Published Vegetation Indices Research Articles

Predicting leaf nitrogen content in wolfberry trees by hyperspectral transformation and machine learning for precision agriculture.

Leaf nitrogen content (LNC) is an important indicator for scientific diagnosis of the nutrition status of crops. It plays an important role in the growth, yield and quality of wolfberry. This study aimed to develop new spectral indices (NSIs) and constructed machine learning regression (MLR) models for predicting wolfberry tree LNC. By utilizing four smoothing methods and five mathematic transformation methods, we obtained the original spectral dataset and five spectral transformation datasets for quantitative analysis and model establishment. Subsequently, published vegetation indices (PVIs) were acquired, sensitive wavelengths (SWs) were screened and NSIs were calculated based on SWs. Then MLR models were constructed by combining NSIs from six spectral datasets with three machine learning algorithms. Finally, a comparison was made among the MLR models. The study indicated that the application of mathematical transformation highlighted the differences in spectra, the square root, first-derivative and second-derivative transformation improved the prediction accuracy of MLR models constructed based on NSIs (MLR-NSIs models). However, these transformations had little impact on improving the prediction ability of MLR models constructed based on PVIs (MLR-PVIs models). Additionally, The optimal model for predicting the LNC of wolfberry tree was obtained by using the Random Forest (RF) algorithm combined with NSIs developed by first-derivative transformation spectra. The determination coefficient of validation (Rv2) and ratio of percentage deviation (RPD) was 0.71 and 1.90, respectively. In conclusion, this study has demonstrated that the combination of hyperspectral transformation and machine learning is useful for improving the accuracy of LNC estimation in wolfberry trees.

Development of new indices and use of CARS-Ridge algorithm for wheat fusarium head blight detection using in-situ hyperspectral data

Wheat Fusarium head blight (FHB) poses a significant threat to wheat quality and yield. However, accurately identifying the disease in wheat ears remains challenging due to limited data and weak hyperspectral signals. This study aimed to address these issues by obtaining effective wheat canopy hyperspectral data over three years of successive experiments. To reduce band redundancy and improve accuracy, nine different models were constructed, and the optimal algorithm was determined. Additionally, two types of new indices, were developed based on the spectral response mechanism of the wheat disease and published vegetation indices. Our study demonstrated that these newly constructed indices outperformed the published vegetation indices in terms of detection capability. By fusing the optimal algorithm with the new indices, a detection accuracy of 91.4% for the disease was achieved, surpassing the current level of wheat FHB detection. The high-accuracy model developed in this study not only provides methodological support for detecting wheat FHB but also serves as a reference for diagnosing diseases in other crops.

Estimation of sugar content in sugar beet root based on UAV multi-sensor data

Sugar beet (Beta vulgaris L.) is the second largest sugar source crop in the world. Sugar content in its beetroot is an important quality index of sugar beet. Rapid and accurate estimation of sugar content in beetroot is helpful to high-throughput phenotype and genotype breeding. The purpose of the current study is to explore the potential of UAV-based multimodal remote sensing data in sugar content estimation. Multimodal data were collected by different UAV-platform carried with RGB, multi-spectral, thermal infrared, hyperspectral and LiDAR. Spectral, structure and thermal features of canopy were extracted from different sensor combinations to estimate sugar content during sugar accumulation period of beetroot by using Partial Least Squares Regression (PLSR), Bayesian Ridge Regression (BRR) and Support Vector Regression (SVR). The main results are as follows: (i) In estimating sugar content, published vegetation indices (PBI, NDVI, TCARI, etc.) calculated from hyperspectral as spectral features outperformed these of multispectral. Structure features calculated from LiDAR outperformed those from RGB. (ii) Eight newly developed vegetation indices presented better performance than published vegetation indices. Among them (R730-R734)/(R730 + R734) index using two bands and (R694-R714)/(R710-R714) using three bands presented the best performance. (iii) Multimodal data combination from different sensors improved estimation accuracy of sugar content than data from individual sensors. PLSR based multimodal data from LiDAR, hyperspectral, thermal infrared sensor presented the best accuracy with R2 = 0.64 and relative root mean square error (rRMSE) = 7.2 %. These results showed that UAV-based canopy features from multimodal data can be used to quickly and accurately obtain sugar content of beetroot. This study provides a basic reference for estimating bioactive substances in roots of root tuber crops based on multimodal canopy remote sensing data.

