Red-edge Normalized Difference Vegetation Index Research Articles

Detection of pine wood nematode infestation using hyperspectral drone images

The pine wood nematode (PWN) is one of the most threatening pests of pine forests in China. An essential management response involves felling and removing the infested trees before the PWN can spread over large areas, but this requires early detection. In this study, we analyzed the temporal changes in the spectral signatures of healthy and infested trees throughout the entire growing season (May to October) using unmanned aerial vehicle (UAV) hyperspectral images (HSI). To explore the potential of these seasonal spectral changes in detecting PWN, we investigated the separability of healthy and infested trees using both absolute values and changes in values. A random forest (RF) classifier was applied to separate healthy and infested trees based on reflectances, derivatives, vegetation indices (VIs), and their seasonal change values. We obtained the following results: (1) the seasonal changes of tree crown spectra following a PWN infestation were similar to those observed using absolute spectral values, though the latter performed better in early detection; (2) in the early stage of infestation (June and July), the green and red-edge regions contained the most sensitive bands, whereas the near-infrared portion played a more important role in later stages; (3) derivatives achieved higher overall accuracy (OA) than the reflectance in the early stage of PWN infestation (June and July); (4) in June, the Pigment Specific Simple Ratio (PSSR) had the highest separability (OA = 0.77) for PWN infestation, followed by the Plant Senescence Reflectance Index (PSRI) and the Normalized Difference Red-edge Index (NDRE), both with a OA of 0.70. In July, PSRI, Red Edge Normalized Difference Vegetation Index (RENDVI), and the Modified Red Edge Simple Ratio (MRESR) had the highest OA of 0.77. These VIs were calculated based on the red-edge regions. In August, RENDVI achieved the best accuracy (0.87), followed by the Normalized Difference Vegetation Index (NDVI, OA = 0.83) and the Plant Stress Index (PSI, OA = 0.80). In September and October, PSRI, PSI, the Greenness Index (GI), and RENDVI showed the strongest separability (0.87 to 0.90). Similar results were obtained using change values of VIs. In June, RENDVI, PSI, and NDVI showed the highest accuracy (OA = 0.73). In July, MRESR had the best accuracy (0.77), followed by RENDVI (0.73) and PSI (0.73). In the late stage of PWN infestation, NDVI, PSRI, PSI, RENDVI, GI, and PSSR obtained the highest accuracies (0.83 to 0.93), surpassing the accuracy achieved using absolute spectra; (5) in RF classification model, seasonal variation values derived from reflectances, derivatives, and VIs performed well in the late stages of the PWN infestation (September and October) but were not as good as absolute values in the early and middle stages (June to August). Our study explored the potential of seasonal changes in UAV spectra for early detection of forest pests.

Phenotyping and Exploitation of Kompetitive Allele-Specific PCR Assays for Genes Underpinning Leaf Rust Resistance in New Spring Wheat Mutant Lines.

Leaf rust (Puccinia triticina Eriks) is a wheat disease causing substantial yield losses in wheat production globally. The identification of genetic resources with permanently effective resistance genes and the generation of mutant lines showing increased levels of resistance allow the efficient incorporation of these target genes into germplasm pools by marker-assisted breeding. In this study, new mutant (M3 generation) lines generated from the rust-resistant variety Kazakhstanskaya-19 were developed using gamma-induced mutagenesis through 300-, 350-, and 400-Gy doses. In field trials after leaf rust inoculation, 75 mutant lines showed adult plant resistance. These lines were evaluated for resistance at the seedling stage via microscopy in greenhouse experiments. Most of these lines (89.33%) were characterized as resistant at both developmental stages. Hyperspectral imaging analysis indicated that infected leaves of wheat genotypes showed increased relative reflectance in visible and near-infrared light compared to the non-infected genotypes, with peak means at 462 and 644 nm, and 1936 and 2392 nm, respectively. Five spectral indexes, including red edge normalized difference vegetation index (RNDVI), structure-insensitive pigment index (SIPI), ratio vegetation index (RVSI), water index (WI), and normalized difference water index (NDWI), demonstrated significant potential for determining disease severity at the seedling stage. The most significant differences in reflectance between susceptible and resistant mutant lines appeared at 694.57 and 987.51 nm. The mutant lines developed were also used for the development and validation of KASP markers for leaf rust resistance genes Lr1, Lr2a, Lr3, Lr9, Lr10, and Lr17. The mutant lines had high frequencies of "a" resistance alleles (0.88) in all six Lr genes, which were significantly associated with seedling resistance and suggest the potential of favorable haplotype introgression through functional markers. Nine mutant lines characterized by the presence of "b" alleles in Lr9 and Lr10-except for one line with allele "a" in Lr9 and three mutant lines with allele "a" in Lr10-showed the progressive development of fungal haustorial mother cells 72 h after inoculation. One line from 300-Gy-dosed mutant germplasm with "b" alleles in Lr1, Lr2a, Lr10, and Lr17 and "a" alleles in Lr3 and Lr9 was characterized as resistant based on the low number of haustorial mother cells, suggesting the contribution of the "a" alleles of Lr3 and Lr9.

