Handheld Spectroradiometer Research Articles

Target Detection and Characterization of Multi-Platform Remote Sensing Data

Detecting targets in remote sensing imagery, particularly when identifying sparsely distributed materials, is crucial for applications such as defense, mineral exploration, agriculture, and environmental monitoring. The effectiveness of detection and the precision of the results are influenced by several factors, including sensor configurations, platform properties, interactions between targets and their background, and the spectral contrast of the targets. Environmental factors, such as atmospheric conditions, also play a significant role. Conventionally, target detection in remote sensing has relied on statistical methods that typically assume a linear process for image formation. However, to enhance detection performance, it is critical to account for the geometric and spectral variabilities across multiple imaging platforms. In this research, we conducted a comprehensive target detection experiment using a unique benchmark multi-platform hyperspectral dataset, where man-made targets were deployed on various surface backgrounds. Data were collected using a hand-held spectroradiometer, UAV-mounted hyperspectral sensors, and airborne platforms, all within a half-hour time window. Multi-spectral space-based sensors (i.e., Worldview and Landsat) also flew over the scene and collected data. The experiment took place on 23 July 2021, at the Rochester Institute of Technology’s Tait Preserve in Penfield, NY, USA. We validated the detection outcomes through receiver operating characteristic (ROC) curves and spectral similarity metrics across various detection algorithms and imaging platforms. This multi-platform analysis provides critical insights into the challenges of hyperspectral target detection in complex, real-world landscapes, demonstrating the influence of platform variability on detection performance and the necessity for robust algorithmic approaches in multi-source data integration.

A Wavelet Decomposition Method for Estimating Soybean Seed Composition with Hyperspectral Data

Soybean seed composition, particularly protein and oil content, plays a critical role in agricultural practices, influencing crop value, nutritional quality, and marketability. Accurate and efficient methods for predicting seed composition are essential for optimizing crop management and breeding strategies. This study assesses the effectiveness of combining handheld spectroradiometers with the Mexican Hat wavelet transformation to predict soybean seed composition at both seed and canopy levels. Initial analyses using raw spectral data from these devices showed limited predictive accuracy. However, by using the Mexican Hat wavelet transformation, meaningful features were extracted from the spectral data, significantly enhancing prediction performance. Results showed improvements: for seed-level data, Partial Least Squares Regression (PLSR), a method used to reduce spectral data complexity while retaining critical information, showed R2 values increasing from 0.57 to 0.61 for protein content and from 0.58 to 0.74 for oil content post-transformation. Canopy-level data analyzed with Random Forest Regression (RFR), an ensemble method designed to capture non-linear relationships, also demonstrated substantial improvements, with R2 increasing from 0.07 to 0.44 for protein and from 0.02 to 0.39 for oil content post-transformation. These findings demonstrate that integrating handheld spectroradiometer data with wavelet transformation bridges the gap between high-end spectral imaging and practical, accessible solutions for field applications. This approach not only improves the accuracy of seed composition prediction at both seed and canopy levels but also supports more informed decision-making in crop management. This work represents a significant step towards making advanced crop assessment tools more accessible, potentially improving crop management strategies and yield optimization across various farming scales.

Carbon Flux Models in a Zonally Dry Tropical Forest Area (Caatinga)

Studies on energy exchange in ecosystems are critical for understanding carbon flows amid different vegetation patterns. In semi-arid areas, comprehending the variability in carbon absorption is essential for quantifying and anticipating the impacts of changes in the caatinga ecosystem. This study aims to calibrate and evaluate models for Gross Primary Production (GPP), Net Ecosystem Exchange (NEE), and Ecosystem Respiration (Reco) in the caatinga biome. NEE measurements were obtained and calculated at 30-minute intervals using EddyPro 3.6 software, from raw data measured at 10 Hz. GPP was estimated by partitioning NEE and Reco, all measured in micromoles of CO2 per square meter per second (μmol CO2m⁻²s⁻¹) using the eddy covariance (EC) tower, installed in a legal reserve at Embrapa Semiárido, in Petrolina, Pernambuco, within a section of the caatinga canopy. Following the measurement of carbon fluxes and ecosystem respiration, field measurements were conducted using a portableFieldSpec HandHeld spectroradiometer to obtain the reflectance of the canopy around the turbulent eddy covariance tower. These measurements were carried out in 2015 in a preserved caatinga area. Multiple linear regression models weredeveloped to estimate carbon fluxes from orbital images, enabling accurate estimation of GPP, NEE, and Reco, using the MODIS/Terra Daily Surface Reflectance product (MOD09GA). The primary results demonstrate the effectiveness of the developed models, particularly the GPP model, which exhibited the best statistical indices (R = 0.97; R² = 0.95; Root Mean Square Error = 0.20). Observations from the EC tower indicated that the models developed to estimate NEE, GPP, and Reco, using visible and near-infrared reflectance data, accurately represented the dry period and effectively captured the phenological aspects of the caatinga ecosystem.

