Green Vegetation Index Research Articles

Ongoing Fen–Bog Transition in a Boreal Aapa Mire Inferred from Repeated Field Sampling, Aerial Images, and Landsat Data

Northern aapa mire complexes are characterized by patterned fens with flarks (wet fen surfaces) and bog zone margins with Sphagnum moss cover. Evidence exists of a recent increase in Sphagnum over fens that can alter ecosystem functions. Contrast between flarks and Sphagnum moss cover may enable remote sensing of these changes with satellite proxies. We explored recent changes in hydro-morphological patterns and vegetation in a south-boreal aapa mire in Finland and tested the performance of Landsat bands and indices in detecting Sphagnum increase in aapa mires. We combined aerial image analysis and vegetation survey, repeated after 60 years, to support Landsat satellite image analysis. Aerial image analysis revealed a decrease in flark area by 46% between 1947 and 2019. Repeated survey showed increase in Sphagnum mosses (S. pulchrum, S. papillosum) and deep-rooted vascular plants (Menyanthes trifoliata, Carex rostrata). A supervised classification of high-resolution UAV image recognized the legacy of infilled flarks in the patterning of Sphagnum carpets. Among Landsat variables, all separate spectral bands, the Green Difference Vegetation Index (GDVI), and the Automated Water Extraction Index (AWEI) correlated with the flark area. Between 1985 and 2020, near-infrared (NIR) and GDVI increased in the central flark area, and AWEI decreased throughout the mire area. In aapa mire complexes, flark fen and Sphagnum bog zones have contrasting Landsat NIR reflectance, and NIR band is suggested for monitoring changes in flarks. The observed increase in Sphagnum mosses supports the interpretation of ongoing fen–bog transitions in Northern European aapa mires, indicating significant ecosystem-scale changes.

Integrating satellite-derived climatic and vegetation indices to predict smallholder maize yield using deep learning

Timely and accurately estimating smallholder crop yield is essential for optimizing agronomic management, guiding investment and policy-making to reduce poverty and improve food security. However, the productivity of most smallholder farms around the world is still poorly estimated due to absence of technologies and field-scale data, especially for the largest smallholders across China. Here, we integrated 11,857 of field-surveyed yields across maize cultivation areas in China and heterogeneous geospatial data to predict field-level maize yield using three data-driven approaches, i.e., Least Absolute Shrinkage and Selection Operator (LASSO), Light Gradient Boosting Machine (LightGBM) and Long Short-Term Memory (LSTM). We determined the most suitable vegetation index (VI), compared the performances of satellite-derived and ground-observed climate data, and identified the optimal combination of input variables and the best method for maize yield estimation. We found that the green chlorophyll vegetation index (GCVI) outperformed the traditional visible and near-infrared-based VIs. Combining Land Surface Temperature (LST), cumulative precipitation (Pgs) and standardized precipitation index (SPI) explained about 70% of the yield variation, which was comparable to ground-observed indices. Integrating GCVI improved R2 by 0.01–0.20 depending on the methods, suggesting valuable information on biotic or abiotic stress contained by GCVI. Maize yield could be predicted 1–2-month ahead before harvesting in all agro-ecological zones. The data collected during silking period contributed more information. The LSTM model did better than LightGBM and LASSO, because of its neural network characterizing the cumulative effects of environmental factors on yield. Our study demonstrates an effective mean for large-scale crop yield estimation using publicly available data, particularly for smallholder systems where ground observations are currently limited and sparse.

