Red-edge Spectral Bands Research Articles

750 nm laser based on an BaGa4Se7 optical parametric oscillator

The red edge effect of plants is extensively utilized in vegetation remote sensing, particularly by applying hyperspectral LiDAR (HSL) technology. This technology effectively captures spectral information from targets together with range measurements by processing recorded waveforms in the red-edge spectral bands. Despite its widespread use, there is still potential for enhancing the tuning accuracy and the energy output of each channel. What we believe to be a novel nonlinear crystal, BaGa4Se7 (BGSe), has been employed to achieve laser output in the red edge spectral band with a wide tuning range and high tuning precision for the first time. Successful generation of laser radiation at 1512 nm was achieved, with an angular tuning resolution of 35.9 nm/°. When the pump light energy was 17.81 mJ, the energy of the 1512 nm near-infrared laser was 3.210 mJ, with a slope efficiency of 31.2% and an optical-to-optical conversion efficiency (pump to signal) of 18.0%. Subsequent pumping of the second harmonic generation crystal KTiOPO4 (KTP) with the 1512 nm laser output from the BGSe optical parametric oscillator (OPO) facilitated the generation of 756 nm red light laser output. Angle tuning of the BGSe OPO eventually enabled the tunable output of the red edge spectral laser ranging from 701 nm to 780 nm with output energy of approximately 2 mJ, which is several orders of magnitude higher than traditional supercontinuum laser source solution. Such improvement becomes a solid cornerstone for long-range HSL applications.

Assessment of Atmospheric Correction Algorithms for Correcting Sunglint Effects in Sentinel-2 MSI Imagery: A Case Study in Clean Lakes

The Sentinel-2 Multi-Spectral Instrument (MSI) is characterized by short revisit times (5 days), red-edge spectral bands (665 nm and 705 nm), and a high spatial resolution (10 m), making it highly suitable for monitoring water quality in both inland and coastal waters. Unlike SeaWiFS, which can adjust its viewing angles to minimize sunglint, the Sentinel-2 MSI operates with fixed near-nadir angles, which makes it more susceptible to sunglint. Additionally, the complex optical properties of water pose challenges in accurately determining its water-leaving reflectance. Therefore, we compared the effectiveness of six atmospheric correction (AC) algorithms (POLYMER, MUMM, DSF, C2RCC, BP, and GRS) in correcting sunglint using two typical lakes in Xinjiang, China, as examples. The results indicated that POLYMER achieved the highest overall evaluation score (1.61), followed by MUMM (1.21), while BP exhibited the lowest performance (0.62). Specifically, POLYMER showed robust performance at the 665 nm band with RMSE = 0.0012 sr−1, R2 = 0.74, and MAPE = 30.68%, as well as at the 705 nm band with RMSE = 0.0014 sr−1, R2 = 0.42, and MAPE = 38.44%. At the 443, 490, and 560 nm bands, MUMM showed better performance (RMSE ≤ 0.0026 sr−1, R2 ≥ 0.86, MAPE ≤ 28.20%). In terms of band ratios, POLYMER exhibited the highest accuracy (RMSE ≤ 0.093 and MAPE ≤ 22.2%), particularly for the ratio Rrs(490)/Rrs(560) (R2 = 0.71). In general, POLYMER is the best choice for the sunglint correction of Xinjiang’s clean lakes. This study assessed the capability of different AC algorithms for sunglint correction and enhanced the monitoring capability of MSI data in clean waters.

Chlorophyll-a unveiled: unlocking reservoir insights through remote sensing in a subtropical reservoir