Vegetation Indices for Early Grey Mould Detection in Lettuce Grown under Different Lighting Conditions.

Early detection of pathogenic fungi in controlled environment areas can prevent major food production losses. Grey mould caused by Botrytis cinerea is often detected as an infection on lettuce. This paper explores the use of vegetation indices for early detection and monitoring of grey mould on lettuce under different lighting conditions in controlled environment chambers. The aim was focused on the potential of using vegetation indices for the early detection of grey mould and on evaluating their changes during disease development in lettuce grown under different lighting conditions. The experiment took place in controlled environment chambers, where day/night temperatures were 21 ± 2/17 ± 2 °C, a 16 h photoperiod was established, and relative humidity was 70 ± 10% under different lighting conditions: high-pressure sodium (HPS) and light-emitting diode (LED) lamps. Lettuces were inoculated by 7-day-old fungus Botrytis cinerea isolate at the BBCH 21. As a control, non-inoculated lettuces were grown under HPS and LEDs (non-inoculated). Then, the following were evaluated: Anthocyanin Reflectance Index 2 (ARI2); Carotenoid Reflectance Index 2 (CRI2); Structure Intensive Pigment Index (SIPI); Flavanol Reflectance Index (FRI); Greenness (G); Greenness 2 (G2); Redness (R); Blue (B); Blue Green Index 2 (BGI2); Browning Index 2 (BRI2); Lichtenthaler Index 1 (LIC1); Pigment Specific Simple Ratio (PSSRa and PSSRb); Gitelson and Merzlyak (GM1 and GM2); Zarco Tejada-Miller Index (ZMI); Normalized Difference Vegetation Index (NDVI); Simple Ratio (SR); Red-Eye Vegetation Stress Index (RVSI); Photochemical Reflectance Index (PRI); Photochemical Reflectance Index 515 (PRI515); Water Band Index (WBI); specific disease index for individual study (fD); Healthy Index (HI); Plant Senescence Reflectance (PSRI); Vogelmann Red Edge Index (VREI1); Red Edge Normalized Difference Vegetation Index (RENDVI); and Modified Red Edge Simple Ratio (MRESRI). Our results showed that the PSRI and fD vegetation indices significantly detected grey mould on lettuce grown under both lighting systems (HPS and LEDs) the day after inoculation. The results conclusively affirmed that NDVI, PSRI, HI, fD, WBI, RVSI, PRI, PRI515, CRI2, SIPI, chlorophyll index PSSRb, and coloration index B were identified as the best indicators for Botrytis cinerea infection on green-leaf lettuce (Lactuca sativa L. cv Little Gem) at the early stage of inoculated lettuce's antioxidative response against grey mould with a significant increase in chlorophyll indices.

