Modified Chlorophyll Absorption Ratio Index Research Articles

Using vegetation and chlorophyll indices to model above ground biomass over time in an urban arboretum in subtropical queensland

As urban forests grow, they can provide a range of ecosystem services, including by sequestering carbon in the form of above ground biomass (AGB). Data from satellites is starting to be used to estimate ecological functions of urban forests including AGB. In this study, we compare individually and in combination five vegetation index and three chlorophyll specific indices based on Sentinel-2 satellite images in spring (September to November) of a newly planted multi-species arboretum over eight years in subtropical Queensland, Australia. There was variation in the eight indices within the arboretum, but some clear patterns among indices over time reflecting the growth of the forest. Using a published simple regression model of AGB as a function of Leaf Area Index (LAI), we found Modified Soil Adjusted Vegetation Index (MSAVI) and Modified Chlorophyll Absorption Ratio Index (MCARI) best predicted AGB (r-square = 0.52 and r-square = 0.84, respectively), and in combination gave the best estimate (r-square = 0.85). The performance of these chlorophyll-specific indices (MSAVI and MCARI) reinforces their benefits over traditional vegetation indices in characterizing vegetation patterns and could complement field-based sampling when modelling carbon sequestration potentials including in mixed species urban forests.

Multi-Sensor Remote Sensing to Estimate Biophysical Variables of Green-Onion Crop (Allium cepa L.) under Different Sources of Magnesium in Ismailia, Egypt

Foliar feeding has been confirmed to be the fastest way of dealing with nutrient deficiencies and increasing the yield and quality of crop products. The synthesis of chlorophyll and photosynthesis are directly related to magnesium (Mg), which operates in the improvement of plant tissues and enhances the appearance of plants. This study aimed to analyze the correlation between two biophysical variables, including the leaf area index (LAI), the fraction of absorbed photosynthetically active radiation (FAPAR), and seven spectral vegetation indices. The spectral indices under investigation were Atmospherically Resistant Vegetation Index (ARVI), Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Disease–Water Stress Index (DSWI), Modified Chlorophyll Absorption Ratio Index (MCARI), the Red-Edge Inflection Point Index (REIP), and Pigment-Specific Simple Ratio (PSSRa). These indices were derived from Sentinel-2 data to investigate the impact of applying foliar applications of Mg from various sources in the production of green-onion crops. The biophysical variables were derived using field measurements and Sentinel-2 data under the effects of different sources of Mg foliar sprays. The correlation coefficient between field-measured LAI and remotely sensed, calculated LAI was 0.72 in two seasons. Concerning FAPAR, it was found that the correlation between remotely sensed calculated FAPAR and field-measured FAPAR was 0.66 in the first season and 0.89 in the second season. The magnesium oxide nanoparticle (nMgO) treatments resulted in significantly higher yields than the different treatments of foliar applications. The LAI and FAPAR variables showed a positive correlation with yield in the first season (October) and in the second season (March). Yield in treatment by nMgO varied significantly from that in the other treatments, ranging from 69-ton ha−1 in the first season to 74.9-ton ha−1 in the second season. Linear regression between LAI and PSSRa showed the highest correlation coefficient (0.90) compared with other vegetation indices in the first season. In the same season, the highest correlation coefficient (0.94) was found between FAPAR and PSSRa. In the second season, the highest accuracy to the estimate LAI was found in the correlation between MCARI and PSSRa, with correlation coefficients of 0.9 and 0.91, respectively. In the second season, the highest accuracy to the estimate FAPAR was found with the correlation between PSSRa, ARVI, and NDVI, with correlation coefficients 0.97 and 0.96, respectively. The highest correlation coefficients between vegetation indices and yield were found with ARVI and NDVI in the first season, and only with NDVI in the second season.