Thermo-gas dynamics affect the leaf canopy shape and moisture content of aquaponic lettuce in a modified partially diffused microclimatic chamber

Precise and timely monitoring of leaf water content and growth is necessary for precision agriculture. In this study, the impact of temperature and gas concentration variations was synthesized with leaf canopy shape and moisture content of aquaponic lettuce in a customized partially diffused microclimatic chamber equipped with an RGB camera. Using the acquired thermo-gas sensor data during the light-dependent photosynthetic reaction and dark-period aerobic respiration, it was analyzed with the extracted spectro-morphological leaf canopy signatures. Applying spectro-morphological leaf canopy data as input to the recurrent neural network it was possible to sensitively predict full moisture content (FMT) and equivalent water thickness (EWT) with 92.34% and 85.96% accuracy, respectively. A decrease in red green blue vegetation index (RGBVI) and an increase in excess of green (ExG) optical vegetation stress index corresponds to an increase in FMT. According to our data, it was possible to observe that the water content balance during the third week after sowing, follows the same trend observed for the oxygen production increase during the light period. Additionally, it was also possible to observe that uneven leaf broadening (canopy shape index > 1), low EWT, high photosynthetic quotient (PQ), and photothermal unit (PTU), and minimal temperature and humidity stretch resulted in higher oxygen production rates. A growing degree day of 23.46 ± 0.30 °C day is ideal for aquaponic lettuce. Plants with six (6) weeks exhibited the highest thermal sensitivity (Q1) of 0.205. Overall, the developed model and observations allow concluding that the combined temperature-O2CO2 dynamics monitoring must be considered to achieve maximum production rates. Moreover, in the framework of the new approaches developed within precision agriculture schemes, the collected optical data allied to the recurrent neural network are suitable tools for this continuous monitoring in an automated way.

Heritable variation in tree growth and needle vegetation indices of slash pine (Pinus elliottii) using unmanned aerial vehicles (UAVs)

Vegetation indices (VIs) are mainly associated with plant yield, and yield-related physiological traits can assist in breeding selection. Tree breeding programs require a rapid assessment of a large number of individual trees across multiple regions. Unmanned aerial vehicle (UAV) multispectral platforms combined with high spatial, temporal and spectral resolutions can readily assess the VIs at various plant growth phases, which has been deeply detailed for evaluating the physiological status of agricultural crops and forest stands. However, the underlying genetics of foliar spectral bands, VIs and growth traits from a large scale of samples, especially in the case of forest trees, have been less investigated. In this paper, two sites of slash pine breeding populations were completely investigated to monitor the spectral bands, VIs, and tree growth traits. This allows us to determine the genotypic variation of these traits and to estimate the heritability (h2). The results show that the estimated h2 ranges from 0.04 to 0.36, and the site may affect tree growth because the traits between two sites come to different h2 values. At the two sites, the h2 values of the green red difference vegetation index (GNDVI) and anthocyanin reflectance index (ARI) at Site 1 and the red edge chlorophyll index (RECI), leaf chlorophyll index (LCI), red green blue vegetation index (RGBVI), normalized difference red edge index (NDRE), NIR and red edge at Site 2 were less than 0.1. However, breeding selection is still possible for most VIs and growth traits at both sites based on different breeding targets and families (families 3, 5, 10, 11, 14 and 17 at Site 1 and 4, 6, 8, 19 and 13 at Site 2 for optimal spectral bands; families 2, 8, 7, 13, 16, and 18 at Site 1 and 6, 7, 11, 14, 15, 18, and 20 at Site 2 for optimal VIs; families 8 and 16 at Site 1 and families 7 and 18 at Site 2 for optimal growth traits) with a variety of optimal traits are selected. This paper demonstrates the potential use of UAV-based imagery technologies in the breeding selection of slash pine trees.

Evaluating the Spectral and Physiological Responses of Grapevines (Vitis vinifera L.) to Heat and Water Stresses under Different Vineyard Cooling and Irrigation Strategies