Inversion of Leaf Water Content of Cinnamomum camphora Based on Preferred Spectral Index and Machine Learning Algorithm

Plant leaf water content significantly influences photosynthetic efficiency and crop yield. Leaf water content (LWC) and equivalent water thickness (EWT) are indicators that reflect the water state within plant tissues, and they play a crucial role in assessing plant water supply and usage. In recent years, there has been a growing focus on the rapid and precise determination of plant water content. In this study, Cinnamomum camphora (C. camphora) was chosen as the subject of investigation. After acquiring spectral data, three types of vegetation indices were computed: the empirical vegetation index, the random combination dual-band vegetation index, and the ‘trilateral’ parameter. Four groups of optimal spectral index screening strategies were established, namely an empirical vegetation index group (G1), a random combination dual-band vegetation index group (G2), a ‘trilateral’ parameter group (G3), and a mixed group (G4). Three algorithms, specifically random forest (RF), radial basis function neural network (RBFNN), and support vector machine (SVM), were employed for the estimation of leaf water content (LWC) and equivalent water thickness (EWT) in mature C. camphora. The results demonstrated that the G4 group displayed superior performance, yielding five optimal spectral indices for LWC: water index (WI), optimized soil-adjusted vegetation index (OSAVI), difference vegetation index (DVI) at wavelengths 734 and 956 nm, first-order difference vegetation index (DVI-FD) at wavelengths 1009 and 774 nm, and red-edge amplitude (Dr). With regard to EWT estimation, the five optimal spectral indices encompassed the red-edge normalized difference vegetation index (RE-NDVI), simple ratio water index (SRWI), difference vegetation index (DVI) at wavelengths 700 and 1167 nm, first-order difference vegetation index (DVI-FD) at wavelengths 1182 and 1514 nm, and red-edge area (SDr). Utilizing these indices as inputs significantly enhanced the accuracy of the models, with the RF model emerging as the most effective for estimating LWC and EWT in C. camphora. Based on the LWC estimation model of the G4 group and the RF algorithm, the determination coefficient (R2) for both the training and test sets reached 0.848 and 0.871, respectively. The root mean square error (RMSE) was 0.568% for the training set and 0.582% for the test set, while the average relative error (MRE) stood at 0.806% and 0.642%, respectively. Regarding the EWT estimation model, R2 values of 0.887 and 0.919 were achieved for the training and test sets, accompanied by RMSE values of 0.6 × 10−3 g·cm−2 and 0.7 × 10−3 g·cm−2, and MRE values of 3.198% and 2.901%, respectively. These findings lay a solid foundation for hyperspectral moisture monitoring in C. camphora and offer valuable reference for the rapid assessment of crop growth status.

Effect Analysis of Hydrogen Peroxide Using Hyperspectral Reflectance in Sorghum [Sorghum bicolor (L.) Moench] under Drought Stress.

Due to global climate change, adverse environments like drought in agricultural production are occurring frequently, increasing the need for research to ensure stable crop production. This study was conducted to determine the effect of artificial hydrogen peroxide treatment on sorghum growth to induce stress resistance in drought conditions. Hyperspectral analysis was performed to rapidly find out the effects of drought and hydrogen peroxide treatment to estimate the physiological parameters of plants related to drought and calculate the vegetation indices through PLS analysis based on hyperspectral data. The partial least squares (PLS) analysis collected chlorophyll fluorescence variables, photosynthetic parameters, leaf water potential, and hyperspectral reflectance during the stem elongation and booting stage. To find out the effect of hydrogen peroxide treatment in sorghum plants grown under 90% and 60% of field capacity in greenhouses, growth and hyperspectral reflectance were measured on the 10th and 20th days after foliar application of H2O2 at 30 mM from 1st to 5th leaf stage. The PLS analysis shows that the maximum variable fluorescence of the dark-adapted leaves was the most predictable model with R2 = 0.76, and the estimation model suitability gradually increased with O (R2 = 0.51), J (R2 = 0.73), and P (R2 = 0.75) among OJIP parameters of chlorophyll fluorescence analysis. However, the estimation suitability of predictions for moisture-related traits, vapor pressure deficit (VPD, R2 = 0.18), and leaf water potential (R2 = 0.15) using hyperspectral data was low. The hyperspectral reflectance was 10% higher at 20 days after treatment (DAT) and 3% at 20 DAT than the non-treatment in the far red and infra-red light regions under drought conditions. Vogelmann red edge index (VOG REI) 1, chlorophyll index red edge (CIR), and red-edge normalized difference vegetation index (RE-NDVI) efficiently reflected moisture stress among the vegetation indices. Photochemical reflectance index (PRI) can be used as an indicator for early diagnosis of drought stress because hydrogen peroxide treatment showed higher values than untreated in the early stages of drought damage.

