Ratio Spectral Index Research Articles

Assessment of Burned Forest Area Severity and Postfire Regrowth in Chapada Diamantina National Park (Bahia, Brazil) Using dNBR and RdNBR Spectral Indices

Fire scar detection through orbital data can be done using specific techniques, such as the use of spectral indices like the normalized burn ratio (NBR), which are designed to help identify burnt areas as they have typical spectral responses. This paper aims to characterize burn severity and regrowth in areas hit by three fires in the Chapada Diamantina National Park (Bahia, Brazil) and its surrounding area through the differenced normalized burn ratio (dNBR) and relative differenced normalized burn ratio (RdNBR) spectral indices. The data acquired were pretreated and prepared adequately to calculate the indices. We conclude that for the study area, considering the limitations of fieldwork, the multitemporal index dNBR and the relative index RdNBR are important tools for classifying burnt areas and can be used to assess the regrowth of vegetation.

Detecting Burn Severity across Mediterranean Forest Types by Coupling Medium-Spatial Resolution Satellite Imagery and Field Data

In Mediterranean countries, in the year 2017, extensive surfaces of forests were damaged by wildfires. In the Vesuvius National Park, multiple summer wildfires burned 88% of the Mediterranean forest. This unprecedented event in an environmentally vulnerable area suggests conducting spatial assessment of the mixed-severity fire effects for identifying priority areas and support decision-making in post-fire restoration. The main objective of this study was to compare the ability of the delta Normalized Burn Ratio (dNBR) spectral index obtained from Landsat-8 and Sentinel-2A satellites in retrieving burn severity levels. Burn severity levels experienced by the Mediterranean forest communities were defined by using two quali-quantitative field-based composite burn indices (FBIs), namely the Composite Burn Index (CBI), its geometrically modified version CBI (GeoCBI), and the dNBR derived from the two medium-resolution multispectral remote sensors. The accuracy of the burn severity map produced by using the dNBR thresholds developed by Key and Benson (2006) was first evaluated. We found very low agreement (0.15 < K < 0.21) between the burn severity class obtained from field-based indices (CBI and GeoCBI) and satellite-derived metrics (dNBR) from both Landsat-8 and Sentinel-2A. Therefore, the most appropriate dNBR thresholds were rebuilt by analyzing the relationships between two field-based (CBI and GeoCBI) and dNBR from Landsat-8 and Sentinel-2A. By regressing alternatively FBIs and dNBRs, a slightly stronger relationship between GeoCBI and dNBR metrics obtained from the Sentinel-2A remote sensor (R2 = 0.69) was found. The regressed dNBR thresholds showed moderately high classification accuracy (K = 0.77, OA = 83%) for Sentinel-2A, suggesting the appropriateness of dNBR-Sentinel 2A in assessing mixed-severity Mediterranean wildfires. Our results suggest that there is no single set of dNBR thresholds that are appropriate for all burnt biomes, especially for the low levels of burn severity, as biotic factors could affect satellite observations.

Detection of Soil Total Nitrogen, Phosphorus and Potassium Content Based on the Spectral Information of Citrus Canopy

The content of total nitrogen, phosphorus and potassium in the soil is an important basis for calculating the best fertilization amount and realizing the reasonable fertilization of the orange planting soil in time and accurately. In this research, hyper-spectral technology is applied to detect the three soil nutrient elements in the cultivated soil of citrus trees. Four spectral bands with central wavelengths of 660, 780, 870 and 970 nm and width of 20 nm are selected to measure the spectral reflectance of the potted kumquats canopy by hand-held spectrometer. At the same time, the content of total nitrogen, phosphorus and potassium in the cultivated soil of kumquat is determined using traditional methods. The correlation between the spectral information of kumquat canopy and the content of soil nutrient elements is analyzed by correlation coefficient method. The prediction model of soil nutrient element content based on the spectral information is established by multiple linear regression method. The results showed that the total potassium content of soil is significantly correlated with the spectral reflectance at 660 nm wave band and significantly correlated with the spectral indexes of R660/R780, R660/R870 and R660/R970. R2 of the prediction model of total potassium content established on the basis of spectral reflectance and ratio spectral index of the selected band is 0.756 and 0.857 respectively, which shows good prediction effect of the model. There is no significant correlation between the spectral reflectance and ratio spectral index of the selected band towards the total soil nitrogen and phosphorus content and it cannot be used for the rapid measurement of total nitrogen and phosphorus content. Further study is needed to select suitable spectral band and spectral index.

