scholarly journals Remote Sensing of Leaf and Canopy Nitrogen Status in Winter Wheat (Triticum aestivum L.) Based on N-PROSAIL Model

2018 ◽  
Vol 10 (9) ◽  
pp. 1463 ◽  
Author(s):  
Zhenhai Li ◽  
Xiuliang Jin ◽  
Guijun Yang ◽  
Jane Drummond ◽  
Hao Yang ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Spectral Response ◽  
Triticum Aestivum L ◽  
Hyperspectral Data ◽  
Area Index ◽  
Plant Nitrogen ◽  
Model Inversion ◽  
Leaf N Concentration ◽  
Canopy Nitrogen ◽  
Leaf N

Plant nitrogen (N) information has widely been estimated through empirical techniques using hyperspectral data. However, the physical model inversion approach on N spectral response has seldom developed and remains a challenge. In this study, an N-PROSAIL model based on the N-based PROSPECT model and the SAIL model canopy model was constructed and used for retrieving crop N status both at leaf and canopy scales. The results show that the third parameter (3rd-par) retrieving strategy (leaf area index (LAI) and leaf N density (LND) optimized where other parameters in the N-PROSAIL model are set at different values at each growth stage) exhibited the highest accuracy for LAI and LND estimation, which resulted in R2 and RMSE values of 0.80 and 0.69, and 0.46 and 21.18 µg·cm−2, respectively. It also showed good results with R2 and RMSE values of 0.75 and 0.38% for leaf N concentration (LNC) and 0.82 and 0.95 g·m−2 for canopy N density (CND), respectively. The N-PROSAIL model retrieving method performed better than the vegetation index regression model (LNC: RMSE = 0.48 − 0.64%; CND: RMSE = 1.26 − 1.78 g·m−2). This study indicates the potential of using the N-PROSAIL model for crop N diagnosis on leaf and canopy scales in wheat.

Optical sensing estimation of leaf nitrogen concentration in maize across a range of water-stress levels

2011 ◽  
Vol 62 (6) ◽  
pp. 474 ◽  
Author(s):  
Tong-Chao Wang ◽  
B. L. Ma ◽  
You-Cai Xiong ◽  
M. Farrukh Saleem ◽  
Feng-Min Li
Keyword(s):  
Water Stress ◽  
Vegetation Index ◽  
Optical Sensing ◽  
Leaf Nitrogen ◽  
Growth Stages ◽  
N Concentration ◽  
Leaf N Concentration ◽  
Chlorophyll Index ◽  
Plant Moisture ◽  
Leaf N

Optical sensing techniques offer an instant estimation of leaf nitrogen (N) concentration during the crop growing season. Differences in plant-moisture status, however, can obscure the detection of differences in N levels. This study presents a vegetation index that robustly measures differences in foliar N levels across a range of plant moisture levels. A controlled glasshouse study with maize (Zea mays L.) subjected to both water and N regimes was conducted in Ottawa, Canada. The purpose of the study was to identify spectral waveband(s), or indices derived from different wavebands, such as the normalised difference vegetation index (NDVI), that are capable of detecting variations in leaf N concentration in response to different water and N stresses. The experimental design includes three N rates and three water regimes in a factorial arrangement. Leaf chlorophyll content and spectral reflectance (400–1075 nm) were measured on the uppermost fully expanded leaves at the V6, V9 and V12 growth stages (6th, 9th and 12th leaves fully expanded). N concentrations of the same leaves were determined using destructive sampling. A quantitative relationship between leaf N concentration and the normalised chlorophyll index (normalised to well fertilised and well irrigated plants) was established. Leaf N concentration was also a linear function (R2 = 0.9, P < 0.01) of reflectance index (NDVI550, 760) at the V9 and V12 growth stages. Chlorophyll index increased with N nutrition, but decreased with water stress. Leaf reflectance at wavebands of 550 ± 5 nm and 760 ± 5 nm were able to separate water- and N-stressed plants from normal growing plants with sufficient water and N supply. Our results suggest that NDVI550, 760 and normalised chlorophyll index hold promise for the assessment of leaf N concentration at the leaf level of both normal and water-stressed maize plants.

