Red edge Research Papers - Academia.edu (original) (raw)

2025, Ecological Complexity

2025, International Journal of Remote Sensing

Previous studies have shown the potential of remote-sensing tools to monitor coastal wetlands at a landscape scale. Several biophysical parameters are typically used to evaluate ecosystem conditions such as the chlorophyll content, an indicator of photosynthesis activity. In natural environments characterized by a large fraction of standing litter -such as the marshes on the Atlantic coast of the Buenos Aires Province -spectral indices designed for green vegetation change their typical response and, hence, their biophysical interpretation requires more attention. In this work, a theoretical study was performed to determine if it is possible to detect and eventually quantify the abrupt reductions of leaf chlorophyll content (C aþb ) in Sporobolus densiflorusthe dominant vegetation in these marshes -using hyperspectral data. To achieve this, in situ radiometric measurements in the VIS-NIR-SWIR (Visible, Near-Infrared and Short Wave Infrared) spectral region and biological data, acquired over S. densiflorus specimens in several campaigns, were used to set up an inversion procedure based on the radiative transfer model PROSAIL. By applying this model, simulated reflectances that fit the measured reflectances were obtained and by means of this inversion a theoretical canopy reflectance data set for S. densiflorus was modelled using the PROSAIL parameters. The performance of several vegetation indices typically used to estimate chlorophyll content was studied using the simulated and modelled reflectances, among which MTCI (MERIS Terrestrial Chlorophyll Index), Macc and MCARI/OSAVI (Modified Chlorophyll Absorption in Reflectance Index/Optimized Soil-Adjusted Index) indices showed significant correlation with C aþb . By means of addressing the performance of these indices together with BLRs (Baseline Residuals), a two-step VI-LUT (Vegetation Index -Look-Up Table ) inversion model was proposed to retrieve C aþb , which first corrects the effect of variable leaf area index (LAI) using a BLR index (using bands at 800, 1100 and 1300 nm), and then retrieves C aþb by either using MCARI/OSAVI or ARTICLE HISTORY

2025, ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences

In the context of HYPERNETS project, which is developing a relatively low cost hyperspectral radiometer (and associated pointing system and embedded calibration device for automated measurement of water and land bidirectional reflectance), the tidal coastal marsh in the Mar Chiquita (Argentina) lagoon is being characterized as a test site for validation of radiometric variables. High quality in situ measurements will be available at all spectral bands at this site (and other sites over land and water around the world) for the validation of the surface reflectance data issued from all earth observation missions. This site, dominanted by Sporobolus densiflorus vegetation, is a coastal habitat that provides ecosystem services essential to people and the environment. There is evidence that growth and photosynthetic apparatus of S. densiflorus is negatively affected by the herbicide glyphosate, which is extensively used in the Argentinian agricultural production. As a way to monitor this risk, in this work a theoretical study was performed to establish if it is possible to estimate the chlorophyll content (C a+b in S. densiflorus), which concentrations are known to be affected by the herbicide, using hyperspectral reflectance. Signatures recorded in situ plus other parameters obtained from a biochemical characterization of the plant were used to obtain a simulated reflectance with the radiative transfer model PROSAIL. Then, a BaseLine Residual approach, based on close band triplets, was proposed to retrieve C a+b . As a result, we found that it is possible to distinguish between two levels of C a+b . * Corresponding author Many effects were observed but in particular it was shown that photosynthetic pigments such as chlorophyll a, chlorophyll b and carotenoids decrease with glyphosate concentration after 30 days of treatment.

2025, Canadian Journal of Remote Sensing

In this study, the capability of Landsat-8 (L8), Sentinel-2 (S2), Sentinel-1 (S1), and their combination was investigated for estimating aboveground biomass (AGB). A pure stand of Fagus Orientalis located in the Hyrcanian forest of Iran was selected as the study area. The performance of a parametric approach, i.e., Multiple Linear Regression (MLR) model, and non-parametric approaches, i.e., k-Nearest Neighbor (k-NN), Random Forest (RF), and Support Vector Regression (SVR), was also evaluated for AGB estimations. Our results indicated that among S2 metrics, the FAPAR canopy biophysical index and NDVI index based on the red-edge band (NIR-b8a) have the highest correlation coefficient (r) of 0.420 and 0.417, respectively. The results of AGB estimation showed that a combination of S2 and S1 datasets using the k-NN algorithm had the best accuracy (R2 of 0.57 and rRMSE of 14.68%). The best rRMSE using L8, S2, and S1 datasets was 18.95, 16.99, and 19.17% using k-NN, k-NN, and MLR algorithms, respectively. The combination of L8 with the S1 dataset also improved the rRMSE relative to L8 and S1 separately by 0.96 and 1.18%, respectively. We concluded that the combination of optical data (L8 or S2) with SAR data (S1) improves the broadleaved Hyrcanian AGB estimation.

2025, Agronomy

Several narrow or broadband spectral indices can be calculated at varying spatial and spectral resolution, which can then be correlated with the physiological and nutritional status of the leaves. In a three-year trial carried out on fruiting, potted cv. Barbera grapevines subjected to full (N+) or no (N0) nitrogen supply, seasonal evolution of different leaf spectral indices were correlated with non-destructive chlorophyll readings (Minolta SPAD meter), leaf gas exchange, and vine performance. Throughout the entire trial, N starvation resulted in greater-than-proportional limitation of vine yield as compared to vegetative growth (55% compared to 26% less than values measured on N+). Indices calculated within the red-edge spectral domain had highest sensitivity to relative change between N+ and N0, also indicating that the promptest response was recorded at the median shoot zone level. Twelve broadband indices were linearly correlated with leaf blade N concentration at veraison, ind...

2025, Journal of Environmental Quality

graphic interpretation (API) . While these assessments provide valuable information to for-Leaf and crown damage and discoloration characteristics are imest managers they are acknowledged to be subjective, portant variables when defining the health of eucalypt tree species and have been used as key indicators of environmental quality. These labor intensive, and often cannot reveal physiological indicators can vary significantly over a few hectares, especially in changes that characterize early stress responses (Sampmixed-species forests, making field-based environmental surveillance son et al., 2001; Zarco-Tejada et al., 2002). of crown condition an extremely expensive and logistically impractical Advances in remote sensing technology are now routask. Reflectance in narrow spectral wavelengths obtained from a tinely demonstrating the possibility of developing forest field-based spectroradiometer and a Compact Airborne Spectrographic canopy condition indicators based on detection of leaf Imager 2 (CASI-2) were collected over eucalypt vegetation of varying pigments , and condition in southeastern Australia and compared with leaf-and biochemicals , foliage biomass (Spancrown-based attributes including percent red foliage discoloration, ner et al., 1990a, 1990b; Coops et al., 1999), and structure percent leaf damage, and crown density and crown foliage condition.

