Green Chlorophyll Index Research Articles

Monitoring invasive exotic grass species in ecological restoration areas of the Brazilian savanna using UAV images

Identifying and monitoring invasive exotic grasses (IEG) is critical for the ecological restoration of grasslands and savannas, as they are the main barrier to the successful recovery of native grasslands and savannas. The integration of high-resolution remote sensing data, acquired through UAVs (Unmanned Aerial Vehicles), with machine learning algorithms is advancing restoration monitoring. The present study aimed to estimate IEG cover and identify plots with different invasive species dominance in ecological restoration areas in the Brazilian savanna. For ground truth, species cover estimates were carried out in plots through point-line intercept sampling. Then, the areas were classified according to the dominance of each invasive species (>40% vegetation cover) or of a mix of native species. A multispectral camera onboard a UAV was used to acquire images in the visible to near-infrared spectrum. From the images, vegetation indices and texture metrics were derived as predictor variables. The Random Forest (RF) algorithm was used to estimate the percentage of invasive species cover and to classify plots in terms of species dominance. The final RF regression for invasive species cover percentage presented an R2 of 0.71 and selected the blue band, NIR and Ratio Vegetation Index (RVI) as the most important variables. The overall accuracy of plot classification according to species dominance was 84%. The most prominent predictors were the Green Chlorophyll Index (GCI), the atmospherically resistant vegetation index (ARVI), and the RVI. The structural and photosynthetic characteristics of exotic and native species influenced the spectral responses. In conclusion, multispectral images acquired with UAV can be used to estimate the proportion of invasion in restoration sites and to map areas dominated by different invasive grass species in grasslands and savannas. This is a useful tool for evaluating restoration success and can help indicate areas that require management interventions.

Impact Assessment of Nematode Infestation on Soybean Crop Production Using Aerial Multispectral Imagery and Machine Learning

Accurate and prompt estimation of geospatial soybean yield (SY) is critical for the producers to determine key factors influencing crop growth for improved precision management decisions. This study aims to quantify the impacts of soybean cyst nematode (SCN) infestation on soybean production and the yield of susceptible and resistant seed varieties. Susceptible varieties showed lower yield and crop vigor recovery, and high SCN population (20 to 1080) compared to resistant varieties (SCN populations: 0 to 340). High-resolution (1.3 cm/pixel) aerial multispectral imagery showed the blue band reflectance (r = 0.58) and Green Normalized Difference Vegetation Index (GNDVI, r = −0.6) have the best correlation with the SCN populations. While GDNVI, Green Chlorophyll Index (GCI), and Normalized Difference Red Edge Index (NDRE) were the best differentiators of plant vigor and had the highest correlation with SY (r = 0.59–0.75). Reflectance (REF) and VIs were then used for SY estimation using two statistical and four machine learning (ML) models at 10 different train–test data split ratios (50:50–95:5). The ML models and train–test data split ratio had significant impacts on SY estimation accuracy. Random forest (RF) was the best and consistently performing model (r: 0.84–0.97, rRMSE: 8.72–20%), while a higher train–test split ratio lowered the performances of the ML models. The 95:5 train–test ratio showed the best performance across all the models, which may be a suitable ratio for modeling over smaller or medium-sized datasets. Such insights derived using high spatial resolution data can be utilized to implement precision crop protective operations for enhanced soybean yield and productivity.

Impacts of mining on vegetation phenology and sensitivity assessment of spectral vegetation indices to mining activities in arid/semi-arid areas

Measuring the impact of mining activities on vegetation phenology and assessing the sensitivity of vegetation indices (VIs) to it are crucial for understanding land degradation in mining areas and enhancing the carbon sink capacity following the ecological restoration of mines. To this end, we have developed a novel technical framework to quantify the impact of mining activities on vegetation, and applied it to the Bainaimiao copper mining area in Inner Mongolia. Phenological indices are extracted based on the VI time series data of Sentinel-2, and changes in phenological differences in various directions are used to quantify the impact of mining activities on vegetation. Finally, indicators such as mean difference, standard deviation, index value distribution interval, and concentration of index value distribution were selected to assess the sensitivity of the Enhanced Vegetation Index (EVI), Green Chlorophyll Index (GCI), Global Environmental Monitoring Index (GEMI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Vegetation Index (NDVI), Renormalized Difference Vegetation Index (RDVI), Red-Edge Chlorophyll Index (RECI), and Soil-Adjusted Vegetation Index (SAVI) to mining activities. The results of the study show that the impact of mining activities on surrounding vegetation extends to an area three times larger than the actual mining activity area. When compared with the reference and unaffected areas, the affected area experienced a delay of approximately 10 days in seasonal vegetation development. Environmental pollution caused by the tailings pond was identified as the primary factor influencing this delay. Significant variations in the sensitivity of each VI to assess mining activities in arid/semi-arid areas were observed. Notably, GCI, GNDVI and RDVI displayed relatively high sensitivity to discrepancies in the spectral attributes of vegetation within the affected area, while SAVI reflected the overall spectral stability of the vegetation in the affected area. The research findings have the potential to provide valuable technical guidance for holistic environmental management in mining areas and hold great significance in preventing further land degradation and supporting ecological restoration in mining areas.

