Red Edge Inflection Point Research Articles

A new method for satellite-based remote sensing analysis of plant-specific biomass yield patterns for precision farming applications

AbstractThis study describes a new method for satellite-based remote sensing analysis of plant-specific biomass yield patterns for precision farming applications. The relative biomass potential (rel. BMP) serves as an indicator for multiyear stable and homogeneous yield zones. The rel. BMP is derived from satellite data corresponding to specific growth stages and the normalized difference vegetation index (NDVI) to analyze crop-specific yield patterns. The development of this methodology is based on data from arable fields of two research farms; the validation was conducted on arable fields of commercial farms in southern Germany. Close relationships (up to r > 0.9) were found between the rel. BMP of different crop types and study years, indicating stable yield patterns in arable fields. The relative BMP showed moderate correlations (up to r = 0.64) with the yields determined by the combine harvester, strong correlations with the vegetation index red edge inflection point (REIP) (up to r = 0.88, determined by a tractor-mounted sensor system) and moderate correlations with the yield determined by biomass sampling (up to r = 0.57). The study investigated the relationship between the rel. BMP and key soil parameters. There was a consistently strong correlation between multiyear rel. BMP and soil organic carbon (SOC) and total nitrogen (TN) contents (r = 0.62 to 0.73), demonstrating that the methodology effectively reflects the impact of these key soil properties on crop yield. The approach is well suited for deriving yield zones, with extensive application potential in agriculture.

Effects of biochar in combination with cattle slurry and mineral nitrogen on crop yield and nitrogen use efficiency in a three-year field experiment

Applying biochar can improve soil characteristics and functions, resulting in significant increases in yield in tropical soils. However, whether biochar can boost soil yield in temperate climates with high soil fertility is mostly unknown. The optimal biochar application rates and combinations with organic and mineral nitrogen (N) fertilizers remain to be determined. To address this research gap, a factorial three-year field experiment in southern Germany was conducted using mineral N fertilizer (0, 60, 120, and 180 kg ha−1 a−1), and biochar (0, 500, and 1000 kg ha−1 a−1 biochar C) mixed with cattle slurry before application (0 and 100 kg ha−1 a−1 slurry N). The effects on crop (silage maize, winter wheat, and winter barley) yield, N uptake, and N use efficiency were studied. Spectral measurements were taken at selected growth stages, and the vegetation index REIP (Red-Edge Inflection Point) was calculated.The silage maize dry matter yield reached 27.2 t ha−1 while the winter wheat and winter barley grain yield reached 10.4 t ha−1, and 9.6 t ha−1, respectively. Biochar consistently, but mostly non-significantly, decreased crop yield (e.g., winter wheat, 5.09, 4.74, and 4.50 t ha−1) and N uptake (74.3, 70.8, and 67.4 kg ha−1) at biochar C rates of 0, 500, and 1000 kg ha−1 a−1 when biochar was mixed with cattle slurry before field application in treatments without mineral N input in all trial years. In the third trial year, the biochar-related yield reductions were significant. Consequently, N use efficiency decreased slightly but significantly. At high additional mineral N fertilizer rates no yield-reducing effects were observed. Biochar applied alone (without cattle slurry or mineral N fertilizer) did not affect crop yield. Positive yield effects of biochar were not observed in any of the trial years.The vegetation index REIP decreased non-significantly with increasing biochar addition to cattle slurry in all three years and correlated very closely (up to r = 0.97) with crop yield.The results show that the yield effect of biochar depends on the combination with organic and mineral fertilizers. The derivation of optimal ratios of biochar, cattle slurry and mineral fertilizer requires further investigation. Based on the yield effects, however, it can be deduced that the more biochar is used, the higher the compensation with nitrogen fertilizer must be to maintain the yield level.

Spatial Resolution Enhancement of Vegetation Indexes via Fusion of Hyperspectral and Multispectral Satellite Data

