Soil Nitrogen Content Influence on Canopy Reflectance Spectra

2011 ◽  
Vol 54 (6) ◽  
pp. 2343-2349 ◽  
Author(s):  
Y. Shao ◽  
Y. Bao ◽  
Y. He
Keyword(s):  
Nitrogen Content ◽  
Soil Nitrogen ◽  
Reflectance Spectra ◽  
Canopy Reflectance

ESTIMATION OF SOIL NITROGEN STATUS WITH CANOPY REFLECTANCE SPECTRA IN RICE

2006 ◽  
Vol 30 (4) ◽  
pp. 675-681 ◽  
Author(s):  
XUE Li_Hong ◽  
◽  
LU Ping ◽  
YANG Lin_Zhang ◽  
SHAN Yu_Hua ◽  
...  
Keyword(s):  
Soil Nitrogen ◽  
Reflectance Spectra ◽  
Canopy Reflectance ◽  
Nitrogen Status

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

2007 ◽  
Vol 10 (4) ◽  
pp. 400-411 ◽  
Author(s):  
Yan Zhu ◽  
Yongchao Tian ◽  
Xia Yao ◽  
Xiaojun Liu ◽  
Weixing Cao
Keyword(s):  
Leaf Nitrogen ◽  
Reflectance Spectra ◽  
Canopy Reflectance ◽  
Nitrogen Concentrations

Monitoring leaf nitrogen in wheat using canopy reflectance spectra

2006 ◽  
Vol 86 (4) ◽  
pp. 1037-1046 ◽  
Author(s):  
Yan Zhu ◽  
Yingxue Li ◽  
Wei Feng ◽  
Yongchao Tian ◽  
Xia Yao ◽  
...  
Keyword(s):  
Nitrogen Concentration ◽  
Leaf Nitrogen ◽  
Reflectance Spectra ◽  
Nutrition Status ◽  
Nitrogen Accumulation ◽  
Canopy Reflectance ◽  
Leaf Nitrogen Concentration ◽  
Non Destructive ◽  
N Status ◽  
Leaf N

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 normalized difference index (NDI) of 1220 and 710 nm of canopy reflectance spectra described the dynamic pattern of change in LNC in wheat. The ratios of several near infrared (NIR) bands to visible light were linearly related to LNA, with the ratio index (RI) of the average reflectance over 760, 810, 870, 950 and 1100 nm to 660 nm having the best index for quantitative estimation of LNA in wheat. When independent data were fit to the derived equations, the average root mean square error (RMSE) values for the predicted LNC and LNA relative to the observed values were no more than 15.1 and 15.2%, respectively, indicating a good fit. Our relationships of leaf N status to spectral indices of canopy reflectance can be potentially used for non-destructive and real-time monitoring of leaf N status in wheat. Key words: Wheat, leaf nitrogen concentration, leaf nitrogen accumulation, canopy reflectance, spectral index, nitrogen monitoring

Spectroscopic retrieval of above-ground crop nitrogen content with a hybrid machine learning regression method

2020 ◽  
Author(s):  
Katja Berger ◽  
Gustau Camps-Valls ◽  
Jochem Verrelst ◽  
Jean-Baptiste Féret ◽  
Matthias Wocher ◽  
...  
Keyword(s):  
Machine Learning ◽  
Nitrogen Content ◽  
Confidence Intervals ◽  
Vegetation Indices ◽  
Imaging Spectroscopy ◽  
Remote Sensing Data ◽  
Specific Protein ◽  
Canopy Reflectance ◽  
Absorption Bands ◽  
Model Inversion

<p>Proteins are the major nitrogen-containing biochemical constituents of plants. Since nitrogen (N) cannot be measured directly using remote sensing data, leaf protein content constitutes a valid proxy for this main limiting plant nutrient. In the past, mainly linear parametric algorithms, such as vegetation indices, have been employed to retrieve this non-state variable from optical reflectance data. Moreover, most studies solely relied on the relationship of chlorophyll content with nitrogen. In contrast, our study presents a hybrid model inversion scheme of a physically-based approach via protein retrieval combined with advanced machine learning regression. The leaf optical properties PROSPECT-PRO model, including the newly calibrated specific absorption coefficients (SAC) of proteins, was coupled with the canopy reflectance model 4SAIL to PROSAIL-PRO. A generic synthetic database of model input parameters with corresponding reflectance was simulated and used for training two different machine learning regression methods: a standard homoscedastic Gaussian Process (GP) and a variational heteroscedastic GP regression that accounts for signal-to-noise correlations. Both GP methods have the interesting feature of providing confidence intervals for the estimates. As part of multiple field campaigns, carried out in the scientific preparation framework of the Environmental Mapping and Analysis Program (EnMAP), spectra of maize and winter wheat were acquired to simulate EnMAP data and plant-organ-specific nitrogen measurements were destructively collected for validation. Both GP models yielded excellent performance in learning the nonlinear relationship between specific protein absorption bands and area-based above-ground N. They also performed similar or even outperformed other nonlinear nonparametric approaches. Physical validation of the estimates against in situ nitrogen measurements from leaves plus stalks yielded a root mean square error (RMSE) of 2.5 g/m². The variational heteroscedastic GP provided a more differentiated pattern of uncertainty with tighter confidence intervals within low-value regimes compared to the standard GP. The inclusion of fruit nitrogen content for validation deteriorated the results of all models, which can be explained by the inability of radiation in the optical domain to penetrate the thick tissues of maize cobs and wheat ears. Following some further validation exercises, we aim to implement GP-based algorithms for global agricultural monitoring of above-ground N derived from future satellite imaging spectroscopy data.</p>

Variations and Typical Impact Factors of Soil Nitrogen Content during the Vegetation Succession in Limestone Mountains

2013 ◽  
Vol 664 ◽  
pp. 142-145
Author(s):  
Shan Shan Zhang ◽  
Li Yuan Yang ◽  
Hui Wang ◽  
Qing Lin Chen ◽  
Qian Li
Keyword(s):  
Organic Carbon ◽  
Nitrogen Content ◽  
Total Nitrogen ◽  
Soil Nitrogen ◽  
Mineral Soil ◽  
Impact Factors ◽  
Organic Carbon Content ◽  
Soil Mineral Nitrogen ◽  
Platycladus Orientalis ◽  
Positive Correlations

In order to explore the variations and impact factors of soil nitrogen contents, 0-20 cm mineral soil under herb, shrub, Platycladus orientalis plantation of limestone mountains after restoration for 5 years and 10 years were collected and examined in Jinan, Shandong province. The results showed that there was different soil mineral nitrogen content under different vegetation during the natural succession and artificial restoration succession. Shrub community (14.35 mg/Kg) > herb community (12.73 mg/Kg); Platycladus orientalis plantation restored for 10 years (27.82 mg/Kg) > Platycladus orientalis plantation restored for 5 years (20.76 mg/Kg). NO3--N has highly significant positive correlations with soil organic carbon and total nitrogen content (r = 0.626, 0.564, p 4+-N has not significantly correlated with total nitrogen and organic carbon content (r = 0.218, 0.155). However, it has highly significant positive correlation with the NO3--N (r = 0.531, p 3--N and NH4+-N have highly significant negative correlations with soil pH (r = -0.657, -0.605, p < 0.01), respectively. But the correlation with the soil moisture was not significant (r = -0.181, 0.114). The research provided base information for the evaluation of restoration effects and restoration practice on the limestone mountains.

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