Comparing the Utility of Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) on Sentinel-2 MSI to Estimate Dry Season Aboveground Grass Biomass

Grasslands are biomes of significant fiscal, social and environmental value. Grassland or rangeland management often monitors and manages grassland productivity. Productivity is determined by various biophysical parameters, one such being grass aboveground biomass. Advancements in remote sensing have enabled near-real-time monitoring of grassland productivity. Furthermore, the increase in sophisticated machine learning algorithms has provided a powerful tool for remote sensing analytics. This study compared the performance of two neural networks, namely, Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN), in predicting dry season aboveground biomass using open-access Sentinel-2 MSI data. Sentinel-2 spectral bands and derived vegetation indices were used as input data for the two algorithms. Overall, findings in this study showed that the deep CNN outperformed the ANN in estimating aboveground biomass with an R2 of 0.83, an RMSE of 3.36 g/m2 and an RMSE% of 6.09. In comparison, the ANN produced an R2 of 0.75, an RMSE of 5.78 g/m2 and an RMSE% of 8.90. The sensitivity analysis suggested that the blue band, Green Chlorophyll Index (GCl), and Green Normalised Difference Vegetation Index (GNDVI) were the most significant for model development for both neural networks. This study can be considered a pilot study as it is one of the first to compare different neural network performances using freely available satellite data. This is useful for more rapid biomass estimation, and this study exhibits the great potential of deep learning for remote sensing applications.

Spatiotemporal Variations in Biophysical Water Quality Parameters: An Integrated In Situ and Remote Sensing Analysis of an Urban Lake in Chile

This study aims to develop and implement a methodology for retrieving bio-optical parameters in a lagoon located in the Biobío region, South-Central Chile, by analyzing time series of Landsat-8 OLI satellite images. The bio-optical parameters, i.e., chlorophyll-a (Chl-a, in mg·m−3) and turbidity (in NTU) were measured in situ during a satellite overpass to minimize the impact of atmospheric distortions. To calibrate the satellite images, various atmospheric correction methods (including ACOLITE, C2RCC, iCOR, and LaSRC) were evaluated during the image preprocessing phase. Spectral signatures obtained from the scenes for each atmospheric correction method were then compared with spectral signatures acquired in situ on the water surface. In short, the ACOLITE model emerged as the best fit for the calibration process, reaching R2 values of 0.88 and 0.79 for Chl-a and turbidity, respectively. This underlies the importance of using inversion models, when processing water surfaces, to mitigate errors due to aerosols and the sun-glint effect. Subsequently, reflectance data derived from the ACOLITE model were used to establish correlations between various spectral indices and the in situ data. The empirical retrieval models (based on band combinations) yielding superior performance, with higher R2 values, were subjected to a rigorous statistical validation and optimization by applying a bootstrapping approach. From this process the green chlorophyll index (GCI) was selected as the optimal choice for constructing the Chl-a retrieval model, reaching an R2 of 0.88, while the red + NIR spectral index achieved the highest R2 value (0.79) for turbidity analysis, although in the last case, it was necessary to incorporate data from several seasons for an adequate model training. Our analysis covered a broad spectrum of dates, seasons, and years, which allowed us to search deeper into the evolution of the trophic state associated with the lake. We identified a striking eight-year period (2014–2022) characterized by a decline in Chl-a concentration in the lake, possibly attributable to governmental measures in the region for the protection and conservation of the lake. Additionally, the OLI imagery showed a spatial pattern varying from higher Chl-a values in the northern zone compared to the southern zone, probably due to the heat island effect of the northern urban areas. The results of this study suggest a positive effect of recent local regulations and serve as the basis for the creation of a modern monitoring system that enhances traditional point-based methods, offering a holistic view of the ongoing processes within the lake.