Effective water resources management and monitoring are essential amid increasing challenges posed by population growth, industrialization, urbanization, and climate change. Earth observation techniques offer promising opportunities to enhance water resources management and support informed decision-making. This study utilizes Landsat-8 OLI and Sentinel-2 MSI satellite data to estimate chlorophyl-a (chl-a) concentrations in the Nandoni reservoir, Thohoyandou, South Africa. The study estimated chl-a concentrations using random forest models with spectral bands only, spectral indices only (blue difference absorption (BDA), fluorescence line height in the violet region (FLH_violet), and normalized difference chlorophyll index (NDCI)), and combined spectral bands and spectral indices. The results showed that the models using spectral bands from both Landsat-8 OLI and Sentinel-2 MSI performed comparably. The model using Sentinel-2 MSI had a higher accuracy of estimating chl-a when spectral bands alone were used. Sentinel-2 MSI’s additional red-edge spectral bands provided a notable advantage in capturing subtle variations in chl-a concentrations. Lastly, the –chl-a concentration was higher at the edges of the Nandoni reservoir and closer to the reservoir wall. The findings of this study are crucial for improving the management of water reservoirs, enabling proactive decision-making, and supporting sustainable water resource management practices. Ultimately, this research contributes to the broader understanding of the application of earth observation techniques for water resources management, providing valuable information for policymakers and water authorities.

ESTIMATION OF SUGARCANE YIELD USING MULTI-TEMPORAL SENTINEL 2 SATELLITE IMAGERY AND RANDOM FOREST REGRESSION

Abstract. Advancements in remote sensing techniques have greatly enhanced crop monitoring and yield estimation, with spectral vegetation indices (VIs) serving as a key component. Our study investigates the use of Sentinel-2 data, notable for its red-edge bands, in estimating sugarcane yield in Ethiopia's Awash Basin. Utilizing 22 VIs from S2 imagery, our approach combines Random Forest (RF) regression with the Recursive Feature Elimination (RFE) algorithm to improve the accuracy of sugarcane yield predictions. The results demonstrate the superior performance of the RF-RFE method over traditional RF with full datasets and Stepwise Multiple Regression (SMR). Particularly, VIs focusing on the red-edge spectral bands of S2 - such as NDVIre1n, NDVIre2n, NDVIre3n, NDRE1 and NDRE2 - were crucial in enhancing prediction precision. These indices from the red-edge and NIR narrow bands consistently influenced yield estimations in both the Wonji-Shoa and Metehara estates. The study underscores the critical role of the RFE algorithm in optimal variable selection, reinforcing earlier findings that precise variable choice can substantially boost model accuracy. The enhanced performance of the RF model when paired with the RFE algorithm was evident, emphasizing the importance of variable selection in accurate yield predictions. Employing the Out-of-Bag RMSE (OOB_RMSE) error estimate for evaluation, we observed variations in OOB_RMSE performance with different RF parameters, identifying the ntree value of 500 as optimal for the studied regions. The RF-RFE model’s estimations showed lower errors and higher correlation coefficients, proving its efficacy over a full dataset approach, which faced challenges with traditional VIs' saturation. Our findings align with earlier studies, highlighting the efficiency of S2’s red-edge bands in diverse estimation tasks and a shift towards using freely available broadband images like S2, over hyperspectral imagery, due to reduced data redundancy and processing costs. In conclusion, our findings reveal that RF regression, particularly when integrated with the RFE algorithm, is a powerful tool in remote sensing applications. The S2 imagery is optimal VIs, predominantly from the red-edge bands, exhibit significant potential for sugarcane yield estimations. The impressive results of the RF-RFE method, evident in metrics like MAE, MAPE, RMSE, Mean percentage, and R2, advocate its invaluable role in sugarcane yield prediction, highlighting its potential for optimizing irrigation management strategies and broad-spectrum agricultural planning.

An integrative data-driven approach for monitoring corn biomass under irrigation water and nitrogen levels based on UAV-based imagery.

Unmanned aerial vehicle (UAV)-based remote sensing has been widely considered recently in field scale crop yield estimation. In this research, the capability of 13 spectral indices in the form of 5 groups was studied under different irrigation water and N fertilizer managements in terms of corn biomass monitoring and estimation. Farm experiments were conducted at Urmia University, Iran. The research was done using a randomized complete block design at three levels of 60, 80, and 100% of irrigation water and nitrogen requirements during four replications. The aerial imagery operations were performed using a fixed-wing UAV equipped with a Sequoia sensor during three plant growth stages including stem elongation, flowering, and silking. The effect of different irrigation water and nitrogen levels on vegetation indices and crop biomass was examined using variance decomposition analysis. Then, the correlation of the vegetation indices with corn biomass was evaluated by fitting linear regression models. Based on the obtained results, the indices based on near infrared (NIR) and red-edge spectral bands showed a better performance. Thus, the MERIS terrestrial chlorophyll index (MTCI) indicated the highest accuracy at estimating corn biomass during the growing season with the R2 and RMSE values of 0.92 and 8.27 ton/ha, respectively. Finally, some Bayesian model averaging (BMA) models were proposed to estimate corn biomass based on the selected indices and different spectral bands. Results of the BMA models revealed that the accuracy of biomass estimation models could be improved using the capabilities and advantages of different vegetation indices.