Digital mapping of soil organic carbon density using newly developed bare soil spectral indices and deep neural network

Soil organic carbon density (SOCD) is an important parameter of agricultural soils and is useful for the improvement of environment and agricultural production. Proximal and remote sensing techniques are effective methods for digital soil mapping of SOCD. The current study used three types of spectral data, including laboratory proximal spectra, airborne hyperspectral and Sentinel 2 multispectral images, to predict SOCD in an agricultural land. Bare soil spectral indices (BSSIs) were developed to predict SOCD and compared with the published vegetation indices (VIs). With 45 soil samples, the partial least square regression (PLSR), back propagation neural network (BPNN) and deep neural network (DNN) prediction models were established to map SOCD. The results showed that the laboratory spectra (R2 = 0.70–0.80) had the best performance of SOCD prediction, followed by the airborne (R2 = 0.43–0.81) and Sentinel 2 (R2 = 0.14–0.57) spectra. The SOCD maps derived from airborne and Sentinel 2 images had similar spatial distribution trends. The BSSIs (R2 = 0.24–0.81) obtained higher accuracy than the VIs (R2 = 0.14–0.74) in SOCD prediction. Moreover, DNN model was the best for digital soil mapping of SOCD among three prediction models. This study offered an effective approach for mapping SOCD in bare soil areas.

Identification and Comprehensive Evaluation of Resistant Weeds Using Unmanned Aerial Vehicle-Based Multispectral Imagery.

Atrazine is one of the most widely used herbicides in weed management. However, the widespread use of atrazine has concurrently accelerated the evolution of weed resistance mechanisms. Resistant weeds were identified early to contribute to crop protection in precision agriculture before visible symptoms of atrazine application to weeds in actual field environments. New developments in unmanned aerial vehicle (UAV) platforms and sensor technologies promote cost-effective data collection by collecting multi-modal data at very high spatial and spectral resolution. In this study, we obtained multispectral and RGB images using UAVs, increased available information with the help of image fusion technology, and developed a weed spectral resistance index, WSRI = (RE-R)/(RE-B), based on the difference between susceptible and resistant weed biotypes. A deep convolutional neural network (DCNN) was applied to evaluate the potential for identifying resistant weeds in the field. Comparing the WSRI introduced in this study with previously published vegetation indices (VIs) shows that the WSRI is better at classifying susceptible and resistant weed biotypes. Fusing multispectral and RGB images improved the resistance identification accuracy, and the DCNN achieved high field accuracies of 81.1% for barnyardgrass and 92.4% for velvetleaf. Time series and weed density influenced the study of weed resistance, with 4 days after application (4DAA) identified as a watershed timeframe in the study of weed resistance, while different weed densities resulted in changes in classification accuracy. Multispectral and deep learning proved to be effective phenotypic techniques that can thoroughly analyze weed resistance dynamic response and provide valuable methods for high-throughput phenotyping and accurate field management of resistant weeds.

Exploring hyperspectral reflectance indices for the estimation of water and nitrogen status of spinach

Optimizing irrigation and nitrogen (N) fertilizer management in vegetable production is key to meeting economic and ecological demands. Inadequate application increases the risk of quality and yield losses as well as environmental pollution. Accurate determination of both crops' water and N supply status is essential for an effective management strategy that couples N and water supply, thus accounting for the water–N interaction. In a field experiment with spinach as model crop, hyperspectral reflectance was measured and related to the relative water content (RWC), nitrogen and chlorophyll (Chl) content of leaves indicating plants' supply status. Three statistical methods were applied to estimate plants’ water and N status by reflectance spectroscopy: i) Published Vegetation Indices (VI) for N and water status, ii) Competitive Adaptive Reweighted Sampling - Partial Least Squares Regression (CARS-PLSR) to identify key wavelengths and to build a simple robust model based on full spectra, and iii) inflection points (IP) based on the first and second derivatives. The best model fit for Chl was obtained for VI REIP and IP1. For N, best results were found for mrNDVI and IP1. The estimation of RWC was not significant. Higher Chl content was found in stressed plants due to smaller and thicker leaves. At full water supply, REIP and IP1 as well as CARS-PLSR differentiated N status levels. In water-deficient plants, VIs and IPs successfully detected water stress levels. The basis for a differentiating nitrogen and water management using spectral data has been established.