2025, Revista Geografica De America Central

2025, Sensors

ESA's upcoming satellite Sentinel-2 will provide Earth images of high spatial, spectral and temporal resolution and aims to ensure continuity for Landsat and SPOT observations. In comparison to the latter sensors, Sentinel-2 incorporates three new spectral bands in the red-edge region, which are centered at 705, 740 and 783 nm. This study addresses the importance of these new bands for the retrieval and monitoring of two important biophysical parameters: green leaf area index (LAI) and chlorophyll content (Ch). With data from several ESA field campaigns over agricultural sites (SPARC, AgriSAR, CEFLES2) we have evaluated the efficacy of two empirical methods that specifically make use of the new Sentinel-2 bands. First, it was shown that LAI can be derived from a generic normalized difference index (NDI) using hyperspectral data, with 674 nm with 712 nm as best performing bands. These bands are positioned closely to the Sentinel-2 B4 (665 nm) and the new red-edge B5 (705 nm) band. The method has been applied to simulated Sentinel-2 data. The resulting green LAI map was validated against field data of various crop types, thereby spanning a LAI between 0 and 6, and yielded a RMSE of 0.6. Second, the recently developed -Normalized Area Over reflectance Curve‖ (NAOC), an index that derives Ch from hyperspectral data, was studied on its compatibility with simulated Sentinel-2 data. This index integrates the reflectance curve between 643 and 795 nm, thereby including the new Sentinel-2 bands in the red-edge region. We found that these new bands significantly improve the accuracy of Ch estimation. Both methods emphasize

2025

This manuscript delves further into the assessment of narrow-band vegetation indices derived from hyperspectral imagery acquired at 1 m spatial resolution with the Compact Airborne Spectrographic Imager (CASI). Narrow-band indices proposed in this study were assessed as indicators of biochemical and structural parameters in Vitis vinifera L., observing their relationships with foliar variables such as N, P, K, Ca, Fe, Mg and chlorophyll a+b concentration (Ca+b). Hyperspectral indices were assessed to study their capability for vegetation condition monitoring as a function of fertilization treatments applied (basically extracts of Ascophyllum nodosum seaweed and chelates), showing associations with field variables. Narrow-band vegetation indices displayed sensitivity to vineyard growth and condition as a function of seaweed fertilization and other supplementary mineral correctors, such as chelates. This work shows the interest of using new narrow-band hyperspectral remote sensing ind...

2025, Sensors

Active-optical sensor readings from an N non-limiting area standard established within a farm field are used to predict yield in the standard. Lower yield predictions from sensor readings obtained from other parts of the field outside of the N non-limiting standard area indicate a need for supplemental N. Active-optical sensor algorithms for predicting corn (Zea mays, L.) yield to direct in-season nitrogen (N) fertilization in corn utilize red NDVI (normalized differential vegetative index). Use of red edge NDVI might improve corn yield prediction at later growth stages when corn leaves cover the inter-row space resulting in "saturation" of red NDVI readings. The purpose of this study was to determine whether the use of red edge NDVI in two active-optical sensors (GreenSeeker™ and Holland Scientific Crop Circle™) improved corn yield prediction. Nitrogen rate experiments were established at 15 sites in North Dakota (ND). Sensor readings were conducted at V6 and V12 corn. Red NDVI and red edge NDVI were similar in the relationship of readings with yield at V6. At V12, the red edge NDVI was superior to the red NDVI in most comparisons, indicating that it would be most useful in developing late-season N application algorithms.

2025

While the mapping of LAI green (LAI G ) is well established, current operational products are not calibrated for LAI brown (LAI B ), i.e. LAI estimation over senescent vegetation. With Sentinel-2 (S2) new opportunities are opened to estimate LAI brown. An explicit distinction between LAI G and LAI B can be achieved thanks to the S2 bands in the red edge (B5: 705 nm and B6: 740 nm) and in the shortwave infrared (B11: 1610 nm). By using LAI ground measurements data from multiple campaigns together with available S2 data, independent LAI G and LAI B models were optimized using Gaussian processes regression (LAI G : R 2 = 0.89, NRMSE= 7.1%; LAI B : R 2 = 0.75, NRMSE= 13.7%). These models can then be combined into LAI GB composite maps. The uncertainty estimates were used to map only those LAI estimated values that fall within a 50% uncertainty threshold. As only the vegetated areas fall within that threshold there is no need to apply additional masks. For multiple European core test sites, LAI GB composite maps were generated from S2 images, enabling to quantify when crops start senescing across the European regions.

2025, Journal of Environmental Treatment Techniques

Climate change is one of the most debatable reasons for changing plant performance. Meanwhile, a higher amount of carbon dioxide (CO2) directly or indirectly affects the growth and development of corn and soybean oil plants; CO2 concentration changes also affect the dry weight characteristics of the plant. In this experiment, three effective levels of CO2 concentration have been evaluated on corn and soybean plants. Increasing the effectiveness of CO2 concentration from 400 PPM to 800 PPM depicted a significant increase in the plant's physiological traits, a 54% and 8.5% increase in Soybean biomass and Corn biomass, respectively. Furthermore, based on leaf area, the results reveal a 4% and 5% increase in Soybean and Corn, respectively. In contrast, increasing the CO2 concentration to 1000 PPM resulted in a decrease in plant performance and physiological traits, both directly and indirectly. With increasing CO2 concentration, a direct increase in physiological traits is observed.