The Effect of Controlled Tile Drainage on Growth and Grain Yield of Spring Barley as Detected by UAV Images, Yield Map and Soil Moisture Content

Controlled tile drainage (CTD) practices are a promising tool for improving water balance, water quality and increasing crop yield by raising shallow groundwater level and capillary rise due to drainage flow retardation. We tested the effect of CTD on growth and grain yield of spring barley, at a study site in central Bohemia using vegetation indices from unmanned aerial vehicle (UAV) imagery and Sentinel-2 satellite imagery. Tile drainage flow was slowed by fixed water level control structures that increased soil moisture in the surrounding area according to the terrain slope. Vegetation indices based on red-edge spectral bands in combination with near-infrared and red bands were selected, of which the Normalized Red Edge-Red Index (NRERI) showed the closest relationships with shoot biomass parameters (dry biomass, nitrogen concentration and uptake, nitrogen nutrition index) from point sampling at the tillering stage. The CTD sites showed significantly more biomass using NRERI compared to free tile drainage (FTD) sites. In contrast, in the period prior to the implementation of CTD practices, Sentinel-2 satellite imagery did not demonstrate higher biomass based on NRERI at CTD sites compared to FTD sites. The grain yields of spring barley as determined from the yield map also increased due to CTD (by 0.3 t/ha, i.e., by 4%). The positive impact of CTD on biomass development and grain yield of spring barley was confirmed by the increase in soil moisture at depths of 20, 40 and 60 cm compared to FTD. The largest increase in soil water content of 3.5 vol% due to CTD occurred at the depth of 40 cm, which also had a higher degree of saturation of available water capacity and the occurrence of crop water stress was delayed by 14 days compared to FTD.

Retrieving Surface Soil Moisture over Wheat-Covered Areas Using Data from Sentinel-1 and Sentinel-2

Surface soil moisture (SSM) is a major factor that affects crop growth. Combined microwave and optical data have been widely used to improve the accuracy of SSM retrievals. However, the influence of vegetation indices derived from the red-edge spectral bands of multi-spectral optical data on retrieval accuracy has not been sufficiently analyzed. In this study, we retrieved soil moisture from wheat-covered surfaces using Sentinel-1/2 data. First, a modified water cloud model (WCM) was proposed to remove the influence of vegetation from the backscattering coefficient of the radar data. The vegetation fraction (FV) was then introduced in this WCM, and the vegetation water content (VWC) was calculated using a multiple linear regression model. Subsequently, the support vector regression technique was used to retrieve the SSM. This approach was validated using in situ measurements of wheat fields in Hebi, located in northern Henan Province, China. The key findings of this study are: (1) Based on vegetation indices obtained from Sentinel-2 data, the proposed VWC estimation model effectively eliminated the influence of vegetation; (2) Compared with vertical transmit and horizontal receive (VH) polarization, vertical transmit and vertical receive (VV) polarization was better for detecting changes in SSM key phenological phases of wheat; (3) The validated model indicates that the proposed approach successfully retrieved SSM in the study area using Sentinel-1 and Sentinel-2 data.