Estimation of the vertically integrated leaf nitrogen content in maize using canopy hyperspectral red edge parameters

Real-time monitoring of leaf nitrogen (N) content by remote sensing can accurately diagnose crop nutrient status and facilitate precision N management. However, the methods used to estimate of vertically integrated leaf N content do not consider different cropping systems, in which the maize growth stages are not synchronized, resulting in decreased practical value of the results. The purpose of this study was to propose an optimized red-edge absorption area (OREA) index in which the prediction accuracy of vertically integrated leaf N content is improved within spring- and summer-sown maize canopies. The results showed that vertical distributions of N existed regardless of variations in the maize growth stages, that is, the leaf N density of the upper and middle layers was higher than that of the lower layers. These published vegetation indices (VIs) provided relatively good correlations with leaf N density at different layers across all of the datasets. When predicting leaf N density of each leaf layer, an optimal VI is generated, and inconsistent VIs will limit its practical application. To further overcome the drawbacks of the inconsistency of each VI when estimating the leaf N density at different layers, a new OREA index was proposed based on red-edge absorption area parameter. The OREA index showed the highest prediction accuracy with leaf N density for entire canopies (r2 = 0.811, RMSE = 0.374, RE = 13.17%) and canopies without top first leaves (r2 = 0.795, RMSE = 0.269, RE = 15.20%) compared with the other published VIs. It is concluded that the vertically integrated leaf N content under different field experiments can be accurately estimated by the OREA index.

Estimation of Multi-Species Leaf Area Index Based on Chinese GF-1 Satellite Data Using Look-Up Table and Gaussian Process Regression Methods

Leaf area index (LAI) is an important biophysical parameter, which can be effectively applied in the estimation of vegetation growth status. At present, amounts of studies just focused on the LAI estimation of a single plant type, while plant types are usually mixed rather than single distribution. In this study, the suitability of GF-1 data for multi-species LAI estimation was evaluated by using Gaussian process regression (GPR), and a look-up table (LUT) combined with a PROSAIL radiative transfer model. Then, the performance of the LUT and GPR for multi-species LAI estimation was analyzed in term of 15 different band combinations and 10 published vegetation indices (VIs). Lastly, the effect of the different band combinations and published VIs on the accuracy of LAI estimation was discussed. The results indicated that GF-1 data exhibited a good potential for multi-species LAI retrieval. Then, GPR exhibited better performance than that of LUT for multi-species LAI estimation. What is more, modified soil adjusted vegetation index (MSAVI) was selected based on the GPR algorithm for multi-species LAI estimation with a lower root mean squared error (RMSE = 0.6448 m2/m2) compared to other band combinations and VIs. Then, this study can provide guidance for multi-species LAI estimation.

Optimal vegetation index for assessing leaf water potential using reflectance factors from the adaxial and abaxial surfaces

Leaf water potential (LWP) is an effective indicator of plant water deficit. Determining the LWP by remote sensing can contribute to quick and non-destructive drought assessment. The purpose of this study was to investigate the quantitative relationship between LWP and different vegetation indices (VIs). Some previous indices and new indices proposed in this study were used to determine LWP. Our results showed that most of the previously published vegetation indices had a strong correlation with LWP based on measurements conducted on the adaxial leaf surface; however, the correlation between LWP and VIs derived from the reflectance factor of both the abaxial and adaxial surfaces was relatively poor. Based on the optimization process of the reflectance factor indices, a modified DATT (MDATT) index (R1740-R2370)/(R1740-R1750), which had the strongest correlation with LWP, is proposed as a new indicator for the remote assessment of LWP considering different leaf structures (R2 = 0.85, P < 0.01 for two leaf types based on measurements from both the adaxial and abaxial surfaces). This new index is insensitive to the adaxial and abaxial leaf surface structures; therefore, LWP can be accurately estimated. This new index can provide scientific guidance for monitoring the water status, growth, and health conditions of vegetation.