The definition and calculation of a spectral index suitable for characterizing vegetated landscapes depend on the number and widths of the bands of the imaging instrument. Here, we point out the advantages of performing the fusion of hyperspectral (HS) satellite data with the multispectral (MS) bands of Sentinel-2 to calculate such vegetation indexes as the normalized area over reflectance curve (NAOC) and the red-edge inflection point (REIP), which benefit from the availability of quasi-continuous pixel spectra. Unfortunately, MS data may be acquired from satellite platforms with very high spatial resolution; HS data may not. Despite their excellent spectral resolution, satellite imaging spectrometers currently resolve areas not greater than 30 × 30 m2, where different thematic classes of landscape may be mixed together to form a unique pixel spectrum. A way to resolve mixed pixels is to perform the fusion of the HS dataset with the same dataset produced by an MS scanner that images the same scene with a finer spatial resolution. The HS dataset is sharpened from 30 m to 10 m by means of the Sentinel-2 bands that have all been previously brought to 10 m. To do so, the hyper-sharpening protocol, that is, m:n fusion, is exploited in two nested steps: the first one to bring the 20 m bands of Sentinel-2 all to 10 m, the second one to sharpen all the 30 m HS bands to 10 m by using the Sentinel-2 bands previously hyper-sharpened to 10 m. Results are presented on an agricultural test site in The Netherlands imaged by Sentinel-2 and by the satellite imaging spectrometer recently launched as a part of the environmental mapping and analysis program (EnMAP). Firstly, the excellent match of statistical consistency of the fused HS data to the original MS and HS data is evaluated by means of analysis tools, existing and developed ad hoc for this specific case. Then, the spatial and radiometric accuracy of REIP and NAOC calculated from fused HS data are analyzed on the classes of pure and mixed pixels. On pure pixels, the values of REIP and NAOC calculated from fused data are consistent with those calculated from the original HS data. Conversely, mixed pixels are spectrally unmixed by the fusion process to resolve the 10 m scale of the MS data. How the proposed method can be used to check the temporal evolution of vegetation indexes when a unique HS image and many MS images are available is the object of a final discussion.

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

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

Mapping Forage Biomass and Quality of the Inner Mongolia Grasslands by Combining Field Measurements and Sentinel-2 Observations

Grasslands provide essential forage sources for global livestock production. Remote sensing approaches have been widely used to estimate the biomass production of grasslands from regional to global scales, but simultaneously mapping the forage biomass and quality metrics (e.g., crude fiber and crude protein) is still relatively lacking despite an increasing need for better livestock management. We conducted novel gradient grass-cutting experiments and measured hyperspectral reflectance, forage biomass, crude fiber per area (CFarea), and crude protein per area (CParea) across 19 temperate grassland sites in the Xilingol region, Inner Mongolia, China. Based on these measurements, we identified sensitive spectral bands, calculated nine potential spectral indices (Normalized Difference Vegetation Index, Enhanced Vegetation Index, Red Edge Normalized Difference Vegetation Index, Red-Edge Inflection Point, Inverted Red-Edge Chlorophyll Index algorithm, Normalized Difference Red Edge Index, Nitrogen Reflectance Index, Normalized Greenness Index, Land Surface Water Index) and established Random Forest (RF) models that well predicted forage biomass (R2 = 0.67, NRMSE = 12%), CFarea (R2 = 0.59, NRMSE = 14%), and CParea (R2 = 0.77, NRMSE = 10%). Among these nine indices, Land Surface Water Index (LSWI, calculated by R785-900 and R2100-2280) was identified to be the most important predictor and was then used to establish empirical power law models, showing comparable prediction accuracies (forage biomass, R2 = 0.53; NRMSE = 14%; CFarea, R2 = 0.40, NRMSE = 17%; CParea, R2 = 0.72, NRMSE = 11%) in comparison to Random Forest models. Combining the empirical power law models with the LSWI calculated from Sentinel-2 observations, we further mapped the forage biomass and quality and estimated the livestock carrying capacity. The predicted forage biomass, CFarea, and CParea all showed a significant increase with higher mean annual precipitation, but showed no significant correlations with mean annual temperature. Compared with the estimates based on crude protein, the conventional approach solely based on forage biomass consistently overestimated livestock carrying capacity, especially in wetter areas. Our work provides an approach to simultaneously map the forage biomass and quality metrics and recommends a LSWI-based power law model for rapid and low-cost assessment of regional forage status to guide better livestock management.