Species-Level Classification and Mapping of a Mangrove Forest Using Random Forest—Utilisation of AVIRIS-NG and Sentinel Data

Although studies on species-level classification and mapping using multisource data and machine learning approaches are plenty, the use of data with ideal placement of central wavelength and bandwidth at appropriate spatial resolution, for the classification of mangrove species is underreported. The species composition of a mangrove forest has been estimated utilising the red-edge spectral bands and chlorophyll absorption information from AVIRIS-NG and Sentinel-2 data. In this study, three dominant species, Heritiera fomes, Excoecaria agallocha and Avicennia officinalis, have been classified using the random forest (RF) model for a mangrove forest in Bhitarkanika Wildlife Sanctuary, India. Various combinations of reflectance/backscatter bands and vegetation indices derived from Sentinel-2, AVIRIS-NG, and Sentinel-1 were used for species-level discrimination and mapping. The RF model showed maximum accuracy using Sentinel-2, followed by the AVIRIS-NG, in discriminating three dominant species and two mixed compositions. This study indicates the potential of Sentinel-2 data for discriminating various mangrove species owing to the appropriate placement of central wavelength and bandwidth in Sentinel-2 at ≥10 m spatial resolution. The variable importance plots proved that species-level classification could be attempted using red edge and chlorophyll absorption information. This study has wider applicability in other mangrove forests around the world.

Multichannel Object Detection for Detecting Suspected Trees With Pine Wilt Disease Using Multispectral Drone Imagery

In this article, a multichannel convolutional neural network (CNN) based object detection was used to detect suspected trees of pine wilt disease after acquiring aerial photographs through a rotorcraft drone equipped with a multispectral camera. The acquired multispectral aerial photographs consist of RGB, green, red, NIR, and red edge spectral bands per shooting point. The aerial photographs for each band performed image calibration to correct radiation distortion, image alignment to correct the distance error of the lenses of a multispectral camera, and image enhancement to edge enhancement to highlight the features of objects in the image. After that, a large amount of data obtained through data augmentation were put into multichannel CNN-based object detection for training and test. As a result of verifying the detection performance of the trained model, excellent detection results were obtained with mAP 86.63% and average intersection over union 71.47%.

Exploring the potential of Sentinel-2A satellite data for aboveground biomass estimation in fragmented Himalayan subtropical pine forest

The Sentinel-2A satellite having embedded advantage of red edge spectral bands offers multispectral imageries with improved spatial, spectral and temporal resolutions as compared to the other contemporary satellites providing medium resolution data. Our study was aimed at exploring the potential of Sentinel-2A imagery to estimate Above Ground Biomass (AGB) of Subtropical Pine Forest in Pakistan administered Kashmir. We developed an AGB predictive model using field inventory and Sentinel 2A based spectral and textural parameters along with topographic features derived from ALOS Digital Elevation Model (DEM). Field inventory data was collected from 108 randomly distributed plots (0.l ha each) across the study area. The stepwise linear regression method was employed to investigate the potential relationship between field data and corresponding satellite data. Biomass and carbon mapping of the study area was carried out through established AGB estimation model with R (0.86), R2 (0.74), adjusted R2 (0.72) and RMSE value of 33 t/ha. Our results showed that first order textures (mean, standard deviation and variance) significantly contributed in AGB predictive modeling while only one spectral band ratio made contribution from spectral domain. Our study leads to the conclusion that Sentinel-2A optical data is a potential source for AGB estimation in subtropical pine forest of the area of interest with added benefit of its free of cost availability, higher quality data and long-term continuity that can be utilized for biomass carbon distribution mapping in the resource constraint study area for sustainable forest management.

Potential use of hyperspectral data to monitor sugarcane nitrogen status

Nitrogen management in crops is a key activity for agricultural production. Methods that can determine the levels of this element in plants in a quick and non-invasive way are extremely important for improving production systems. Within several fronts of study on this subject, proximal and remote sensing methods are promising techniques. In this regard, this research sought to demonstrate the relationships between variations in leaf nitrogen content (LNC) and sugarcane spectral behaviour. The work was carried out in three experimental areas in São Paulo State, Brazil, with different soils, varieties and nitrogen rates during the 2012/13 and 2013/14 seasons. A significant correlation was observed between the LNC and variations in the sugarcane spectra. The green and red-edge spectral bands were the most consistent and stable predictors of LNC among the evaluated harvests. Stepwise multiple linear regression analysis (MSLR) generated better models for LNC estimation when calibrated with experimental area, independent of the variety. The present research demonstrates that specific wavelengths are associated with the variation in LNC in sugarcane, and these are reported in the green region (near 550 nm) and in the red-edge wavelengths (680 to 720 nm). These results may help in future research on the direct in situ application of nitrogen fertilizers.