Comparison of UAV RGB Imagery and Hyperspectral Remote-sensing Data for Monitoring Winter-wheat Growth

2021 ◽  
Author(s):  
Haikuan Feng ◽  
Huilin Tao ◽  
Chunjiang Zhao ◽  
Zhenhai Li ◽  
Guijun Yang
Keyword(s):  
Remote Sensing ◽  
Spectral Index ◽  
Vegetation Index ◽  
Crop Growth ◽  
Hyperspectral Data ◽  
Growth Stages ◽  
Growth Monitoring ◽  
Estimation Model ◽  
Area Index ◽  
Simple Ratio

Abstract Background: Although crop-growth monitoring is important for agricultural managers, it has always been a difficult research topic. However, unnamed aerial vehicles (UAVs) equipped with RGB and hyperspectral cameras can now acquire high-resolution remote-sensing images, which facilitates and accelerates such monitoring. Results: To explore the effect of monitoring a single crop-growth indicator and multiple indicators, this study combines six growth indicators (plant nitrogen content, above-ground biomass, plant water content, chlorophyll, leaf area index, and plant height) into a new comprehensive growth index (CGI). We investigate the performance of RGB imagery and hyperspectral data for monitoring crop growth based on multi-time estimation of the CGI. The CGI is estimated from the vegetation indices based on UAV hyperspectral data treated by linear, nonlinear, and multiple linear regression (MLR), partial least squares (PLSR), and random forest (RF). The results show that (1) the RGB-imagery indices red reflectance (r), the excess-red index(EXR), the vegetation atmospherically resistant index(VARI), and the modified green-red vegetation index(MGRVI) , as well as the spectral indices consisting of the linear combination index (LCI), the modified simple ratio index(MSR), the simple ratio vegetation index(SR), and the normalized difference vegetation index (NDVI)are more strongly correlated with the CGI than a single growth-monitoring indicator (2) The CGI estimation model is constructed by comparing a single RGB-imagery index and a spectral index, and the optimal RGB-imagery index corresponding to each of the four growth stage in order is r, r, r, EXR; the optimal spectral index is LCI for all four growth stages. (3) The MLR, PLSR, and RF methods are used to estimate the CGI. The MLR method produces the best estimates. (4) Finally, the CGI is more accurately estimated using the UAV hyperspectral indices than using the RGB-image indices.Conclusions: UAVs carrying RGB cameras and hyperspectral cameras have high inversion CGI accuracy and can judge the overall growth of wheat can provide a reference for monitoring the growth of wheat.

Predicting Winter Wheat Grain Quality Using Hyperspectral Data Based on Plant Nitrogen Status

2012 ◽  
Vol 524-527 ◽  
pp. 2132-2138 ◽  
Author(s):  
Hui Fang Wang ◽  
Ji Hua Wang ◽  
Mei Chen Feng ◽  
Qian Wang ◽  
Wen Jiang Huang ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Nitrogen Content ◽  
Vegetation Index ◽  
Grain Quality ◽  
Leaf Nitrogen ◽  
Hyperspectral Data ◽  
Leaf Nitrogen Content ◽  
Wheat Grain ◽  
Quality Model ◽  
Plant Nitrogen

Quality of winter wheat from hyperspectral data would provide opportunities to manage grain harvest differently, and to maximize output by adjusting input in fields. In this study, two varieties winter wheat as the object, hyperspectral data were utilized to predict grain quality. Firstly, the leaf and stem nitrogen content at winter wheat anthesis stage was proved to be signification correctly with crude content and wet gluten. And the leaf relatedcoefficient more than stem at the anthesis. Then, spectral indices significantly correlated to plant nitrogen content at anthesis stage were potential indicators for grain qualities. The vegetation index, VI derived from the canopy spectral reflectance was signification correlated to the leaf nitrogen content at anthesis stage, and highly significantly correlated to the leaf nitrogen content. Based on above analysis, the predict grain quality model were build and the related coefficient were 0.86, 0.68, 0.84, 0.58 which were reached a very significant.The result demonstrated the model based on SIPI and RVI to predict different cultivars wheat grain quality were practical and feasible.