Estimating Salt Concentrations Based on Optimized Spectral Indices in Soils with Regional Heterogeneity

Soil salinity is one of the most damaging environmental problems worldwide, especially in arid and semiarid regions. The objectives of this study were to improve the inversion accuracy of soil salt content (SSC) in soils with spectral heterogeneity by using optimized spectral indices. Soil samples at a 0–20 cm depth were taken from Keriya Oasis (98 soil samples), Ugan-Kuqa Oasis (49 soil samples), and Ebinur Lake Basin (57 soil samples). SSC and spectral reflectance (SR) of all the 204 soil samples were determined. To comprehensively analyze the field-collected hyperspectral data, various band combinations were used to calculate a normalized difference spectral index (NDSI) and ratio spectral index (RSI). Then, the relationships between the indices and SSC were examined, and the most robust relationships were demonstrated. The partial least squares regression (PLSR) method was utilized to develop a predictive model of SSC, and the variable importance in the projection (VIP) method was used during modeling. The results revealed that (i) the salinized soils in different regions had apparent differences in both reflectance and spectral curve morphology, but the optimized spectral indices method effectively overcame the regional heterogeneity of salinized soil hyperspectral characteristics, and the correlation with SSC was always kind, with correlation coefficients up to 0.748 at 0.001 level of significance; (ii) the VIP filtering method effectively selected the optimal independent model, and the modeling accuracy was better than the single optimization index (R2Pre = 0.83 and RMSEPre = 2.31 g·kg−1) by using the combination of two optimal indices; (iii) although the global modeling accuracy was significantly lower than the local modeling accuracy due to the inconsistent salt sensitivity bands of salinized soils in different regions, combined with cross-validation analysis, the global model had the ability to predict soil salinization accurately (R2Pre = 0.69 and RMSEPre = 8.45 g·kg−1). The methods developed in this study can be applied in other arid and semiarid areas. Besides, the study also provides examples for aerospace hyperspectral remote sensing of cross-regional soil salinization.

Estimating leaf nitrogen concentration considering unsynchronized maize growth stages with canopy hyperspectral technique

Abstract Accurate monitoring of the leaf nitrogen concentration (LNC) in maize can provide a fundamental basis for effective N management. In recent decades, many spectral indices and algorithms can accurately estimate the crop N status during different growth stages of maize. However, the effect of unsynchronized maize growth stages on these spectral models is rarely considered. The objectives of this study were to verify the predictive ability of the published vegetation indices (VIs), the partial least squares (PLS) regression and the two-band optimal combinations algorithms, and to determine the most accurate method for assessing the LNC of unsynchronized growth stages in maize. Canopy raw and first-derivative reflectance (FDR) spectra, and destructive measurements of the maize LNC were collected in 2016–2018 in different ecological areas, unsynchronized growth stages, cultivars, plant densities, and N rates. The published VIs and new 2-band VIs and their band combinations varied across different growth stages and were not affected by unsynchronized growth stages. The red-edge chlorophyll index (CIred edge), which was identified across growth stages, performed quite well for LNC estimation, but performed unsatisfactorily when compared to the new 2-band VIs that were developed in this study. The best spectral index of the green or red and red-edge or near-infrared band combination based on FDR spectra had a good diagnostic effect for LNC of maize across the four growth stages, in which the novel normalized difference spectral indices (NDSI) effect was optimal in the V9 (9-leaf stage), VT (tasseling stage) and R1 (silking stage) stage and the novel ratio spectral indices (RSI) was the best in the R3 (milk stage) stage. Compared to PLS regression based on raw full-range hyperspectral data, the PLS regression for estimating LNC across four growth stages based on FDR full-range hyperspectral data showed a higher accuracy, with an average coefficient of determination (rval2) of 0.87 and average root mean square error (RMSEval) of 0.18. In addition, the average rval2 for the PLS regression based on selected FDR wavelengths increased by 2.40% and the average RMSEval decreased by 14.8%, compared with the best performing VIs during the four growth stages. It is concluded that the best 2-band VIs and the PLS regression based on selected FDR wavelengths provide a useful explorative tool for estimating LNC of maize across years, ecological areas, and unsynchronized growth stages.