Detection of calcium, magnesium, and chlorophyll variations of wheat genotypes on sodic soils using hyperspectral red edge parameters

Plants grown on sodic soils can suffer from macronutrient deficiencies, such as calcium (Ca), magnesium (Mg), and potassium (K), reducing health and growth. Nutrient concentrations in plant tissue could potentially provide a signal to identify cultivars tolerant to sodic conditions. However, conventional approaches to diagnosing crop nutrient and chlorophyll status involve determining total elemental content in plant tissues. These methods are time-consuming, tedious, and expensive, requiring destructive sampling of plant parts and complex laboratory analyses. Here, we propose a novel approach using hyperspectral sensing to determine macronutrient and chlorophyll variations/deficiencies of 18 different wheat genotypes grown in moderately sodic (MS) and highly sodic (HS) soil conditions in north-eastern Australia. Canopy reflectance was measured using a handheld spectroradiometer close to flowering to compute red edge spectral indices, such as normalized difference red edge index (NDRE), red edge inflection point (REIP), and red edge chlorophyll index (Cl rededge). Plant Ca, Mg, and K concentrations were also measured by destructive sampling of young mature leaves followed by laboratory analysis. The maximum first derivative of reflectance spectra for 18 wheat genotypes were observed at 722–728 nm and 719–725 nm for the MS and HS site, respectively and was used to determine REIP for the genotypes using a four-point linear interpolation method. Ca and Mg had a significant positive association with both REIP and NDRE, with Ca more closely correlated than either Mg or K. REIP was more closely associated with Ca (R2=0.72; RMSE=0.02 for the MS site and R 2=0.57; RMSE=0.02 for the HS site) than NDRE. This suggests that REIP has a great potential to detect structural variations of wheat genotypes in sodic soil environment. Furthermore, Ca was also significantly (p<0.0001) and positively correlated with Cl rededge at both sites with R2=0.53 and 0.51 for the MS and HS site. This suggests that plant structural variations in sodic soil can regulate leaf chlorophyll concentration and, in turn, photosynthetic activities. Overall, results demonstrate that hyperspectral sensing can be efficiently used to detect plant Ca, Mg, and chlorophyll concentrations. The study improves understanding of genotypic nutrient variation for tolerance to different levels of sodic soil conditions using optical properties of plant structure and can be beneficial to the plant science community for developing new approaches to study plant physiology.

Prediction of multi-year winter wheat yields at the field level with satellite and climatological data

• Estimation of daily crop water requirements. • Costly comparison of different features for yield prediction at the field level. • High quality multi-year winter wheat yield data at the field level. Long-term yield mapping is key to Precision Farming and needs to consider yield-relevant factors such as the water demand and nitrogen uptake. For the prediction of winter wheat yields, we incorporated climatological data and daily crop water requirements ( CWR ), Sentinel-2 time series, and the derived indices such as the normalised difference red edge ( NDRE ), red edge inflection point ( REIP ) and the normalized difference water index ( NDWI ). These factors were evaluated by using stepwise linear regression (LR) and random forest (RF). Robust field data sets from 2016 to 2018 with weighed yields from three regions in southern Germany in the range of 49 dt ha −1 to 102 dt ha −1 were used for calibration and validation. Indices reflecting the field variability were not better suited for yield prediction than climatological data alone. A new approach using all raw bands of Sentinel-2 in combination with evapotranspiration and precipitation, delivered a reduced number of features and explained 84% of the yield variance with a RMSE of 5.6 dt ha −1 . Alternatively, the NDWI -based CWR merged all significant parameters into one feature and explained 79% of the variance with a RMSE of 6.46 dt ha −1 . A sufficiently precise prediction of yields at the field level could contribute to an optimised agricultural management.

Selection of Hyperspectral Vegetation Indices for Monitoring Yield and Physiological Response in Sweet Maize under Different Water and Nitrogen Availability

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 emerged through PCA. Moreover, DD, REIP, and Clred-edge VIs were able to discriminate transient temporary stress at the mid-season stage, as well as to separate water and N stress levels. Therefore, hyperspectral reflectance VIs can provide valid information to growers, helping them identify and discriminate between different stress conditions.

VENµS-Derived NDVI and REIP at Different View Azimuth Angles

The bidirectional reflectance distribution function (BRDF) is crucial in determining the quantity of reflected light on the earth’s surface as a function of solar and view angles (i.e., azimuth and zenith angles). The Vegetation and ENvironment monitoring Micro-Satellite (VENµS) provides a unique opportunity to acquire data from the same site, with the same sensor, with almost constant solar and view zenith angles from two (or more) view azimuth angles. The present study was aimed at exploring the view angles’ effect on the stability of the values of albedo and of two vegetation indices (VIs): the normalized difference vegetation index (NDVI) and the red-edge inflection point (REIP). These products were calculated over three polygons representing urban and cultivated areas in April, June, and September 2018, under a minimal time difference of less than two minutes. Arithmetic differences of VIs and a change vector analysis (CVA) were performed. The results show that in urban areas, there was no difference between the VIs, whereas in the well-developed field crop canopy, the REIP was less affected by the view azimuth angle than the NDVI. Results suggest that REIP is a more appropriate index than NDVI for field crop studies and monitoring. This conclusion can be applied in a constellation of satellites that monitor ground features simultaneously but from different view azimuth angles.