2025, Jurnal Geografi, Edukasi dan Lingkungan (JGEL)

Kelapa sawit merupakan komoditas penting bagi ekonomi Indonesia, dengan peran besar dalam sektor ekspor dan penyerapan tenaga kerja. Pemantauan dan manajemen nutrisi pada perkebunan kelapa sawit sangat penting untuk memastikan produktivitas dan kualitas hasil yang optimal. Penelitian ini bertujuan mengembangkan sistem prediksi nutrisi untuk tanaman kelapa sawit dengan menggunakan teknologi penginderaan jauh berbasis foto drone multispektral, yang meliputi unsur nitrogen (N), fosfor (P), kalium (K), dan magnesium (Mg). Dengan memanfaatkan metode Random Forest Regression (RFR), penelitian ini dapat mengidentifikasi defisiensi nutrisi tanaman secara lebih efisien dan akurat. Hasil penelitian menunjukkan bahwa model RFR mampu memberikan prediksi nutrisi dengan tingkat akurasi yang memadai, di mana nilai R-squared (R²) untuk nutrisi nitrogen, fosfor, kalium, dan magnesium masing-masing mencapai 0,641, 0,601, 0,558, dan 0,765. Penelitian ini menyimpulkan bahwa metode penginderaan jauh dan model RFR merupakan alternatif yang efektif untuk monitoring nutrisi tanaman secara luas, sehingga dapat membantu perkebunan dalam pengambilan keputusan terkait pemupukan yang lebih efisien dan berkelanjutan.

2025, Remote Sensing

Hyperspectral remote sensing is considered to be an effective tool in crop monitoring and estimation of biomass. Many of the previous approaches are from single year or single date measurements, even though the complete crop growth with multiple years would be required for an appropriate estimation of biomass. The aim of this study was to estimate the fresh matter biomass (FMB) by terrestrial hyperspectral imaging of the three crops (lablab, maize and finger millet) under different levels of nitrogen fertiliser and water supply. Further, the importance of the different spectral regions for the estimation of FMB was assessed. The study was conducted in two experimental layouts (rainfed (R) and irrigated (I)) at the University of Agricultural Sciences, Bengaluru, India. Spectral images and the FMB were collected over three years (2016)(2017)(2018) during the growing season of the crops. Random forest regression method was applied to build FMB models. R 2 validation (R 2 val ) and relative root mean square error prediction (rRMSEP) was used to evaluate the FMB models. The Generalised model (combination of R and I data) performed better for lablab (R 2 val = 0.53, rRMSEP = 13.9%), maize (R 2 val = 0.53, rRMSEP = 18.7%) and finger millet (R 2 val = 0.46, rRMSEP = 18%) than the separate FMB models for R and I. In the best derived model, the most important variables contributing to the estimation of biomass were in the wavelength ranges of 546-910 nm (lablab), 750-794 nm (maize) and 686-814 nm (finger millet). The deviation of predicted and measured FMB did not differ much among the different levels of N and water supply. However, there was a trend of overestimation at the initial stage and underestimation at the later stages of crop growth.

2025

The goal of this work is to develop non-destructive techniques that can conveniently, rapidly and accurately assess crop physiological status and objectively evaluate plant responses to environmental factors, both natural and anthropogenic. High spectral resolution reflectance and absorption spectra of different and unrelated plant species were analyzed to determine spectral variability and information content in the visible and near-infrared spectrum at leaf and canopy levels. Techniques were developed to quantitatively retrieve chlorophyll, carotenoid and anthocyanin content from reflectance in a wide range of pigment content and composition. Techniques for vegetation fraction retrieval those based on channels in visible range of the spectrum were developed and validated. Despite the fact that the reflectance contrast among the visible channels is much smaller than between the visible and near infrared, the sensitivity to moderate to high values of vegetation fraction is much high...

2025, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Cutting-edge remote sensing technology has a significant role for managing the natural resources as well as the any other applications about the earth observation. Crop monitoring is the one of these applications since remote sensing provides us accurate, up-to-date and cost-effective information about the crop types at the different temporal and spatial resolution. In this study, the potential use of three different vegetation indices of RapidEye imagery on crop type classification as well as the effect of each indices on classification accuracy were investigated. The Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Normalized Difference Red Edge Index (NDRE) are the three vegetation indices used in this study since all of these incorporated the near-infrared (NIR) band. RapidEye imagery is highly demanded and preferred for agricultural and forestry applications since it has red-edge and NIR bands. The study area is located in Aegean region of Turkey. Radial Basis Function (RBF) kernel was used here for the Support Vector Machines (SVMs) classification. Original bands of RapidEye imagery were excluded and classification was performed with only three vegetation indices. The contribution of each indices on image classification accuracy was also tested with single band classification. Highest classification accuracy of 87, 46% was obtained using three vegetation indices. This obtained classification accuracy is higher than the classification accuracy of any dual-combination of these vegetation indices. Results demonstrate that NDRE has the highest contribution on classification accuracy compared to the other vegetation indices and the RapidEye imagery can get satisfactory results of classification accuracy without original bands. .

2025, Remote Sensing

The retrieval of nutrient concentration in sugarcane through hyperspectral remote sensing is widely known to be affected by canopy architecture. The goal of this research was to develop an estimation model that could explain the nitrogen variations in sugarcane with combined cultivars. Reflectance spectra were measured over the sugarcane canopy using a field spectroradiometer. The models were calibrated by a vegetation index and multiple linear regression. The original reflectance was transformed into a First-Derivative Spectrum (FDS) and two absorption features. The results indicated that the sensitive spectral wavelengths for quantifying nitrogen content existed mainly in the visible, red edge and far near-infrared regions of the electromagnetic spectrum. Normalized Differential Index (NDI) based on FDS (750/700) and Ratio Spectral Index (RVI) based on FDS (724/700) are best suited for characterizing the nitrogen concentration. The modified estimation model, generated by the Stepwise Multiple Linear Regression (SMLR) technique from FDS centered at 410, 426, 720, 754, and 1,216 nm, yielded the highest correlation coefficient value of 0.86 and Root Mean Square Error of the Estimate (RMSE) value of 0.033%N (n = 90) with nitrogen concentration in sugarcane. The results of this research demonstrated that the estimation model developed by SMLR yielded a higher correlation coefficient with nitrogen content OPEN ACCESS than the model computed by narrow vegetation indices. The strong correlation between measured and estimated nitrogen concentration indicated that the methods proposed in this study could be used for the reliable diagnosis of nitrogen quantity in sugarcane. Finally, the success of the field spectroscopy used for estimating the nutrient quality of sugarcane allowed an additional experiment using the polar orbiting hyperspectral data for the timely determination of crop nutrient status in rangelands without any requirement of prior cultivar information.