First Results of Phytoplankton Spatial Dynamics in Two NW-Mediterranean Bays from Chlorophyll-a Estimates Using Sentinel 2: Potential Implications for Aquaculture

Shellfish aquaculture has a major socioeconomic impact on coastal areas, thus it is necessary to develop support tools for its management. In this sense, phytoplankton monitoring is crucial, as it is the main source of food for shellfish farming. The aim of this study was to assess the applicability of Sentinel 2 multispectral imagery (MSI) to monitor the phytoplankton biomass at Ebro Delta bays and to assess its potential as a tool for shellfish management. In situ chlorophyll-a data from Ebro Delta bays (NE Spain) were coupled with several band combination and band ratio spectral indices derived from Sentinel 2A levels 1C and 2A for time-series mapping. The best results (AIC = 72.17, APD < 10%, and MAE < 0.7 mg/m3) were obtained with a simple blue-to-green ratio applied over Rayleigh corrected images. Sentinel 2–derived maps provided coverage of the farm sites at both bays allowing relating the spatiotemporal distribution of phytoplankton with the environmental forcing under different states of the bays. The applied methodology will be further improved but the results show the potential of using Sentinel 2 MSI imagery as a tool for assessing phytoplankton spatiotemporal dynamics and to encourage better future practices in the management of the aquaculture in Ebro Delta bays.

Monitoring leaf potassium content using hyperspectral vegetation indices in rice leaves

Potassium (K) is one of three main crop nutrients, and the high rate of potash fertilizer utilization (second only to nitrogen) leads to high prices. Therefore, efficient application, as well as rapid and time monitoring of K in crops is essential. Several turnover box and field experiments were conducted across multiple years and cultivation factors (i.e., potassium levels and plant varieties) yielding 340 groups of leaf samples with different K contents; these samples were used to examine the relationship between reflectance spectra (350–2500 nm) and leaf K content (LKC). The correlation between LKC and the two-band spectral indices computed with random two bands from 350 to 2500 nm were determined for the published K vegetation indices in rice. Results showed that the spectral reflectance, R, of the shortwave infrared (1300–2000 nm) region was sensitive to the K levels and significantly correlated with rice LKC. New shortwave infrared two-band spectral indices, Normalized difference spectral index [NDSI (R1705, R1385)], Ratio spectral index [RSI (R1385, R1705)], and Difference spectral index [DSI (R1705, R1385)], showed good correlations with LKC (R2 up to 0.68). Moreover, the three-band spectral indices (R1705 − R700)/(R1385 − R700) and (R1705 − R1385)/(R1705 + R1385 − 2 × R704) were developed by adding red edge bands to improve accuracy. Three-band spectral indices had an improved prediction accuracy for rice LKC (R2 up to 0.74). However, several previously published K-sensitive vegetation indices did not yield good results in this study. Validation with independent samples showed that the indices (R1705 − R700)/(R1385 − R700) and (R1705 − R1385)/(R1705 + R1385 − 2 × R704) had higher accuracies and stabilities than two-band indices and are suitable for quantitatively estimating rice LKC. The widescale application of these proposed vegetation indices in this paper still needs to be verified in different environmental conditions. This study provides a technical basis for LKC monitoring using spectral remote sensing in rice.

Phenotyping flag leaf nitrogen content in rice using a three-band spectral index

Accurate, rapid and non-destructive measurements of rice flag leaf nitrogen content (LNC) are crucial for geneticists and breeders. To help design a less expensive, non-destructive, real-time LNC sensor, we developed a Three-Band Difference Ratio (TBDR) spectral index, TBDR (R755, R513, R508). This spectral index could accurately and rapidly estimate rice LNC in a population of chromosome segment substitution lines with small variation in LNC. The model estimating LNC was validated using a leave-one-out cross-validation technique; the achieved root mean square error was 0.13% and the relative error was 4.74%. In comparison with SPAD-502plus chlorophyll meter readings and commonly used spectral indices, including GreenSeeker- and Crop Circle-based indices, TBDR (R755, R513, R508) produced higher accuracy in LNC estimation.