Determining improvements in Landsat spectral sampling for inland water quality monitoring

Inland waters are optically complex and provide an ongoing challenge to effective water quality monitoring through remote sensing. Imaging satellites with spectral sampling designed for this task often have coarse spatial resolutions, preventing any capture of information from small lakes. Medium resolution satellite systems such as Landsat 8 have the appropriate spatial resolution and sensitivity required to resolve these waterbodies, but the spectral sampling is not optimal. This work uses system simulation to explore potential changes to Landsat spectral sampling to determine if its ability to monitor inland waters could be improved. The HydroLight and MODTRAN radiative transfer models are used for simulation in a Look Up Table and spectrum matching approach to provide maximum flexibility intesting spectral sampling scenarios. To isolate the testing to the impacts of spectral sampling, all simulations were performed based on the known system noise characteristics of Landsat 8. Spectral sampling changes tested include the addition of yellow and red edge spectral bands as well as conversion to an imaging spectrometer. Simulated spectra of inland waters undergoing a cyanobacteria bloom, including atmospheric effects and sensor noise, were implemented with the Look-Up-Table retrieval process to extract estimated concentrations of waterbody components. The retrieval accuracy of each potential system is compared to that of a modeled Landsat 8 baseline. All potential systems show an increase of retrieval accuracy over the baseline. The best performing system design is an imaging spectrometer, followed by the addition of both a yellow and red edge band simultaneously, and the addition of either band individually. Testing also demonstrates that resampling an imaging spectrometer with 20 ​nm spectral resolution to the Landsat 8 band responses produces outputs matching those available from Landsat 8. Our results indicate that future Landsat missions should aim to add as much spectral sampling as is feasible, while maintaining at least the same sensitivity. The minimum change to improve water quality monitoring capability is the addition of a red edge spectral band.

Leaf chlorophyll content retrieval of wheat by simulated RapidEye, Sentinel-2 and EnMAP data

Leaf chlorophyll content (LCC) is an important physiological indicator of the actual health status of individual plants. An accurate estimation of LCC can therefore provide valuable information for precision field management. Red-edge information from hyperspectral data has been widely used to estimate crop LCC. However, after the advent of red-edge bands in satellite imagery, no systematic evaluation of the performance of satellite data has been conducted. Toward this end, we analyze herein the performance of winter wheat LCC retrieval of currant and forthcoming satellites (RapidEye, Sentinel-2 and EnMAP) and their new red-edge bands by using partial least squares regression (PLSR) and a vegetation-index-based approach. These satellite spectral data were obtained by resampling ground-measured hyperspectral data under various field conditions and according to specific spectral response functions and spectral resolution. The results showed: 1) This study confirmed that RapidEye, Sentinel-2 and EnMAP data are suitable for winter wheat LCC retrieval. For the PLSR approach, Sentinel-2 data provided more accurate estimates of LCC (R2=0.755, 0.844, 0.805 for 2002, 2010, and 2002+2010) than do RapidEye data (R2=0.689, 0.710, 0.707 for 2002, 2010, and 2002+2010) and EnMAP data (R2=0.735, 0.867, 0.771 for 2002, 2010, and 2002+2010). For index-based approaches, the MERIS terrestrial chlorophyll index, which is a vegetation index with two red-edge bands, was the most sensitive and robust index for LCC for both the Sentinel-2 and EnMAP data (R2≥0.628), and the indices (NDRE1, SRRE1 and CIRE1) with a single red-edge band were the most sensitive and robust indices for the RapidEye data (R2≥0.420); 2) According to the analysis of the effect of the wavelength and number of used red-edge spectral bands on LCC retrieval, the short-wavelength red-edge bands (from 699 to 734 nm) provided more accurate predictions when using the PLSR approach, whereas the long-wavelength red-edge bands (740 to 783 nm) gave more accurate predictions when using the vegetation indice (VI) approach. In addition, the prediction accuracy of RapidEye, Sentinel-2 and EnMAP data was improved gradually because of more number of red-edge bands and higher spectral resolution; VI regression models that contain a single or multiple red-edge bands provided more accurate predictions of LCC than those without red-edge bands, but for normalized difference vegetation index (NDVI)-, simple ratio (SR)- and chlorophyll index (CI)-like index, two red-edge bands index didn't significantly improve the predictive accuracy of LCC than those indices with a single red-edge band. Although satellite data with higher spectral resolution and a greater number of red-edge bands marginally improve the accuracy of estimates of crop LCC, the level of this improvement remains insufficient because of higher spectral resolution, which results in a worse signal-to-noise ratio. The results of this study are helpful to accurately monitor LCC of winter wheat in large-area and provide some valuable advice for design of red-edge spectral bands of satellite sensor in future.