Heat stress (HS) and water stress (WS) pose severe threats to viticulture, and effective management solutions to counter their effects on grapevine performance must be examined. In this study, we evaluated the physiological and spectral responses of Vitis vinifera L. cv. Sauvignon blanc to individual (HS) and combined (HS + WS) stress under four different cooling and irrigation strategies. The treatments were: standard drip irrigation (SI), extra drip irrigation (SI+), extra sprinklers irrigation (SPRI), and sustained deficit irrigation (SDI; 50% of SI). Compared to the other treatments, in the early stages after the occurrence of HS, the vine water status of SPRI and SI+ improved, with high stomatal conductance (gs) (SPRI) and stem water potential (Ψstem; SPRI and SI+). All the physiological indicators measured were significantly lower after the end of HS in the SDI treatment. We also identified the spectral response of grapevine to HS and combined HS and WS (resulting from SDI). Consistent with the physiological analysis, the proximal spectral responses of leaves identified SPRI and SI+ as putative cooling strategies to minimize vine HS. The vines undergoing combined stress (SDI) showed greenness amelioration 10 days after stress, as revealed by the greenness vegetation indices (VIs), i.e., Green Index (GI), Normalized Difference Greenness Vegetation Index (NDGI), and Visible Atmospherically Resistant Index (VARI). However, their physiological recovery was not achieved within this time, as shown by the Simple Ratio Index (SRI), Transformed Chlorophyll Absorption Ratio Index (TCARI), and TCARI/Optimized Soil-Adjusted Vegetation Index (TCARI/OSAVI). A three-step band selection process allowed the identification of the spectral traits’ responsive to HS and combined stress, i.e., 1336–1340 nm, 1967–1971 nm, and 600–604 nm.

Rolling pits of Hartmann's mountain zebra (Zebra equus hartmannae) increase vegetation diversity and landscape heterogeneity in the Pre-Namib.

Microsites created by soil‐disturbing animals are important landscape elements in arid environments. In the Pre‐Namib, dust‐bathing behavior of the near‐endemic Hartmann's mountain zebra creates unique rolling pits that persist in the landscape. However, the ecohydrological characteristics and the effects of those microsites on the vegetation and on organisms of higher trophic levels are still unknown. In our study, we characterized the soil grain size composition and infiltration properties of rolling pits and reference sites and recorded vegetation and arthropod assemblages during the rainy season of five consecutive years with different amounts of seasonal rainfall. We further used the excess green vegetation index derived from drone imagery to demonstrate the different green up and wilting of pits and references after a rainfall event. In contrast to the surrounding grassland, rolling pits had finer soil with higher nutrient content, collected runoff, showed a higher infiltration, and kept soil moisture longer. Vegetation in the rolling pits was denser, dominated by annual forbs and remained green for longer periods. The denser vegetation resulted in a slightly higher activity density of herbivorous arthropods, which in turn increased the activity density of omnivorous and predatory arthropods. In times of drought, the rolling pits could act as safe sites and refuges for forbs and arthropods. With their rolling pits, Hartmann's mountain zebras act as ecosystem engineers, contributing to the diversity of forb communities and heterogeneity of the landscape in the Pre‐Namib.

Measuring vegetation greenery in park using iPhone panoramic image and a new green vegetation extraction index

Park greenery contributes substantial health benefits for local residents. Few data source however is available to quantitatively measure the vegetation greenery along park trails. In this study, a typical park region in Homestead, Florida was selected as the study area. The iPhone panoramic images in 400 randomly generated sampling sites were examined to depict the vegetation greenery along park trails. A green vegetation extraction index (GVEI) was proposed to distinguish the green vegetation in the images, and the green vegetation index was computed to grade the sampling sites based on the GVEI results. The results demonstrated that the overall (kappa coefficient) accuracies of green vegetation classification in the sampling sites using the GVEI all exceeded 0.90 (0.87). The GVEI values obtained from iPhone and commonly used Google Street View (GSV) images showed a high correlation coefficient of 0.94, which indicated that the iPhone panoramic images were compatible with the GSV images, and suited for green vegetation identification and monitoring. The green view index of sampling sites showed discrepant patterns, 50 sites possessed the green view index of more than 30 %, indicating that people in these areas can enjoy visual and spiritual pleasure. Park managers and planners may strive to improve the vegetation greenery in the other sites to offer better health benefits for people. The method established in this study can be used as a reference for vegetation greenery monitoring in other parks.