An optimized non-linear vegetation index for estimating leaf area index in winter wheat

Using hyperspectral remote sensing technology to monitor leaf area index (LAI) in a timely, fast and non-destructive manner is essential for accurate quantitative crop management. The relationships between existing vegetation indices (VIs) and LAI usually tend to saturate under dense canopies in crop production. The purpose of this study was to propose a new VI in which the estimating saturation is greatly weakened, and prediction accuracy is improved under conditions of high LAI in winter wheat (Triticum aestivum L.). The quantitative relationship between ground-based canopy spectral reflectance and LAI in wheat was investigated. The results showed that the optimized band combination, namely, the form of non-linear vegetation index (NLI) was more sensitive to changes in LAI. When λ(x1) = 798 nm and λ(y2) = 728 nm, the band combination NLI (798,728) had the highest R2 of 0.757. Among the common VIs, the modified triangular vegetation index 2 (MTVI2), the ratio spectral index [RSI (760,730)] and the 2-band enhanced vegetation index (EVI2) gave superior performance (R2 > 0.710) in terms of LAI estimation, but were worse than NLI (798,728). Inspired by the modified non-linear vegetation index (MNLI), NLI (798,728) was further optimized to become a novel optimized non-linear vegetation index (ONLI), which can be calculated by the formula {{left( { 1 { + 0} . 0 5} right) , times , left( { 0. 6, times ,R_{ 7 9 8}^{2} , - ,R_{ 7 2 8} } right)} mathord{left/ {vphantom {{left( { 1 { + 0} . 0 5} right) , times , left( { 0. 6, times ,R_{ 7 9 8}^{2} , - ,R_{ 7 2 8} } right)} { left( { 0. 6, times ,R_{ 7 9 8}^{2} , + ,R_{ 7 2 8} { + 0} . 0 5} right)}}} right. kern-0pt} { left( { 0. 6, times ,R_{ 7 9 8}^{2} , + ,R_{ 7 2 8} { + 0} . 0 5} right)}}. The unified ONLI model gave an R2 of 0.779 and root mean square error (RMSE) of 1.013 across all datasets. These results indicate that the novel ONLI has strong adaptability to various cultivation conditions and can provide a good estimate of LAI in winter wheat.

Exploring the potential of leaf reflectance spectra for retrieving the leaf maximum carboxylation rate

ABSTRACT The maximum carboxylation rate (V cmax) is a key photosynthetic parameter that is determined by the leaf biochemistry and environmental conditions. Numerous studies have shown that plant biochemical, physiological and structural parameters can be estimated from reflectance spectra. Therefore, it is reasonable to assume that V cmax can be spectrally determined. Here, we investigate the potential of leaf reflectance spectra for retrieving the maximum carboxylation rate of leaves. Measurements of leaf reflectance, carbon dioxide (CO2) response curves, leaf chlorophyll-a + b (chl-a + b) etc., were made on 80 crop, shrub and tree leaves. Then, the leaf V cmax,25 was linked to leaf biochemistry and spectral reflectance. A reliable relationship, with a coefficient of determination (R 2) value of 0.75, was found between the leaf chl-a + b content and Vcmax,25. The leaf Vcmax,25 values were also significantly correlated with chl-a + b-sensitive spectral indices with the highest R 2 value that was found being 0.83 for the ratio spectral index (RSI) using reflectances at 1089 nm and 695 nm. Finally, multiple stepwise regression (MSR) and a partial least-squares regression (PLSR) modelling approach were used to estimate V cmax,25 from leaf reflectances. The results confirmed that V cmax,25 can be reliably estimated from leaf reflectance spectra and give an R 2 value >0.80. These findings show that leaf chl-a + b can be used as a proxy for leaf V cmax,25 and that leaf V cmax,25 can be spectrally determined using leaf reflectance data.

Assessment of factors driving high fire severity potential and classification in a Mediterranean pine ecosystem

Fire severity is an increasingly critical issue for forest managers for estimating fire impacts. Estimating high fire severity potential and accurate classification between fire severity levels are essential for integrated fire management planning in fire prone Mediterranean pine ecosystems. This study attempts to determine the role of topography, pre-fire forest stand structure, fuel complex characteristics and fire behavior parameters on high fire severity potential and classification based on a large fire event occurred in Thasos, Greece. Within this framework, the Random Forest (RF) classification algorithm was used to model the relationship between a large set of predictors and fire severity as expressed by the differenced Normalized Burn Ratio (dNBR) spectral index, inferred from differenced pre- and post-fire Landsat 8 Operational Land Imager (OLI) at 30-m resolution. Results from the RF classifier algorithm showed that high fire severity potential and classification between fire severity levels mainly depended on topography variables and fuel complex characteristics. Assessing of factors which drive a fire to turn into high severe fire and classification into fire severity levels will substantially help land and forest managers to increase fire prevention and develop of concrete actions for successful post fire management at landscape level.