Analysis Of The Mangrove Structure In The Dong Rui Commune Based On Multispectral Unmanned Aerial Vehicle Image Data

Mangroves are one of the most important types of wetlands in coastal areas and perform many different functions. Assessing the structure and function of mangroves is a premise for the management, monitoring and development of this most diverse and vulnerable ecosystem. In this study, the unmanned aerial vehicle (UAV) Phantom 4 Multispectral was used to analyse the structure of a mangrove forest area of approximately 50 hectares in Dong Rui commune, Tien Yen district, Quang Ninh Province – one of the most diverse wetland ecosystems in northern Vietnam. Based on the visual classification method combined with the results of field taxonomic sampling, a mangrove tree classification map was established for UAV with three species, Bruguiera gymnorrhiza, Rhizophora stylosa, and Kandelia obovata, achieving an overall accuracy = 86.28%, corresponding to a Kappa coefficient =0.84. From the images obtained from the UAV, we estimated and developed maps and assessed the difference in tree height and four vegetation indices, including the normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), enhanced vegetation index (EVI), and green chlorophyll index (GCI), for three mangrove plant species in the flying area. Bruguiera gymnorrhiza and Rhizophora stylosa reach an average height of 4 to 5 m and are distributed mainly in high tide areas. Meanwhile, Kandelia obovata has a lower height (ranging from 2 to 4 m), distributed in low-tide areas, near frequent flows. This study confirms the superiority of UAV with red edge and near-infrared wave bands in classifying and studying mangrove structures in small-scale areas.

Use of Remote Sensing Data to Assess the Effects of the Floodplain Vegetation Burning in the Lower Dniester National Park (Odesa Region)

The aim of the study was to determine the burnt areas, response of the floodplain ecosystems to burning, and to determine the difference between them and the control sites using the remote sensing data. The impact of burning was assessed using the spectral indices: Normalized Difference Vegetation Index (NDVI), Green Chlorophyll Index (GCI), Specific Leaf Area Vegetation Index (SLAVI), Normalized Difference Moisture Index (NDMI) and Moisture Stress Index (MSI). It was established that during the winter/spring period of 2020, fires in the territory of the Lower Dniester NNP covered almost 4453 hectares (33.1% of the floodplain ecosystems area). Simulation showed that the difference between the burned and control areas gradually decreased over the vegetation period. Maximal difference was observed at the beginning of the growing season (the quality of group selection was 43.2%), which indicates forming of relatively individual environmental conditions for the floodplain vegetation development. In spring, the difference was equal to 20.3%, due to development of <i>Phragmites australis</i>, the edifier of the plant complexes of wetland ecosystems, whose stems have developed a leaf blade. The summer period was characterized by further decrease in the group selection quality (19.9%), owing to equalization of environmental conditions for vegetation development. The autumn period was characterized by dry conditions and end of the growing season, which also contributes to the almost complete disappearance of differences between the plots (14.5%). Based on the analysis of similarity (ANOSIM), it was found that degree of difference between the burned plots was small, owing to local conditions and burning intensity, and amount of dry organic matter in certain areas. It was found that gradual decrease in differences during the vegetation season took place only at winter (February) burning. The influence of spring (March, April) burning resulted in significant variability of the similarity degree over the vegetation period.

Enhancing the potential of phenomic and genomic prediction in winter wheat breeding using high-throughput phenotyping and deep learning.

Integrating high-throughput phenotyping (HTP) based traits into phenomic and genomic selection (GS) can accelerate the breeding of high-yielding and climate-resilient wheat cultivars. In this study, we explored the applicability of Unmanned Aerial Vehicles (UAV)-assisted HTP combined with deep learning (DL) for the phenomic or multi-trait (MT) genomic prediction of grain yield (GY), test weight (TW), and grain protein content (GPC) in winter wheat. Significant correlations were observed between agronomic traits and HTP-based traits across different growth stages of winter wheat. Using a deep neural network (DNN) model, HTP-based phenomic predictions showed robust prediction accuracies for GY, TW, and GPC for a single location with R2 of 0.71, 0.62, and 0.49, respectively. Further prediction accuracies increased (R2 of 0.76, 0.64, and 0.75) for GY, TW, and GPC, respectively when advanced breeding lines from multi-locations were used in the DNN model. Prediction accuracies for GY varied across growth stages, with the highest accuracy at the Feekes 11 (Milky ripe) stage. Furthermore, forward prediction of GY in preliminary breeding lines using DNN trained on multi-location data from advanced breeding lines improved the prediction accuracy by 32% compared to single-location data. Next, we evaluated the potential of incorporating HTP-based traits in multi-trait genomic selection (MT-GS) models in the prediction of GY, TW, and GPC. MT-GS, models including UAV data-based anthocyanin reflectance index (ARI), green chlorophyll index (GCI), and ratio vegetation index 2 (RVI_2) as covariates demonstrated higher predictive ability (0.40, 0.40, and 0.37, respectively) as compared to single-trait model (0.23) for GY. Overall, this study demonstrates the potential of integrating HTP traits into DL-based phenomic or MT-GS models for enhancing breeding efficiency.