Monitoring wheat area using sentinel-2 imagery and In-situ spectroradiometer data in heterogeneous field conditions

Crop statistics are crucial for developing a demand-based export and import strategy to ensure a country’s sustainable food security. Remote sensing efficiently generates essential crop statistics, while ground-based supplementary sensor data offers sufficient information for crop delineation. This study explored the multispectral satellite imagery using in-situ ground-based hyperspectral reflectance phenology information as training data to delineate wheat from other competitive winter crops in Northwestern Bangladesh as a case study. Wheat spectral signatures were primarily obtained through a hand-held Spectroradiometer at various phenological stages, aligned with Sentinel-2 data availability. Five vegetation indices (VIs), namely Normalized Difference Vegetation Index (NDVI), Red-edge NDVI (RENDVI), Enhanced Vegetation Index (EVI), Greenness Chromatic Coordinate (GCC) and Soil-Adjusted Vegetation Index (SAVI), were derived from Spectroradiometer-data across six wheat growth stages: seedling, tillering, booting, flowering, grain development, and maturity. Maximum and minimum threshold values for the VIs at those six growth stages were determined from regression analysis of the values collected from Spectroradiometer and Sentinel-2. A rule-based classification technique was then used to categorize Sentinel-2 for wheat crop delineation based on those threshold values. The results revealed that maps based on NDVI, EVI, and SAVI showed overall accuracies of 83.33%, 85.18%, and 81.48%, respectively. These accuracies were found to be statistically acceptable (p < 0.05) outcomes. A positive agreement was observed when comparing the remotely sensed area at the union (4th tier administrative level) with the officially reported data of Bangladesh. This innovative method has the potential to be extended for developing phenology and area delineation for other major crops locally and globally.

Bazı Ekmeklik ve Makarnalık Buğday Çeşitlerinde Sarı Pas Hastalığının Fenolojik Dönemlere göre Hastalık-Verim İlişkisinin Çok Bantlı (Hiperspektral) Veriler Kullanılarak Araştırılması

The aim of this study was to evaluate the severity of yellow rust in different phenological periods by &#x0D; subjecting bread (Bayraktar 2000, Demir 2000, Eser and Kenanbey) and durum (Çeşit-1252, Eminbey, Kızıltan 91 and Mirzabey 2000) wheat varieties to different spore doses (0%, 25%, 50% and 100%) under controlled epidemic conditions. The research was conducted in Yenimahalle, Ankara, Turkey during the 2018-2019 growing season. In the study, the morphological changes in yellow rust severity were determined at different phenological developmental stages of the test materials with the reflectance values obtained by using handheld spectroradiometer in different spore dose applications during the period from tillering to stalk emergence. These reflectance values were converted into vegetation index values expressed by mathematical formulae and used in determining yield estimates. Considering the results obtained, it was determined that the spectral indices calculated especially in the early flowering period (25 May 2019, Feekes 10.5.1) were effective in yield estimation for all bread varieties except Kenanbey variety (15 June 2019, Feekes 10.5.4). It was determined that the spectral band region of 25 May 2019 (Feekes 10.5.1), which includes all indices determined to predict yield in all bread and durum varieties and which is the beginning of flowering, was effective. In grain yield estimation, it was determined that there was a decrease in the correlation values of the spectral indices starting from the early flowering period (Feekes 10.5.1) towards the grain setting period (Feekes 10.5.3) and milk maturity period &#x0D; (Feekes 10.5.4). When the correlations between these index values and yield values were examined, it was &#x0D; determined that prominent phenological periods and high correlation indices could be calculated for these periods. Nowadays, with the use of optical sensor technology instead of traditional disease surveillance methods, the way has paved the way for the development of new approaches for early, fast and accurate yield estimation as a result of the verification of images taken by unmanned aerial vehicles on which multispectral and hyperspectral cameras are located with ground data using artificial intelligence and deep learning techniques.