A Study on the Distribution Pattern of Banana Blood Disease (BBD) and Fusarium Wilt Using Multispectral Aerial Photos and a Handheld Spectrometer in Subang, Indonesia

Knowledge of the health of banana trees is critical for farmers in order to profit from banana cultivation. Fusarium wilt and banana blood disease (BBD), two significant diseases that infect banana trees, are caused by Fusarium oxysporum and Ralstonia syzygii, respectively. They have caused a decline in crop yield, as they destroy trees, starting sequentially from the pseudostem to the fruit. The entire distribution of BBD and fusarium on a plantation can be understood using advanced geospatial information obtained from multispectral aerial photographs taken using unmanned aerial vehicles (UAVs) and a reliable data field for infected trees. Vegetation and soil indices derived from multispectral aerial photographs, such as the normalized difference vegetation index, the modified chlorophyll absorption ratio index, the normalized difference water index (NDWI), and soil pH, may have to be relied upon to explain the precise location of these two diseases. This study used a random forest algorithm to handle a large dataset consisting of multispectral and spectral models. The results show that the soil indices, soil pH, and NDWI are the most important variables for predicting the spatial distribution of these two diseases. Simultaneously, the plantation area affected by BBD is more extensive than that affected by fusarium if variations in planted banana cultivars are not considered.

Quantification of Biophysical Parameters and Economic Yield in Cotton and Rice Using Drone Technology

New agronomic opportunities for more informed agricultural decisions and enhanced crop management have been made possible by drone-based near-ground remote sensing. Obtaining precise non-destructive information regarding crop biophysical characteristics at spatial and temporal scales is now possible. Drone-mounted multispectral and thermal sensors were used to assess crop phenology, condition, and stress by profiling spectral vegetation indices in crop fields. In this study, vegetation indices, viz., Atmospherically Resistant Vegetation Index (ARVI), Modified Chlorophyll Absorption Ratio Index (MCARI), Wide Dynamic Range Vegetation Index (WDRVI), Normalized Red–Green Difference Index (NGRDI), Excess Green Index (ExG), Red–Green Blue Vegetation Index (RGBVI), and Visible Atmospherically Resistant Index (VARI) were generated. Furthermore, Pearson correlation analysis showed a better correlation between WDRVI and VARI with LAI (R = 0.955 and R = 0.982) ground truth data. In contrast, a strong correlation (R = 0.931 and R = 0.844) was recorded with MCARI and NGRDI with SPAD chlorophyll ground truth data. Then, the best-performing indices, WDRVI and MCARI in cotton, and VARI and NGRDI in rice, were further used to generate the yield model. This study for determining LAI and chlorophyll shows that high spatial resolution drone imageries are accurate and fast. As a result, finding out the LAI and chlorophyll and how they affect crop yield at a regional scale is helpful. The widespread use of unmanned aerial vehicles (UAV) and yield prediction were technical components of large-scale precision agriculture.

Improved estimation of vegetation water content and its impact on L-band soil moisture retrieval over cropland

Satellite-based soil moisture (SM) retrieval within agricultural regions is challenging due to pronounced vegetation and land cover variations during the growing season. Vegetation water content (VWC)–used to describe vegetation opacity in both the Soil Moisture Active Passive (SMAP) Dual Channel Algorithm (DCA) and V-polarization Single Channel Algorithm (SCA-V)–is approximated using a climatological Normalized Difference Vegetation Index (NDVI) time series. To understand the impact of VWC algorithms on SMAP SM retrievals, we investigated optimal vegetation indices for VWC estimation, and further evaluated the impact of VWC uncertainty on DCA and SCA-V SM retrievals. Besides NDVI, we applied four dynamic vegetation indices (VIs) acquired from Sentinel-2–including two red-edge VIs (Modified Chlorophyll Absorption Ratio Index (MCARI) and Inverted Red-edge Chlorophyll Index (IRECI)) and two non-red-edge VIs (Normalized Difference Water Index (NDWI1 and NDWI2)) to estimate growing-season corn VWC and the impact of different empirical relationships on VWC estimates. We find that climatological NDVI from Moderate Resolution Imaging Spectroradiometer (MODIS) significantly underestimates VWC, and therefore underestimates SM over croplands in Northwest China during the growing season, whereas dynamic VIs can improve the accuracy of VWC and SM retrievals. With respect to the choice of VI for VWC estimation, MCARI is more accurate during the peak growing season compared to its counterpart non-red-edge VIs; however, it also overestimates VWC during the early growing stage. In addition, the influence of VWC uncertainty on SM accuracy is shown to be higher in SCA-V than in DCA. The results of this study provide insight into preferred approaches for the accurate estimation of VWC and associated improvement in passive microwave algorithms for SM retrieval.