RSPD: A Novel Remote Sensing Index of Plant Biodiversity Combining Spectral Variation Hypothesis and Productivity Hypothesis

Plant diversity (PD) plays an important role in maintaining the healthy function of an ecosystem through affecting the productivity, stability, and nutrient utilization of a terrestrial ecosystem. Remote sensing is a vital way to monitor the status and changes of PD. Most of the existing methods rely on a field botany survey to construct a statistical relationship between PD and remote sensing observations. However, a field botany survey is too costly to be applied widely. In this study, we constructed a new remote sensing index of PD (RSPD), combining the spectral variation hypothesis and productivity hypothesis. Concretely, the RSPD integrated the multi-band spectral reflectance and several spectral greenness, moisture, and red-edge vegetation indices with the principles of Shannon information entropy and Euclidean distance. The RSPD was evaluated by comparing the classical coefficient of variation (CV) method and the Shannon and Simpson diversity indices based on vegetation classification results. Two cases were selected, where Case I was in Beijing and Case II was located in part of Huai’an, China. Sentinel-2 data in three years of 2016, 2018, and 2020 and higher-resolution Pléiades-1 data in 2018 were also utilized. The results demonstrate that: (1) the RSPD is basically consistent with the CV in spatiotemporal variation; (2) the RSPD outperforms the CV as compared with Shannon and Simpson diversity indices that are based on vegetation classification results with Sentinel-2 and Pléiades-1 data; (3) the RSPD outperforms the CV as compared with visual interpretations with Google Earth image. The suggested index can reflect the richness and evenness of plant species, which is inherent in its calculation formula. Moreover, it has a great potential for large-scale regional and long-term series monitoring.

Estimation of nitrogen status and yield of rice crop using unmanned aerial vehicle equipped with multispectral camera

Nitrogen is one of the essential nutrients required for crop growth, and hence should be applied efficiently for attaining optimum yield. To fulfil nitrogen need, absorbed nitrogen in the plant is required to be estimated. Various methods are available to estimate crop nitrogen such as tissue analysis using the methods of Kjeldahl and Dumas, which are accurate, but time-consuming and destructive. Satellite imagery provides a more extensive field view. However, they are limited to their spatial and temporal resolution. Unmanned aerial vehicle (UAV) is emerging as a promising tool that can provide the status of crop nitrogen rapidly with high spatial and temporal resolution. The objective of the study was to evaluate UAV-based imageries to show nitrogen status and predict rice yield at different growth stages. The experiments were conducted using two rice cultivars, six nitrogen applications, two water management practices, and with three replications. Soil plant analysis development (SPAD) meter readings were collected at various growth stages. First, aerial imageries of experimental site were collected using an octocopter UAV equipped with a multispectral sensor that provides reflectance values in four different bands (red, green, red edge, and near-infrared) along with SPAD values for respective flight. Second, aerial images were processed in pix4D software, to identify the most appropriate vegetation index that shows nitrogen status variation in the field and to predict yield using different vegetation indices. Nine vegetation indices were considered: ratio vegetation index, normalized difference vegetation index, normalized green red difference index, red edge difference vegetation index, green ratio vegetation index, green normalized difference vegetation index (GNDVI), wide dynamic range vegetation index, transformed normalized difference vegetation index (TNDVI), and normalized difference red edge. After that, a linear regression model was developed between the representative index and SPAD values. Finally, linear regression models developed by using VI and SPAD values were evaluated and results revealed that GNDVI-based model simulates SPAD values with R2 of 0.49, 0.49, and 0.74 at panicle, milky, and booting stages, respectively. It is also found that TNDVI-based linear regression model predicts yield with R2 of 0.71 at milky stage.