Detection and infected area segmentation of apple fire blight using image processing and deep transfer learning for site-specific management

Advanced sensing technologies and deep learning models are needed for automatic recognition of pathogens to protect trees in orchards. This study developed a fire blight disease detection and infected area segmentation system using image processing and deep learning approaches to automate the detection process in a complex apple orchard environment for site-specific management. Two types of images were acquired: multispectral images from an unmanned aerial vehicle (UAV) using a multispectral camera and red–greenblue (RGB) images from the ground using two different cameras. Multispectral images were preprocessed and used for image feature analysis by calculating vegetation indices, including excessive blue (ExB), normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), red-edge normalized difference vegetation index (RENDVI), modified ratio vegetation index (RVI), and triangular blueness index (TBI). Vegetation indices were calculated from a total of 60 multispectral images (30 heathy and 30 fire blight infected). Results showed that RVI was most sensitive to fire blight infection among the six indices. A support vector machine model was used to classify unhealthy tree canopies. A Mask Region-Convolutional Neural Network (Mask R-CNN) based deep learning model was developed from RGB infected images. A total of 880 images were used for training, and 220 images were used for validation. Another 110 images were used for testing the trained Mask R-CNN model. A precision of 92.8 % and recall of 91.2 % were obtained by detecting the infected canopies using a ResNet-101 backbone and intersection over union (IoU) threshold of 0.7. The high precision demonstrates the effectiveness of Mask R-CNN for the identification and segmentation of fire blight infection in images taken in complex orchard conditions. These results prove the potential of this non-invasive sensing method in detecting disease in commercial fruit production for site-specific infected canopies removing.

Combined use of urban Atlas and Corine land cover datasets for the implementation of an ecological network using graph theory within a multi-species approach

Ecological sustainability has recently risen to prominence in scientific research and management applications. Approaches to measuring ecological connectivity and their application to optimize ecological network (EN) design are powerful tools against landscape fragmentation and biodiversity loss.We focused on building an EN by identifying the most sensitive areas for ecological connectivity within the Reggio Calabria (Italy) metropolitan area. We also proposed a defragmentation scenario to improve the obtained EN.The CORINE Land Cover and the Urban Atlas 2018 were used to obtain a fine-scale representation of the study area. Ten terrestrial mammal species were used to model connectivity following a multi-species approach. Dispersal distance, patch size, and resistance to species movement were used to identify patches and corridors. Vegetational fractional coverage based on three years time series of Sentinel-2 red-edge normalized difference vegetation index was used to discriminate areas with higher naturalness. We used graph theory and connectivity metrics to test the EN’s robustness and identify locations for restoration in a defragmentation scenario.The obtained EN, formed by three separate components, was composed of 724 arcs and 300 nodes with an average patch area of 27.04 ha. After the defragmentation hypothesis, the EN, formed by only one component, was composed of 771 arcs and 328 nodes with an average patch area of 26.82 ha.It was possible to analyze an EN’s connectivity and evaluate the impact of a scenario intended to enhance multi-species connectivity. By comparing several connectivity metrics, we highlighted the potential of land interventions as a planning tool to enhance future ecological sustainability and biodiversity conservation.

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.