2024, Communications in Soil Science and Plant Analysis

Estimating the nitrogen (N) status of plants as a function of their spectral response is a promising technique to diagnose and optimize N fertilization. An experiment was conducted in Jiquilpan (Michoacán, México) in which three N levels (0.3, 3, and 20 mM of NO 3 2 in the irrigation water) were applied to strawberry (Fragaria vesca) in a randomized complete block design with three replicates. The spectral response of strawberry was measured at both the canopy and leaf level using individual wavebands as well as vegetation indices. Individual leaves were separated into three strata (young, mature, and old) and leaf dry matter, leaf area, and N content (% dry matter) were measured in each stratum. Leaf area, biomass, and N content differed significantly between strata. Leaf area, biomass, and N content in all strata were affected by N fertilization. At the canopy level, N content was highly correlated with green reflectance (R550) (r 2 ¼ 0.50) and red reflectance (R680) (r 2 ¼ 0.60) as well as the vegetation indices simple ratio (SR) (r 2 ¼ 0.56), normalized difference vegetation index (NDVI) (r 2 ¼ 0.56), and hyperspectral NDVI (HNDVI) (r 2 ¼ 0.56). For individual leaves, significant differences between strata were found with normalized total pigment to chlorophyll a ratio index (NPCI) and MERIS terrestrial chlorophyll index (MTCI) (p , 0.001) as well as R550, photochemical reflectance index (PRI), red edge position (REP), and REP calculated using the MERIS satelite wavelengths (p , 0.01). Relationships between spectral indices and N content at the leaf level

2024, ltid.inpe.br

The chlorophyll (Chl) content of a crop canopy is a biophysical variable that quantitatively expresses the photosynthetic capacity of a vegetation stand and it is related to many important plant functions and parameters. Therefore, it is not surprising that many remote sensing studies have focused on the estimation of Chl content of vegetation canopies to asses the vitality of plants and to detect vegetation stress. However, there is little information regarding how the distribution of Chl within vegetation canopies defines the reflectance signatures measured remotely and the derived spectral indexes. The goal of this study was to determine how deep into a maize canopy, a spectral vegetation index, based on the red edge and NIR spectral bands, senses the Chl content of individual leaves. Reflectance was measured using a hand-held radiometer at both the leaf and canopy level in order to retrieve foliar and total canopy Chl content, respectively. A hierarchical regression analysis was used to find (i) how many maize leaves contribute significantly to total canopy Chl content, and (ii) how many leaves, from top to bottom, are sensed by a field radiometer and by the red edge chlorophyll index, CI red edge. Results showed that CI red edge senses the chlorophyll content of the top 7 to 8 leaves in the maize canopy and, thus, is able to accurately estimate total chlorophyll content in canopy.

2024

To this day, the NIR (Near-Infrared) remains one of the most useful extra-visible bands in the EM (electromagnetic) spectrum. Aerial photogrammetry have long relied on NIR imagery to capture the landscape with the greatest possible... more

2024, International journal of applied earth observation and geoinformation

Invasive plants pose significant threats to biodiversity and ecosystem function globally, leading to costly monitoring and management effort. While remote sensing promises cost-effective, robust and repeatable monitoring tools to support intervention, it has been largely restricted to airborne platforms that have higher spatial and spectral resolutions, but which lack the coverage and versatility of satellite-based platforms. This study tests the ability of the WorldView-2 (WV2) eight-band satellite sensor for detecting the invasive shrub mesquite (Prosopis spp.) in the north-west Pilbara region of Australia. Detectability was challenged by the target taxa being largely defoliated by a leaf-tying biological control agent (Gelechiidae: Evippe sp. #1) and the presence of other shrubs and trees. Variable importance in the projection (VIP) scores identified bands offering greatest capacity for discrimination were those covering the near-infrared, red, and red-edge wavelengths. Wavelengths between 400 nm and 630 nm (coastal blue, blue, green, yellow) were not useful for species level discrimination in this case. Classification accuracy was tested on three band sets (simulated standard multispectral, all bands, and bands with VIP scores ≥1). Overall accuracies were comparable amongst all band-sets (Kappa = 0.71-0.77). However, mesquite omission rates were unacceptably high (21.3%) when using all eight bands relative to the simulated standard multispectral band-set (9.5%) and the band-set informed by VIP scores (11.9%). An incremental cover evaluation on the latter identified most omissions to be for objects <16 m 2 . Mesquite omissions reduced to 2.6% and overall accuracy significantly improved (Kappa = 0.88) when these objects were left out of the confusion matrix calculations. Very high mapping accuracy of objects >16 m 2 allows application for mapping mesquite shrubs and coalesced stands, the former not previously possible, even with 3 m resolution hyperspectral imagery. WV2 imagery offers excellent portability potential for detecting other species where spectral/spatial resolution or coverage has been an impediment. New generation satellite sensors are removing barriers previously preventing widespread adoption of remote sensing technologies in natural resource management.

2024, Remote Sensing

Kelp forests are commonly classified within remote sensing imagery by contrasting the high reflectance in the near-infrared spectral region of kelp canopy floating at the surface with the low reflectance in the same spectral region of water. However, kelp canopy is often submerged below the surface of the water, making it important to understand the effects of kelp submersion on the above-water reflectance of kelp, and the depth to which kelp can be detected, in order to reduce uncertainties around the kelp canopy area when mapping kelp. Here, we characterized changes to the above-water spectra of Nereocystis luetkeana (Bull kelp) as different canopy structures (bulb and blades) were submerged in water from the surface to 100 cm in 10 cm increments, while collecting above-water hyperspectral measurements with a spectroradiometer (325–1075 nm). The hyperspectral data were simulated into the multispectral bandwidths of the WorldView-3 satellite and the Micasense RedEdge-MX unoccupied ...