Detecting the distribution of grass aboveground biomass on a rangeland using Sentinel-2 MSI vegetation indices

Quantifying the grass biomass content of rangelands is potentially useful to rangeland managers. This study assessed the potential of image-derived vegetation indices to assess in-situ spatial variations in grass aboveground biomass (AGB). Sampling was conducted at the peak growth stage, at widely distributed sampling sites whose grass cover homogeneity was wider than 20 m. Up to three AGB samples per site were collected, in 1 m quadrats. Dry biomass weights were determined and averaged per site. Rainy season Sentinel-2 MSI images of the rangeland that corresponded with the fieldwork dates were obtained. Atmospheric correction of the images was performed using the Sen2Cor algorithm, which yielded 20 m resolution visible (vis), red edge (RE), near infrared (NIR), and short wave infrared (SWIR) bands. Seven biomass-sensitive vegetation indices that utilised Sentinel-2 MSI vis-RE-NIR-SWIR spectral ranges were then tested using the images, to determine which correlated best with the field-derived AGB. They were the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Red Edge index (NDRE), Red Edge Inflection Point (REIP), Aerosol Free Vegetation Index (AFRI 2.1μm ), and Normalized Difference Water Index (NDWI). In decreasing order of magnitude, the EVI, SAVI, NDVI, AFRI 2.1μm , and NDWI had statistically significant correlations with AGB ( p ≤ 0.05). The discontinuous canopies but high biomass of tall-grass species weakened the correlations. The EVI model of AGB was then used for depicting rangeland-scale, location-context spatial variations in AGB. Pixels with woody vegetation cover ≥ 50% were excluded using a binary mask that was developed through sub-pixel classification of a grass-senescence period Sentinel-2 MSI image. A statistically significant linear relationship ( F = 21.192, p = 0.000) between EVI-predicted and actual AGB was established ( r = 0.765, p < 0.001). The results from the studied savannah rangeland suggest higher vegetation index prediction accuracy at short-grass than tall-grass sites.

Relationships between the Spatio-Temporal Variation in Reflectance Data from the Sentinel-2 Satellite and Potato (Solanum Tuberosum L.) Yield and Stem Density

Satellite Image Time Series (SITS) have been used to build models for predicting Potato (Solanum tuberosum L.) yields at regional scales, but evidence of extension of such models to local field scale for practical use in precision agriculture is lacking. In this study, multispectral data from the Sentinel-2 satellite were used to interpolate continuous spectral signatures of potato canopies and generate vegetation indices and the red edge inflection point (REIP) to relate to marketable yield and stem density. The SITS data were collected from 94 sampling locations across five potato fields in England, United Kingdom. The sampling locations were georeferenced and the number of stems per square meter, as well as marketable yield, were determined at harvest. The first principal components of the temporal variation of each SITS wavelength were extracted and used to generate 54 vegetation indices to relate to the response variables. Marketable yield was negatively related to the overall seasonal reflectance (first principal component) at 559 nm with a beta coefficient of −0.53 (±0.18 at p = 0.05). Seasonal reflectance at 703 nm had a positive significant relationship with Marketable yield. Marketable yield was modeled with a normalized root mean square error (nRMSE) of 0.16 and R2 of 0.65. On the other hand, Stem density was significantly related to the Specific Leaf Area Vegetation Index (β = 1.66 ± 1.59) but the REIP’s farthest position during the season was reached later in dense canopies (β = 1.18 ± 0.79) with a higher reflectance (β = 3.43 ± 1.9). This suggested that denser canopies took longer to reach their maximum chlorophyll intensity and the intensity was lower than in sparse canopies. Potato stem density was modeled with an nRMSE of 0.24 and R2 of 0.51. These results reinforce the importance of SITS analysis as opposed to the use of single-instance intrinsic indices.