scholarly journals Incorporating Multi-Scale, Spectrally Detected Nitrogen Concentrations into Assessing Nitrogen Use Efficiency for Winter Wheat Breeding Populations

2021 ◽  
Vol 13 (19) ◽  
pp. 3991
Author(s):  
Raquel Peron-Danaher ◽  
Blake Russell ◽  
Lorenzo Cotrozzi ◽  
Mohsen Mohammadi ◽  
John Couture
Keyword(s):  
Winter Wheat ◽  
Leaf Nitrogen ◽  
Hyperspectral Data ◽  
Plant Nitrogen ◽  
Nitrogen Levels ◽  
Nitrogen Concentrations ◽  
Use Efficiency ◽  
Canopy Nitrogen ◽  
Wheat Lines

Annually, over 100 million tons of nitrogen fertilizer are applied in wheat fields to ensure maximum productivity. This amount is often more than needed for optimal yield and can potentially have negative economic and environmental consequences. Monitoring crop nitrogen levels can inform managers of input requirements and potentially avoid excessive fertilization. Standard methods assessing plant nitrogen content, however, are time-consuming, destructive, and expensive. Therefore, the development of approaches estimating leaf nitrogen content in vivo and in situ could benefit fertilization management programs as well as breeding programs for nitrogen use efficiency (NUE). This study examined the ability of hyperspectral data to estimate leaf nitrogen concentrations and nitrogen uptake efficiency (NUpE) at the leaf and canopy levels in multiple winter wheat lines across two seasons. We collected spectral profiles of wheat foliage and canopies using full-range (350–2500 nm) spectroradiometers in combination with leaf tissue collection for standard analytical determination of nitrogen. We then applied partial least-squares regression, using spectral and reference nitrogen measurements, to build predictive models of leaf and canopy nitrogen concentrations. External validation of data from a multi-year model demonstrated effective nitrogen estimation at leaf and canopy level (R2 = 0.72, 0.67; root-mean-square error (RMSE) = 0.42, 0.46; normalized RMSE = 12, 13; bias = −0.06, 0.04, respectively). While NUpE was not directly well predicted using spectral data, NUpE values calculated from predicted leaf and canopy nitrogen levels were well correlated with NUpE determined using traditional methods, suggesting the potential of the approach in possibly replacing standard determination of plant nitrogen in assessing NUE. The results of our research reinforce the ability of hyperspectral data for the retrieval of nitrogen status and expand the utility of hyperspectral data in winter wheat lines to the application of nitrogen management practices and breeding programs.

The Use of Remote Sensing to Assess the Effects of Water Stress on Wheat

1991 ◽  
Vol 27 (4) ◽  
pp. 423-429 ◽  
Author(s):  
R. K. Mahey ◽  
Rajwant Singh ◽  
S. S. Sidhu ◽  
R. S. Narang
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Grain Yield ◽  
Spectral Data ◽  
Growth And Development ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spectral Response ◽  
Canopy Cover ◽  
Area Index

SUMMARYGround-based radiometric measurements in the red and infrared bands were used to monitor the growth and development of wheat under irrigated and stressed conditions throughout the 1987–88 and 1988–89 growth cycles. Spectral data were correlated with plant height, leaf area index, total fresh and total dry biomass, plant water content and grain yield. The radiance ratio (R) and normalized difference vegetation index (NDVI) were highly and linearly correlated with yield, establishing the potential which remote sensing has for predicting grain yield. The correlation for R and NDVI was at a maximum between 75 and 104 days after sowing, corresponding with maximum green crop canopy cover. The differences in spectral response over time between irrigated and unirrigated crops allowed detection of water stress effects on the crop, indicating that a hand-held radiometer can be used to collect spectral data which can supply information on wheat growth and development.Efectos de lafalta de agua en el trigo

scholarly journals Machine Learning in Evaluating Multispectral Active Canopy Sensor for Prediction of Corn Leaf Nitrogen Concentration and Yield