2024, Journal of agricultural science

Crambe is an oleaginous plant mainly cultivated in Brazil due to its oil characteristics that provide stability to oxidation, qualifying it for the use in a variety of products. Understanding the spectral-temporal pattern of the crambe crop is important to identify and quantify already cultivated areas via remote sensing. This study spectrally characterised the plant, seeking to relate the spectral pattern to the phenological stages of the crop throughout its development. The spectral information was obtained by passive terrestrial sensors in two harvests, thus generating a spectral-temporal pattern and the crambe temporal profile through the vegetation indices NDVI and SAVI. During the phenological stages of the seedling and the beginning of the vegetative growth, the red spectral band showed higher values of reflectance; this occurred because the crop had not yet completely covered the soil. Stages at the end of the vegetative growth and the beginning of the flowering, there was a higher reflectance in the near infrared and a lower reflectance in the mid-infrared. For the granulation and maturation stages, the reflectance in the mean and near infrared reduced due to leaf senescence and loss of cellular water content. The NDVI and SAVI temporal profiles demonstrate linear growth up to the vegetative peak, which occurs between the end of the phenological stage of the vegetative growth and the beginning of the flowering and highest amount of green biomass. At the beginning of grain formation and filling, yellowing of leaves and senescence, granulation and maturation stages, the values reduced.

2024, Crop Science

Nitrogen deficiencies can seriously reduce yield and economic returns for farmers. Remote sensing could provide inexpensive, largearea estimates of N status and be used to monitor N status since leaf chlorophyll (Chi) A content is mainly determined by N availability. The objective was to determine if remote sensing of wheat (Triticum aestivum L.) Chi A content would provide a rapid estimation of wheat N status. We measured the reflectance of a wheat crop submitted to five different fertilization treatments throughout the growth cycle. We tested several empirical reflectance indices of pigment content: reflectance at 550 nm (R550), reflectance at 680 nm (R680), three parameters of the red edge [wavelength ()."), amplitude in the first derivative of the reflectance spectra (dR re), and sum of amplitudes between 680 and 780 nm in the first derivative of the reflectance spectra (£dR 680. 78 o nm)], and pigment simple ratio (PSR) and normalized pigment chlorophyll index (NPCI) (indices of carotenoid/Chl ratio). We also measured leaf Chi A and N content, and leaf area index. There were significant correlations between canopy Chi A content and R550, R680, and all the red edge parameters. The NPCI and PSR followed phenological evolution of the carotenoids/Chl A ratio and separated the different treatments. By discriminant analysis based on the pigment indices reflectance at 430 nm (R430), R550, R680, X re , dR,e, and NPCI, each reflectance spectrum can be assigned to a different N status class. Thus, the use of these optical techniques offers a potential for assessing N status of wheat.

2024

Water deficit can cause chlorophyll degradation which decreases foliar chlorophyll concentration (Chls). Few studies investigated the effectiveness of spectral indices under water stress conditions. Chlorophyll meters have been extensively used for a wide variety of leaf chlorophyll and nitrogen estimations. Since a chlorophyll meter works BGD

2024, Agronomy Journal

Interactions between water and N may impact remote‐sensing‐based N recommendations. The objectives of this study were to determine the influence of water and N stress on reflectance from a corn (Zea mays L.) crop, and to evaluate the impacts of implementing a remote‐sensing‐based model on N recommendations. A replicated N and water treatment factorial experiment was conducted in 2002, 2003, and 2004. Yield losses due to water (YLWS) and N (YLNS) stress were determined using the 13C discrimination (Δ) approach. Reflectance data (400–1800 nm) collected at three growth stages (V8–V9, V11–VT, and R1–R2) were used to calculate six different remote sensing indices (normalized difference vegetation index [NDVI], green normalized vegetation index, normalized difference water index [NDWI], N reflectance index, and chorophyll green and red edge indices). At the V8–V9 growth stage, increasing the N rate from 0 to 112 kg N ha−1 decreased reflectance in the blue (485 nm), green (586 nm), and red...

2024, Computers and Electronics in Agriculture

Two years experiments were set up to evaluate the performance of different vegetation indices (VI) to estimate shoot N concentration (N c) and shoot dry matter (DM) for a potato crop grown under different nitrogen (N) treatments. Possibilities to improve the performance of VI using normalization by leaf area index (LAI) or camera-derived ground cover fraction (GC) were also investigated. Results indicated that N c was significantly correlated to RRE (Near-infrared divided by red edge reflectance) and RRE/GC with a coefficient of determination (R 2) of 0.62 and 0.78, respectively, indicating that inclusion of auxiliary parameter GC together with RRE substantially improved the correlation as compared to using only RRE. However, no significant correlation between N c and RVI (Ratio Vegetation Index, near-infrared divided by red reflectance) or NDVI (Normalized Difference Vegetation Index) was found. However, DM was highly correlated to RVI and NDVI. Moreover, DM showed significant relationship (R 2 = 0.86) with GC, highlighting its versatile usefulness in estimating agronomic variables DM and N c , which are the core variables to assess N status of crops for a better N application.

2024, Microorganisms

One of the main challenges facing the development of aquaponics is disease control, due on one hand to the fact that plants cannot be treated with chemicals because they can lead to mortality in cultured fish. The aim of this study was to apply the visible-near-infrared spectroscopy and vegetation index approach to test aquaponically cultivated lettuce (Lactuca sativa L.) infected with different fungal pathogens (Aspergillus niger, Fusarium oxysporum, and Alternaria alternata). The lettuces on the third leaf formation were placed in tanks (with dimensions 1 m/0.50 m/0.35 m) filled up with water from the aquaponics system every second day. In this study, we included reference fungal strains Aspergillus niger NBIMCC 3252, Fusarium oxysporum NBIMCC 125, and Alternaria alternata NBIMCC 109. Diffuse reflectance spectra of the leaves of lettuce were measured directly on the plants using a USB4000 spectrometer in the 450-1100 nm wavelength range. In near-infrared spectral range, the reflectance values of infected leaves are lower than those of the control, which indicates that some changes in cell structures occurred as a result of the fungal infection. All three investigated pathogens had a statistically significant effect on leaf water content and water band index. Vegetative indices such as Chlorophyll Absorption in Reflectance Index (CARI), Modified chlorophyll absorption in reflectance index (MCARI), Plant Senescence Reflectance Index (PSRI), Red Edge Index (REI2), Red Edge Index (REI3), and Water band index (WBI) were found to be effective in distinguishing infected plants from healthy ones, with WBI demonstrating the greatest reliability.