Development of a Rapid Mangrove Zonation Mapping Workflow Using Sentinel 2-Derived Indices and Biophysical Dataset

Moderate to high resolution satellite imageries are commonly used in mapping mangrove cover from local to global scales. In addition to extent information, studies such as mangrove composition, ecology, and distribution analysis require further information on mangrove zonation. Mangrove zonation refers to unique sections within a mangrove forest being dominated by a similar family, genus, or species. This can be observed both in natural and planted mangrove forests. In this study, a mapping workflow was developed to detect zonation in test mangrove forest sites in Katunggan-It Ibajay (KII) Ecopark (Aklan), Bintuan (Coron), Bogtong, and Sagrada (Busuanga) in the Philippines and Fukido Mangrove Park (Ishigaki, Japan) using Sentinel-2 imagery. The methodology was then applied to generate a nationwide mangrove zonation map of the Philippines for year 2020. Combination of biophysical products, water, and vegetation indices were used as classification inputs including leaf area index (LAI), fractional vegetation cover (FVC), fraction of photosynthetically-active radiation (FAPAR), Canopy chlorophyll content (Cab), canopy water content (Cw), Normalized Difference Vegetation Index (NDVI), modified normalized difference water index (MNDWI), modified chlorophyll absorption in reflectance index (MCARI), and red-edge inflection point (REIP). Mangrove extents were first mapped using either the Maximum Likelihood Classification (MLC) algorithm or the Mangrove Vegetation Index (MVI)-based methodology. The biophysical and vegetation indices within these areas were stacked and transformed through Principal Component Analysis (PCA). Regions of Interest (ROIs) were selected on the PCA bands as training input to the MLC. Results show that mangrove zonation maps can highlight the major mangrove zones in the study sites, commonly limited up to genera level only except for genera with only one known species thriving in the area. Four zones were detected in KII Ecopark: Avicennia zone, Nypa zone, Avicennia mixed with Nypa zone, and mixed mangroves zones. For Coron and Busuanga, the mapped mangrove zones are mixed mangroves, Rhizophora zone and sparse/damaged zones. Three zones were detected in Fukido site: Rhizophora stylosa-dominant zone, Bruguiera gymnorrhiza-dominant zone, and mixed mangrove zones. The zonation maps were validated using field plot data and orthophotos generated from Unmanned Aerial System (UAS) surveys, with accuracies ranging from 75 to 100%.

Remote sensing retrieval of winter wheat leaf area index and canopy chlorophyll density at different growth stages

ABSTRACT Leaf area index (LAI) and canopy chlorophyll density (CCD) are key indicators of crop growth status. In this study, we compared several vegetation indices and their red-edge modified counterparts to evaluate the optimal red-edge bands and the best vegetation index at different growth stages. The indices were calculated with Sentinel-2 MSI data and hyperspectral data. Their performances were validated against ground measurements using R2, RMSE, and bias. The results suggest that indices computed with hyperspectral data exhibited higher R2 than multispectral data at the late jointing stage, head emergence stage, and filling stage. Furthermore, red-edge modified indices outperformed the traditional indices for both data genres. Inversion models indicated that the indices with short red-edge wavelengths showed better estimation at the early jointing and milk development stage, while indices with long red-edge wavelength estimate the sought variables better at the middle three stages. The results were consistent with the red-edge inflection point shift at different growth stages. The best indices for Sentinel-2 LAI retrieval, Sentinel-2 CCD retrieval, hyperspectral LAI retrieval, and hyperspectral CCD retrieval at five growth stages were determined in the research. These results are beneficial to crop trait monitoring by providing references for crop biophysical and biochemical parameters retrieval.

Effect of Time of Day and Sky Conditions on Different Vegetation Indices Calculated from Active and Passive Sensors and Images Taken from UAV

Optical sensors have been widely reported to be useful tools to assess biomass, nutrition, and water status in several crops. However, the use of these sensors could be affected by the time of day and sky conditions. This study aimed to evaluate the effect of time of day and sky conditions (sunny versus overcast) on several vegetation indices (VI) calculated from two active sensors (the Crop Circle ACS-470 and Greenseeker RT100), two passive sensors (the hyperspectral bidirectional passive spectrometer and HandySpec Field sensor), and images taken from an unmanned aerial vehicle (UAV). The experimental work was conducted in a wheat crop in south-west Germany, with eight nitrogen (N) application treatments. Optical sensor measurements were made throughout the vegetative growth period on different dates in 2019 at 9:00, 14:00, and 16:00 solar time to evaluate the effect of time of day, and on a sunny and overcast day only at 9:00 h to evaluate the influence of sky conditions on different vegetation indices. For most vegetation indices evaluated, there were significant differences between paired time measurements, regardless of the sensor and day of measurement. The smallest differences between measurement times were found between measurements at 14:00 and 16:00 h, and they were observed for the vehicle-carried and the handheld hyperspectral passive sensor being lower than 2% and 4%, respectively, for the indices NIR/Red edge ratio, Red edge inflection point (REIP), and the water index. Differences were lower than 5% for the vehicle-carried active sensors Crop Circle ACS-470 (indices NIR/Red edge and NIR/Red ratios, and NDVI) and Greenseeker RT100 (index NDVI). The most stable indices over measurement times were the NIR/Red edge ratio, water index, and REIP index, regardless of the sensor used. The most considerable differences between measurement times were found for the simple ratios NIR/Red and NIR/Green. For measurements made on a sunny and overcast day, the most stable were the indices NIR/Red edge ratio, water index, and REIP. In practical terms, these results confirm that passive and active sensors could be used to measure on-farm at any time of day from 9:00 to 16:00 h by choosing optimized indices.