Identification of plants responding to natural gas microleakage stress using solar-induced chlorophyll fluorescence

With the increasing use of natural gas, gas emissions (predominantly methane) from natural gas infrastructure are exacerbating the greenhouse effect. Therefore, it is crucial to detect natural gas leaks in a timely manner. The remote sensing of plants offers the potential to identify leakage from the reflectance responses of the plants to elevated concentrations of natural gas in the soil. Solar-induced chlorophyll fluorescence (SIF) is directly related to photosynthesis and is thus proposed to track the impact of natural gas leakage on vegetation growth. In order to explore the feasibility and advantage of SIF for detection of natural gas leaks, a field experiment of natural gas microleakage is performed, and soybean and grass are chosen as the test plants. Sixteen plots, eight with soybean and eight with grass, are established, and four of each plant are chosen for the injection of natural gas into the soil. The other plots act as controls. We present weekly ground measurements of canopy spectra over plots of gassed and ungassed soybean and grass. SIF at 760 nm (F760) and other published vegetation indices are calculated and analyzed. The indices can identify grasses under natural gas microleakage stress throughout the whole experiment, whereas only F760 can identify soybeans under natural gas microleakage stress during the later stage of the experiment. When the canopy chlorophyll content of soybean is at high levels, F760 can still increase along with it, whereas other indices fail to increase because of saturation. The results suggest that the F760 index can stably and reliably identify plants under natural gas microleakage stress. Therefore, in the future, the location of leakage of natural gas can be detected by monitoring the F760 of plants with hyperspectral remote sensing.

Estimating Peanut Leaf Chlorophyll Content with Dorsiventral Leaf Adjusted Indices: Minimizing the Impact of Spectral Differences between Adaxial and Abaxial Leaf Surfaces

Relatively little research has assessed the impact of spectral differences among dorsiventral leaves caused by leaf structure on leaf chlorophyll content (LCC) retrieval. Based on reflectance measured from peanut adaxial and abaxial leaves and LCC measurements, this study proposed a dorsiventral leaf adjusted ratio index (DLARI) to adjust dorsiventral leaf structure and improve LCC retrieval accuracy. Moreover, the modified Datt (MDATT) index, which was insensitive to leaves structure, was optimized for peanut plants. All possible wavelength combinations for the DLARI and MDATT formulae were evaluated. When reflectance from both sides were considered, the optimal combination for the MDATT formula was ( R 723 − R 738 ) / ( R 723 − R 722 ) with a cross-validation R2cv of 0.91 and RMSEcv of 3.53 μg/cm2. The DLARI formula provided the best performing indices, which were ( R 735 − R 753 ) / ( R 715 − R 819 ) for estimating LCC from the adaxial surface (R2cv = 0.96, RMSEcv = 2.37 μg/cm2) and ( R 732 − R 754 ) / ( R 724 − R 773 ) for estimating LCC from reflectance of both sides (R2cv = 0.94, RMSEcv = 2.81 μg/cm2). A comparison with published vegetation indices demonstrated that the published indices yielded reliable estimates of LCC from the adaxial surface but performed worse than DLARIs when both leaf sides were considered. This paper concludes that the DLARI is the most promising approach to estimate peanut LCC.

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.

A New Integrated Vegetation Index for the Estimation of Winter Wheat Leaf Chlorophyll Content