Cotton yield prediction using drone derived LAI and chlorophyll content

The unmanned aerial vehicles (UAV) have become a better solution for agricultural growers due to advanced features such as minimal maintenance costs, quick set-up time, low acquisition costs, and live data capturing. Near-ground remote sensing (drone) has opened up new agronomic opportunities for better crop management. This study predicted the seed cotton yield for a cotton field area located at Tamil Nadu Agricultural University, Coimbatore. Pearson correlation analysis and regression analysis were done for ground truth data and vegetation indices for validation and accuracy and also to find the best-performing indices. It was concluded that the Wide Dynamic Range Vegetation Index (WDRVI) showed a better correlation coefficient (R=0.959) with LAI ground truth data (R2=0.919). In contrast, the Modified Chlorophyll Absorption Ratio Index (MCARI) showed a better correlation coefficient (R=0.919) with SPAD chlorophyll ground truth data (R2=0.845). Then the best performing indices WDRVI and MCARI were further used for generating the yield model. High spatial resolution drone imageries for determining LAI and chlorophyll are reliable and rapid, as per the study. It helps to determine the LAI and chlorophyll at a spatial scale and their influence on yield production. This yield prediction was technical support for the widespread adoption and application of unmanned aerial vehicle (UAV) remote sensing in large-scale precision agriculture.

Sugarcane Yield Prediction Using Vegetation Indices in Northern Karnataka, India

The integration of remote sensing (RS) technology with machine learning (ML) algorithms can facilitate accurate prediction of sugarcane yield. This paper presents an assessment of the random forest (RF)-based prediction model and second-degree polynomial regression models for sugarcane yield prediction. The models are developed utilizing vegetation indices (VIs) computed from the Sentinel-2 satellite and sugarcane yield data. The sugarcane yield data were acquired from sugarcane fields around the Godavari Bio-refineries Limited (GBL) factory in Karnataka, India, during the 2017–2018 sugarcane growing season. A dataset detailing agronomic information and VIs was prepared for yield prediction. The study area comprises seven sugarcane growing talukas. The second-degree polynomial regression was used for predicting the sugarcane yield as it had the best fit for the distribution of variables. The green normalized difference vegetation index (GNDVI) recorded the highest R2, i.e., 0.71 during November month with a coefficient of variance of 0.83, with all other indices characterized by R2 values ranging from 0.42(modified chlorophyll absorption ratio index) to 0.69 (normalized difference red edge), suggesting the GNDVI’s potential for sugarcane yield prediction. Comparing the actual yield with the predicted yield, the RF prediction and second-degree polynomial regression model exhibited accuracies of 90.42% and 88%, respectively. This indicates that the models are sufficiently accurate and beneficial in decision-making for sugar mill operational planning.

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

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

Evaluating automated endmember extraction for classifying hyperspectral data and deriving spectral parameters for monitoring forest vegetation health.

The hyperspectral remote sensing datasets possess high capability in differentiating the spectrally similar features, and thus, they are immensely important in various forestry activities, especially vegetation classifications. But extracting endmembers for data training is a challenging task. The present study is focused on the use of automated endmember extraction technique for deriving endmembers during the unavailability of ground spectra. We used the Sequential Maximum Angle Convex Cone (SMACC) method on EO-1 Hyperion data for endmember extraction in the Barkot forest range of Dehradun district, Uttarakhand which were used for classification of the study area using support vector machine (SVM). Further, we estimated the vegetation health of the region by assigning the threshold weights for various derived environmental variables such as NDVI (Normalised Difference Vegetation Index), CRI (Carotenoid Reflectance Index), Anthocyanin Reflectance Index (ARI), Modified Simple Ratio (MSR), Modified Chlorophyll Absorption Ratio Index (MCARI) and WBI (Water Band Index). Then, to further validate the health of the forest types, we correlated it with the Land Surface Temperature (LST) from LANDSAT 5 ETM + data. The results showed a high classification accuracy of 89.13%. The healthy vegetation area coverage of the area was about 78.6% with most healthy class as Tectona grandis and Shorea robusta and its correlation with LST showed lower temperature range in healthy vegetation areas and vice versa. The study was useful in determining the superiority of SMACC automated endmember extraction and estimating the vegetation health.