Machine Learning Optimised Hyperspectral Remote Sensing Retrieves Cotton Nitrogen Status

Hyperspectral imaging spectrometers mounted on unmanned aerial vehicle (UAV) can capture high spatial and spectral resolution to provide cotton crop nitrogen status for precision agriculture. The aim of this research was to explore machine learning use with hyperspectral datacubes over agricultural fields. Hyperspectral imagery was collected over a mature cotton crop, which had high spatial (~5.2 cm) and spectral (5 nm) resolution over the spectral range 475–925 nm that allowed discrimination of individual crop rows and field features as well as a continuous spectral range for calculating derivative spectra. The nominal reflectance and its derivatives clearly highlighted the different treatment blocks and were strongly related to N concentration in leaf and petiole samples, both in traditional vegetation indices (e.g., Vogelman 1, R2 = 0.8) and novel combinations of spectra (R2 = 0.85). The key hyperspectral bands identified were at the red-edge inflection point (695–715 nm). Satellite multispectral was compared against the UAV hyperspectral remote sensing’s performance by testing the ability of Sentinel MSI to predict N concentration using the bands in VIS-NIR spectral region. The Sentinel 2A Green band (B3; mid-point 559.8 nm) explained the same amount of variation in N as the hyperspectral data and more than the Sentinel Red Edge Point 1 (B5; mid-point 704.9 nm) with the lower 10 m resolution Green band reporting an R2 = 0.85, compared with the R2 = 0.78 of downscaled Sentinel Red Edge Point 1 at 5 m. The remaining Sentinel bands explained much lower variation (maximum was NIR at R2 = 0.48). Investigation of the red edge peak region in the first derivative showed strong promise with RIDAmid (R2 = 0.81) being the best index. The machine learning approach narrowed the range of bands required to investigate plant condition over this trial site, greatly improved processing time and reduced processing complexity. While Sentinel performed well in this comparison and would be useful in a broadacre crop production context, the impact of pixel boundaries relative to a region of interest and coarse spatial and temporal resolution impacts its utility in a research capacity.

Monitoring the Vertical Distribution of Maize Canopy Chlorophyll Content Based on Multi-Angular Spectral Data

Remote sensing approaches have several advantages over traditional methods in determining information on physical and chemical parameters, including timely data acquisition, low costs, and wide coverage. Thus, remote sensing is widely used in crop growth monitoring. Unlike vertical observations, multi-angular remote sensing technology can obtain the vertical distribution information of the central and lower leaves of a crop. Furthermore, applications of remote sensing on the vertical distribution of maize canopy components is complicated, and related research is limited. In the current paper, we employed multi-angular spectral data, measured by a self-designed multi-angular observation instrument at view zenith angles (VZAs) of 0°, 10°, 20°, 30°, 40°, 50°, and 60°, to explore the monitoring strategy and monitoring precision of the vertical distribution of chlorophyll content in the maize canopy. This was then used to determine the optimal monitoring method for the chlorophyll content (soil and plant analyzer development (SPAD) value) of each layer. The correlation between SPAD value and chlorophyll sensitivity indices at different growth stages was used as the basis for screening indices and VZAs. The correlation between the selected EPI (eucalyptus pigment index) and REIP (red edge inflection point) indices and chlorophyll content indicated view zenith angles (VZAs) of 0°, 30°, and 40° as optimal for the early growth stage monitoring of chlorophyll content in the 1st, 2nd, and 3rd layers, respectively. These values were associated with RMSEs of 4.14, 1.71, and 1.11 for EPI, respectively; and 4.61, 2.31, and 1.00 for REIP, respectively. In addition, a VZA of 50° was selected to monitor the chlorophyll content of the 1st, 2nd, 3rd, and 4th layers at the late growth stage, with RMSE values of 2.97, 3.50, 2.80, and 4.80 for EPI, respectively; and 3.16, 5.02, 4.55, and 7.85 for REIP, respectively. The results demonstrated the ability of canopy multi-angular spectral reflectance to accurately estimate the maize canopy chlorophyll content vertical distribution, with the VZAs of different vertical layers varying between the early and late growth stages.