Comparison of various modelling approaches for water deficit stress monitoring in rice crop through hyperspectral remote sensing

This study was conducted to understand the behaviour of ten rice genotypes for different water deficit stress levels. The spectroscopic hyperspectral reflectance data in the range of 350–2500 nm was recorded and relative water content (RWC) of plants was measured at different stress levels. The optimal wavebands were identified through spectral indices, multivariate techniques and neural network technique, and prediction models were developed. The new water sensitive spectral indices were developed and existing water band spectral indices were also evaluated with respect to RWC. These indices based models were efficient in predicting RWC with R2 values ranging from 0.73 to 0.94. The contour plotting using the ratio spectral indices (RSI) and normalized difference spectral indices (NDSI) was done in all possible combinations within 350–2500 nm and their correlations with RWC were quantified to identify the best index. Spectral reflectance data was also used to develop partial least squares regression (PLSR) followed by multiple linear regression (MLR) and Artificial Neural Networks (ANN), support vector machine regression (SVR) and random forest (RF) models to calculate plant RWC. Among these multivariate models, PLSR-MLR was found to be the best model for prediction of RWC with R2 as 0.98 and 0.97 for calibration and validation respectively and Root mean square error of prediction (RMSEP) as 5.06. The results indicate that PLSR is a robust technique for identification of water deficit stress in the crop. Although the PLSR is robust technique, if PLSR extracted optimum wavebands are fed into MLR, the results are found to be improved significantly. The ANN model was developed with all spectral reflectance bands. The 43 developed model didn’t produce satisfactory results. Therefore, the model was developed 44 with PLSR selected optimum wavebands as independent x variables and PLSR-ANN model 45 was found better than the ANN model alone. The study successfully conducts a comparative 46 analysis among various modelling approaches to quantify water deficit stress. The methodology developed would help to identify water deficit stress more accurately by predicting RWC in the crops.

Monitoring litchi canopy foliar phosphorus content using hyperspectral data

Phosphorus (P) is an important element to litchi yield and fruit quality in addition to nitrogen (N) and potassium (K). This study was undertaken to explore the ability to predict P content using canopy reflectance. Some published indices and two ratio spectral indices (Ratio of reflectance index, RRI; Ratio of reflectance difference index, RRDI) developed by band interactive-optimization algorithms were investigated to determine their performance in predicting litchi canopy foliar P content. The results showed that optimal spectral indices selected by correlation analyses reached the highest level of accuracy in the retrieval of P content at each growth stage (R2cv = 0.54–0.98, RMSEcv = 0.02–0.03). The particular wavelengths of importance in the significant RRIs and RRDIs changed with the growing stages, cultivars and planting conditions. The sensitive wavebands ranged from the visible to the short-wave infrared (SWIR) regions, which are related to the absorption features of pigments (e.g., anthocyanin, chlorophyll), proteins, nitrogen, starch, sugar, oil, cellulose, and lignin. And the wavebands in SWIR region were used in the optimal RRIs and RRDIs for growth stages. This study demonstrates that the optimal RRDI is useful in predicting litchi foliar P content. The successes of use of SWIR in foliar nutrient monitoring is important for precision agriculture.

Wildfires as a major challenge for natural regeneration in Atlantic Forest

The natural regeneration management is a good strategy of ecological restoration of the Atlantic Forest, one of the most devastated biomes on the planet. However, the frequent occurrence of wildfires is one of the challenges to the success of this method. The objective of this study was to evaluate the effects of wildfires on forest dynamics in Atlantic Forest. The studied area was explored during the coffee cycle when plantations replaced primary forests. We used remote sensing data to analyze the forest dynamics over a period of 50 years (1966–2016). We used the INPE burn database to find the occurrence of hot spots from 1998 to 2016. During this period, we selected the years most affected by the fires for the identification of fire scars using the Normalized Burn Ratio spectral index. From this set of information, we used the methodology of weights of evidence to relate forest dynamics and wildfire events with biophysical and anthropic variables. The results showed that in 1966 the forest area accounted for 8.01% of the land cover, and in 2016 this number rose to 18.55% due to the spontaneous natural regeneration process. The regenerating areas were mainly related to the proximity of the remaining fragments and the portions of the landscape receiving the least amount of global solar radiation. The proximity to urban areas, roads and highways, damaged regeneration and favored both deforestation and wildfire events. Fire scars preferentially occur where there is greater sun exposure. It is possible to observe a negative correlation between the natural regeneration process and the fire scars. We concluded that fire severity is one of the factors that shape the landscape of the region while slowing the regeneration process in preferential areas.