2024, Microorganisms

2024, Revista Brasileira de Zootecnia

Este trabalho foi realizado para determinar, entre seis índices de vegetação baseados em dados de refletância espectral, aquele que melhor discrimina doses de nitrogênio e que possui maior correlação com leituras de clorofila e massa seca do capim-tanzânia (Panicum maximum Jacq.). Os índices testados foram o NDVI (índice de vegetação por diferença normalizada, calculado utilizando a banda do vermelho e a banda do infravermelho próximo), VARI (índice resistente à atmosfera na região do visível, calculado utilizando a banda de transição do vermelho ao infravermelho e a banda do verde) e WDRVI (índice de vegetação de amplo alcance, calculado utilizando três coeficientes de ponderação, 0,05; 0,1 e 0,2). Foram avaliadas quatro doses de nitrogênio (0, 80, 160 e 320 kg/ha) em delineamento de blocos ao acaso com subamostras, com três repetições e três subamostras por bloco. Os índices VARI utilizando a banda de transição do vermelho ao infravermelho e o WDRVI utilizando os coeficientes 0,05...

2024, Proceedings of US …

2024, International Journal of Agricultural and Biological Engineering

This study was carried out to analyze the spectral reflectance response of different nitrogen levels for corn crops. Four different nitrogen treatments of 0%, 80%, 100% and 120% BMP (best management practice) were studied. Principal component analysis-loading (PCA-loading) was used to identify the effective wavelengths. Partial least squares (PLS) and multiple linear regression (MLR) models were built to predict different nitrogen values. Vegetation indices (VIs) were calculated and then used to build more prediction models. Both full and selected wavelengths-based models showed similar prediction trends. The overall PLS model obtained the coefficient of determination (R 2) of 0.6535 with a root mean square error (RMSE) of 0.2681 in the prediction set. The selected wavelengths for overall MLR model obtained the R 2 of 0.6735 and RMSE of 0.3457 in the prediction set. The results showed that the wavelengths in visible and near infrared region (350-1000 nm) performed better than the two either spectral regions (1001-1350/1425-1800 nm and 2000-2400 nm). For each data set, the wavelengths around 555 nm and 730 nm were identified to be the most important to predict nitrogen rates. The vogelmann red edge index 2 (VOG 2) performed the best among all VIs. It demonstrated that spectral reflectance has the potential to be used for analyzing nitrogen response in corn.

2024, Agronomy

This study used hyperspectral reflectance data to evaluate the crop physiological parameters of sweet maize. Principal component analysis (PCA) was applied to identify the wavelengths that primarily contributed to each selected PC. Correlation analysis and multiple linear regression, with a stepwise algorithm, were used to select the best-performing vegetation indices (VIs) for monitoring the yield and physiological response of sweet maize grown under different water and nitrogen availability. The spectral reflectance measurements of crops were taken during the mid-season stage, for two consecutive growing seasons. The multivariate regression results showed that red-edge group indices, such as CARI (Chlorophyll Absorption Reflectance Index), DD (Double Difference Index), REIP (Red-Edge Inflection Point), and Clred-edge (Chlorophyll Red-Edge) indices were good predictors of yield and physiological parameters, confirming the crucial role of the red-edge spectral region that also emerg...

2024, Geocarto International

Testing the capability of spectral resolution of the new multispectral sensors on detecting the severity of grey leaf spot disease in maize crop Abstract Development of techniques for early detection of maize grey leaf spot (GLS) infection is valuable in preventing crop damage and minimising yield loss. In this study, we tested whether GLS field symptoms on maize can be detected using hyperspectral field spectra resampled to different sensor resolutions. First, field spectra were acquired from healthy, moderate and severely infected maize leaves during the 2013 and 2014 growing seasons. The spectra were then resampled to four sensor spectral resolutions-WorldView-2, Quickbird, RapidEye, and Sentinel-2. In each case, the Random Forest algorithm was used to classify the 2013 resampled spectra to represent the three identified disease severity categories. Classification accuracy was evaluated using an independent test dataset obtained during the 2014 growing season. Results showed that Sentinel-2, with 13 spectral bands, achieved the highest overall accuracy and kappa value of 84% and 0.76, respectively while the WorldView-2, with 8 spectral bands, yielded the second highest overall accuracy and kappa value of 82% and 0.73, respectively. Results also showed that the 705 and 710nm red edge bands were the most valuable in detecting the GLS for Sentinel-2 and RapidEye, respectively. On the resampled WorldView 2 and Quickbird sensor resolutions, the respective 608 nm and 660 nm in the yellow and red bands were identified as the most valuable for discriminating all categories of infection. Overall, our results imply that opportunities exist for developing operational remote sensing systems based on multispectral sensors, especially Sentinel-2 and WorldView-2 for early detection of GLS. Adoption of such datasets is particularly valuable for minimizing crop damage and improving yield.

2024, Proceedings of SPIE

Sentinel-2 is intended to improve vegetation assessment at local to global scales. Today, estimation of leaf nitrogen (N) as an indicator of rangeland quality is possible using hyperspectral systems. However, few studies based on commercial imageries have shown a potential of the red-edge band to accurately predict leaf N at the broad landscape scale. We intend to investigate the utility of Sentinel-2 for estimating leaf N concentration in the African savanna. Grass canopy reflectance was measured using the analytical spectral device (ASD) in concert with leaf sample collections for leaf N chemical analysis. ASD reflectance data were resampled to the spectral bands of Sentinel-2 using published spectral response functions. Random forest (RF), partial least square regression (PLSR), and stepwise multiple linear regression (SMLR) were used to predict leaf N using all 13 bands. Using leave-one-out cross validation, the RF model explained 90% of leaf N variation, with a root mean square error of 0.04 (6% of the mean), which is higher than that of PLSR and SMLR. Using RF, spectral bands centered at 705 nm (red edge) and two shortwave infrared bands centered at 2190 and 1610 nm were found to be the most important bands in predicting leaf N.

2024, Journal of Applied Remote Sensing

2024, Applied Sciences

The role of biodiversity in improving the primary productivity within terrestrial ecosystems is well documented. Each species in an ecosystem has a role to play in the overall productivity of an ecosystem. Grass species nitrogen (N) estimation is essential in rangelands, especially in rugged terrain such as mountainous regions. It is an indicator of forage quality, which has nutritional implications for grazing animals. This research sought to improve and test the predictability of grass N by applying a combination of remotely sensed spectral bands and vegetation indices as input. Recursive feature selection was used to select the optimal spectral bands and vegetation indices for predicting grass N. Subsequently, the selected vegetation indices and bands were used as input into the non-parametric random forest (RF) regression to predict grass N. The prediction of grass N improved slightly in the vegetation indices model (81%) compared to the bands model (80%), and the highest predic...