Assessing the Leaf Blade Nutrient Status of Pinot Noir Using Hyperspectral Reflectance and Machine Learning Models

Monitoring grape nutrient status, from flowering to veraison, is important for viticulturists when implementing vineyard management strategies, in order to produce quality wines. However, traditional methods for measuring nutrient elements incur high labour costs. The aim of this study is to explore the potential of predicting grapevine leaf blade nutrient concentration based on hyperspectral data. Leaf blades were collected at two Pinot Noir commercial vineyards at Martinborough, New Zealand. The leaf blade spectral data were obtained with a handheld spectroradiometer, to evaluate surface reflectance and derivative spectra in the spectrum range between 400 and 2400 nm. Afterwards, leaf blades nutrient concentrations (N, P, K, Ca, and Mg) were measured, and their relationships with the hyperspectral data were modelled by machine learning models; partial least squares regression (PLSR), random forest regression (RFR), and support vector regression (SVR) were used. Pearson correlation and recursive feature elimination, based on cross-validation, were used as feature selection methods for RFR and SVR, to improve the model’s performance. The variable importance score of PLSR, and permutation variable importance of RFR and SVR, were used to determine the most sensitive wavelengths, or spectral regions related to each biochemical variable. The results showed that the best predictive performance for leaf blade N concentration was based on PLSR to raw reflectance data (R2 = 0.66; RMSE = 0.15%). The combination of support vector regression with the Pearson correlation selected method and second derivative reflectance provided a high accuracy for K and Ca modelling (R2 = 0.7; RMSE = 0.06%; R2 = 0.62; RMSE = 0.11%, respectively). However, the modelling performance for P and Mg, by different feature groups and variable selection methods, was poor (R2 = 0.15; RMSE = 0.02%; R2 = 0.43; RMSE = 0.43%, respectively). Thus, a larger dataset is needed for improving the prediction of P and Mg. The results indicated that for Pinot Noir leaf blades, raw reflectance data had potential for the prediction of N concentration, while the second-derivative spectra were more suitable to predict K and Ca. This study led to the provision of rapid and non-destructive measurements of grapevine leaf nutrient status.

Early Identification of Plant Drought Stress Responses: Changes in Leaf Reflectance and Plant Growth Promoting Rhizobacteria Selection-The Case Study of Tomato Plants

Drought is a worldwide problem, especially in arid and semi-arid regions. Detection of drought stress at the initial stages, before visible signs, to adequately manage irrigation and crop fertilization to avoid crop yield loss, is a desire of most farmers. Here, we evaluated the response of tomato plants to water scarcity, through changes in leaf reflectance due to modification in leaf pigments’ content, which translates into differences in spectral reflectance indices (SRI) values. Our methodology is innovative, as we were able to easily calculate and identify several SRIs for the early detection of drought stress “invisible” responses. We used a handheld spectro-radiometer to obtain SRI values from leaves of tomato plants growing under two different water regimes for 37 days. In an ensemble of 25 SRIs, we identified 12 that showed a consistent trend of significant differences between treatments along the experiment and within these, NDVI, SR, ZMI, Ctr2, GM1, and GM2 were already significantly different between treatments at day 7 after the start of the experiment and Ctr1 at day 9; although, no signs of damage were visible. Therefore, our results pinpoint these SRIs as promising proxies for the early detection of “invisible” responses to drought onset. We also analyzed the relationship between the monitored SRIs and plant morphological parameters measured during the experiment, highlighting a relationship between GM1 and plant height and leaf number. Finally, we observed a high abundance of putative beneficial bacteria among isolates collected from the tomato water-limited rhizo-environment at the terminus of the experiment, suggesting the active recruitment or selection of Plant Growth Promoting Rhizobacteria by tomato roots as a response to drought. Our work may be adapted into an easy protocol, of rapid execution, to be used in small-scale fields for early drought stress detection.