Detection of Stress in Cotton (Gossypium hirsutum L.) Caused by Aphids Using Leaf Level Hyperspectral Measurements.

Remote sensing can be a rapid, accurate, and simple method for assessing pest damage on plants. The objectives of this study were to identify spectral wavelengths sensitive to cotton aphid infestation. Then, the normalized difference spectral indices (NDSI) and ratio spectral indices (RSI) based on the leaf spectrum were obtained within 350–2500 nm, and their correlation with infestation were qualified. The results showed that leaf spectral reflectance decreased in the visible range (350–700 nm) and the near-infrared range (NIR, 700–1300 nm) as aphid damage severity increased, and significant differences were found in blue, green, red, NIR and short-wave infrared (SWIR) band regions between different grades of aphid damage severity. Decrease in Chlorophyll a (Chl a) pigment was more significant than that in Chlorophyll (Chl b) in the infested plants and the Chl a/b ratio showed a decreasing trend with increase in aphid damage severity. The sensitive spectral bands were mainly within NIR and SWIR ranges. The best spectral indices NDSI (R678, R1471) and RSI (R1975, R1904) were formulated with these sensitive spectral regions through reducing precise sampling method. These new indices along with 16 other stress related indices compiled from literature were further tested for their ability to detect aphid damage severity. The two indices in this study showed significantly higher coefficients of determination (R2 of 0.81 and 0.81, p < 0.01) and the least RMSE values (RMSE of 0.50 and 0.49), and hence have potential application in assessing aphid infestation severity in cotton.

Exploring new spectral bands and vegetation indices for estimating nitrogen nutrition index of summer maize

Abstract Accurately and timely diagnosis of plant nitrogen (N) status is imperative for N fertilization management and yield prediction of summer maize. This study was aimed to identify the most sensitive/appropriate spectral band combinations to estimate the N nutrition index (NNI) by comprehensive analyses on canopy spectral reflectance from visible to near-infrared light, to develop the optimum vegetation indices for NNI during V6-V12 growth period, and to validate the regression models for estimating NNI of summer maize by comparing the two methods (direct and indirect) to determine the most appropriate method for practical use. Five multi-locational and multi-N rates (0–320 kg ha−1) field experiments were conducted during three growing seasons (2015, 2016 and 2017) using five summer maize cultivars. The measurements regarding canopy spectral reflectance, plant biomass, and plant N concentration were taken at critical stages of summer maize under the various N treatments. Comprehensive analyses on the different regression models of NNI for normalized difference spectral index (NDSI) and ratio spectral index (RSI) composed of any two bands between 325 and 905 nm of summer maize were made by using the reduced precise sampling method. The NNI values in the present study ranged from 0.68 to 1.15 under different N treatments. The most sensitive spectral bands were located at 710 nm (red edge band) and 512 nm (visible light band) and the optimum spectral vegetation index for estimating NNI was NDSI (R710, R512). The linear regression model between NDSI (R710, R512) and NNI was NNI = 0.95 NDSI (R710, R512) + 0.14. Additionally, the soil-adjusted vegetation index (SAVI) was used to correct NDSI(R710, R512), and the performance of the linear regression model was best when the parameter L (soil-brightness correction factor) of SAVI (R710, R512) was 0.05. The performances of the direct and indirect NNI estimation methods were compared. The validation results showed that the performance of the newly developed vegetation indices (NDSI (R710, R512) and SAVI (R710, R512)(L=0.05)) was the best with the relative root mean square error (RRMSE) values ranging from 11.4% and 13.1% in the direct method; while the performance of the existing vegetation indices (Ratio Vegetation Index II and modified SAVI) were best with RRMSE value of 16.9% in the indirect method. It was concluded that both the direct and indirect methods can be used to estimate NNI of summer maize, but the construction of the newly developed vegetation indices was easier in the direct method. The projected results will provide a technical basis for potential application of remote sensing technology for monitoring and diagnosis of plant N nutrition in summer maize production.