Use of RPA Images in the Mapping of the Chlorophyll Index of Coffee Plants

Coffee trading is an important source of income for the Brazilian commercial balance. Chlorophyll (Chl) are pigments responsible for converting radiation into energy; these pigments are closely related to the photosynthetic efficiency of plants, and the evaluation of the nutritional status of the coffee tree. The inversion method can be used for estimating the canopy chlorophyll content (Chlcanopy) using the leaf chlorophyll content (Chlleaf) and the leaf area index (LAI). The application of vegetation indices (VIs) in high spatial resolution images obtained from remotely piloted aircraft (RPA) can assist in the characterization of Chlcanopy in addition to providing vital and fast information for monitoring crops and aiding decision-making. This study aimed to identify which VIs adequately explain the Chl and evaluate the relationships between the VIs obtained from remotely piloted aircraft (RPA) images and the Chlleaf and Chlcanopy in coffee plants during the wet and dry seasons. The experiment was conducted on a Coffea arabica L. plantation in Lavras, Minas Gerais, Brazil. Images were collected on 26 November 2019 (wet), 11 August 2020 (dry), and 26 August 2021 (dry) by a multispectral camera embedded in a quadcopter. Plant height (H), crow diameter (D), and Chlleaf (a, b and total) data were collected in the field by a metre ruler (H and D) and sensor (Chlleaf). The LAI was calculated based on H and D. The Chlcanopy (a, b, and total) was calculated based on Chlleaf and LAI. The image processing was performed in Pix4D software, and postprocessing and calculation of the 21 VIs were performed in QGIS. Statistical analyses (descriptive, statistical tests, Pearson correlation, residuals calculation, and linear regression) were performed using the software R. The VIs from the RPA that best correlates to Chlcanopy in the wet season were the Modified Chlorophyll Absorption Ratio Index 2 (MCARI2RPA), Modified Simple Ratio (MSRRPA) and Simple Ratio (SRRPA). These VIs had high sensitivity and, therefore, were more affected by chlorophyll variability. For the two dry season studied days, there were no patterns in the relationships between Chlleaf, Chlcanopy, and the VIs. It was possible to use the Chl inversion method for the coffee during the wet season.

Hyperspectral response of agronomic variables to background optical variability: Results of a numerical experiment

Understanding how biophysical and biochemical variables contribute to the spectral characteristics of vegetation canopies is critical for their monitoring. Quantifying these contributions, however, remains difficult due to extraneous factors such as the spectral variability of canopy background materials, including soil/crop-residue moisture, soil-type, and non-photosynthetic vegetation (NPV). This study focused on exploring the spectral response of two important agronomic variables (1) leaf chlorophyll content (Cab) and (2) leaf area index (LAI) under various canopy backgrounds through a global sensitivity analysis of wheat-like canopy spectra simulated using the physically-based PROSAIL radiative transfer model. Our results reveal the following general findings: (1) the contribution of each agronomic variable to the simulated canopy spectral signature varies considerably with respect to the background optical properties; (2) the influence of the soil-type and NPV on the spectral response of canopy to Cab and LAI is more significant than that caused by soil/crop-residue moisture; (3) spectral bands at 560 and 704 nm remain sensitive to Cab while being least affected by the impacts of variations in the NPV, soil-type and moisture; (4) the near-infrared (NIR) spectral bands exhibit higher sensitivity to LAI and lower background effects only in the cases of soil/crop-residue moisture but are relatively strongly affected by soil-type and NPV. Comparative analysis of the correlations of twelve widely used vegetation indices with agronomic variables indicates that LICI (LAI-insensitive chlorophyll index) and Macc01 (Maccioni index) are more effective in estimating Cab, while OSAVI (optimized soil adjusted vegetation index) and MCARI2 (modified chlorophyll absorption ratio index 2) are better LAI predictors under the simulated background variability. Overall, our results highlight that background reflectance variability introduces considerable differences in the agronomic variables’ spectral response, leading to inconsistencies in the VI- Cab /-LAI relationship. Further studies should integrate these results of spectral responsivity to develop trait-specific hyperspectral inversion models.

