Xia Yao

Monitoring leaf nitrogen in wheat using canopy reflectance spectra

Non-destructive monitoring of leaf nitrogen (N) status can assist in growth diagnosis, N management and productivity forecast in field crops. The objectives of this study were to determine the relationships of leaf nitrogen concentration on a leaf dry weight basis (LNC) and leaf nitrogen accumulation per unit soil area (LNA) to ground-based canopy reflectance spectra, and to derive regression equations for monitoring N nutrition status in wheat (Triticum aestivum L.). Four field experiments were conducted with different N application rates and wheat cultivars across four growing seasons, and time-course measurements were taken on canopy spectral reflectance, LNC and leaf dry weights under the various treatments. In these studies, LNC and LNA in wheat increased with increasing N fertilization rates. The canopy reflectance differed significantly under varied N rates, and the pattern of response was consistent across the different cultivars and years. Overall, an integrated regression equation of LNC to norm...

Quantitative relationships of leaf nitrogen status to canopy spectral reflectance in rice

Non-destructive and quick methods for assessing leaf nitrogen (N) status are helpful for precision N management in field crops. The present study was conducted to determine the quantitative relationships of leaf N concentration on a leaf dry weight basis (LNC) and leaf N accumulation per unit soil area (LNA) to ground-based canopy spectral reflectance in rice (Oryza sativa L.). Time-course measurements were taken on canopy spectral reflectance, LNC, and leaf dry weights, with 4 field experiments under different N application rates and rice cultivars across 4 growing seasons. All possible ratio vegetation indices (RVI), difference vegetation indices (DVI), and normalised difference vegetation indices (NDVI) of key wavebands from the MSR16 radiometer were calculated. The results showed that LNC, LNA, and canopy reflectance spectra all markedly varied with N rates, with consistent change patterns among different rice cultivars and experiment years. There were highly significant linear correlations between LNC and canopy reflectance in the visible region from 560 to 710 nm (|r| > 0.85), between LNA and canopy reflectance from 760 to 1100 nm (|r| > 0.79), and from 460 to 710 nm wavelengths (|r| > 0.70). Among all possible RVI, DVI, and NDVI of key wavebands from the MSR16 radiometer, NDVI of 1220 and 710 nm was most highly correlated to LNC, and RVI of 950 and 660 nm and RVI of 950 and 680 nm were the best spectral indices for quantitative monitoring of LNA in rice. The average relative root mean square errors (RRMSE) between the predicted LNC and LNA and the observed values with independent data were no more than 11% and 25%, respectively. These results indicated that the canopy spectral reflectance can be potentially used for non-destructive and real-time monitoring of leaf N status in rice.

Comparison of different hyperspectral vegetation indices for canopy leaf nitrogen concentration estimation in rice

Background and aimsVariations in the water and soil background in the signal path can cause variations in canopy spectral reflectance, which leads to uncertainty in estimating the canopy nitrogen (N) status. The primary objective of this study was to explore the optimum vegetation indices that were highly correlated with canopy leaf N concentration (LNC) but less influenced by the canopy leaf area index (LAI) and vegetation coverage (VC) in rice.MethodsA systematic analysis of the quantitative relationships between various hyperspectral vegetation indices and LNC, VC and LAI was conducted based on 4-year rice field experiments using different rice varieties, N rates and planting densities. New spectral indices were derived to estimate LNC in rice under variable vegetation coverage.ResultsAlthough the newly developed simple green ratio indices, SR (R553, R537) and SR (R545, R538), and the three-band index (R605-R521-R682)/(R605+R521+R682) correlated well with the LNC. Only SR (R553, R537) was less influenced by VC/LAI and showed a stable performance in both the independent calibration and validation datasets. For the published indices tested in the present study, NDVIg-b and ND (R503, R483) showed a good predictive ability for the LNC. However, both of these indices and other published indices were found to be significantly dominated by the VC/LAI.ConclusionSR (R553, R537) was the best index to reliably estimate the LNC in rice under various cultivation conditions, and is recommended for this use. However, other spectral indices need to be examined to determine if they influenced by factors such as VC/LAI. Such studies will improve the applicability of these indices to different types of rice cultivars and production systems.