Detecting peatland vegetation patterns with multi-temporal field spectroscopy

ABSTRACT Peatlands are one of the most significant terrestrial carbon pools, and the processes behind the carbon cycle in peatlands are strongly associated with different vegetation patterns. Handheld spectroradiometer data has been widely applied in ecological research, but there is a lack of studies on peatlands assessing how the temporal and spectral resolution affect the detectability of vegetation patterns. We collected field spectroscopy and vegetation inventory data at two northern boreal peatlands, Lompolojänkkä and Halssiaapa, between late May and August 2019. We conducted multivariate random forest regressions to examine the appropriate periods, benefits of multi-temporal data, and optimal spectral bandwidth and sampling interval for detecting plant communities and the two-dimensional (2D) %-cover, above-ground biomass (AGB) and leaf area index (LAI) of seven plant functional types (PFTs). In the best cross-site regression models for detecting plant community clusters (PCCs), R2 was 42.6–48.0% (root mean square error (RMSE) 0.153–0.193), and for PFT 2D %-cover 53.9–69.8% (RMSE 8.2–17.6%), AGB 43.1–61.5% (RMSE 86.2–165.5 g/m2) and LAI 46.3–51.3% (RMSE 0.220–0.464 m2/m2). The multi-temporal data of the whole season increased R2 by 13.7–24.6%-points and 10.2–33.0%-points for the PCC and PFT regressions, respectively. There was no single optimal temporal window for vegetation pattern detection for the two sites; in Lompolojänkkä the early growing season between late May and mid-June had the highest regression performance, while in Halssiaapa, the optimal period was during the peak season, from July to early August. In general, the spectral sampling interval between 1 to 10 nm yielded the best regression performance for most of the vegetation characteristics in Lompolojänkkä, whereas the optimal range extended to 20 nm in Halssiaapa. Our findings underscore the importance of fieldwork timing and the use of multi-temporal and hyperspectral data in detecting vegetation in spatially heterogeneous landscapes.

Developing and Using Empirical Bio-Optical Algorithms in the Western Part of the Bering Sea in the Late Summer Season

This study aimed to assess the applicability of global bio-optical algorithms for the estimation of chlorophyll-a (chl-a) concentration (C) and develop regional empirical bio-optical algorithms for estimating C and colored dissolved organic matter (CDOM) content (D) from ocean remote sensing reflectance spectra in the western part of the Bering Sea in the late summer period. The analysis took into account possible problems with the different relative contributions of phytoplankton and CDOM to water-leaving radiance and possible errors associated with the atmosphere correction procedure for ocean color satellite data. Shipborne remote sensing measurements obtained using an above-water hyperspectral ASD HandHeld spectroradiometer, satellite measurements collected via MODIS and VIIRS radiometers, and in situ measurements of C and D in seawater were used. The simulated values of the different multispectral satellite radiometers with daily or 2-day global coverage, obtained by applying the corresponding spectral response functions to ship hyperspectral data, were also analyzed. In this paper, a list of recommended regional bio-optical algorithms is presented. Recommendations are given depending on the possible quality of atmospheric correction and the purpose of use. To obtain more precise estimations of C, OC3/OC4-like algorithms should be used. If the atmosphere correction is poor, then use OC2-like algorithms in which spectral bands in the 476–539 nm range should be used to estimate C and bands near 443 nm to estimate D; however, in the last case, this will provide only the order of magnitude. To estimate more independent fields of C and D, it is necessary to use a spectral range of 501–539 nm for chl-a and bands near 412 nm in the case of modern satellite radiometers (e.g., OLCI or SGLI), for which this band is not the first. Additionally, we showed that global bio-optical algorithms can be applied with acceptable accuracy and similar recommendations.