Scale-Aware Pomegranate Yield Prediction Using UAV Imagery and Machine Learning

Highlights UAV-based remote sensing platform and ML methods for individual tree level yield prediction. Decision tree ML model had an accuracy of 85% for yield prediction. Vegetation indices derived from UAV high-resolution imagery were used for yield prediction. Scale-aware prediction model has great potential for efficient field management. Abstract. Accurately estimating the yield is one of the most important research projects for orchard management. Growers need to estimate the yield of trees at an early stage to make smart decisions for field management. However, methods to predict the yield at the individual tree level are currently not available because of the complexity and variability of each tree. To improve the accuracy of yield prediction, the authors evaluate the performance of an unmanned aerial vehicle (UAV)-based remote sensing system and machine learning (ML) approaches for tree-level pomegranate yield estimation. A lightweight multispectral camera was mounted on the UAV platform to acquire high-resolution images. Eight features were extracted from the UAV images, including the normalized difference vegetation index (NDVI), the green normalized vegetation index (GNDVI), the red-edge normalized difference vegetation index (NDVIre), red-edge triangulated vegetation index (RTVIcore), individual tree canopy size, the modified triangular vegetation index (MTVI2), the chlorophyll index-green (CIg), and the chlorophyll index-rededge (CIre). Correlation coefficients (R2) were calculated between these vegetation indices and tree yield. Classic machine learning approaches were applied with the extracted features to predict the yield at the individual tree level. Results showed that the decision tree classifier had the best prediction performance, with an accuracy of 85%. The study demonstrated the potential of using UAV-based remote sensing methods, coupled with ML algorithms, for pomegranate yield estimation. Predicting the yield at the individual tree level will enable the stakeholders to manage the orchard at different scales, thus improving field management efficiency. Keywords: Machine learning, Remote sensing, UAV, Vegetation index, Yield prediction.

Estimation of shrub willow biophysical parameters across time and space from Sentinel-2 and unmanned aerial system (UAS) data

Shrub willow can be used as a perennial energy crop to produce biomass for biofuels, bioproducts, and bioenergy to help address environmental issues. Leaf chlorophyll content (LCC), leaf area index (LAI), and canopy chlorophyll content (CCC), which are closely related to vegetation health status, provide valuable information to assess willow vigor and crop management activities. We developed models for estimating LCC, LAI, and CCC from Sentinel-2 and unmanned aerial system (UAS) data using a number of vegetation indices (VIs) and applying four analytical tools: regression analysis, partial least squares regression (PLSR), random forest (RF), and neural networks (NNs). We investigated twenty VIs using regression analysis, finding the red-edge normalized difference vegetation index (NDVIre) as the optimal VI based on predictive performance and so this was included as an input variable for the PLSR, RF, and NN methods. For RF and NN models, sensitivity analysis was implemented to help tune hyperparameters and provide information to guide future studies. Results showed that the best averaged performance of Sentinel-2 and UAS data for LCC, LAI, and CCC (normalized root mean square error (NRMSE) = 9%, 12%, and 10%) of the same site in the same year was derived from models based on the NN algorithm using blue (B), green (G), red (R), red-edge (RE), and near-infrared (NIR) bands. In order to test model transferability, we collected UAS and Sentinel-2 data across different years and sites. Based on NRMSE, NN models using B, G, R, RE, and NIR bands provided the best averaged predictive performance for LCC estimation (16%) and the regression modeling using NDVIre performed best for LAI (21%) and CCC (13%) estimation for both Sentinel-2 and UAS data. Moreover, CCC estimation had smaller NRMSE values than LCC and LAI estimation for both Sentinel-2 and UAS data. Based on this study, we found that UAS data has the potential to complement Sentinel-2 data by mitigating limitations caused by temporal resolution and cloud cover. We also recommend use of the CCC parameter to evaluate shrub willow health status and perform time-series monitoring across time and space.