Pre-Harvest Corn Grain Moisture Estimation Using Aerial Multispectral Imagery and Machine Learning Techniques

Corn grain moisture (CGM) is critical to estimate grain maturity status and schedule harvest. Traditional methods for determining CGM range from manual scouting, destructive laboratory analyses, and weather-based dry down estimates. Such methods are either time consuming, expensive, spatially inaccurate, or subjective, therefore they are prone to errors or limitations. Realizing that precision harvest management could be critical for extracting the maximum crop value, this study evaluates the estimation of CGM at a pre-harvest stage using high-resolution (1.3 cm/pixel) multispectral imagery and machine learning techniques. Aerial imagery data were collected in the 2022 cropping season over 116 experimental corn planted plots. A total of 24 vegetation indices (VIs) were derived from imagery data along with reflectance (REF) information in the blue, green, red, red-edge, and near-infrared imaging spectrum that was initially evaluated for inter-correlations as well as subject to principal component analysis (PCA). VIs including the Green Normalized Difference Index (GNDVI), Green Chlorophyll Index (GCI), Infrared Percentage Vegetation Index (IPVI), Simple Ratio Index (SR), Normalized Difference Red-Edge Index (NDRE), and Visible Atmospherically Resistant Index (VARI) had the highest correlations with CGM (r: 0.68–0.80). Next, two state-of-the-art statistical and four machine learning (ML) models (Stepwise Linear Regression (SLR), Partial Least Squares Regression (PLSR), Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), and K-nearest neighbor (KNN)), and their 120 derivates (six ML models × two input groups (REFs and REFs+VIs) × 10 train–test data split ratios (starting 50:50)) were formulated and evaluated for CGM estimation. The CGM estimation accuracy was impacted by the ML model and train-test data split ratio. However, the impact was not significant for the input groups. For validation over the train and entire dataset, RF performed the best at a 95:5 split ratio, and REFs+VIs as the input variables (rtrain: 0.97, rRMSEtrain: 1.17%, rentire: 0.95, rRMSEentire: 1.37%). However, when validated for the test dataset, an increase in the train–test split ratio decreased the performances of the other ML models where SVM performed the best at a 50:50 split ratio (r = 0.70, rRMSE = 2.58%) and with REFs+VIs as the input variables. The 95:5 train–test ratio showed the best performance across all the models, which may be a suitable ratio for relatively smaller or medium-sized datasets. RF was identified to be the most stable and consistent ML model (r: 0.95, rRMSE: 1.37%). Findings in the study indicate that the integration of aerial remote sensing and ML-based data-run techniques could be useful for reliably predicting CGM at the pre-harvest stage, and developing precision corn harvest scheduling and management strategies for the growers.

Evaluation of Coffee Plants Transplanted to an Area with Surface and Deep Liming Based on Multispectral Indices Acquired Using Unmanned Aerial Vehicles

The use of new technologies to monitor and evaluate the management of coffee crops allowed for a significant increase in productivity. Precision coffee farming has leveraged the development of this commodity by using remote sensing and Unmanned Aerial Vehicles (UAVs). However, the success of coffee farming in the country also resulted from management practices, including liming management in the soils. This study aimed to evaluate the response of coffee seedlings transplanted to areas subjected to deep liming in comparison to conventional (surface) liming, using vegetation indices (VIs) generated by multispectral images acquired using UAVs. The study area was overflown bimonthly by UAVs to measure the plant height, crown diameter, and chlorophyll content in the field. The VIs were generated and compared with the data measured in the field using linear time graphs and a correlation analysis. Linear regression was performed to predict the biophysical parameters as a function of the VIs. A significant difference was found only in the chlorophyll content. Most indices were correlated with the biophysical parameters, particularly the green chlorophyll index (GCI) and the canopy area calculated via vectorization. Therefore, UAVs proved to be effective coffee monitoring tools and can be recommended for coffee producers.