Patterns in Foliar Isotopic Nitrogen, Percent Nitrogen, and Site Index for Managed Forest Systems in the United States

Patterns in foliar nitrogen (N) stable isotope ratios (δ15N) have been shown to reveal trends in terrestrial N cycles, including the identification of ecosystems where N deficiencies limit forest ecosystem productivity. However, there is a gap in our understanding of within-species variation and species-level response to environmental gradients or forest management. Our objective is to examine the relationship between site index, foliar %N, foliar δ15N and spectral reflectance for managed Douglas-fir (Pseudotsuga menziesii) and loblolly pine (Pinus taeda) plantations across their geographic ranges in the Pacific Northwest and the southeastern United States, respectively. Foliage was measured at 28 sites for reflectance using a handheld spectroradiometer, and further analyzed for δ15N and N concentration. Unlike the prior work for grasslands and shrubland species, our results show that foliar δ15N and foliar %N are not well correlated for these tree species. However, multiple linear regression models suggest a strong predictive ability of spectroscopy data to quantify foliar δ15N, with some models explaining more than 65% of the variance in the δ15N. Additionally, moderate to strong explanations of variance were found between site index and foliar δ15N (R2 = 0.49) and reflectance and site index (R2 = 0.84) in the Douglas-fir data set. The development of relationships between foliar spectral reflectance, δ15N and measures of site productivity provides the first step toward mapping canopy δ15N for these managed forests with remote sensing.

Estimation of eggplant yield with machine learning methods using spectral vegetation indices

Estimation of crop yields included in the planning is an essential condition for accurate and timely agricultural planning. Remotely sensed products, such as the spectral vegetation index (VI), are widely used in estimation of crop yields. The integration of remotely sensed data into machine learning methods will have the potential to develop a real-time management system specific to the area of interest. The main aim of the study was to determine the eggplant yield in field conditions, based on VIs obtained from a handheld spectroradiometer, using five different machine learning methods (artificial neural networks (ANN), support vector machines (SVR), k nearest neighbor (kNN), random forests (RF), and Adaptive boosting (AB)), and compare the performances of the methods. The data used in the study were obtained in field experiments focusing on determining the most suitable irrigation program for eggplant production in a semi-humid climate region in northern Turkey during 2015, 2016 and 2017 growing seasons. Irrigation treatments consisted of a total of five applications, which were full water application (I1:100 %) and different deficit ration of full water application (I2:I1x 75 %, I3: I1x50%, I4:I1x25% and I5: rainfed based). Input variables used in yield estimation models were determined by correlation analysis and principal components analysis (PCA). The inputs in the models were different combinations of 10 different VIs, the number of days after planting (DAP) and water application coefficients. In addition, an alternative approach was proposed, in which PCA components were used as input for yield estimation. All machine learning models using PCA-based inputs were estimated with higher accuracy than other input combinations. The best results were obtained with the ANN model based on PCA-based inputs; therefore, this model was chosen for eggplant yield estimation (coefficient of determination (R2) = 0.973, mean absolute error (MAE) = 274.816 kg ha−1, root mean square error (RMSE) = 352.787 kg ha−1 and Nash–Sutcliffe efficiency (NSE) = 0.951). The lowest accuracy for yield estimation was recorded in RF model. The prediction accuracy of the models using a single VI as input was low. Green index (GI) and green vegetation index (GVI) had the highest impact on eggplant yield, and eggplant yield was estimated with higher accuracy with these indices, which are sensitive to chlorophyll absorption. The findings of the current study demonstrate the benefits of using remotely sensed data and PCA together in machine learning models to more reliably and accurately estimate eggplant yield at regional scale.

Rubber-Tree Leaf Diseases Mapping Using Close Range Remote Sensing Images

Currently, close-range remote sensing method using drone-based platform which payload compact sensor has been used for monitoring and mapping in the agriculture sector at large area. Thus, this study is deployed drone with a compact sensor to identify the rubber tree leaf diseases based on two groups of a spectral wavelength which are visible (RGB: 0.4 μm – 0.7 μm) and near infrared (NIR: 0.7μm – 2.0 μm), respectively. Spectral obtained from drone-based platform will be validated using ground observation handheld spectroradiometer. Eight types of rubber tree clones leaf at three different conditions (healthy, unhealthy and severe) were randomly selected within the 9.4-hectare experimental rubber plot, Rubber Research Institute of Malaysia (RRIM), Kota Tinggi, Johor whereby consist RRIM 2000 series, RRIM 3000 series, and PB series, respectively. Based on the result, quantitative analysis shows that the f-value is smaller than Critical-one tail for healthy, unhealthy while for severe the f-value is larger than Critical-one tail. The f-value is 2.887 &lt; 4.283 (healthy), 0.002 &lt; 0.264 (unhealthy) and 1.008 &gt; 0.0526, respectively. Thus, this can be concluded that spectral and estimate is equal at the 0.05 significant levels. While qualitative analysis shows that each rubber clone tree diseases can be distinguished at the near infrared band for healthy, unhealthy, and severe, respectively.