Estimating leaf chlorophyll content using red edge parameters.

Abstract Hyperspectral remote sensing makes it possible to non-destructively monitor leaf chlorophyll content (LCC). This study characterized the geometric patterns of the first derivative reflectance spectra in the red edge region of rapeseed ( Brassica napus L.) and wheat ( Triticum aestivum L.) crops. The ratio of the red edge area less than 718 nm to the entire red edge area was negatively correlated with LCC. This finding allowed the construction of a new red edge parameter, defined as red edge symmetry (RES). Compared to the commonly used red edge parameters (red edge position, red edge amplitude, and red edge area), RES was a better predictor of LCC. Furthermore, RES was easily calculated using the reflectance of red edge boundary wavebands at 675 and 755 nm ( R 675 and R 755 ) and reflectance of red edge center wavelength at 718 nm ( R 718 ), with the equation RES = ( R 718 -R 675 ) / ( R 755 - R 675 ). In addition, RES was simulated effectively with wide wavebands from the airborne hyperspectral sensor AVIRIS and satellite hyperspectral sensor Hyperion. The close relationships between the simulated RES and LCC indicated a high feasibility of estimating LCC with simulated RES from AVIRIS and Hyperion data. This made RES readily applicable to common airborne and satellite hyperspectral data derived from AVIRIS and Hyperion sources, as well as ground-based spectral reflectance data.

Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration

The rapid and non-destructive monitoring of the canopy leaf nitrogen concentration (LNC) in crops is important for precise nitrogen (N) management. Nowadays, there is an urgent need to identify next-generation bio-physical variable retrieval algorithms that can be incorporated into an operational processing chain for hyperspectral satellite missions. We assessed six retrieval algorithms for estimating LNC from canopy reflectance of winter wheat in eight field experiments. These experiments represented variations in the N application rates, planting densities, ecological sites and cultivars and yielded a total of 821 samples from various places in Jiangsu, China over nine consecutive years. Based on the reflectance spectra and their first derivatives, six methods using different numbers of wavelengths were applied to construct predictive models for estimating wheat LNC, including continuum removal (CR), vegetation indices (VIs), stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), artificial neural networks (ANNs), and support vector machines (SVMs). To assess the performance of these six methods, we provided a systematic evaluation of the estimation accuracies using the six metrics that were the coefficients of determination for the calibration (R2C) and validation (R2V) sets, the root mean square errors of prediction (RMSEP) for the calibration and validation sets, the ratio of prediction to deviation (RPD), the computational efficiency (CE) and the complexity level (CL). The following results were obtained: (1) For the VIs method, SAVI(R1200, R705) produced a more accurate estimation of the LNC than other indices, with R²C, R²V, RMSEP, RPD and CE values of 0.844, 0.795, 0.384, 2.005 and 0.10 min, respectively; (2) For the SMLR, PLSR, ANNs and SVMs methods, the SVMs using the first derivative canopy spectra (SVM-FDS) offered the best accuracy in terms of R²C, R²V, RMSEP, RPD, and CE, at 0.96, 0.78, 0.37, 2.02, and 21.17, respectively; (3) The PLSR-FDS, ANN-OS and SVM-FDS methods yield similar accuracies if the CE and CL are not considered, however, ANNs and SVMs performed better on calibration set than the validation set which indicate that we should take more caution with the two methods for over-fitting. Except PLS method, the performance for most methods did not enhance when the spectrum were operated by the first derivative. Moreover, the evaluation of the robustness demonstrates that SVM method may be better suited than the other methods to cope with potential confounding factors for most varieties, ecological site and growth stage; (4) The prediction accuracy was found to be higher when more wavelengths were used, though at the cost of a lower CE. The findings are of interest to the remote sensing community for the development of improved inversion schemes for hyperspectral applications concerning other types of vegetation. The examples provided in this paper may also serve to illustrate the advantages and shortcomings of empirical hyperspectral models for mapping important vegetation biophysical properties of other crops.