High-throughput phenotyping for non-destructive estimation of soybean fresh biomass using a machine learning model and temporal UAV data.

Biomass accumulation as a growth indicator can be significant in achieving high and stable soybean yields. More robust genotypes have a better potential for exploiting available resources such as water or sunlight. Biomass data implemented as a new trait in soybean breeding programs could be beneficial in the selection of varieties that are more competitive against weeds and have better radiation use efficiency. The standard techniques for biomass determination are invasive, inefficient, and restricted to one-time point per plot. Machine learning models (MLMs) based on the multispectral (MS) images were created so as to overcome these issues and provide a non-destructive, fast, and accurate tool for in-season estimation of soybean fresh biomass (FB). The MS photos were taken during two growing seasons of 10 soybean varieties, using six-sensor digital camera mounted on the unmanned aerial vehicle (UAV). For model calibration, canopy cover (CC), plant height (PH), and 31 vegetation index (VI) were extracted from the images and used as predictors in the random forest (RF) and partial least squares regression (PLSR) algorithm. To create a more efficient model, highly correlated VIs were excluded and only the triangular greenness index (TGI) and green chlorophyll index (GCI) remained. More precise results with a lower mean absolute error (MAE) were obtained with RF (MAE = 0.17kg/m2) compared to the PLSR (MAE = 0.20kg/m2). High accuracy in the prediction of soybean FB was achieved using only four predictors (CC, PH and two VIs). The selected model was additionally tested in a two-year trial on an independent set of soybean genotypes in drought simulation environments. The results showed that soybean grown under drought conditions accumulated less biomass than the control, which was expected due to the limited resources. The research proved that soybean FB could be successfully predicted using UAV photos and MLM. The filtration of highly correlated variables reduced the final number of predictors, improving the efficiency of remote biomass estimation. The additional testing conducted in the independent environment proved that model is capable to distinguish different values of soybean FB as a consequence of drought. Assessed variability in FB indicates the robustness and effectiveness of the proposed model, as a novel tool for the non-destructive estimation of soybean FB.

A new four-stage approach based on normalized vegetation indices for detecting and mapping sugarcane hail damage using multispectral remotely sensed data

Hailstorms have increased in frequency and intensity over the past decade causing substantial losses in agriculture. Since hailstorms often hit a wide area with no detectable pattern, relying on traditional field-based methods to assess crop damage is difficult. The aim of this study was to develop and test a new four-stage normalized vegetation index approach for detecting the severity of hailstorm damage on sugarcane plants in a large estate in south eastern Zimbabwe. The following six spectral indices were computed for the period before and after a hailstorm event to assess the extent of hailstorm damage on sugarcane: Green Chlorophyll Index (GCI); Normalized Difference Vegetation Index (NDVI); Normalized Difference Senescent Vegetation Index (NDSVI); Red Edge Chlorophyll Index (RECI); Normalized Difference Tillage Index (NDTI); and Modified Soil Adjusted Vegetation Index (MSAVI2). Then, the spectral differences were computed for each index separately and the difference maps are reported as delta (Δ) indices. The results of this study show that within one week and even two weeks after a hailstorm, ΔNDTI, ΔNDVI and ΔRECI were consistently able to detect and characterise the severity of sugarcane damage. When used in partial least squares-discriminant analysis (PLS-DA), ΔNDTI performed best in mapping the severity of crop damage throughout the large estate. ΔNDTI was able to discriminate three different levels of sugarcane damage with an overall accuracy of 90% and a Kappa value of 0.85. Combined these results imply that ΔNDTI computed using multi-spectral datasets within a fortnight after a hailstorm is a promising tool for generating reliable information about the severity of sugarcane damage by hailstorms. Such spatially explicit information is useful for creating customised crop insurance packages sensitive to damage incurred by the farmer.