Kiwi Plant Canker Diagnosis Using Hyperspectral Signal Processing and Machine Learning: Detecting Symptoms Caused by Pseudomonas syringae pv. actinidiae.

Pseudomonas syringae pv. actinidiae (Psa) has been responsible for numerous epidemics of bacterial canker of kiwi (BCK), resulting in high losses in kiwi production worldwide. Current diagnostic approaches for this disease usually depend on visible signs of the infection (disease symptoms) to be present. Since these symptoms frequently manifest themselves in the middle to late stages of the infection process, the effectiveness of phytosanitary measures can be compromised. Hyperspectral spectroscopy has the potential to be an effective, non-invasive, rapid, cost-effective, high-throughput approach for improving BCK diagnostics. This study aimed to investigate the potential of hyperspectral UV–VIS reflectance for in-situ, non-destructive discrimination of bacterial canker on kiwi leaves. Spectral reflectance (325–1075 nm) of twenty plants were obtained with a handheld spectroradiometer in two commercial kiwi orchards located in Portugal, for 15 weeks, totaling 504 spectral measurements. Several modeling approaches based on continuous hyperspectral data or specific wavelengths, chosen by different feature selection algorithms, were tested to discriminate BCK on leaves. Spectral separability of asymptomatic and symptomatic leaves was observed in all multi-variate and machine learning models, including the FDA, GLM, PLS, and SVM methods. The combination of a stepwise forward variable selection approach using a support vector machine algorithm with a radial kernel and class weights was selected as the final model. Its overall accuracy was 85%, with a 0.70 kappa score and 0.84 F-measure. These results were coherent with leaves classified as asymptomatic or symptomatic by visual inspection. Overall, the findings herein reported support the implementation of spectral point measurements acquired in situ for crop disease diagnosis.

A novel method to simulate AVIRIS-NG hyperspectral image from Sentinel-2 image for improved vegetation/wildfire fuel mapping, boreal Alaska

• - Vegetation mapped from simulated AVIRIS-NG image offers greater details and accuracy. • − 33% improvement in the new vegetation map accuracy. • - A novel approach to generate improved vegetation maps for Alaska’s boreal domain. Detailed vegetation maps are one of the primary inputs for forest and wildfire management. Hyperspectral remote sensing is a proven technique for detailed and accurate vegetation mapping. However, the availability of recent hyperspectral imagery in Alaska is limited because of the logistics and high cost involved in its acquisition. In this study, we simulated AVIRIS-NG (Airborne Visible InfraRed Imaging Spectrometer - Next Generation) hyperspectral data from widely available Sentinel-2 multispectral data using the Universal Pattern Decomposition Method (UPDM). The UPDM is a spectral unmixing technique that uses detailed ground spectra of vegetation classes and the Spectral Response Functions of AVIRIS-NG and Sentinel-2 sensors to simulate imagery with the same number of bands and spectral resolution as an AVIRIS-NG image. We simulated three images (each covering an area of 100 km × 100 km) from two ecoregions to test portability of the approach. We collected ground spectra of vegetation and bare ground during summers (2019–2021) using a PSR+ 3500 hand-held spectroradiometer and created a spectral library for this study. The Iterative Endmember Selection (IES) algorithm was used to optimize the spectral library and to select the most representative endmembers for simulation: birch, spruce, and gravel. We validated the simulated hyperspectral imagery by comparing it with available AVIRIS-NG images. The simulated image was visually and spectrally similar to the AVIRIS-NG image (RMSE of 0.03 and 0.02 for birch and spruce spectra, respectively). We applied the Random Forest image classification model to derive detailed vegetation maps from the simulated images. Our vegetation map showed an improvement of 33% in the map accuracy compared to the LANDFIRE EVT map. This study demonstrated an efficient and cost-effective approach to derive detailed vegetation maps at the Sentinel scene scale by simulating hyperspectral images in Google’s cloud environment. It offers a novel pathway to generate detailed vegetation and fuel maps for the whole boreal region of Alaska to aid effective forest and fire 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.