Common Spectral Bands and Optimum Vegetation Indices for Monitoring Leaf Nitrogen Accumulation in Rice and Wheat

Real-time monitoring of nitrogen status in rice and wheat plant is of significant importance for nitrogen diagnosis, fertilization recommendation, and productivity prediction. With 11 field experiments involving different cultivars, nitrogen rates, and water regimes, time-course measurements were taken of canopy hyperspectral reflectance between 350-2 500 nm and leaf nitrogen accumulation (LNA) in rice and wheat. A new spectral analysis method through the consideration of characteristics of canopy components and plant growth status varied with phenological growth stages was designed to explore the common central bands in rice and wheat. Comprehensive analyses were made on the quantitative relationships of LNA to soil adjusted vegetation index (SAVI) and ratio vegetation index (RVI) composed of any two bands between 350-2 500 nm in rice and wheat. The results showed that the ranges of indicative spectral reflectance were largely located in 770-913 and 729-742 nm in both rice and wheat. The optimum spectral vegetation index for estimating LNA was SAVI (R822, R738) during the early-mid period (from jointing to booting), and it was RVI (R822, R738) during the mid-late period (from heading to filling) with the common central bands of 822 and 738 nm in rice and wheat. Comparison of the present spectral vegetation indices with previously reported vegetation indices gave a satisfactory performance in estimating LNA. It is concluded that the spectral bands of 822 and 738 nm can be used as common reflectance indicators for monitoring leaf nitrogen accumulation in rice and wheat.

Analysis of Common Canopy Reflectance Spectra for Indicating Leaf Nitrogen Concentrations in Wheat and Rice

Abstract Non-destructive monitoring and diagnosis of plant nitrogen (N) concentration are of significant importance for precise N management and productivity forecasting in field crops. The present study was conducted to identify the common spectra wavebands and canopy reflectance spectral parameters for indicating leaf nitrogen concentration (LNC, mg N g-1 DW) and to determine quantitative relationships of LNC to canopy reflectance spectra in both rice (Oryza sativa L.) and wheat (Triticum aestivum L.). Ground-based canopy spectral reflectance and LNC were measured with seven field experiments consisting of seven different wheat cultivars and five different rice cultivars and varied N fertilization levels across three growing seasons for wheat and four growing seasons for rice. All possible ratio vegetation indices (RVI), difference vegetation indices (DVI), and normalized difference vegetation indices (NDVI) of key wavebands from the MSR16 radiometer were calculated. The results showed that LNC of wheat and rice increased with increasing N fertilization rates. Canopy reflectance, however, was a more complicated relationship under different N application rates. In the near infrared portion of the spectrum (760−1220 nm), canopy spectral reflectance increased with increasing N supply, whereas in the visible region (460−710 nm), canopy reflectance decreased with increasing N supply. For both rice and wheat, LNC was best estimated at 610, 660 and 680 nm. Among all possible RVI, DVI and NDVI of key bands from the MSR16 radiometer, NDVI(1220, 610) and RVI(1220, 610) were most highly correlated to LNC in both wheat and rice. In addition, the correlations of NDVI(1220, 610) and RVI(1220, 610) to LNC were found to be higher than those of individual wavebands at 610, 660 and 680 nm in both wheat and rice. Thus LNC in both wheat and rice could be indicated with common wavebands and vegetation indices, but separate regression equations are necessary for precisely describing the dynamic change patterns of LNC in wheat and rice. When independent data were fit to the derived equations, the root mean square error (RMSE) values for the predicted LNC with NDVI(1220, 610) and RVI(1220, 610) relative to the observed values were 10.50% and 10.52% in wheat, and 13.04% and 12.61% in rice, respectively, indicating a good fit. These results should improve the knowledge on non-destructive monitoring of leaf N status in cereal crops.