Leaf chlorophyll content (LCC) provides valuable information about the nutrition and photosynthesis statuses of crops. Vegetation index-based methods have been widely used in crop management studies for the non-destructive estimation of LCC using remote sensing technology. However, many published vegetation indices are sensitive to crop canopy structure, especially the leaf area index (LAI), when crop canopy spectra are used. Herein, to address this issue, we propose four new spectral indices (The red-edge-chlorophyll absorption index (RECAI), the red-edge-chlorophyll absorption index/optimized soil-adjusted vegetation index (RECAI/OSAVI), the red-edge-chlorophyll absorption index/ the triangular vegetation index (RECAI/TVI), and the red-edge-chlorophyll absorption index/the modified triangular vegetation index(RECAI/MTVI2)) and evaluate their performance for LCC retrieval by comparing their results with those of eight published spectral indices that are commonly used to estimate LCC. A total of 456 winter wheat canopy spectral data corresponding to physiological parameters in a wide range of species, growth stages, stress treatments, and growing seasons were collected. Five regression models (linear, power, exponential, polynomial, and logarithmic) were built to estimate LCC in this study. The results indicated that the newly proposed integrated RECAI/TVI exhibited the highest LCC predictive accuracy among all indices, where R2 values increased by more than 13.09% and RMSE values reduced by more than 6.22%. While this index exhibited the best association with LCC (0.708** ≤ r ≤ 0.819**) among all indices, RECAI/TVI exhibited no significant relationship with LAI (0.029 ≤ r ≤ 0.167), making it largely insensitive to LAI changes. In terms of the effects of different field management measures, the LCC predictive accuracy by RECAI/TVI can be influenced by erective winter wheat varieties, low N fertilizer application density, no water application, and early sowing dates. In general, the newly developed integrated RECAI/TVI was sensitive to winter wheat LCC with a reduction in the influence of LAI. This index has strong potential for monitoring winter wheat nitrogen status and precision nitrogen management. However, further studies are required to test this index with more diverse datasets and different crops.

High-Throughput Field Phenotyping of Leaves, Leaf Sheaths, Culms and Ears of Spring Barley Cultivars at Anthesis and Dough Ripeness.

To optimize plant architecture (e.g., photosynthetic active leaf area, leaf-stem ratio), plant physiologists and plant breeders rely on destructively and tediously harvested biomass samples. A fast and non-destructive method for obtaining information about different plant organs could be vehicle-based spectral proximal sensing. In this 3-year study, the mobile phenotyping platform PhenoTrac 4 was used to compare the measurements from active and passive spectral proximal sensors of leaves, leaf sheaths, culms and ears of 34 spring barley cultivars at anthesis and dough ripeness. Published vegetation indices (VI), partial least square regression (PLSR) models and contour map analysis were compared to assess these traits. Contour maps are matrices consisting of coefficients of determination for all of the binary combinations of wavelengths and the biomass parameters. The PLSR models of leaves, leaf sheaths and culms showed strong correlations (R2 = 0.61–0.76). Published vegetation indices depicted similar coefficients of determination; however, their RMSEs were higher. No wavelength combination could be found by the contour map analysis to improve the results of the PLSR or published VIs. The best results were obtained for the dry weight and N uptake of leaves and culms. The PLSR models yielded satisfactory relationships for leaf sheaths at anthesis (R2 = 0.69), whereas only a low performance for all of sensors and methods was observed at dough ripeness. No relationships with ears were observed. Active and passive sensors performed comparably, with slight advantages observed for the passive spectrometer. The results indicate that tractor-based proximal sensing in combination with optimized spectral indices or PLSR models may represent a suitable tool for plant breeders to assess relevant morphological traits, allowing for a better understanding of plant architecture, which is closely linked to the physiological performance. Further validation of PLSR models is required in independent studies. Organ specific phenotyping represents a first step toward breeding by design.

Estimation of arsenic in agricultural soils using hyperspectral vegetation indices of rice

This study systematically analyzed the performance of multivariate hyperspectral vegetation indices of rice (Oryza sativa L.) in estimating the arsenic content in agricultural soils. Field canopy reflectance spectra was obtained in the jointing-booting growth stage of rice. Newly developed and published multivariate vegetation indices were initially calculated to estimate soil arsenic content. The well-performing vegetation indices were then selected using successive projections algorithm (SPA), and the SPA selected vegetation indices were adopted to calibrate a multiple linear regression model for estimating soil arsenic content. Results showed that a three-band vegetation index (R716−R568)/(R552−R568) performed best in the newly developed vegetation indices in estimating soil arsenic content. The photochemical reflectance index (PRI) and red edge position (REP) performed well in the published vegetation indices. Moreover, the linear combination of two vegetation indices ((R716−R568)/(R552−R568) and REP) selected using SPA improved the estimation of soil arsenic content. These results indicated that the newly developed three-band vegetation index (R716−R568)/(R552−R568) might be recommended as an indicator for estimating soil arsenic content in the study area. PRI and REP could be used as universal vegetation indices for monitoring soil arsenic contamination.