A Novel Spectral Index to Identify Cacti in the Sonoran Desert at Multiple Scales Using Multi-Sensor Hyperspectral Data Acquisitions

Accurate identification of cacti, whether seen as an indicator of ecosystem health or an invasive menace, is important. Technological improvements in hyperspectral remote sensing systems with high spatial resolutions make it possible to now monitor cacti around the world. Cacti produce a unique spectral signature because of their morphological and anatomical characteristics. We demonstrate in this paper that we can leverage a reflectance dip around 972 nm, due to cacti’s morphological structure, to distinguish cacti vegetation from non-cacti vegetation in a desert landscape. We also show the ability to calculate two normalized vegetation indices that highlight cacti. Furthermore, we explore the impacts of spatial resolution by presenting spectral signatures from cacti samples taken with a handheld field spectroradiometer, drone-based hyperspectral sensor, and aerial hyperspectral sensor. These cacti indices will help measure baseline levels of cacti around the world and examine changes due to climate, disturbance, and management influences.

Effect of Chlorophyll Content &amp; Solar Irradiance on Spectral Reflectance of Vegetation Canopies Acquired By Spectro-Radiometer

The aims of the study were: (i) to observe the effect of leaf chlorophyll content, Solar Irradiance and Normalized Difference Vegetation Index (NDVI) on spectral reflectance at Visible(Blue,Green,Red), Near Infrared (NIR) and Short Wave Infrared (SWIR) spectrum for a given number of vegetation types including Rongon (Ixora Coccinea), Hibiscus, Jhau, Grass and Togor(Tabernaemontana Divaricata).(ii) to investigate the relationship of Solar Irradiance with Normalized Difference Vegetation Index (NDVI) for the same number of vegetation types. This study used a five band hand-held spectro-radiometer “Multispectral Radiometer MSR-5” centered at wavelength 485nm, 560nm, 660nm, 830nm and 1650nm corresponding to bands 2, 3, 4, 5, 6 of Landsat 8 operational Land Imager (OLI) sensor. This spectro-radiometer provides solar irradiance and spectral reflectance values in the visible, NIR and SWIR spectrum which indirectly help to calculate Normalized Difference Vegetation Index (NDVI) for the given number of vegetation types. This study also used a Chlorophyll Meter (SPAD 502) to estimate chlorophyll concentration from the leaf of the vegetation types. The result shows that the value of the spectral reflectance correlated linearly with chlorophyll content at wavelength at 560nm and 1650 nm where the coefficient of determination R2 is 0.8761 and 0.6289 respectively. The spectral reflectance correlated inversely with NDVI at wavelength 485nm and 660nm where the coefficient of determination R2 was 0.5317 and 0.6191 respectively. This result also shows that solar irradiance relates inversely with chlorophyll content at wavelength 830nm where the coefficient of determination R2 was 0.8523.Lastly we have found that solar irradiance correlated inversely with NDVI where the coefficient of determination R2 was 0.7617.

Integrating remote sensing data, GIS analysis and field studies for mapping alteration zones at Wadi Saqia area, central Eastern Desert, Egypt

Remote sensing data, spectral analyses, Geographic Information System (GIS) tools and field work were integrated for detecting the spatial distribution of the alteration zones in the study area. Remote sensing studies including; spectral signatures analyses and satellite image processing, were used for identifying the alteration minerals setting of the study area then extracting the alteration zones. A field study was carried out to the study area and several tens of samples were collected. The spectral behavior of the collected samples was obtained using the “Advanced Spectral Devices (ASD) TerraSpec Halo mineral identifier” handheld spectro-radiometer. Spectral analyses were carried out on signatures of selected samples to obtain the characterizing absorption features and to reveal the identity of the alteration minerals (end-members) representing the alteration zones in the area. The identified end-members included; Clay minerals, white Mica, Epidote and Iron oxides. Spectral Angle Mapper (SAM), Spectral Feature Fitting (SFF), Constrained Energy Minimization (CEM), and Mixture Tuned Matched Filtering (MTMF) alteration mapping techniques were used to map the abundance of the identified alteration minerals from ASTER satellite image data. Lineaments were extracted automatically using combined hill-shade images obtained at multi-azimuths derived from Allos PALSAR 12.5 m Digital Elevation Model (DEM) data. The alteration and lineament data were integrated together using the GIS “Weighted Overlay Analysis” tool to delineate the most potential areas for alteration zones existence. The results were validated through plotting the field observation points on the resulted final alteration map; the accuracy was estimated by about 80%.