Towards improving the precision agriculture management of the wheat crop using remote sensing: A case study in Central Non-Black Earth region of Russia

Smart management of the wheat crop requires understanding the various variables affecting crop quality and quantity. The use of remote sensing data contributes to improve the application of precision agriculture (PA).The current paper aims to evaluate the efficiency of remote sensing in monitoring wheat growth and enhance the management practices. Three cultivation technologies for winter wheat were implemented: basic (CT1), intensive (CT2), and highly intensive (CT3) were investigated. Sentinel-2 with a resolution of 10 m was used to monitor the change in wheat growth under different management systems during 2019. The following variables: yield quantity, grain quality (measured protein and gluten), in addition, five vegetation indices: Soil Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI),Green Chlorophyll Index (GCI), Green Leaf Index (GLI) were retrieved during the growing season. The results showed that, GCI has the highest performance in predicting crop yield where r was 0.98. In addition, the SAVI and NDVI have the same performance; r was 0.96 for both protein and gluten contents. The yield production of CT3 has increased by 3 t/ha, in addition, the grain quality was superior compared to the CT1. The economic efficiency results showed that the CT3 was the most profitable for Moscovskaya 40 variety (WV1) with 2,72 Payback. For Nemchinovskaya 17 variety (WV2), the most profitable cultivation technology was the CT1 and CT2 with 2,44 Payback, and for the new variety Nemchinovskaya 85, the CT2 was the most profitable with the Payback of 3,03. Finally, remote sensing shows the spatial variation in crop growth, which enhances crop management to achieve optimal production in terms of quantity and quality.

UAV-derived photogrammetric point clouds and multispectral indices for fuel estimation in Mediterranean forests

Sensors attached to unmanned aerial vehicles (UAVs) allow estimating a large number of forest attributes related to forest fuels. This study assesses photogrammetric point clouds and multispectral indices obtained from a fixed-wing UAV for the classification of Prometheus fuel types in 82 forest plots in Aragón (NE Spain). Images captured by an RGB camera and a multispectral sensor allowed generating high density photogrammetric point clouds (RGB: 3000 points/m2; multispectral: 85 points/m2), which were normalized using alternatively a Digital Elevation Model (DEM) of 0.5, 1, and 2 m resolution. A set of structural and textural variables were derived from the normalized point cloud heights, and for the latter, the gray-level co-occurrence matrix (GLCM) approach was used. Multispectral images were also used to create seven spectral vegetation indices. The most relevant structural, textural, and spectral variables to introduce into the fuel types classification models were selected using Dunn's test, which included: the vegetation height at the 50th percentile, the coefficient of variation of the heights, the percentage of returns above 4 m, the mean textural dissimilarity, and the mean of the Green Chlorophyll Index. Three different data samples were introduced in the models: i) the relevant structural and textural variables from the RGB camera (RGB data sample); ii) the relevant structural, textural, and spectral variables from the multispectral sensor (MS data sample); and iii) the relevant structural and textural variables from the RGB camera plus the relevant spectral variable from the multispectral sensor (integrated data sample). After comparing three machine learning classification techniques (Random Forest, and Linear and Radial Support Vector Machine), the best results were obtained with Random Forest with k-fold cross-validation (k-10) and the integrated data sample with normalized point clouds at 0.5 m DEM resolution (overall accuracy = 71%). The variables successfully identified the Prometheus main fire carriers (i.e., shrubs or trees) and confusions were mainly located within the fuel types of the same dominant stratum, especially in fuel types 3 and 6. These results demonstrate the ability of UAV imagery to classify forest fuels in Mediterranean environments when RGB and multispectral data are combined.

An improved light use efficiency model by considering canopy nitrogen concentrations and multiple environmental factors

Although the light use efficiency (LUE) models are widely employed to estimate ecosystem gross primary production (GPP), the majority of these models inadequately consider the effects of environmental and biological factors on GPP, resulting in considerable uncertainty. In addition, most developed LUE models have assumed that the maximum LUE (εmax) is a fixed value for different vegetation types, while εmax should be dynamic under environmental changes. The canopy nitrogen (N) concentrations were considered to have a significant linear relationship with εmax and could be estimated using various vegetation indices. In this study, we selected a vegetation index to characterize the canopy N concentrations and further simulate the dynamic εmax. We then developed an improved LUE model that simultaneously integrated the effects of canopy N concentrations, temperature, water, atmospheric carbon dioxide (CO2) and radiation components on the GPP estimates. Different forms of LUE models that partially integrate the above factors were also constructed for comparison. Our results showed that (1) the green chlorophyll index (CIgreen) correlated well with measured canopy N concentrations (R2 = 0.68), and the model using the CIgreen to characterize canopy N concentrations performed the best; (2) the GPP estimated using the improved model gave the best accuracy (R2 = 0.69, RMSE = 2.13 gC/m2/d, MAE=1.36 gC/m2/d, IOA = 0.915) and performed well for different vegetation types when validated against the FLUXNET GPP; and (3) the estimated GPP had the best accuracy compared with MOD17 GPP and the revised EC-LUE GPP on a both daily and yearly scale. Overall, this study was an attempt to integrate N into the LUE model to obtain the spatiotemporally dynamic εmax while simultaneously taking into account the impacts of multiple environmental variables on the GPP estimates. The proposed model has the potential for satisfactory GPP simulations on a global or regional scale.