2021 ◽  
Vol 14 (1) ◽  
pp. 120
Author(s):  
Razieh Barzin ◽  
Hossein Lotfi ◽  
Jac J. Varco ◽  
Ganesh C. Bora
Keyword(s):  
Machine Learning ◽  
Grain Yield ◽  
Vegetation Index ◽  
Growth Stages ◽  
Fertilizer N ◽  
Red Edge ◽  
Leaf N Concentration ◽  
Different Growth Stages ◽  
N Status ◽  
Leaf N

Applying the optimum rate of fertilizer nitrogen (N) is a critical factor for field management. Multispectral information collected by active canopy sensors can potentially indicate the leaf N status and aid in predicting grain yield. Crop Circle multispectral data were acquired with the purpose of measuring the reflectance data to calculate vegetation indices (VIs) at different growth stages. Applying the optimum rate of fertilizer N can have a considerable impact on grain yield and profitability. The objectives of this study were to evaluate the reliability of a handheld Crop Circle ACS-430, to estimate corn leaf N concentration and predict grain yield of corn using machine learning (ML) models. The analysis was conducted using four ML models to identify the best prediction model for measurements acquired with a Crop Circle ACS-430 field sensor at three growth stages. Four fertilizer N levels from deficient to excessive in 50/50 spilt were applied to corn at 1–2 leaves, with visible leaf collars (V1-V2 stage) and at the V6-V7 stage to establish widely varying N nutritional status. Crop Circle spectral observations were used to derive 25 VIs for different growth stages (V4, V6, and VT) of corn at the W. B. Andrews Agricultural Systems farm of Mississippi State University. Multispectral raw data, along with Vis, were used to quantify leaf N status and predict the yield of corn. In addition, the accuracy of wavelength-based and VI-based models were compared to examine the best model inputs. Due to limited observed data, the stratification approach was used to split data to train and test set to obtain balanced data for each stage. Repeated cross validation (RCV) was then used to train the models. Results showed that the Simplified Canopy Chlorophyll Content Index (SCCCI) and Red-edge ratio vegetation index (RERVI) were the most effective VIs for estimating leaf N% and that SCCCI, Red-edge chlorophyll index (CIRE), RERVI, Soil Adjusted Vegetation Index (SAVI), and Normalized Difference Vegetation Index (NDVI) were the most effective VIs for predicting corn grain yield. Additionally, among the four ML models utilized in this research, support vector regression (SVR) achieved the most accurate results for estimating leaf N concentration using either spectral bands or VIs as the model inputs.

scholarly journals Analyzing the Effects of Hyperspectral ZhuHai-1 Band Combinations on LAI Estimation Based on the PROSAIL Model

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1869
Author(s):  
Yangyang Zhang ◽  
Jian Yang ◽  
Lin Du
Keyword(s):  
Satellite Data ◽  
Vegetation Index ◽  
Hyperspectral Data ◽  
Quantitative Detection ◽  
Inversion Method ◽  
Vegetation Monitoring ◽  
Area Index ◽  
Band Combination ◽  
Band Combinations ◽  
Sentinel 2

Leaf area index (LAI) is a key biophysical variable to characterize vegetation canopy. Accurate and quantitative LAI estimation is significant for monitoring vegetation growth status. ZhuHai-1 (ZH-1), which is a commercial remote sensing micro-nano satellite, provides a possibility for quantitative detection of vegetation with high spatial and spectral resolution. However, the band characteristics of ZH-1 are closely related to the accuracy of vegetation monitoring. In this study, a simulation dataset containing 32 bands of ZH-1 was generated by using the PROSAIL model, which was used to analyze the performance of 32 bands for LAI estimation by using the hybrid inversion method. Meanwhile, the effect of different band combinations on LAI estimation was discussed based on sensitivity analysis and the correlation between bands. Then, the optimal band combination from ZH-1 hyperspectral satellite data for LAI estimation was obtained. LAI estimation was performed based on the selected optimal band combination of ZH-1 satellite images in Xiantao city, Hubei province, and compared with the Sentinel-2 normalized difference vegetation index (NDVI) values and LAI product. The results demonstrated that the obtained LAI map based on the optimal band combination of ZH-1 was generally consistent with the overall distribution of Sentinel-2 NDVI and the LAI product, but had a moderate correlation with Sentinel-2 LAI (R = 0.60), which may not favorably indicate the validity of indirect validation. However, the method of this study on the analysis of hyperspectral data bands has application potential to provide a reference for selecting appropriate bands of hyperspectral satellite data to estimate LAI and improve the application of hyperspectral data such as ZH-1 in vegetation monitoring.