Detecting European Aspen (Populus tremula L.) in Boreal Forests Using Airborne Hyperspectral and Airborne Laser Scanning Data

Sustainable forest management increasingly highlights the maintenance of biological diversity and requires up-to-date information on the occurrence and distribution of key ecological features in forest environments. European aspen (Populus tremula L.) is one key feature in boreal forests contributing significantly to the biological diversity of boreal forest landscapes. However, due to their sparse and scattered occurrence in northern Europe, the explicit spatial data on aspen remain scarce and incomprehensive, which hampers biodiversity management and conservation efforts. Our objective was to study tree-level discrimination of aspen from other common species in northern boreal forests using airborne high-resolution hyperspectral and airborne laser scanning (ALS) data. The study contained multiple spatial analyses: First, we assessed the role of different spectral wavelengths (455–2500 nm), principal component analysis, and vegetation indices (VI) in tree species classification using two machine learning classifiers—support vector machine (SVM) and random forest (RF). Second, we tested the effect of feature selection for best classification accuracy achievable and third, we identified the most important spectral features to discriminate aspen from the other common tree species. SVM outperformed the RF model, resulting in the highest overall accuracy (OA) of 84% and Kappa value (0.74). The used feature set affected SVM performance little, but for RF, principal component analysis was the best. The most important common VI for deciduous trees contained Conifer Index (CI), Cellulose Absorption Index (CAI), Plant Stress Index 3 (PSI3), and Vogelmann Index 1 (VOG1), whereas Green Ratio (GR), Red Edge Inflection Point (REIP), and Red Well Position (RWP) were specific for aspen. Normalized Difference Red Edge Index (NDRE) and Modified Normalized Difference Index (MND705) were important for coniferous trees. The most important wavelengths for discriminating aspen from other species included reflectance bands of red edge range (724–727 nm) and shortwave infrared (1520–1564 nm and 1684–1706 nm). The highest classification accuracy of 92% (F1-score) for aspen was achieved using the SVM model with mean reflectance values combined with VI, which provides a possibility to produce a spatially explicit map of aspen occurrence that can contribute to biodiversity management and conservation efforts in boreal forests.

Sensitivity analysis of artificial neural network for chlorophyll prediction using hyperspectral data

Hyperspectral acquisition provides the spectral response in narrow and continuous spectral channel. The high number of contiguous bands in hyperspectral remote sensing provides significant improvements in assessing subtle changes as compared to the multispectral satellite datasets in context of spectral resolution. Therefore, the main goal of the present research is to evaluate the sensitivity of the artificial neural networks (ANNs) for chlorophyll prediction in the winter wheat crop using different hyperspectral spectral indices. For evaluating relative variable significance in the study, the Olden’s function has been applied. The Lek’s profile method is used for sensitivity analysis of ANNs for chlorophyll prediction using the vegetation indices such as Red Edge Inflection Point (REIP), Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and Structure-Insensitive Pigment Index (SIPI) derived from hyperspectral radiometer. The analysis indicates a high sensitivity of SAVI followed by NDVI, REIP and SIPI for chlorophyll retrieval using ANNs. The statistical performance indices obtained from calibration (RMSE = 0.27; index of agreement = 0.96) and validation (RMSE = 0.66; index of agreement = 0.83) suggested that the ANN model is appropriate for chlorophyll prediction with good efficiency. The outcome of this work can be used by upcoming hyperspectral missions such as Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Hyperspectral Infrared Imager (HyspIRI) for large-scale estimation of chlorophyll and could help in the real-time monitoring of crop health status.