Corn Biomass Estimation by Integrating Remote Sensing and Long-Term Observation Data Based on Machine Learning Techniques

The accurate and timely estimation of regional crop biomass at different growth stages is of great importance in guiding crop management decision making. The recent availability of long time series of remote sensing data offers opportunities for crop monitoring. In this paper, four machine learning models, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), and extreme gradient boosting (XGBoost) were adopted to estimate the seasonal corn biomass based on field observation data and moderate resolution imaging spectroradiometer (MODIS) reflectance data from 2012 to 2019 in the middle reaches of the Heihe River basin, China. Nine variables were selected with the forward feature selection approach from among twenty-seven variables potentially influencing corn biomass: soil-adjusted total vegetation index (SATVI), green ratio vegetation index (GRVI), Nadir_B7 (2105–2155 nm), Nadir_B6 (1628–1652 nm), land surface water index (LSWI), normalized difference vegetation index (NDVI), Nadir_B4 (545–565 nm), and Nadir_B3 (459–479 nm). The results indicated that the corn biomass was suitably estimated (the coefficient of determination (R2) was between 0.72 and 0.78) with the four machine learning models. The XGBoost model performed better than the other three models (R2 = 0.78, root mean squared error (RMSE) = 2.86 t/ha and mean absolute error (MAE) = 1.86 t/ha). Moreover, the RF model was an effective method (R2 = 0.77, RMSE = 2.91 t/ha and MAE = 1.91 t/ha), with a performance comparable to that of the XGBoost model. This study provides a reference for estimating crop biomass from MOD43A4 datasets. In addition, the research demonstrates the potential of machine learning techniques to achieve a relatively accurate estimation of daily corn biomass at a large scale.

Multisensor Fusion of Remotely Sensed Vegetation Indices Using Space-Time Dynamic Linear Models

Abstract High spatiotemporal resolution maps of surface vegetation from remote sensing data are desirable for vegetation and disturbance monitoring. However, due to the current limitations of imaging spectrometers, remote sensing datasets of vegetation with high temporal frequency of measurements have lower spatial resolution, and vice versa. In this research, we propose a space-time dynamic linear model to fuse high temporal frequency data (MODIS) with high spatial resolution data (Landsat) to create high spatiotemporal resolution data products of a vegetation greenness index. The model incorporates the spatial misalignment of the data and models dependence within and across land cover types with a latent multivariate Matérn process. To handle the large size of the data, we introduce a fast estimation procedure and a moving window Kalman smoother to produce a daily, 30-m resolution data product with associated uncertainty.

Improved Estimation of Aboveground Biomass of Disturbed Grassland through Including Bare Ground and Grazing Intensity

Accurate approaches to aboveground biomass (AGB) estimation are required to support appraisal of the effectiveness of land use measures, which seek to protect grazing-adapted grasslands atop the Qinghai-Tibet Plateau (QTP). This methodological study assesses the effectiveness of one commonly used visible band vegetation index, Red Green Blue Vegetation Index (RGBVI), obtained from unmanned aerial vehicle (UAV), in estimating AGB timely and accurately at the local scale, seeking to improve the estimation accuracy by taking into account in situ collected information on disturbed grassland. Particular emphasis is placed upon the mapping and quantification of areas disturbed by grazing (simulated via mowing) and plateau pika (Ochotona curzoniae) that have led to the emergence of bare ground. The initial model involving only RGBVI performed poorly in AGB estimation by underestimating high AGB by around 10% and overestimating low AGB by about 10%. The estimation model was modified by the mowing intensity ratio and bare ground metrics. The former almost doubled the estimation accuracy from R2 = 0.44 to 0.81. However, this modification caused the bare ground AGB to be overestimated by about 38 and 19 g m−2 for 2018 and 2019, respectively. Although further modification of the model by bare ground metrics improved the accuracy slightly to 0.88, it markedly reduced the overestimation of low AGB values. It is recommended that grazing intensity be incorporated into the micro-scale estimation of AGB, together with the bare ground modification metrics, especially for severely disturbed meadows with a sizable portion of bare ground.

