrogen fertilizers.

Quantification of the Optical Properties of Perovskite Nanocrystals Using a Combination of Polarized Resonance Synchronous and Polarized Anti-Stokes, On-Resonance, and Stokes-Shifted Spectroscopy

Reliable quantification of the optical properties of perovskite nanocrystals (PNCs) is necessary for rational PNC design and applications in optoelectronics. Presented herein is the experimental evaluation of the photon scattering, absorption, and on-resonance fluorescence (ORF) cross sections of lead halide PNCs. Four CsPbX3 (X = Cl, Br, or I) PNCs fabricated through anion exchanges with the same parent PNC CsPbCl3 were studied using UV–vis, fluorescence, polarized resonance synchronous spectroscopic (PRS2), and the polarized anti-Stokes-shifted, on-resonance, and Stokes-shifted (PAOS) spectroscopic methods. All of the PNCs exhibit strong ORF in the wavelength region where the PNC absorption and fluorescence spectra overlap. The PNC optical properties under resonance excitation and detection conditions differ in different wavelength regions. They are simultaneous light absorbers and scatterers in the wavelength region shorter than the blue-edge wavelength of the PNC ORF peak; simultaneously, photon absorbers, scatterers, and emitters in the wavelength region where the PNC is ORF active; and light scatterers alone in the wavelength region longer than the red-edge wavelength of the PNC ORF peak. The PNC peak ORF wavelength and absorption extinction cross section increase with increasing fraction of heavy halides. However, the ORF cross section and quantum yield (QY) of the iodide-containing PNCs are several folds lower than those of their counterpart iodide-free PNCs. The effects of sample aging on the PNC optical properties have been quantified in terms of PNC absorption, scattering, and ORF cross sections and ORF QY. In addition to providing a series of quantitative information not accessible before, we anticipate that the devised methodology will be extended to fluorescent nanomaterials for which the existing tools are inadequate for decoupling the complex interplay among light UV–vis absorption, scattering, and fluorescence emission that can concurrently occur under the same excitation and detection conditions.

A Systematic Review of the Factors Influencing the Estimation of Vegetation Aboveground Biomass Using Unmanned Aerial Systems

Interest in the use of unmanned aerial systems (UAS) to estimate the aboveground biomass (AGB) of vegetation in agricultural and non-agricultural settings is growing rapidly but there is no standardized methodology for planning, collecting and analyzing UAS data for this purpose. We synthesized 46 studies from the peer-reviewed literature to provide the first-ever review on the subject. Our analysis showed that spectral and structural data from UAS imagery can accurately estimate vegetation biomass in a variety of settings, especially when both data types are combined. Vegetation-height metrics are useful for trees, while metrics of variation in structure or volume are better for non-woody vegetation. Multispectral indices using NIR and red-edge wavelengths normally have strong relationships with AGB but RGB-based indices often outperform them in models. Including measures of image texture can improve model accuracy for vegetation with heterogeneous canopies. Vegetation growth structure and phenological stage strongly influence model accuracy and the selection of useful metrics and should be considered carefully. Additional factors related to the study environment, data collection and analytical approach also impact biomass estimation and need to be considered throughout the workflow. Our review shows that UASs provide a capable tool for fine-scale, spatially explicit estimations of vegetation AGB and are an ideal complement to existing ground- and satellite-based approaches. We recommend future studies aimed at emerging UAS technologies and at evaluating the effect of vegetation type and growth stages on AGB estimation.

Integration of Time Series Sentinel-1 and Sentinel-2 Imagery for Crop Type Mapping over Oasis Agricultural Areas

Timely and accurate crop type mapping is a critical prerequisite for the estimation of water availability and environmental carrying capacity. This research proposed a method to integrate time series Sentinel-1 (S1) and Sentinel-2 (S2) data for crop type mapping over oasis agricultural areas through a case study in Northwest China. Previous studies using synthetic aperture radar (SAR) data alone often yield quite limited accuracy in crop type identification due to speckles. To improve the quality of SAR features, we adopted a statistically homogeneous pixel (SHP) distributed scatterer interferometry (DSI) algorithm, originally proposed in the interferometric SAR (InSAR) community for distributed scatters (DSs) extraction, to identify statistically homogeneous pixel subsets (SHPs). On the basis of this algorithm, the SAR backscatter intensity was de-speckled, and the bias of coherence was mitigated. In addition to backscatter intensity, several InSAR products were extracted for crop type classification, including the interferometric coherence, master versus slave intensity ratio, and amplitude dispersion derived from SAR data. To explore the role of red-edge wavelengths in oasis crop type discrimination, we derived 11 red-edge indices and three red-edge bands from Sentinel-2 images, together with the conventional optical features, to serve as input features for classification. To deal with the high dimension of combined SAR and optical features, an automated feature selection method, i.e., recursive feature increment, was developed to obtain the optimal combination of S1 and S2 features to achieve the highest mapping accuracy. Using a random forest classifier, a distribution map of five major crop types was produced with an overall accuracy of 83.22% and kappa coefficient of 0.77. The contribution of SAR and optical features were investigated. SAR intensity in VH polarization was proved to be most important for crop type identification among all the microwave and optical features employed in this study. Some of the InSAR products, i.e., the amplitude dispersion, master versus slave intensity ratio, and coherence, were found to be beneficial for oasis crop type mapping. It was proved the inclusion of red-edge wavelengths improved the overall accuracy (OA) of crop type mapping by 1.84% compared with only using conventional optical features. In comparison, it was demonstrated that the synergistic use of time series Sentinel-1 and Sentinel-2 data achieved the best performance in the oasis crop type discrimination.