2024, Remote Sensing

Leaf nitrogen concentration (leaf N, %) is an essential component for understanding biogeochemical cycling. Leaf N is a good indicator of grass or forage quality, which is important for understanding the movements and feeding patterns of herbivores. Leaf N can be used as input for rangeland carrying capacity and stocking rate models. The estimation of leaf N has been successful using hyperspectral and commercial high spatial resolution satellite data such as WorldView-2 and RapidEye. Empirical methods have been used successfully to estimate leaf N, on the basis that it correlates with leaf chlorophyll. As such, leaf N was estimated using red edge based indices. The new Sentinel-2 sensor has two red edge bands, is freely available, and could further improve the estimation of leaf N at a regional scale. The objective of this study is to develop red edge based Sentinel-2 models derived from an analytical spectral device (ASD) spectrometer to map and monitor leaf N using Sentinel-2 images. Field work for leaf N and ASD data were collected in 2014 (December) in and around Kruger National Park, South Africa. ASD data were resampled to the Sentinel-2 spectral configuration using the spectral response function. The Sentinel-2 data for various dates were acquired from the European Space Agency (ESA) portal. The Sentinel-2 atmospheric correction (Sen2Cor) process was implemented. Simple empirical regression was used to estimate leaf N. High leaf N prediction accuracy was achieved at the ASD level and the best model was inverted on Sentinel-2 images to explain leaf N distribution at a regional scale over time. The spatial distribution of leaf N is influenced by the underlying geological substrate, fire frequency and other environmental variables. This study is a demonstration of how ASD data can be used to calibrate Sentinel-2 for leaf N estimation and mapping.

2024, International Journal of Applied Earth Observation and Geoinformation

The regional mapping of grass nutrients is of interest in the sustainable planning and management of livestock and wildlife grazing. The objective of this study was to estimate and map foliar and canopy Nitrogen (N) at a regional scale using a recent high resolution spaceborne multispectral sensor (i.e. RapidEye) in the Kruger National Park (KNP) and its surrounding areas, South Africa. The RapidEye sensor contains five spectral bands in the visible-to-near infrared (VNIR), including a red-edge band centered at 710 nm. The importance of the red-edge band for estimating foliar chlorophyll and N concentrations has been demonstrated in many previous studies, mostly using field spectroscopy. The utility of the red-edge band of the RapidEye sensor for estimating grass N was investigated in this study. A two-step approach was adopted involving (i) vegetation indices and (ii) the integration of vegetation indices with environmental or ancillary variables using a stepwise multiple linear regression (SMLR) and a non-linear spatial least squares regression (PLSR). To ensure that the estimation of grass N was not compromised by biomass variability, the field work was undertaken during peak productivity. The model involving the simple ratio (SR) index (R 805 /R 710) defined as SR54, altitude and the interaction between SR54 and altitude (SR54*altitude) yielded the highest accuracy for canopy N estimation, while the non-linear PLSR yielded the highest accuracy for *Manuscript Click here to view linked References 2 foliar N estimation through the integration of remote sensing (SR54) and environmental variables. The spatial pattern of foliar N concentrations corresponded with the soil fertility gradient induced by the geological parent material. The study demonstrated the possibility to map grass nutrients at a regional scale provided there is a spaceborne sensor encompassing the red edge waveband with a high spatial resolution. Regional maps of the grass nutrients could be used for planning and management of the savanna ecosystems by farmers, resource managers and land use planners.

2024, SPIE Proceedings

2024, Indian Journal of Virology

Remote sensing technique is useful for monitoring large crop area at a single time point, which is otherwise not possible by visual observation alone. Yellow mosaic disease (YMD) is a serious constraint in soybean production in India. However, hardly any basic information is available for monitoring YMD by remote sensing. Present study examines spectral reflectance of soybean leaves due to Mungbean yellow mosaic India virus (MYMIV) infection in order to identify YMD sensitive spectral ratio or reflectance. Spectral reflectance measurement indicated significant (p \ 0.001) change in reflectance in the infected soybean canopy as compared to the healthy one. In the infected canopy, reflectance increased in visible region and decreased in near infra-red region of spectrum. Reflectance sensitivity analysis indicated wavelength *642, *686 and *750 nm were sensitive to YMD infection. Whereas, in yellow leaves induced due to nitrogen deficiency, the sensitive wavelength was *589 nm. Due to viral infection, a shift occurred in red and infra-red slope (called red edge) on the left in comparison to healthy one. Red edge shift was a good indicator to discriminate yellow mosaic as chlorophyll gets degraded due to MYMIV infection. Correlation of reflectance at 688 nm (R688) and spectral reflectance ratio at 750 and 445 nm (R750/R445) with the weighted mosaic index indicated that detection of yellow mosaic is possible based on these sensitive bands. Our study for the first time identifies the yellow mosaic sensitive band as R688 and R750/R445, which could be utilized to scan satellite data for monitoring YMD affected soybean cropping regions. Keywords Soybean yellow mosaic Á Mungbean yellow India mosaic virus Á Spectral indices Á Red edge Á Remote sensing

2024, Remote Sensing of Environment

Many algorithms have been developed for the remote estimation of biophysical characteristics of vegetation, in terms of combinations of spectral bands, derivatives of reflectance spectra, neural networks, inversion of radiative transfer models, and several multi-spectral statistical approaches. However, the most widespread type of algorithm used is the mathematical combination of visible and near-infrared reflectance bands, in the form of spectral vegetation indices. Applications of such vegetation indices have ranged from leaves to the entire globe, but in many instances, their applicability is specific to species, vegetation types or local conditions. The general objective of this study is to evaluate different vegetation indices for the remote estimation of the green leaf area index (Green LAI) of two crop types (maize and soybean) with contrasting canopy architectures and leaf structures. Among the indices tested, the chlorophyll Indices (the CI Green , the CI Red-edge and the MERIS Terrestrial Chlorophyll Index, MTCI) exhibited strong and significant linear relationships with Green LAI, and thus were sensitive across the entire range of Green LAI evaluated (i.e., 0.0 to more than 6.0 m 2 /m 2). However, the CI Red-edge was the only index insensitive to crop type and produced the most accurate estimations of Green LAI in both crops (RMSE = 0.577 m 2 /m 2). These results were obtained using data acquired with close range sensors (i.e., field spectroradiometers mounted 6 m above the canopy) and an aircraft-mounted hyperspectral imaging spectroradiometer (AISA). As the CI Red-edge also exhibited low sensitivity to soil background effects, it constitutes a simple, yet robust tool for the remote and synoptic estimation of Green LAI. Algorithms based on this index may not require re-parameterization when applied to crops with different canopy architectures and leaf structures, but further studies are required for assessing its applicability in other vegetation types (e.g., forests, grasslands).