The importance of spectral correction of UAV-based phenotyping with RGB cameras

Spectral correction of colour (RGB) cameras mounted on unmanned aerial vehicles (UAV) is considered important to produce accurate data for quantitative studies in agricultural field research and breeding nurseries. This study investigated the extent to which a simplified spectral correction improves the precision and accuracy of early vigour assessments in winter wheat and winter barley genotypes based on the excess green vegetation Index (nExG) from a pairwise comparison of colour-corrected orthomosaics produced with two different RGB cameras. Two methods of spectral correction were compared, the empirical line method (ELM) and the spectral correction performed in a commercial photogrammetry software package (Pix4D Mapper). Both methods improved the accuracy, with the ELM spectral correction performing better than the Pix4D Mapper spectral correction. Despite high precision and improved accuracy after spectral correction, there were still statistically significant camera effects on vigour assessments because of the minute differences in nExG between genotypes. However, camera effects were evaluated as marginal and with little practical and agronomical relevance under field conditions and in breeding nurseries, as the minute differences between genotypes is overall difficult to reproduce in outdoor field experiments.

The Application of an Unmanned Aerial System and Machine Learning Techniques for Red Clover-Grass Mixture Yield Estimation under Variety Performance Trials

A significant trend has developed with the recent growing interest in the estimation of aboveground biomass of vegetation in legume-supported systems in perennial or semi-natural grasslands to meet the demands of sustainable and precise agriculture. Unmanned aerial systems (UAS) are a powerful tool when it comes to supporting farm-scale phenotyping trials. In this study, we explored the variation of the red clover-grass mixture dry matter (DM) yields between temporal periods (one- and two-year cultivated), farming operations [soil tillage methods (STM), cultivation methods (CM), manure application (MA)] using three machine learning (ML) techniques [random forest regression (RFR), support vector regression (SVR), and artificial neural network (ANN)] and six multispectral vegetation indices (VIs) to predict DM yields. The ML evaluation results showed the best performance for ANN in the 11-day before harvest category (R2 = 0.90, NRMSE = 0.12), followed by RFR (R2 = 0.90 NRMSE = 0.15), and SVR (R2 = 0.86, NRMSE = 0.16), which was furthermore supported by the leave-one-out cross-validation pre-analysis. In terms of VI performance, green normalized difference vegetation index (GNDVI), green difference vegetation index (GDVI), as well as modified simple ratio (MSR) performed better as predictors in ANN and RFR. However, the prediction ability of models was being influenced by farming operations. The stratified sampling, based on STM, had a better model performance than CM and MA. It is proposed that drone data collection was suggested to be optimum in this study, closer to the harvest date, but not later than the ageing stage.

Assessment of Regression Models for Predicting Rice Yield and Protein Content Using Unmanned Aerial Vehicle-Based Multispectral Imagery

Unmanned aerial vehicle-based multispectral imagery including five spectral bands (blue, green, red, red-edge, and near-infrared) for a rice field in the ripening stage was used to develop regression models for predicting the rice yield and protein content and to select the most suitable regression analysis method for the year-invariant model: partial least squares regression, ridge regression, and artificial neural network (ANN). The regression models developed with six vegetation indices (green normalization difference vegetation index (GNDVI), normalization difference red-edge index (NDRE), chlorophyll index red edge (CIrededge), difference NIR/Green green difference vegetation index (GDVI), green-red NDVI (GRNDVI), and medium resolution imaging spectrometer terrestrial chlorophyll index (MTCI)), calculated from the spectral bands, were applied to single years (2018, 2019, and 2020) and multiple years (2018 + 2019, 2018 + 2020, 2019 + 2020, and all years). The regression models were cross-validated through mutual prediction against the vegetation indices in nonoverlapping years, and the prediction errors were evaluated via root mean squared error of prediction (RMSEP). The ANN model was reproducible, with low and sustained prediction errors of 24.2 kg/1000 m2 ≤ RMSEP ≤ 59.1 kg/1000 m2 in rice yield and 0.14% ≤ RMSEP ≤ 0.28% in rice-protein content in all single-year and multiple-year analyses. When the importance of each vegetation index of the regression models was evaluated, only the ANN model showed the same ranking in the vegetation index of the first (MTCI in both rice yield and protein content) and second importance (CIrededge in rice yield and GRNDVI in rice-protein content). Overall, this means that the ANN model has the highest potential for developing a year-invariant model with stable RMSEP and consistent variable ranking.