Advanced Imaging for Quantitative Evaluation of Aphanomyces Root Rot Resistance in Lentil.

Aphanomyces root rot (ARR) is a soil-borne disease that results in severe yield losses in lentil. The development of resistant cultivars is one of the key strategies to control this pathogen. However, the evaluation of disease severity is limited to visual scores that can be subjective. This study utilized image-based phenotyping approaches to evaluate Aphanomyces euteiches resistance in lentil genotypes in greenhouse (351 genotypes from lentil single plant/LSP derived collection and 191 genotypes from recombinant inbred lines/RIL using digital Red-Green-Blue/RGB and hyperspectral imaging) and field (173 RIL genotypes using unmanned aerial system-based multispectral imaging) conditions. Moderate to strong correlations were observed between RGB, multispectral, and hyperspectral derived features extracted from lentil shoots/roots and visual scores. In general, root features extracted from RGB imaging were found to be strongly associated with disease severity. With only three root traits, elastic net regression model was able to predict disease severity across and within multiple datasets (R2 = 0.45–0.73 and RMSE = 0.66–1.00). The selected features could represent visual disease scores. Moreover, we developed twelve normalized difference spectral indices (NDSIs) that were significantly correlated with disease scores: two NDSIs for lentil shoot section – computed from wavelengths of 1170, 1160, 1270, and 1280 nm (0.12 ≤ |r| ≤ 0.24, P < 0.05) and ten NDSIs for lentil root sections – computed from wavelengths in the range of 630–670, 700–840, and 1320–1530 nm (0.10 ≤ |r| ≤ 0.50, P < 0.05). Root-derived NDSIs were more accurate in predicting disease scores with an R2 of 0.54 (RMSE = 0.86), especially when the model was trained and tested on LSP accessions, compared to R2 of 0.25 (RMSE = 1.64) when LSP and RIL genotypes were used as train and test datasets, respectively. Importantly, NDSIs – computed from wavelengths of 700, 710, 730, and 790 nm – had strong positive correlations with disease scores (0.35 ≤r ≤ 0.50, P < 0.0001), which was confirmed in field phenotyping with similar correlations using vegetation index with red edge wavelength (normalized difference red edge, 0.36 ≤ |r| ≤ 0.57, P < 0.0001). The adopted image-based phenotyping approaches can help plant breeders to objectively quantify ARR resistance and reduce the subjectivity in selecting potential genotypes.

The Use of Low-Altitude UAV Imagery to Assess Western Juniper Density and Canopy Cover in Treated and Untreated Stands

Monitoring vegetation characteristics and ground cover is crucial to determine appropriate management techniques in western juniper (Juniperus occidentalis Hook.) ecosystems. Remote-sensing techniques have been used to study vegetation cover; yet, few studies have applied these techniques using unmanned aerial vehicles (UAV), specifically in areas of juniper woodlands. We used ground-based data in conjunction with low-altitude UAV imagery to assess vegetation and ground cover characteristics in a paired watershed study located in central Oregon, USA. The study was comprised of a treated watershed (most juniper removed) and an untreated watershed. Research objectives were to: (1) evaluate the density and canopy cover of western juniper in a treated (juniper removed) and an untreated watershed; and, (2) assess the effectiveness of using low altitude UAV-based imagery to measure juniper-sapling population density and canopy cover. Ground- based measurements were used to assess vegetation features in each watershed and as a means to verify analysis from aerial imagery. Visual imagery (red, green, and blue wavelengths) and multispectral imagery (red, green, blue, near-infrared, and red-edge wavelengths) were captured using a quadcopter-style UAV. Canopy cover in the untreated watershed was estimated using two different methods: vegetation indices and support vector machine classification. Supervised classification was used to assess juniper sapling density and vegetation cover in the treated watershed. Results showed that vegetation indices that incorporated near-infrared reflectance values estimated canopy cover within 0.7% to 4.1% of ground-based calculations. Canopy cover estimates at the untreated watershed using supervised classification were within 0.9% to 2.3% of ground-based results. Supervised classification applied to fall imagery using multispectral bands provided the best estimates of juniper sapling density compared to imagery taken in the summer or to using visual imagery. Study results suggest that low-altitude multispectral imagery obtained using small UAV can be effectively used to assess western juniper density and canopy cover.

Fotografía multiespectral para el diagnóstico fitosanitario del pasto kikuyo (Cechrus clandestinus (Hochst ex Chiov) Morrone)

Se evaluó la posibilidad de diagnosticar el estado fitosanitario del pasto kikuyo (Cechrus clandestinum (Hochst ex Chiov)), empleando un sensor multiespectral acoplado a un dron. Para esto se registraron las reflectancias de 109 puntos de muestreo en las longitudes de onda Red (R), Green (G), Blue (B), Red Edge (RE) y NIR; las cuales se relacionaron con niveles de afección por plagas determinadas bajo metodologías de campo. Las longitudes de onda R, G, B y RE se correlacionaron moderadamente con el grado de afección foliar, cuyos coheficientes fueron 0.63, 0.51, 0.51 y 0.50, respectivamente y la longitud del NIR se relacionó de manera positiva baja (r =0.14). Se evaluaron 25 modelos lineales generalizados (MLG) calculados a partir de los valores de reflectancia, donde el modelo y= (41.330* R) + (1.38*NIR), con Deviance de 0.69 se estableció como el modelo de mayor parsimonia. Además es un modelo de facil aplicación porque utiliza únicamente dos bandas.