Improved Global Gross Primary Productivity Estimation by Considering Canopy Nitrogen Concentrations and Multiple Environmental Factors

The terrestrial gross primary productivity (GPP) plays a crucial role in regional or global ecological environment monitoring and carbon cycle research. Many previous studies have produced multiple products using different models, but there are still significant differences between these products. This study generated a global GPP dataset (NI-LUE GPP) with 0.05° spatial resolution and at 8 day-intervals from 2001 to 2018 based on an improved light use efficiency (LUE) model that simultaneously considered temperature, water, atmospheric CO2 concentrations, radiation components, and nitrogen (N) index. To simulate the global GPP, we mapped the global optimal ecosystem temperatures (Topteco) using satellite-retrieved solar-induced chlorophyll fluorescence (SIF) and applied it to calculate temperature stress. In addition, green chlorophyll index (CIgreen), which had a strong correlation with the measured canopy N concentrations (r = 0.82), was selected as the vegetation index to characterize the canopy N concentrations to calculate the spatiotemporal dynamic maximum light use efficiency (εmax). Multiple existing global GPP datasets were used for comparison. Verified by FLUXNET GPP, our product performed well on daily and yearly scales. NI-LUE GPP indicated that the mean global annual GPP is 129.69 ± 3.11 Pg C with an increasing trend of 0.53 Pg C/yr from 2001 to 2018. By calculating the SPAtial Efficiency (SPAEF) with other products, we found that NI-LUE GPP has good spatial consistency, which indicated that our product has a reasonable spatial pattern. This product provides a reliable and alternative dataset for large-scale carbon cycle research and monitoring long-term GPP variations.

Estimating canopy-scale chlorophyll content in apple orchards using a 3D radiative transfer model and UAV multispectral imagery

Chlorophyll content is a key trait for understanding the functioning of agroforestry ecosystems and has important implications for leaf and canopy photosynthesis. However, fine-scale monitoring of canopy chlorophyll content (CCC) of individual fruit trees is rather challenging. This study aims to use a 3D radiative transfer model (RTM) and proposes a joint inversion model based on prior knowledge to estimate the CCC of individual tree crowns (ITCs) in apple orchards. The widely recognized 3D RTM LESS (large-scale remote sensing data and image simulation framework over heterogeneous 3D scenes) was adopted for large-scale apple orchard 3D scenes radiative transfer computing and image simulation. LESS was first evaluated with unmanned aerial vehicle (UAV) multispectral imagery and the results showed that it reasonably characterized the reflectance of apple tree canopies (RMSE = 0.02). An original look-up table (LUT) with reflectance was then produced using LESS, and the final vegetation indices LUT (VI LUT) including Normalized Difference Vegetation Index (NDVI), Green Chlorophyll Index (CIgreen), Red edge Chlorophyll Index (CIred edge) and Green NDVI (GNDVI) was generated from the original LUT form VI interpolation. A physically-based joint inversion model coupling prior knowledge of leaf pigments and leaf area index (LAI) was developed to estimate the CCC of ITCs from high-resolution UAV images. The solution first used linear interpolation to produce a weighted VI LUT corresponding to the sample based on estimated LAI. Linear interpolation was then adopted to screen multiple combinations of leaf chlorophylla+b (Cab) and leaf carotenoids (Cxc) contents from the VI LUT. A prior relationship between Cab and Cxc was finally used to regularize the constraints on multiple VI combinations and determine the estimation of Cab and CCC. The joint inversion model demonstrated an accurate estimation of CCC of ITCs. The model driven by GNDVI yielded the highest result for CCC estimation (R2 = 0.84, RMSE = 24.12 μg/cm2). In addition, CIgreen (R2 = 0.82, RMSE = 32.22 μg/cm2) and CIred edge (R2 = 0.81, RMSE = 34.05 μg/cm2) also achieved satisfactory results. The proposed model facilitates CCC estimation of ITCs from high-resolution imagery in heterogeneous orchard canopies, which is important for advancing the precise nutrition management of fruit trees.