A Novel Index to Detect Vegetation in Urban Areas Using UAV-Based Multispectral Images

Unmanned aerial vehicles (UAVs) equipped with high-resolution multispectral cameras have increasingly been used in urban planning, landscape management, and environmental monitoring as an important complement to traditional satellite remote sensing systems. Interest in urban regeneration projects is on the rise in Korea, and the results of UAV-based urban vegetation analysis are in the spotlight as important data to effectively promote urban regeneration projects. Vegetation indices have been used to obtain vegetation information in a wide area using the multispectral bands of satellites. UAV images have recently been used to obtain vegetation information in a more rapid and precise manner. In this study, multispectral images were acquired using a UAV equipped with a Micasense RedEde MX camera to analyze vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Blue Normalized Difference Vegetation Index (BNDVI), Red Green Blue Vegetation Index (RGBVI), Green Red Vegetation Index (GRVI), and Soil Adjusted Vegetation Index (SAVI). However, in the process of analyzing urban vegetation using the existing vegetation indices, it became clear that the vegetation index values of long-run steel roofing, waterproof coated roofs, and urethane-coated areas are often similar to, or slightly higher than, those of grass. In order to improve the problem of misclassification of vegetation, various equations were tested by combining multispectral bands. Kappa coefficient analysis showed that the squared Red-Blue NDVI index produced the best results when analyzing vegetation reflecting urban land cover. The novel vegetation index developed in this study will be very useful for effective analysis of vegetation in urban areas with various types of land cover, such as long-run steel roofing, waterproof coated roofs, and urethane-coated areas.

Integration of Sentinel-3 OLCI Land Products and MERRA2 Meteorology Data into Light Use Efficiency and Vegetation Index-Driven Models for Modeling Gross Primary Production

Accurately and reliably estimating total terrestrial gross primary production (GPP) on a large scale is of great significance for monitoring the carbon cycle process. The Sentinel-3 satellite provides the OLCI FAPAR and OTCI products, which possess a higher spatial and temporal resolution than MODIS products. However, few studies have focused on using LUE models and VI-driven models based on the Sentinel-3 satellites to estimate GPP on a large scale. The purpose of this study is to evaluate the performance of Sentinel-3 OLCI FAPAR and OTCI products combined with meteorology reanalysis data in estimating GPP at site and regional scale. Firstly, we integrated OLCI FAPAR and meteorology reanalysis data into the MODIS GPP algorithm and eddy covariance light use efficiency (EC-LUE) model (GPPMODIS-GPP and GPPEC-LUE, respectively). Then, we combined OTCI and meteorology reanalysis data with the greenness and radiation (GR) model and vegetation index (VI) model (GPPGR and GPPVI, respectively). Lastly, GPPMODIS-GPP, GPPEC-LUE, GPPGR, and GPPVI were evaluated against the eddy covariance flux data (GPPEC) at the site scale and MODIS GPP products (GPPMOD17) at the regional scale. The results showed that, at the site scale, GPPMODIS-GPP and GPPEC-LUE agreed well with GPPEC for the US-Ton site, with R2 = 0.73 and 0.74, respectively. The performance of GPPGR and GPPVI varied across different biome types. Strong correlations were obtained across deciduous broadleaf forests, mixed forests, grasslands, and croplands. At the same time, there are overestimations and underestimations in croplands, evergreen needleleaf forests and deciduous broadleaf forests. At the regional scale, the annual mean and maximum daily GPPMODIS-GPP and GPPEC-LUE agreed well with GPPMOD17 in 2017 and 2018, with R2 > 0.75. Overall, the above findings demonstrate the feasibility of using Sentinel-3 OLCI FAPAR and OTCI products combined with meteorology reanalysis data through LUE and VI-driven models to estimate GPP, and fill in the gaps for the large-scale evaluation of GPP via Sentinel-3 satellites.