scholarly journals Remote Sensing Using Canopy and Leaf Reflectance for Estimating Nitrogen Status in Red-blush Pears

HortScience ◽  
2018 ◽  
Vol 53 (1) ◽  
pp. 78-83 ◽  
Author(s):  
Eileen M. Perry ◽  
Ian Goodwin ◽  
David Cornwall
Keyword(s):  
Vegetation Index ◽  
Spatial Information ◽  
Leaf Reflectance ◽  
N Concentration ◽  
Leaf Level ◽  
Leaf N Concentration ◽  
Reflectance Indices ◽  
Reflectance Measurements ◽  
Leaf N ◽  
Multispectral Camera

Reflectance measurements at leaf and canopy scales were made in a red-blush pear (Pyrus communis) orchard for two growing seasons. Canopy reflectance measurements were obtained using a multispectral camera flown on board an unmanned aerial vehicle (UAV), and leaf reflectance measurements were undertaken in a laboratory using a portable spectrometer. These measurements were used to compute reflectance indices as surrogates for direct leaf nitrogen (N) concentration measurements. The indices were evaluated against laboratory analysis of leaf N concentration. Regression results for leaf %N on canopy-level measurements with the multispectral camera resulted in the highest R2 value [R2 = 0.67; root mean square error (RMSE) = 0.24%N] with a new index, Modified Canopy Chlorophyll Content Index (M3CI)_710 nm. Regression results for leaf %N on leaf-level measurements in-laboratory resulted in the highest R2 value (R2 = 0.65) with two other indices, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red-edge Index (NDRE)_720 nm. The corresponding RMSE values were 0.26%N. The results indicate that reflectance indices measured at the leaf level, with a controlled light source and calibration, could be used to estimate leaf %N. An analysis of uncertainty indicated that if leaf %N is estimated from leaf-level reflectance values, 10 or more leaves (from the same tree) should be averaged. The results support the use of a UAV-based assessment for canopy %N using the M3CI_710 nm, which could provide spatial information of leaf N concentration across an orchard.

scholarly journals Comparison of Vegetation Indices for Leaf Area Index Estimation in Vertical Shoot Positioned Vine Canopies with and without Grenbiule Hail-Protection Netting

2019 ◽  
Vol 11 (9) ◽  
pp. 1073 ◽  
Author(s):  
Pedro C. Towers ◽  
Albert Strever ◽  
Carlos Poblete-Echeverría
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Linear Models ◽  
Dynamic Range ◽  
Spectral Response ◽  
Incident Light ◽  
Vegetation Indices ◽  
Area Index

Leaf area per unit surface (LAI—leaf area index) is a valuable parameter to assess vine vigour in several applications, including direct mapping of vegetative–reproductive balance (VRB). Normalized difference vegetation index (NDVI) has been successfully used to assess the spatial variability of estimated LAI. However, sometimes NDVI is unsuitable due to its lack of sensitivity at high LAI values. Moreover, the presence of hail protection with Grenbiule netting also affects incident light and reflection, and consequently spectral response. This study analyses the effect of protective netting in the LAI–NDVI relationship and, using NDVI as a reference index, compares several indices in terms of accuracy and sensitivity using linear and logarithmic models. Among the indices compared, results show NDVI to be the most accurate, and ratio vegetation index (RVI) to be the most sensitive. The wide dynamic range vegetation index (WDRVI) presented a good balance between accuracy and sensitivity. Soil-adjusted vegetation index 2 (SAVI2) appears to be the best estimator of LAI with linear models. Logarithmic models provided higher determination coefficients, but this has little influence over the normal range of LAI values. A similar NDVI–LAI relationship holds for protected and unprotected canopies in initial vegetation stages, but different functions are preferable once the canopy is fully developed, in particular, if tipping is performed.

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