Multi-Temporal and Spectral Analysis of High-Resolution Hyperspectral Airborne Imagery for Precision Agriculture: Assessment of Wheat Grain Yield and Grain Protein Content.

This study evaluates the potential of high resolution hyperspectral airborne imagery to capture within-field variability of durum wheat grain yield (GY) and grain protein content (GPC) in two commercial fields in the Yaqui Valley (northwestern Mexico). Through a weekly/biweekly airborne flight campaign, we acquired 10 mosaics with a micro-hyperspectral Vis-NIR imaging sensor ranging from 400–850 nanometres (nm). Just before harvest, 114 georeferenced grain samples were obtained manually. Using spectral exploratory analysis, we calculated narrow-band physiological spectral indices—normalized difference spectral index (NDSI) and ratio spectral index (RSI)—from every single hyperspectral mosaic using complete two by two combinations of wavelengths. We applied two methods for the multi-temporal hyperspectral exploratory analysis: (a) Temporal Principal Component Analysis (tPCA) on wavelengths across all images and (b) the integration of vegetation indices over time based on area under the curve (AUC) calculations. For GY, the best R2 (0.32) were found using both the spectral (NDSI—Ri, 750 to 840 nm and Rj, ±720–736 nm) and the multi-temporal AUC exploratory analysis (EVI and OSAVI through AUC) methods. For GPC, all exploratory analysis methods tested revealed (a) a low to very low coefficient of determination (R2 ≤ 0.21), (b) a relatively low overall prediction error (RMSE: 0.45–0.49%), compared to results from other literature studies, and (c) that the spectral exploratory analysis approach is slightly better than the multi-temporal approaches, with early season NDSI of 700 with 574 nm and late season NDSI of 707 with 523 nm as the best indicators. Using residual maps from the regression analyses of NDSIs and GPC, we visualized GPC within-field variability and showed that up to 75% of the field area could be mapped with relatively good predictability (residual class: −0.25 to 0.25%), therefore showing the potential of remote sensing imagery to capture the within-field variation of GPC under conventional agricultural practices.

Quantitative monitoring of sucrose, reducing sugar and total sugar dynamics for phenotyping of water-deficit stress tolerance in rice through spectroscopy and chemometrics

In the present investigation, the changes in sucrose, reducing and total sugar content due to water-deficit stress in rice leaves were modeled using visible, near infrared (VNIR) and shortwave infrared (SWIR) spectroscopy. The objectives of the study were to identify the best vegetation indices and suitable multivariate technique based on precise analysis of hyperspectral data (350 to 2500nm) and sucrose, reducing sugar and total sugar content measured at different stress levels from 16 different rice genotypes. Spectral data analysis was done to identify suitable spectral indices and models for sucrose estimation. Novel spectral indices in near infrared (NIR) range viz. ratio spectral index (RSI) and normalised difference spectral indices (NDSI) sensitive to sucrose, reducing sugar and total sugar content were identified which were subsequently calibrated and validated. The RSI and NDSI models had R2 values of 0.65, 0.71 and 0.67; RPD values of 1.68, 1.95 and 1.66 for sucrose, reducing sugar and total sugar, respectively for validation dataset. Different multivariate spectral models such as artificial neural network (ANN), multivariate adaptive regression splines (MARS), multiple linear regression (MLR), partial least square regression (PLSR), random forest regression (RFR) and support vector machine regression (SVMR) were also evaluated. The best performing multivariate models for sucrose, reducing sugars and total sugars were found to be, MARS, ANN and MARS, respectively with respect to RPD values of 2.08, 2.44, and 1.93. Results indicated that VNIR and SWIR spectroscopy combined with multivariate calibration can be used as a reliable alternative to conventional methods for measurement of sucrose, reducing sugars and total sugars of rice under water-deficit stress as this technique is fast, economic, and noninvasive.