A new method for dating the surface exposure age of granite rock walls in the Mont Blanc massif by reflectance spectroscopy

In the high mountain rock walls of the Mont Blanc massif, changes in the granite surface colour are related to its exposure age. The light grey colour of fresh rock surfaces turns orange when is long exposed to weathering. In order to study this colour/age relationship, reflectance spectroscopy was performed on 73 samples, and Terrestrial Cosmogenic Nuclide (TCN) dating was used to obtain their surface exposure age. The standard deviation of the reflectance values was calculated for each wavelength of the visible spectrum to study the behaviour of each spectral region. The aim was to find two colour regions that showed opposite behaviour, and once they are combined, they could provide a representative index of the rock colour changes that are linked to the degree of weathering. As an adaptation of the Green Red Vegetation Index used to measure colour changes in vegetation, the GReen-Infrared GRanite Index (GRIGRI), a normalized difference between the granite 770 nm and 530 nm reflectance values, was developed. The GRIGRI value of a weathered granite surface has a close relationship with its exposure age (R 2 = 0.85). The reflectance spectra of seven samples for which TCN dating failed and two samples for which the TCN age was considered to be an outlier were used to calculate their GRIGRI value to assess the colour-based ages, that were plausible according to rock wall morphology and the TCN exposure ages of the surrounding surfaces. We propose a new method of surface dating for the rock walls of the Mont Blanc massif using reflectance spectroscopy. • Mont Blanc granite surface colour is related to the time that it has been exposed to weathering. • Reflectance spectroscopy was used to quantify granite surface colour. • TCN dating was used to estimate granite surface exposure age. • We use different approaches to explore the relationship between the granite colour and its exposure age. • The ratio between the Green and Infrared reflectance values can be used to estimate granite exposure age.

Fijiを利用したイネ科牧草条播圃場空撮画像の植生指標・Green Red Vegetation Index(GRVI)解析マニュアル

Fijiを利用したイネ科牧草条播圃場空撮画像の植生指標・Green Red Vegetation Index(GRVI)解析マニュアル

Remote Sensing Analysis of Agricultural Drone

Farmers have more requirements for the completion of cultivations. Remote sensing is a big technology for reducing this requirement. Now, we need an organic spraying system at a low cost. We have two methods, first one neural network algorithm of quantum geographic information system (QGIS) and another one global positioning system (GPS) with drone. This paper describes the analysis of drone remote sensing using the normalized difference vegetation index (NDVI)/Near-infrared band (NIR) sensor in a multispectral view of agricultural land. NIR and NDVI images had water content values and precision values which is mixed in managing water resources. NDVI sensors are loaded to produce high-density images. Real-time monitoring coupled in NIR imaging geometrically and radiometrically adjusted to measure temperature. Multispectral and hyperspectral views had used for analyzing the tested data. Standard irrigation level is 60% to produce the plant growing. Irrigation techniques followed the treatment of the plant within continuous data per second. The implemented view focused only on growth controlling of plant in-depth irrigation between 30 and 90 cm in 60% deviation. NDVI, green normalized difference vegetation index (GNDVI), soil brightness index (SBI), green vegetation index (GVI), degree of yellow vegetation index (YVI), nitrogen sufficiency index (NSI), perpendicular vegetation index (PVI), transformed vegetation index (TVI), soil adjusted vegetation index (SAVI) and vegetation condition index (VCI) vegetation indices are used to the correlation of plant growth control with managing leaf strength and import python packages display the Vegetation various Real-time value in QGIS. Correlation of plant growth p ≤0.01, r = 0.77 and − 0.77 with conductance. It measured degree and demonstrated GPS view using irrigation techniques to control water stress. It had used to estimate the leaf conductance rate with the variation of atmospherically changing. It can calculate real-time leaf stress analysis. This report provided a drone survey analysis of compost percentage and vegetation indices of agricultural land.