Monitoring the Spring 2021 Drought Event in Taiwan Using Multiple Satellite-Based Vegetation and Water Indices

The monitoring of droughts is practically important yet challenging due to the complexity of the phenomena. The occurrence of drought involves changes in meteorological conditions, vegetation coverage and soil moisture. To advance the techniques for detecting and monitoring droughts, this study explores the usage of a suite of vegetation and water indices derived from high-resolution images produced by geostationary satellite Himawari-8. The technique is tested on the detection of the drought event in Spring 2021 across Taiwan due to deficit of precipitation in that season. It is found that the time series analysis of green chlorophyll index (CIgreen) and normalized difference vegetation index (NDVI) helps detect the initiation of drought before its severity intensifies. The vegetation condition index (VCI) and vegetation health index (VHI) derived from GIgreen and NDVI are similarly useful for the early warning of a drought event. In addition to vegetation indices, the normalized difference water index (NDWI) is adopted for quantifying the deficit in precipitation. It is found that NDWI provides a better early warning system of drought compared to the vegetation indices. Combining the vegetation and water indices allows a more complete description of the evolution of drought for the Spring 2021 event. The potential for using the new framework for the early warning of future drought events is discussed.

Non-invasive Estimation of Foliar Nitrogen Concentration Using Spectral Characteristics of Menthol Mint (Mentha arvensis L.).

Menthol mint (Mentha arvensis L., Family: Lamiaceae), popularly known as corn mint or Japanese mint, is an important industrial crop that is widely grown for its valued essential oil. Nitrogen (N) is an important macro-nutrient and an essential factor for optimizing the yield and quality of crops. Hence, rapid and accurate estimation of the N content is crucial for nutrient diagnosis in plants and to make precise N fertilizer recommendations. Generally, N concentration is estimated by destructive sampling methods; however, an indirect assessment may be possible based on spectral characteristics. This study aimed to compare the foliar N concentration based on non-destructive (reflectance) and destructive (laboratory analyses) methods in menthol mint. Foliar N concentration was measured through the Kjeldahl method and reflectance by Miniature Leaf Spectrometer C-710 (CID Bio-Science). Using reflectance data, several vegetation indices (VIs), that is, normalized difference red edge (NDRE), red edge normalized difference vegetation index (reNDVI), simple ratio (SR), green–red vegetation index (GRVI), canopy chlorophyll content index (CCCI), photochemical reflectance index (PRI), green chlorophyll index (CIGreen), red edge chlorophyll index (CIRed Edge), canopy chlorophyll index (CCI), normalized pigment chlorophyll ratio index (NPCI), and structure insensitive pigment index (SIPI), were developed to determine the foliar N concentration. The highest correlation (r) between VIs and foliar N concentrations was achieved by NDRE (0.89), followed by reNDVI (0.84), SR (0.83), GRVI (0.78), and CCCI (0.76). Among the VIs, the NDRE index has been found to be the most accurate index that can precisely predict the foliar N concentration (R2 = 0.79, RMSE = 0.18). In summary, the N deficiencies faced by the crop during its growth period can be detected effectively by calculating NDRE and reNDVI, which can be used as indicators for recommending precise management strategies for the application of nitrogenous fertilizers.

Alternative Procedure to Improve the Positioning Accuracy of Orthomosaic Images Acquired with Agisoft Metashape and DJI P4 Multispectral for Crop Growth Observation

Vegetation indices (VIs), such as the green chlorophyll index and normalized difference vegetation index, are calculated from visible and near-infrared band images for plant diagnosis in crop breeding and field management. The DJI P4 Multispectral drone combined with the Agisoft Metashape Structure from Motion/Multi View Stereo software is some of the most cost-effective equipment for creating high-resolution orthomosaic VI images. However, the manufacturer's procedure results in remarkable location estimation inaccuracy (average error: 3.27–3.45 cm) and alignment errors between spectral bands (average error: 2.80–2.84 cm). We developed alternative processing procedures to overcome these issues, and we achieved a higher positioning accuracy (average error: 1.32–1.38 cm) and better alignment accuracy between spectral bands (average error: 0.26–0.32 cm). The proposed procedure enables precise VI analysis, especially when using the green chlorophyll index for corn, and may help accelerate the application of remote sensing techniques to agriculture.