Improved Surface Soil Moisture Estimation Model in Semi-Arid Regions Using the Vegetation Red-Edge Band Sensitive to Plant Growth

Accurate description of surface soil moisture (SSM) in vegetation-covered areas is of great significance to water resource management and drought monitoring. To remove the effect of vegetation on SSM estimation, the vegetation index obtained from Sentinel-2 (S2) was applied for vegetation water content (VWC) estimation. The VWC model was substituted into the water cloud model (WCM), and thus, the SSM estimation model was developed based on the WCM. The methodology was tested at Daxing, Beijing, and Gu’an, Hebei, in which training and validation data of SSM were acquired by in situ measurements. The results can be described as follows: (1) For the vegetation-covered areas, the Modified Chlorophyll Absorption Ratio Index (MCARI) obtained from the B3, B4, and B5 bands of S2 was the most suitable for removing the influence of vegetation on SSM estimation; (2) Compared to Sentinel-1 (S1) vertical–horizontal (VH) polarization, vertical–vertical (VV) polarization was more suitable for SSM estimation and achieved higher accuracy; (3) The developed model could be used to estimate SSM under crop cover with high accuracy, which indicated the correlation coefficients (R2) between in situ measured and estimated SSM were 0.867, the root mean square error (RMSE) was 0.028 cm3/cm3, and the MAE was 0.023 cm3/cm3. Thus, this methodology has the potential for SSM estimation in vegetated areas.

High throughput analysis of leaf chlorophyll content in sorghum using RGB, hyperspectral, and fluorescence imaging and sensor fusion

BackgroundLeaf chlorophyll content plays an important role in indicating plant stresses and nutrient status. Traditional approaches for the quantification of chlorophyll content mainly include acetone ethanol extraction, spectrophotometry and high-performance liquid chromatography. Such destructive methods based on laboratory procedures are time consuming, expensive, and not suitable for high-throughput analysis. High throughput imaging techniques are now widely used for non-destructive analysis of plant phenotypic traits. In this study three imaging modules (RGB, hyperspectral, and fluorescence imaging) were, separately and in combination, used to estimate chlorophyll content of sorghum plants in a greenhouse environment. Color features, spectral indices, and chlorophyll fluorescence intensity were extracted from these three types of images, and multiple linear regression models and PLSR (partial least squares regression) models were built to predict leaf chlorophyll content (measured by a handheld leaf chlorophyll meter) from the image features.ResultsThe models with a single color feature from RGB images predicted chlorophyll content with R2 ranging from 0.67 to 0.88. The models using the three spectral indices extracted from hyperspectral images (Ration Vegetation Index, Normalized Difference Vegetation Index, and Modified Chlorophyll Absorption Ratio Index) predicted chlorophyll content with R2 ranging from 0.77 to 0.78. The model using the fluorescence intensity extracted from fluorescence images predicted chlorophyll content with R2 of 0.79. The PLSR model that involved all the image features extracted from the three different imaging modules exhibited the best performance for predicting chlorophyll content, with R2 of 0.90. It was also found that inclusion of SLW (Specific Leaf Weight) into the image-based models further improved the chlorophyll prediction accuracy.ConclusionAll three imaging modules (RGB, hyperspectral, and fluorescence) tested in our study alone could estimate chlorophyll content of sorghum plants reasonably well. Fusing image features from different imaging modules with PLSR modeling significantly improved the predictive performance. Image-based phenotyping could provide a rapid and non-destructive approach for estimating chlorophyll content in sorghum.

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.

Mapping of the Canopy Openings in Mixed Beech–Fir Forest at Sentinel-2 Subpixel Level Using UAV and Machine Learning Approach

The presented study demonstrates a bi-sensor approach suitable for rapid and precise up-to-date mapping of forest canopy gaps for the larger spatial extent. The approach makes use of Unmanned Aerial Vehicle (UAV) red, green and blue (RGB) images on smaller areas for highly precise forest canopy mask creation. Sentinel-2 was used as a scaling platform for transferring information from the UAV to a wider spatial extent. Various approaches to an improvement in the predictive performance were examined: (I) the highest R2 of the single satellite index was 0.57, (II) the highest R2 using multiple features obtained from the single-date, S-2 image was 0.624, and (III) the highest R2 on the multitemporal set of S-2 images was 0.697. Satellite indices such as Atmospherically Resistant Vegetation Index (ARVI), Infrared Percentage Vegetation Index (IPVI), Normalized Difference Index (NDI45), Pigment-Specific Simple Ratio Index (PSSRa), Modified Chlorophyll Absorption Ratio Index (MCARI), Color Index (CI), Redness Index (RI), and Normalized Difference Turbidity Index (NDTI) were the dominant predictors in most of the Machine Learning (ML) algorithms. The more complex ML algorithms such as the Support Vector Machines (SVM), Random Forest (RF), Stochastic Gradient Boosting (GBM), Extreme Gradient Boosting (XGBoost), and Catboost that provided the best performance on the training set exhibited weaker generalization capabilities. Therefore, a simpler and more robust Elastic Net (ENET) algorithm was chosen for the final map creation.