Mapping leaf chlorophyll content from Sentinel-2 and RapidEye data in spruce stands using the invertible forest reflectance model

Leaf chlorophyll plays an essential role in controlling photosynthesis, physiological activities and forest health. In this study, the performance of Sentinel-2 and RapidEye satellite data and the Invertible Forest Reflectance Model (INFORM) radiative transfer model (RTM) for retrieving and mapping of leaf chlorophyll content in the Norway spruce (Picea abies) stands of a temperate forest was evaluated. Biochemical properties of leaf samples as well as stand structural characteristics were collected in two subsequent field campaigns during July 2015 and 2016 in the Bavarian Forest National Park (BFNP), Germany, parallel with the timing of the RapidEye and Sentinel-2 images. Leaf chlorophyll was measured both destructively and nondestructively using wet chemical spectrophotometry analysis and a hand-held chlorophyll content meter. The INFORM was utilised in the forward mode to generate two lookup tables (LUTs) in the spectral band settings of RapidEye and Sentinel-2 data using information obtained from the field campaigns. Before generating the LUTs, the sensitivity of the model input parameters to the spectral data from RapidEye and Sentinel-2 were examined. The canopy reflectance of the studied plots were obtained from the satellite images and used as input for the inversion of LUTs. The coefficient of determination (R2), root mean square errors (RMSE), and the normalised root mean square errors (NRMSE), between the retrieved and measured leaf chlorophyll, were then used to examine the attained results from RapidEye and Sentinel-2 data, respectively. The use of multiple solutions and spectral subsets for the inversion process were further investigated to enhance the retrieval accuracy of foliar chlorophyll. The result of the sensitivity analysis demonstrated that the simulated canopy reflectance of Sentinel-2 is sensitive to the alternation of all INFORM input parameters, while the simulated canopy reflectance from RapidEye did not show sensitivity to leaf water content variations. In general, there was agreement between the simulated and measured reflectance spectra from RapidEye and Sentinel-2, particularly in the visible and red-edge regions. However, examining the average absolute error from the simulated and measured reflectance revealed a large discrepancy in spectral bands around the near-infrared shoulder. The relationship between retrieved and measured leaf chlorophyll content from the Sentinel-2 data had a higher coefficient of determination with a higher NRMSE (NRMSE = 0.36 μg/cm2, R2 = 0.45) compared to those obtained using the RapidEye data (NRMSE = 0.31 μg/cm2 and R2 = 0.39). Using the mean of the ten best solutions (retrieved chlorophyll) the retrieval error for both Sentinel-2 and RapidEye data decreased (NRMSE = 0.34, NRMSE = 0.26, respectively), as compared to only selecting the single best solution. When the Sentinel-2 red edge bands were used as the spectral subset, the retrieval error of leaf chlorophyll decreased indicating the importance of red edge, as well as properly located spectral bands, for leaf chlorophyll estimation. The chlorophyll maps produced by the inversion of the two LUTs effectively represented the variation of foliar chlorophyll in BFNP and confirmed our earlier findings on the observed stress pattern caused by insect infestation. Our findings emphasise the importance of multispectral satellites which benefits from red edge spectral bands such as Sentinel-2 as well as RapidEye for regional mapping of vegetation foliar properties, particularly, chlorophyll using RTMs such as INFORM.