Evaluation of Point Hyperspectral Reflectance and Multivariate Regression Models for Grapevine Water Status Estimation

Monitoring and management of plant water status over the critical period between flowering and veraison, plays a significant role in producing grapes of premium quality. Hyperspectral spectroscopy has been widely studied in precision farming, including for the prediction of grapevine water status. However, these studies were presented based on various combinations of transformed spectral data, feature selection methods, and regression models. To evaluate the performance of different modeling pipelines for estimating grapevine water status, a study spanning the critical period was carried out in two commercial vineyards at Martinborough, New Zealand. The modeling used six hyperspectral data groups (raw reflectance, first derivative reflectance, second derivative reflectance, continuum removal variables, simple ratio indices, and vegetation indices), two variable selection methods (Spearman correlation and recursive feature elimination based on cross-validation), an ensemble of selected variables, and three regression models (partial least squares regression, random forest regression, and support vector regression). Stem water potential (used as a proxy for vine water status) was measured by a pressure bomb. Hyperspectral reflectance was undertaken by a handheld spectroradiometer. The results show that the best predictive performance was achieved by applying partial least squares regression to simple ratio indices (R2 = 0.85; RMSE = 110 kPa). Models trained with an ensemble of selected variables comprising multicombination of transformed data and variable selection approaches outperformed those fitted using single combinations. Although larger data sizes are needed for further testing, this study compares 38 modeling pipelines and presents the best combination of procedures for estimating vine water status. This may lead to the provision of rapid estimation of vine water status in a nondestructive manner and highlights the possibility of applying hyperspectral data to precision irrigation in vineyards.

Simultaneous assessment of nitrogen and water status in winter wheat using hyperspectral and thermal sensors

Remote sensing is a valuable tool for reducing the environmental impact of agricultural practices by detecting crop nitrogen (N) and water status for site-specific N fertilization and irrigation. The interaction between N and water status may produce confounding effects in the acquired spectral reflectance, making it difficult to separate crop deficiencies. The objective of this study was to evaluate the potential of visible and infrared hyperspectral and thermal imaging sensors for N and water status assessment with reduced confounding effects. A winter wheat (Triticum aestivum L.) field experiment combining four N and two irrigation levels was conducted in Central Spain over 2 years. The Nitrogen Nutrition Index (NNI) was monitored (mid stem elongation, final stem elongation, flowering stage) and the crop water status was measured with a leaf porometer at flowering. Two hyperspectral sensors covering the visible and near infrared regions (400–850 nm) and part of the short-wave infrared (950–1750 nm) together with a thermal camera were installed on-board an aircraft to acquire images 300 m above the experiment. In addition, canopy reflectance (400−1000 nm) was measured with a handheld spectroradiometer at ground level. The relationship between the ground-based determination of N and water status with indicators based on remote sensors was analyzed. The planar domain Canopy Chlorophyll Content Index (CCCI) reduced soil background noise and correlated with the NNI in all cases (R2 > 0.44; P < 0.001). Reliable assessment of water status was achieved by using the Water Deficit Index (WDI), which is calculated using the Vegetation Index-Temperature trapezoid. The CCCI distinguished between N levels reducing the confounding effect of the water status, in contrast to the WDI which was mostly affected by the water status. Combining the CCCI and WDI to assess the crop NNI reduced the root mean square error to 0.109, suggesting that the combination of spectral and thermal information could improve the adjustment of N fertilization and irrigation to crop requirements. However, the approach must be validated in other cultivars and environments before making N fertilization and irrigation recommendations.