Efficacy of PCR-bassed techniqes and remote sensing technology for detecting potato brown rot infection caused by R. solanacearum

Bacterial wilt of potatoes (Solanum tuberosum) caused by Ralstonia solanacearum phylotype II sequevar I (race 3 biovar 2), is one of the most serious diseases of potatoes. This study aimed to detect such deteriorations through the remotely sensed data. In this regard, naturally infested potato fields at Abo Alkhawi, Al Tayareya and Om Saber villages at Behera governorate, Egypt, were used for selective isolation of R. solanacearum. On Semi Selective South Africa (SMSA) medium, 30 isolates were randomly screened and tested by immunofluorescence antibody staining (IFAS) and confirmed biologically by tomato plantlets (cv. Pinto) reaction. All isolates showed positive results with Real-time PCR. Based on the phylotype specific multiplex (Pmx) – PCR data, all isolates in concern were R. solanacearum and affiliated to phylotype II as 372- bp amplicon. Spectral indices were applied on the Landsat 8 and RapidEye satellite images acquired in January and April 2019 including Normalized Difference Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Salinity Index (NDSI) and Modified Chlorophyll Absorption Ratio Index (MCARI). Results from the remote sensing analyses showed medium to relatively high salinity and moisture content of soils concerened. Moreover, plants chlorophyll levels are in medium levels which is not typical to condition associated with usual circumstances. Further investigation, in a greater academic depth, are needed for pathological field surveys.

Assessing Canopy Responses to Thinnings for Sweet Chestnut Coppice with Time-Series Vegetation Indices Derived from Landsat-8 and Sentinel-2 Imagery

Forest management treatments often translate into changes in forest structure. Understanding and assessing how forests react to these changes is key for forest managers to develop and follow sustainable practices. A strategy to remotely monitor the development of the canopy after thinning using satellite imagery time-series data is presented. The aim was to identify optimal remote sensing Vegetation Indices (VIs) to use as time-sensitive indicators of the early response of vegetation after the thinning of sweet chestnut (Castanea Sativa Mill.) coppice. For this, the changes produced at the canopy level by different thinning treatments and their evolution over time (2014–2019) were extracted from VI values corresponding to two trials involving 33 circular plots (r = 10 m). Plots were subjected to one of the following forest management treatments: Control with no intervention (2800–3300 stems ha−1), Treatment 1, one thinning leaving a living stock density of 900–600 stems ha−1 and Treatment 2, a more intensive thinning, leaving 400 stems ha−1. Time series data from Landsat-8 and Sentinel-2 were collected to calculate values for different VIs. Canopy development was computed by comparing the area under curves (AUCs) of different VI time-series annually throughout the study period. Soil-Line VIs were compared to the Normalized Vegetation Index (NDVI) revealing that the Second Modified Chlorophyll Absorption Ratio Index (MCARI2) more clearly demonstrated canopy evolution tendencies over time than the NDVI. MCARI2 data from both L8 and S2 reflected how the influence of treatment on the canopy cover decreases over the years, providing significant differences in the thinning year and the year after. Metrics derived from the MCARI2 time-series also demonstrated the capacity of the canopy to recovery to pretreatment coverage levels. The AUC method generates a specific V-shaped time-signature, the vertex of which coincides with the thinning event and, as such, provides forest managers with another tool to assist decision making in the development of sustainable forest management strategies.

Prediction of Early Season Nitrogen Uptake in Maize Using High-Resolution Aerial Hyperspectral Imagery