EFFECTS OF GF-6 SATELLITE RED-EDGE BANDS ON PEANUT DROUGHT MONITORING

Abstract. Red-edge band is an indicator band to describe the health status of crops. In order to explore the impact of the red-edge bands on the accuracy of agricultural drought monitoring, this study used GF-6 WFV and Landsat8 to calculate the Red-edge Normalized Difference Vegetation Index (NDVI705), Vogelmann red-edge index 1 (VOG1), Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) to construct temperature vegetation drought index (TVDI), this index integrated vegetation index and land surface temperature information, the soil relative humidity data and the measured drought grade data were respectively used for correlation analysis and classification accuracy verification of the TVDI results. The results showed that dry edge equation fit with red edge bands were higher than that of non-red edge bands. The dry edge equation based on VOG1 index has the highest fit, with a maximum coefficient determination of 0.92; TVDI constructed by the above vegetation index have significant negative correlation with soil relative humidity. The TVDI based on the VOG1 index has a better correlation with the relative soil humidity, with a maximum correlation coefficient of 0.85; and realized the dynamic monitoring of peanut drought grade, the overall accuracy rate of peanut drought grade monitoring reached 92.59%, and the result of TVDI classification was in good agreement with the actual drought grade measurement result. The red edge bands of GF-6 satellite can effectively improve the accuracy of peanut drought monitoring and better characterize peanut drought information. This research provides a data reference for the application of GF-6 WFV data in agricultural drought monitoring.

Shrub willow canopy chlorophyll content estimation from unmanned aerial systems (UAS) data: Estimation and uncertainty analysis across time, space, and scales

Remote sensing is an important technology to map land-surface parameters, with many studies demonstrating land-surface parameter estimation, but fewer testing model transferability and quantifying model parameter uncertainty. In this study, we evaluated the uncertainty across time, space, and spatial scales of shrub willow health characterization based on canopy chlorophyll content (CCC) derived from unmanned aerial systems (UAS) data. Shrub willow is a short-rotation woody crop used to produce biomass for bioproducts and CCC is a popular biophysical parameter used to indicate shrub willow health status. We analyzed time-series UAS data at three spatial scales (5 m, 10 m, and 20 m) and stratified field-observed CCC levels. Since scale effects are often related to spatial heterogeneity, we implemented a nested analysis of variance (ANOVA) to evaluate the spatial heterogeneity within different pixel sizes and found that 5 m pixels were the most homogeneous, followed by 10 m and 20 m. Results from regression modeling of shrub willow CCC as a function of red-edge normalized difference vegetation index (NDVIre) at 5 m, 10 m, and 20 m scales showed that the models built at 5 m, 10 m, and 20 m could be applied across time, space, and scales. We also quantified the uncertainty for model parameters using two different inferential frameworks, confidence interval (CI) widths from the frequentist framework and credible interval (CrI) widths from the Bayesian framework. Unlike predictive root mean square error (RMSE), which showed model output uncertainty decreased as pixel size increased, CI and CrI widths indicated that the related model parameter uncertainty increased as pixel size increased. We calculated CI and CrI widths based on time-series model building and different stratified model building to analyze model parameter uncertainty and quantified predictive RMSE for all sampling date combinations. The results showed that, in terms of model complexity and the range of ground observations, both frequentist and Bayesian regression have advantages and disadvantages and demonstrated that the uncertainty quantified by CrI widths is able to guide future experimental design to save resources.

Mapping tree species in natural and planted forests using Sentinel-2 images

ABSTRACT Using remote-sensing technology to accurately map the composition and distribution of tree species is vital for sustainable forest resource management. Sentinel-2 data with the dense time-series observations enable to identify tree species. However, few studies clarify the differences in classification using Sentinel-2 images in natural forest and planted forest. Two study areas with different forest environments (planted forest and natural forest) were selected to evaluate the potential of Sentinel-2 imagery. Our results show that red-edge band, short-wave infrared (SWIR) band and vegetation indices (VIs), such as Red-Edge Normalized Difference Vegetation Index (ReNDVI), Soil-Adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index (EVI), have important effects on tree species classification, especially in the growing season. By using two machine learning algorithms (support vector machine [SVM] and random forest [RF]), the results show that the classification accuracy for planted forests (91.27% in SVM and 88.35% in RF) significantly exceeds that of natural forests (84.34% in SVM and 81.03% in RF). This accuracy difference may be related to the spatial heterogeneity inside the forest and the surrounding environmental implications. Although the multi-temporal Sentinel-2 images produce satisfactory accuracy for classifying tree species, further research is needed to improve the classification accuracy.