2024, Applied Sciences

2024, Remote Sensing of Environment

Remote sensing of terrestrial vegetation uses a wide range of vegetation indices (VIs) to monitor plant characteristics, but these indices can be very sensitive to canopy background reflectance. This study investigated background influences on VIs applied to intertidal microphytobenthos, using a synthetic spectral library constituted by a spectral combination of three contrasting types of sediment (sand, fine sand, and mud) and reflectance spectra of benthic diatom monospecific cultures obtained in controlled conditions. The spectral database exhibited, for the same biomass range (3-182 mg chlorophyll a m − 2), marked differences in albedo and spectral contrast linked to sediment variability in water content, grain size, and organic matter content. Several VIs were evaluated, from ratios using visible and near infrared wavelengths, to hyperspectral indices (derivative analysis, continuum removal). Among the ratios, the Normalized Difference Vegetation Index (NDVI) appeared less sensitive to background effects than VIs with soil corrections such as the Perpendicular Vegetation Index (PVI), the Soil-Adjusted Vegetation Index (SAVI), the Modified second Soil-Adjusted Vegetation Index (MSAVI2) or the Transformed Soil-Adjusted Vegetation Index (TSAVI). The lower efficacy of soil-corrected VIs may be explained by the structural differences and optical behavior of soil vs. canopies compared to sediment vs. microphytobenthos biofilms. The background effects were minimized using Modified Gaussian Model indices at 632 nm and 675 nm, and the second derivative at 632 nm, while poor results were obtained with the red-edge inflection point (REIP) and the second derivative at 675 nm. The least sensitive index was the Phytobenthos Index which is very similar to the NDVI, but uses a red wavelength at 632 nm instead of 675 nm, to account for the absorption by chlorophyll c. The modified NDVI 705 , where the 705 nm wavelength replaces the red band, showed moderate background sensitivity. Moreover, the NDVI 705 and the Phytobenthos Index have the additional relevant property of being less sensitive to the index saturation response with increasing biomass. Unfortunately, these VIs cannot be applied to broad-band multispectral satellite images, and require sensors with a hyperspectral resolution. Nevertheless, this study showed that the background influence was not a limitation to applying the ubiquitous NDVI to map intertidal microphytobenthos using multispectral satellite images.

2024, Scientia Forestalis

Estimation of sanity of a stand of Pinus taeda L. after the attack of Sapajus nigritus Kerr (1972) using vegetation index Estimativa da sanidade de um povoamento de Pinus taeda L. após o ataque de Sapajus nigritus Kerr (1972) utilizando... more

2024, International Journal of Applied Earth Observation and Geoinformation

Indigenous forest degradation is regarded as one of the most important environmental issues facing Sub-Saharan Africa and South Africa in particular. We tested the utility of the unique band settings of the recently launched South African satellite, SumbandilaSat in characterising forest fragmentation in a fragile rural landscape in Dukuduku, northern KwaZulu-Natal. The AISA Eagle hyperspectral image was resampled to the band settings of SumbandilaSat and SPOT 5 (green, red and near infrared bands only) for comparison purposes. Variogram analysis and the red edge shift were used to quantify forest heterogeneity and stress levels, respectively. Results showed that the range values from variograms can quantify differences in spatial heterogeneity across landscapes. The study has also shown that the unique band settings of SumbandilaSat provide additional information for quantifying stress in vegetation as compared to SPOT image data. This is critical in light of the fact that stress levels in vegetation have previously been quantified using hyperspectral sensors, which are more expensive and do not cover large areas as compared to SumbandilaSat satellite. The study moves remote sensing a step closer to operational monitoring of indigenous forests.

2024, IFAC Proceedings Volumes

Many aspects of the instrumentation systems utilized in remote seruring have been discused by so many scientists or researchers, as well as theory and procedures involved in qualitatively analyzing data from such systems. Experience has shown that many earth surfuce featurell can be identified bailie on their spectml chamcterillticll, but some features of interest can not be spectrally separated and identified. A remote sensor has been designed and constructed which has the capability detecting the presence and identification kind of plants, in a tungsten-halogen light beam scanning coverage. This active remote sensor are considered, because of the need for an instrument capable to distinguishing between plants, and also measuring the leaf area index and vegetation index. The system is based on the two wavelenght ratio teclmique of near infrared to visible reflected radiation energy from the scanned object. By using an oscillating mirror as a scanner, this reflected energy is available onto the both silicon detectors used in the instwnent. The output electrical signals from these detectors are then processed by using a multipurpose amplifier in order to get the above mentioned informations.

2024

Empirical and physical approaches,to estimate leaf pigments,in Norway,spruce needles are compared. Foliar samples from 13 stands of Norway spruce, that are heterogeneous in terms of soil nutrient availability were collected (n=78). Foliage was separated by age class and subjected to routine biochemical analysis for chlorophyll a and b. Needle reflectance of stacked layers was measured,using a high spectral resolution spectroradiometer. Three sets of reflectance were used for further analysis: i) 1 nm spectral resolution, ii) degraded to HyMap spectral bands, and iii) HyMap spectral bands with a normally distributed noise component,added (σ=0.002). From reflectance first-derivative of reflectance, continuum removed reflectance, and normalized band-depths were calculated. Relations between,spectra and pigments,were,developed,using stepwise multiple linear regression (SMLR) and partial least square regression (PLSR). The conifer leaf model,LIBERTY was inverted using an artificial neura...