The ability to predict spatially explicit nitrogen uptake (NUP) in maize (Zea mays L.) during the early development stages provides clear value for making in-season nitrogen fertilizer applications that can improve NUP efficiency and reduce the risk of nitrogen loss to the environment. Aerial hyperspectral imaging is an attractive agronomic research tool for its ability to capture spectral data over relatively large areas, enabling its use for predicting NUP at the field scale. The overarching goal of this work was to use supervised learning regression algorithms—Lasso, support vector regression (SVR), random forest, and partial least squares regression (PLSR)—to predict early season (i.e., V6–V14) maize NUP at three experimental sites in Minnesota using high-resolution hyperspectral imagery. In addition to the spectral features offered by hyperspectral imaging, the 10th percentile Modified Chlorophyll Absorption Ratio Index Improved (MCARI2) was made available to the learning models as an auxiliary feature to assess its ability to improve NUP prediction accuracy. The trained models demonstrated robustness by maintaining satisfactory prediction accuracy across locations, pixel sizes, development stages, and a broad range of NUP values (4.8 to 182 kg ha−1). Using the four most informative spectral features in addition to the auxiliary feature, the mean absolute error (MAE) of Lasso, SVR, and PLSR models (9.4, 9.7, and 9.5 kg ha−1, respectively) was lower than that of random forest (11.2 kg ha−1). The relative MAE for the Lasso, SVR, PLSR, and random forest models was 16.5%, 17.0%, 16.6%, and 19.6%, respectively. The inclusion of the auxiliary feature not only improved overall prediction accuracy by 1.6 kg ha−1 (14%) across all models, but it also reduced the number of input features required to reach optimal performance. The variance of predicted NUP increased as the measured NUP increased (MAE of the Lasso model increased from 4.0 to 12.1 kg ha−1 for measured NUP less than 25 kg ha−1 and greater than 100 kg ha−1, respectively). The most influential spectral features were oftentimes adjacent to each other (i.e., within approximately 6 nm), indicating the importance of both spectral precision and derivative spectra around key wavelengths for explaining NUP. Finally, several challenges and opportunities are discussed regarding the use of these results in the context of improving nitrogen fertilizer management.

Prediction of N, K and Mg on Leaves of Four Citrus Rootstocks Seedlings Using Hyper Spectral Data

Sufficient application of nutrients is one of the most important factors in the development of seedlings from quality citrus root stocks. Some of the key guides for preparing citrus fertilizer programs is by tracking the nutrient content of plants directly. This includes, however, an examination of a large number of leaf samples using costly and time-chemical techniques. It has been shown over the last 10 years that it is possible to quantitatively estimate such nutrient elements in citrus leaves using the spectral reflectance values obtained using hyperspectral spectroscopy. This technique is quick, non-destructive, cost-effective and eco- friendly. Therefore, estimating nitrogen, potassium, and magnesium in seedling leaves of citrus rootstocks by this approach would be useful in determining the seedlings ' mineral status. In this research,168 leaf samples from four citrus rootstocks seedlings (Volkamer lemon, Sour orange, Trifoliate orange and Balady Lime) were used to conduct three vegetation indices; normalized difference vegetation index (NDVI), normalized difference nitrogen index (NDNI) and modified chlorophyll absorption ratio index (MCARI) and subsequent nutrient estimates for N, K, and Mg concentration. Simple regression models and chemical analysis were used to produce the best model of estimation to predict the values of the three components. A high correlation coefficient (R2) was verified in the estimate of N (R2=0.982) with the lowest root mean square error (RMSE=0.0472) and k (R2=0.983) with the lowest root mean square error (RMSE=0.0491) and the lowest root mean square error (RMSE=0.0062) was also verified.

Estimation of chlorophyll content in mountain steppe using in situ hyperspectral measurements

Mountain steppe has important production value and ecological function. The chlorophyll content is a critical biochemical parameter of steppe. This study aims at exploring effective spectral parameters for estimating the chlorophyll content in mountain steppe using in situ hyperspectral measurements. The study area is located on the northern slope of Tianshan Mountains with complex terrain and sparse vegetation. The canopy hyperspectral data and leaf chlorophyll content and canopy chlorophyll content data were measured from 101 samples. The empirical models were constructed based on nine vegetation indices and three red-edge parameters. The results showed that the red-edge magnitude, red-edge area, Second Modified Chlorophyll Absorption Ratio Index, Second Modified Chlorophyll Absorption Ratio Index/Second Optimized Soil-Adjusted Vegetation Index, Second Transformed Chlorophyll Absorption Ratio Index and Triangle Vegetation Index estimated canopy chlorophyll content satisfactorily. Leaf chlorophyll content of mountain steppe is not well estimated using canopy spectral parameters. The results revealed the specific vegetation indices and red-edge parameters for estimating vegetation biochemical parameters at canopy scale in mountain steppe using in situ hyperspectral measurements.