Determination of Critical Nitrogen Dilution Curve Based on Stem Dry Matter in Rice

Plant analysis is a very promising diagnostic tool for assessment of crop nitrogen (N) requirements in perspectives of cost effective and environment friendly agriculture. Diagnosing N nutritional status of rice crop through plant analysis will give insights into optimizing N requirements of future crops. The present study was aimed to develop a new methodology for determining the critical nitrogen (Nc) dilution curve based on stem dry matter (SDM) and to assess its suitability to estimate the level of N nutrition for rice (Oryza sativa L.) in east China. Three field experiments with varied N rates (0–360 kg N ha−1) using three Japonica rice hybrids, Lingxiangyou-18, Wuxiangjing-14 and Wuyunjing were conducted in Jiangsu province of east China. SDM and stem N concentration (SNC) were determined during vegetative stage for growth analysis. A Nc dilution curve based on SDM was described by the equation (Nc = 2.17W−0.27with W being SDM in t ha−1), when SDM ranged from 0.88 to 7.94 t ha−1. However, for SDM < 0.88 t ha−1, the constant critical value Nc = 1.76% SDM was applied. The curve was dually validated for N-limiting and non-N-limiting growth conditions. The N nutrition index (NNI) and accumulated N deficit (Nand) of stem ranged from 0.57 to 1.06 and 51.1 to −7.07 kg N ha−1, respectively, during key growth stages under varied N rates in 2010 and 2011. The values of ΔN derived from either NNI or Nand could be used as references for N dressing management during rice growth. Our results demonstrated that the present curve well differentiated the conditions of limiting and non-limiting N nutrition in rice crop. The SDM based Nc dilution curve can be adopted as an alternate and novel approach for evaluating plant N status to support N fertilization decision during the vegetative growth of Japonica rice in east China.

Comparison and intercalibration of vegetation indices from different sensors for monitoring above-ground plant nitrogen uptake in winter wheat.

Various sensors have been used to obtain the canopy spectral reflectance for monitoring above-ground plant nitrogen (N) uptake in winter wheat. Comparison and intercalibration of spectral reflectance and vegetation indices derived from different sensors are important for multi-sensor data fusion and utilization. In this study, the spectral reflectance and its derived vegetation indices from three ground-based sensors (ASD Field Spec Pro spectrometer, CropScan MSR 16 and GreenSeeker RT 100) in six winter wheat field experiments were compared. Then, the best sensor (ASD) and its normalized difference vegetation index (NDVI (807, 736)) for estimating above-ground plant N uptake were determined (R2 of 0.885 and RMSE of 1.440 g·N·m−2 for model calibration). In order to better utilize the spectral reflectance from the three sensors, intercalibration models for vegetation indices based on different sensors were developed. The results indicated that the vegetation indices from different sensors could be intercalibrated, which should promote application of data fusion and make monitoring of above-ground plant N uptake more precise and accurate.

A Knowledge Model System for Wheat Production Management

A knowledge model with temporal and spatial characteristics for the quantitative design of a cultural pattern in wheat production, using systems analysis and dynamic modeling techniques, was developed for wheat management, as a decision-making tool in digital farming. The fundamental relationships and algorithms of wheat growth indices and management criteria to cultivars, ecological environments, and production levels were derived from the existing literature and research data to establish a knowledge model system for quantitative wheat management using Visual C++. The system designed a cultural management plan for general management guidelines and crop regulation indices for time-course control criteria during the wheat-growing period. The cultural management plan module included submodels to determine target grain yield and quality, cultivar choice, sowing date, population density, sowing rate, fertilization strategy, and water management, whereas the crop regulation indices module included submodels for suitable development stages, dynamic growth indices, source-sink indices, and nutrient indices. Evaluation of the knowledge model by design studies on the basis of data sets of different eco-sites, cultivars, and soil types indicated a favorable performance of the model system in recommending growth indices and management criteria under diverse conditions. Practical application of the knowledge model system in comparative field experiments produced yield gains of 2.4% to 16.5%. Thus, the presented knowledge model system overcame some of the difficulties of the traditional wheat management patterns and expert systems, and laid a foundation for facilitating the digitization of wheat management.