Optimal models under multiple resource types for Brix content prediction in sugarcane fields using machine learning

In this paper, multispectral imageries were combined with estimated crop height, age of sugarcane, and weather data (rain event) to predict Brix content in sugarcane fields across multiple sugarcane fields grown with different crop management techniques and under tropical climate over four consecutive growing seasons (2017–2021). Four machine learning regressors were used to train Brix predictive models: (i) lasso, (ii) support vector machine (SVM), (iii) decision tree, and (iv) gradient boosting regression. We compared the model performance in terms of accuracy and practicability based on five different resource types, which used a combination of 10 vegetation indices: normalized difference vegetation index (NDVI), ratio vegetation index (RVI), chlorophyll index green (CIgreen), chlorophyll index Rededge (Cirededge), photosynthetic vigor ratio (PVR), plant pigment ratio (PPR), Red edge NDVI (NDRE), nitrogen reflectance index (NRI), green leaf index (GLI), and the simple inverted reflectance green; and combinations of 10 vegetation indices with estimated crop height, sugarcane age, and rain event information. Overall, gradient boosting regression gave the highest accurate models, followed by SVM, decision tree, and lasso regression. The highest accuracies obtained by models from each resource type are R2 0.54 (RMSEP 3.28 oBrix, RPD 1.5) for a resource that used only a combination of vegetation indices (CIrededge, GLI, PPR, and simple inverted reflectance green), R2 0.67 (RMSEP 2.87 oBrix, RPD 1.7) for a resource that used a combination of CIrededge, GLI, PPR, and simple inverted reflectance green with estimated sugarcane height data, R2 0.66 (RMSEP 2.97 oBrix, RPD 1.7) for a resource that used a combination of CIrededge, PPR, and GLI with the age of sugarcane data, R2 0.71 (RMSEP 2.81 oBrix, RPD 1.8) for a resource that used a combination of CIrededge, NRI, PPR, and simple inverted reflectance green with rain event data, and R2 0.75 (RMSEP 2.81 oBrix, RPD 1.8) for a resource that used a combination of CIrededge, PPR, GLI, and simple inverted reflectance green with estimated sugarcane height and rain event data. The model composed of vegetation indices, estimated sugarcane height, and rain event data is suitable for quantitative prediction, while the other four models are appropriate for screening prediction.

Determining nitrogen deficiencies for maize using various remote sensing indices

Determining a precise nitrogen fertilizer requirement for maize in a particular field and year has proven to be a challenge due to the complexity of the nitrogen inputs, transformations and outputs in the nitrogen cycle. Remote sensing of maize nitrogen deficiency may be one way to move nitrogen fertilizer applications closer to the specific nitrogen requirement. Six vegetation indices [normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), red-edge normalized difference vegetation index (RENDVI), triangle greenness index (TGI), normalized area vegetation index (NAVI) and chlorophyll index-green (CIgreen)] were evaluated for their ability to detect nitrogen deficiency and predict grain maize grain yield. Strip trials were established at two locations in Arkansas, USA, with nitrogen rate as the primary treatment. Remote sensing data was collected weekly with an unmanned aerial system (UAS) equipped with a multispectral and thermal sensor. Relationships among index value, nitrogen fertilizer rate and maize growth stage were evaluated. Green NDVI, RENDVI and CIgreen had the strongest relationship with nitrogen fertilizer treatment. Chlorophyll Index-green and GNDVI were the best predictors of maize grain yield early in the growing season when the application of additional nitrogen was still agronomically feasible. However, the logistics of late season nitrogen application must be considered.

Utilizing spectral vegetation indices for yield assessment of tomato genotypes grown in arid conditions