Evaluation of forest fire on Madeira Island using Sentinel-2A MSI imagery

A forest fire started on August 8th, 2016 in several places on Madeira Island causing damage and casualties. As of August 10th the local media had reported the death of three people, over 200 people injured, over 950 habitants evacuated, and 50 houses damaged. This study presents the preliminary results of the assessment of several spectral indices to evaluate the burn severity of Madeira fires during August 2016. These spectral indices were calculated using the new European satellite Sentinel-2A launched in June 2015. The study confirmed the advantages of several spectral indices such as Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Burn Ratio (NBR) and Normalized Difference Vegetation Index (NDVIreXn) using red-edge spectral bands to assess the post-fire conditions. Results showed high correlation between NDVI, GNDVI, NBR and NDVIre1n spectral indices and the analysis performed by Copernicus Emergency Management Service (EMSR175), considered as the reference truth. Regarding the red-edge spectral indices, the NDVIre1n (using band B5, 705nm) presented better results compared with B6 (740nm) and B7 (783nm) bands. These preliminary results allow us to assume that Sentinel-2 will be a valuable tool for post-fire monitoring. In the future, the two twin Sentinel-2 satellites will offer global coverage of the Madeira Archipelago every five days, therefore allowing the simultaneous study of the evolution of the burnt area and reforestation information with high spatial (up to 10m) and temporal resolution (5days).

LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands

Leaf Area Index (LAI) is an important variable that governs canopy processes and can be monitored by satellites. The current study aims at exploring the potential and limitations of using the red-edge spectral bands of the forthcoming superspectral satellites, namely—Vegetation and Environmental New micro Spacecraft (VENμS) and Sentinel-2, for assessing LAI in field crops. The research was conducted in experimental plots of wheat and potato in the northwestern Negev, Israel. Continuous spectral data were collected by a field spectrometer and LAI data were obtained by a ceptometer. The spectral data were resampled to the superspectral VENμS and Sentinel-2 resolutions. The data were divided into seven datasets (four seasons, two crops, and one including all data). The LAI prediction abilities by Partial Least Squares (PLS) models for continuous spectra and the resampled spectra were compared and evaluated. For wheat and potato of the continuous, VENμS, and Sentinel-2 data formations, the PLS correlation coefficients (r) values were 0.93, 0.93, and 0.92, respectively. In most cases, the red-edge region was found to be the most important spectral region for the three data formations, according to the Variable Importance in Projection (VIP) analysis. Additionally, Normalized Difference Vegetation Index (NDVI) and the Red-Edge Inflection Point (REIP) were computed for the three data formations in order to observe relation to as well as prediction accuracy in retrieving LAI values. The prediction abilities of the calculated indices by the data formations were compared, peaking for wheat, with r values of 0.91 for the REIP for the three data formations. Therefore, it is concluded that VENμS and Sentinel-2 can spectrally assess LAI as good as a hyperspectral sensor. The REIP was found to be a significantly better predictor than NDVI for wheat data and therefore can potentially be implemented for future LAI monitoring applications by superspectral sensors that contain four red-edge bands.

Nondestructive estimation of anthocyanins and chlorophylls in anthocyanic leaves

The anthocyanin and chlorophyll contents in leaves provide valuable information about the physiological status of plants. Thus, there is a need for accurate, efficient, and practical methodologies to estimate these biochemical parameters of vegetation. In this study, we tested the performance and accuracy of several nondestructive, reflectance-based techniques for estimating anthocyanin and chlorophyll contents in leaves of four unrelated species, European hazel (Corylus avellana), Siberian dogwood (Cornus alba =Swida alba), Norway maple (Acer platanoides), and Virginia creeper (Parthenocissus quinquefolia), with widely variable pigment content and composition. An anthocyanin reflectance index, which uses reflectances in the green and red edge spectral bands, and a modified anthocyanin reflectance index, employing, in addition, the near-infrared (NIR) band, were able to accurately estimate leaf anthocyanin for all species taken together with no reparameterization of algorithms. Total chlorophyll content was accurately estimated by a red edge chlorophyll index that uses spectral bands in the red edge and the NIR. These approaches can be used to estimate anthocyanin and chlorophyll nondestructively and allow the development of simple handheld field instrumentation.