Fast and nondestructive method for leaf level chlorophyll estimation using hyperspectral LiDAR

We propose an empirical method for nondestructive estimation of chlorophyll in tree canopies. The first prototype of a full waveform hyperspectral LiDAR instrument has been developed by the Finnish Geodetic Institute (FGI). The instrument efficiently combines the benefits of passive and active remote sensing sensors. It is able to produce 3D point clouds with spectral information included for every point, which offers great potential in the field of environmental remote sensing.The investigation was conducted by using chlorophyll sensitive vegetation indices applied to hyperspectral LiDAR data and testing their performance in chlorophyll estimation. The amount of chlorophyll in vegetation is an important indicator of photosynthetic capacity and stress, and thus important for monitoring of forest condition and carbon sequestration on Earth.Performance of chlorophyll estimation was evaluated for 27 published vegetation indices applied to waveform LiDAR collected from ten Scots pine shoots. Reference data were collected by laboratory chlorophyll concentration analysis. The performance of the indices in chlorophyll estimation was determined by linear regression and leave-one-out cross-validation.The chlorophyll estimates derived from hyperspectral LiDAR linearly correlate with the laboratory analyzed chlorophyll concentrations, and they are able to represent a range of chlorophyll concentrations in Scots pine shoots (R2=0.88, RMSE=0.10mg/g). Furthermore, they are insensitive to measurement scale as nearly the same values of vegetation indices were measured in natural setting while scanning the whole canopy and from clipped shoots re-measured with hyperspectral LiDAR in laboratory. The results indicate that the hyperspectral LiDAR instrument has the potential to estimate vegetation biochemical parameters such as the chlorophyll concentration. The instrument holds much potential in various environmental applications and provides a significant improvement over single wavelength LiDAR or passive optical systems for environmental remote sensing.

Development and implementation of a multiscale biomass model using hyperspectral vegetation indices for winter wheat in the North China Plain

Crop monitoring during the growing season is important for regional management decisions and biomass prediction. The objectives of this study were to develop, improve and validate a scale independent biomass model. Field studies were conducted in Huimin County, Shandong Province of China, during the 2006–2007 growing season of winter wheat (Triticum aestivum L.). The field design had a multiscale set-up with four levels which differed in their management, such as nitrogen fertilizer inputs and cultivars, to create different biomass conditions: small experimental fields (L1), large experimental fields (L2), small farm fields (L3), and large farm fields (L4). L4, planted with different winter wheat varieties, was managed according to farmers’ practice while L1 through L3 represented controlled field experiments. Multitemporal spectral measurements were taken in the fields, and biomass was sampled for each spectral campaign. In addition, multitemporal Hyperion data were obtained in 2006 and 2007. L1 field data were used to develop biomass models based on the relation between the winter wheat spectra and biomass: several published vegetation indices, including NRI, REP, OSAVI, TCI, and NDVI, were investigated. A new hyperspectral vegetation index, which uses a four-band combination in the NIR and SWIR domains, named GnyLi, was developed. Following the multiscale concept, the data of higher levels (L2 through L4) were used stepwise to validate and improve the models of the lower levels, and to transfer the improved models to the next level. Lastly, the models were transferred and validated at the regional scale using Hyperion images of 2006 and 2007. The results showed that the GnyLi and NRI models, which were based on the NIR and SWIR domains, performed best with R2>0.74. All the other indices explained less than 60% model variability. Using the Hyperion data for regionalization, GnyLi and NRI explained 81–89% of the biomass variability. These results highlighted that GnyLi and NRI can be used together with hyperspectral images for both plot and regional level biomass estimation. Nevertheless, additional studies and analyses are needed to test its replicability in other environmental conditions.