Spectral harmonization and red edge prediction of Landsat-8 to Sentinel-2 using land cover optimized multivariate regressors

Multi-sensor remote sensing applications consistently gain importance, boosted by a growing number of freely available earth observation data, increasing computing capacity, and increasingly complex algorithms that need as temporally dense data as possible. Using data provided by different sensors can greatly improve the temporal resolution of time series, fill data gaps and thus improve the quality of land cover monitoring applications. However, multi-sensor approaches are often adversely affected by different spectral characteristics of the sensing instruments, leading to inconsistencies in downstream products. Spectral harmonization, i.e., the transformation of one sensor into the spectral domain of another sensor, may reduce these inconsistencies. It simplifies workflows, increases the reliability of subsequently derived multi-sensor products and may also enable the generation of new products that are not possible with the initial spectral definition. In this paper, we compare the effect of multivariate spectral harmonization techniques on the inter-sensor reflectance consistency and derived products such as spectral indices or land cover classifications. We simulated surface reflectance data of Landsat-8 and Sentinel-2A from airborne hyperspectral data to eliminate any sources of error originating from unequal acquisition geometries, illumination or atmospheric state. We evaluate different methods based on linear, quadratic and random forest regression as well as linear interpolation, and predict not only matching but also unilaterally missing bands (red edge). We additionally consider material-dependent spectral characteristics in the harmonization process by using separate transformation functions for spectral clusters of the input dataset. Our results suggest that spectral harmonization is useful to improve multi-sensor consistency of remote sensing data and subsequently derived products, especially if multiple transformation functions are incorporated. There is a strong dependency between harmonization performance and the similarity of source and target sensor's spectral characteristics. For spectrally transforming Landsat-8 to Sentinel-2A, we achieved the lowest radiometric inter-sensor deviations with 50 spectral clusters and linear regression. Based on simulated data, deviations are below 1.7% reflectance within the red edge spectral region and below 0.3% reflectance for the remaining bands (RMSE). Regarding spectral indices, our results show a reduction of inter-sensor deviation (vegetation pixels only) to 38% of the initial error for NDVI (Normalized Difference Vegetation Index) and to 43% for EVI (Enhanced Vegetation Index). Furthermore, we computed the REIP (Red Edge Inflection Point) with an accuracy of 3.1 nm from Sentinel-2 adapted Landsat-8 data. An exemplary multispectral classification use case revealed an increasing inter-sensor consistency of classification results from 92.3% to 97.3% mean error. Applied to time series of real Landsat-8 and Sentinel-2 data, we observed similar trends, albeit intermingled with non-sensor-induced inconsistencies.

Correlating Sentinel-2 MSI-derived vegetation indices with in-situ reflectance and tissue macronutrients in savannah grass

ABSTRACTThe application of vegetation indices is subject to sensor-dependent errors and uncertainty. This study examines the accuracy of Sentinel-2 Multi-Spectral Instrument (MSI) imagery when estimating biophysical properties of savannah grasses. Six commonly used vegetation indices utilising spectral ranges covered by the MSI were derived from a satellite image coinciding with a field campaign. The imaging and fieldwork dates were at the end of the growing season, at peak grass productivity. Two common grass species were selected for sampling: one broad-leaved, the other narrow-leaved. At widely spread sampling sites under different grazing intensities, five plants with no external manifestation of infection and in close proximity were selected for each species. From each species 35 foliar and 10 stem reflectance measurements were collected using a spectroradiometer which sensed in the 350–2500 nm range in 1.1–1.4 nm bandwidths. The reflectance was later averaged to generate one reflectance profile per species per sampling site. The leaves and stems from which reflectance was measured were collected for laboratory analysis to determine macronutrient (N, P, K, Ca, Mg) concentrations. At three sites where sampling coincided with sunny weather during the satellite overpass window of 09:30–10:30 AM local time, above canopy grass reflectance was measured at ground resolution distance (GRD) of 1 m. Some reflectance was collected within 10 min of image acquisition, which facilitated comparison. The image data were corrected to bottom-of-atmosphere reflectance using the Sent2Cor algorithm whose output included 20 m GRD visible, red edge, near- and short-wave infrared (SWIR) bands, which were used for the respective vegetation indices. The plant level and above canopy reflectance were resampled to the spectral ranges of the MSI bands, and values of the respective indices computed. Plant level values of three red edge indices, which collectively indicated green biomass and chlorophyll, had the strongest significant correlation with N concentrations in both grass species (r = 0.473–0.561; p < 0.01). P and K concentrations had low correlations with the tested indices. Largely due to canopy background reflectance, the above canopy and image-derived vegetation index values differed from corresponding plant level values by up to 9% and 40%, respectively. Despite the attenuation, the Red Edge Chlorophyll Index, Red Edge Inflection Point and a devised SWIR ratio index (ρ1650 nm/ρ2200 nm) showed potential for monitoring relative chlorophyll and green biomass (indicated by N concentrations), Ca and Mg content of savannah grass using Sentinel-2 MSI images.