Tomato is among important vegetable crops cultivated in different climates; however, heat stress can greatly affect fruit quality and overall yield. Crop reflectance measurements based on ground reflectance sensor data are reliable indicators of crop tolerance to abiotic stresses. Here, we report on using non-destructive spectral vegetation indices to monitor yield traits of 10 tomato genotypes transplanted on three different dates (Aug. 2, Sept. 3 and Oct. 1) during 2019 growing season in the Riyadh region. The ten genotypes comprised six commercial cultivars–(Pearson Improved, Strain B, Valentine, Marmande VF, Super Strain B, and Pearson early) ––and four local Saudi cultivars (Al-Ahsa, Al-Qatif, Hail and Najran). Spectral reflectance data were utilized using a FieldSpec 3 spectroradiometer in the range of 350–2500 nm to calculate nine vegetation indices (VIs): Normalized Water Band Index (NWBI), Difference Water Index (NDWI), Photochemical Reflectance Index (PRI), Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Red Edge Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index (SAVI), Red Edge Normalized Difference Vegetation Index (RENDVI), Renormalized Difference Vegetation Index (RDVI), and Normalized Difference Nitrogen Index (NDNI). VIs and yield parameters (total fruit yield, harvest index) revealed that second transplanting date was optimal for all the genotypes. Valentine showed the best growth performance followed by Najran, Hail, Super Strain B and finally Pearson early. For all the three transplanting dates, Valentine recorded the highest total fruit yield. Additionally, some genotypes had no significant differences in the VIs values or the total fruit yield between the second and third transplanting dates. This study indicated that yield parameters could be linked to rapid, non-destructive hyperspectral reflectance data to predict tomato production under heat stress.

At-Sensor Radiometric Correction of a Multispectral Camera (RedEdge) for sUAS Vegetation Mapping.

Rapid advancement of drone technology enables small unmanned aircraft systems (sUAS) for quantitative applications in public and private sectors. The drone-mounted 5-band MicaSense RedEdge cameras, for example, have been popularly adopted in the agroindustry for assessment of crop healthiness. The camera extracts surface reflectance by referring to a pre-calibrated reflectance panel (CRP). This study tests the performance of a Matrace100/RedEdge-M camera in extracting surface reflectance orthoimages. Exploring multiple flights and field experiments, an at-sensor radiometric correction model was developed that integrated the default CRP and a Downwelling Light Sensor (DLS). Results at three vegetated sites reveal that the current CRP-only RedEdge-M correction procedure works fine except the NIR band, and the performance is less stable on cloudy days affected by sun diurnal, weather, and ground variations. The proposed radiometric correction model effectively reduces these local impacts to the extracted surface reflectance. Results also reveal that the Normalized Difference Vegetation Index (NDVI) from the RedEdge orthoimage is prone to overestimation and saturation in vegetated fields. Taking advantage of the camera’s red edge band centered at 717 nm, this study proposes a red edge NDVI (ReNDVI). The non-vegetation can be easily excluded with ReNDVI < 0.1. For vegetation, the ReNDVI provides reasonable values in a wider histogram than NDVI. It could be better applied to assess vegetation healthiness across the site.

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.

The Utility of Sentinel-2 Spectral Data in Quantifying Above-Ground Carbon Stock in an Urban Reforested Landscape

The transformation of the natural landscape into an impervious surface due to urbanization has often been considered an important driver of environmental change, affecting essential urban ecological processes and ecosystem services. Continuous forest degradation and deforestation due to urbanization have led to an increase in atmospheric carbon emissions, risks, and impacts associated with climate change within urban landscapes and beyond them. Hence, urban reforestation has become a reliable long-term alternative for carbon sink and climate change mitigation. However, there is an urgent need for spatially accurate and concise quantification of these forest carbon stocks in order to understand and effectively monitor the accumulation and progress on such ecosystem services. Hence, this study sought to examine the prospect of Sentinel-2 spectral data in quantifying carbon stock in a reforested urban landscape using the random forest ensemble. Results show that Sentinel-2 spectral data estimated reforested forest carbon stock to an RMSE between 0.378 and 0.466 t·ha−1 and R2 of 79.82 and 77.96% using calibration and validation datasets. Based on random forest variable selection and backward elimination approaches, the red-edge normalized difference vegetation index, enhanced vegetation index, modified simple ratio index, and normalized difference vegetation index were the best subset of predictor variables of carbon stock. These findings demonstrate the value and prospects of Sentinel-2 spectral data for predicting carbon stock in reforested urban landscapes. This information is critical for adopting informed management policies and plans for optimizing urban reforested landscapes carbon sequestration capacity and improving their climate change mitigation potential.

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).