Different units of measurement of carotenoids estimation in cotton using hyperspectral indices and partial least square regression

This paper examines the use of canopy reflectance for different units of measurements of carotenoids estimation. Field spectral measurements were collected over cotton in different intensive field campaigns organized during the growing seasons of 2010 and 2011. Three units of measurement were evaluated carotenoids expressed as a mass per unit soil surface area (g/m2), a mass per unit leaf area (μg/cm2), and a mass per unit fresh leaf weight (mg/g), respectively. Four methods were compared to retrieve amount of carotenoids: stepwise multiple linear regression (SMLR), published spectral indices, band combination indices, and partial least square regression (PLSR). Results show that maximum sensitivity of reflectance to variation in different units of measurement of carotenoids was found in the green region at 515–550nm, and at 715nm and 750nm regions in the far-red wavelengths. The predictive accuracies of Car (g/m2), Car (μg/cm2) and Car (mg/g) were tested on a validation data set and the results show that the highest R2 values between estimations and observations were 0.468 for Car (g/m2), 0.563 for Car (μg/cm2), and 0.456 for Car (mg/g), with relative root mean square error (RMSE%, RMSE/mean) of 48.72%, 22.07% and 21.07%, respectively. Compared to Car (g/m2) and Car (mg/g), the model performance indices for Car (μg/cm2) show a high degree of consistency among the R2 values and RMSE% and MAE% values. Further comparison were performed among the estimation accuracies of different unit carotenoids and among the different approaches used in the study by a paired-t-test. The results indicate that although the best estimation results for Car (μg/cm2) and Car (mg/g) were both obtained based on PLSR, they can be estimated by all four adopted methods without significant differences (P>0.1). Whereas for Car (g/m2), the best estimation results were obtained based on published vegetation indices CIred-edge, which were significantly better than the estimation results based on SMLR (P<0.000). In summary, the results of this study show that even the carotenoids expressed on concentration (mg/g) or content (μg/cm2) basis at leaf level can be estimated with the same prediction accuracies to the carotenoids expressed as a mass per unit surface area (g/m2) at canopy level using reflectance measurement at canopy level.

Nitrogen concentration estimation with hyperspectral LiDAR

Abstract. Agricultural lands have strong impact on global carbon dynamics and nitrogen availability. Monitoring changes in agricultural lands require more efficient and accurate methods. The first prototype of a full waveform hyperspectral Light Detection and Ranging (LiDAR) instrument has been developed at the Finnish Geodetic Institute (FGI). The instrument efficiently combines the benefits of passive and active remote sensing sensors. It is able to produce 3D point clouds with spectral information included for every point which offers great potential in the field of remote sensing of environment. This study investigates the performance of the hyperspectral LiDAR instrument in nitrogen estimation. The investigation was conducted by finding vegetation indices sensitive to nitrogen concentration using hyperspectral LiDAR data and validating their performance in nitrogen estimation. The nitrogen estimation was performed by calculating 28 published vegetation indices to ten oat samples grown in different fertilization conditions. Reference data was acquired by laboratory nitrogen concentration analysis. The performance of the indices in nitrogen estimation was determined by linear regression and leave-one-out cross-validation. The results indicate that the hyperspectral LiDAR instrument holds a good capability to estimate plant biochemical parameters such as nitrogen concentration. The instrument holds much potential in various environmental applications and provides a significant improvement to the remote sensing of environment.

Estimating leaf nitrogen concentration with three-band vegetation indices in rice and wheat

Real-time and nondestructive monitoring of crop nitrogen (N) status is of significant importance for precision N management in rice and wheat production. In eight field experiments with different N rates, water regimes and cultivars in rice and wheat crops, a new form of three-band vegetation indices was constructed to reduce saturation in two-band vegetation indices, and the optimal common three-band vegetation index was selected to establish models for canopy leaf N concentration (LNC) monitoring in rice and wheat. The results showed that the linear models for LNC monitoring with (R924−R703+2×R423)/(R924+R703−2×R423) were stable and accurate, with coefficient of determination (R2) of 0.870 and 0.857, and SE of 0.052 and 0.148 in rice and wheat, respectively. Testing of the models with independent data gave R2 of 0.866 and 0.883, RRMSE of 13.1% and 16.9%, and slope of 0.741 and 0.980 in rice and wheat, respectively. Further analysis of the influence of bandwidth change on LNC accuracy indicated that the allowable bandwidths for the central bands were 36nm for 924nm, 15nm for 703nm and 21nm for 423nm. The new three-band vegetation indices with narrow bands and broad bands in the present study are generally more effective for LNC monitoring compared with the other published vegetation indices.