Comparison of different uni- and multi-variate techniques for monitoring leaf water status as an indicator of water-deficit stress in wheat through spectroscopy

Ten different wheat genotypes were studied for understanding their differential behaviour to different water-deficit stress levels. Hyperspectral data (350–2500 nm) and relative water content (RWC) of plants were measured at different stress level for identifying optimal spectral bands, indices and multivariate models to develop non-invasive phenotyping protocols. Evaluation of water sensitive existing spectral indices, proposed indices and band depth analysis at selected wavelengths was done with respect to RWC and prediction models were developed. The prediction models developed were efficient in predicting RWC with the R2 values ranging from 0.73 to 0.88 for spectral indices and 0.74–0.85 with continuum depth. Then, the ratio spectral indices (RSI) and normalised difference spectral indices (NDSI) were obtained in all possible combinations within 350–2500 nm and their correlations with RWC were quantified to identify the best indices. The best spectral indices for estimating RWC in wheat were RSI (R1391, R1830) and NDSI (R1391, R1830) with R2 of 0.86 and 0.81, respectively. Spectral reflectance data were also used to develop partial least squares regression (PLSR) followed by multiple linear regression (MLR), support vector machine regression (SVR), multivariate adaptive regression spline (MARS) and random forest (RF) model to calculate plant RWC. Among these multivariate models, PLSR was the best model for prediction of RWC (R2 and RMSE of 0.96 and 3.88%; 0.91 and 6.52% for calibration and validation, respectively). The methodology developed would help for its further use in high-throughput phenomics of different crops for drought stress.

Limited Effects of Water Absorption on Reducing the Accuracy of Leaf Nitrogen Estimation

Nitrogen is an essential nutrient in many terrestrial ecosystems because it affects vegetation’s primary production. Due to the variety of nitrogen-containing substances and the differences in their composition across species, statistical approaches are now dominant in remote sensing retrieval of leaf nitrogen content. Many studies remove spectral regions characterized by strong water absorptions before retrieving nitrogen content, because water is believed to mask the absorption features of nitrogen. The objectives of this study are to discuss the necessity of this practice and to explore how water absorption affects leaf nitrogen estimation. Spectral measurements and chemical analyses for Maize, Sawtooth Oak, and Sweetgum leaves were carried out in 2014. The leaf optical properties model PROSPECT5 was used to eliminate the influences of water on the measured reflectance spectra. The inversion accuracy of PROPECT5 for chlorophyll, carotenoid, water, and dry matter of Maize was also discussed. Measured, simulated, and water-removed spectra were used to: (1) find the optimal nitrogen-related spectral index; and (2) regress with the area-based leaf nitrogen concentration (LNC) using the partial least square regression technique (PLSR). Two types of spectral indices were selected in this study: Normalized Difference Spectral Index (NDSI) and Ratio Spectral Index (RSI). Additionally, first-order derivative forms of measured, simulated, and water-removed spectra were devised to search for the optimal spectral indices. Finally, species-specific optimal indices and cross-species optimal indices, as well as their root mean square errors (RMSE) and coefficients of determination (R2), were obtained. The Ending Top Percentile (ETP), an indicator of the performance of cross-species optimal indices, was also calculated. PLSR was combined with leave-one-out cross validation (LOOCV) for each species. The predicted root mean square errors (RMSEP) and predicted R2 were finally calculated. The results showed that chlorophyll, carotenoid, and water contents could be estimated with R2 of 0.75, 0.59, and 0.69, respectively, which were acceptable for fresh leaves. The dry matter was retrieved with a relatively lower accuracy because of the fixed absorption coefficients adopted by PROSPECT5. The performances of species-specific optimal indices using water-free spectra were comparable to or worse than the corresponding indices derived with measured or simulated spectra. Compared with measured spectra, ETP did not change much after the effects of water were removed, and the R2 between cross-species optimal spectral indices and area-based LNC for Sawtooth Oak and Sweetgum decreased while it remained almost the same for Maize, suggesting that the water-removed cross-species optimal indices were inferior to the corresponding optimal indices found without water removal. ETP was larger than 30% for all spectra, demonstrating the non-existence of common optimal NDSI or RSI for the three species. After water removal, the accuracy of PLSR for Sawtooth Oak and Sweetgum decreased and increased negligibly for Maize. The results suggest that water absorption has limited effects on reducing the accuracy of leaf nitrogen estimation. On the contrary, the accuracy may decrease due to the loss of spectral information caused by the removal of water-sensitive spectral regions.