scholarly journals Novel Combined Spectral Indices Derived from Hyperspectral and Laser-Induced Fluorescence LiDAR Spectra for Leaf Nitrogen Contents Estimation of Rice

2020 ◽  
Vol 12 (1) ◽  
pp. 185
Author(s):  
Lin Du ◽  
Jian Yang ◽  
Bowen Chen ◽  
Jia Sun ◽  
Biwu Chen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Nitrogen Content ◽  
Laser Induced Fluorescence ◽  
Leaf Nitrogen ◽  
Ecological Environment ◽  
Leaf Nitrogen Content ◽  
Spectral Indices ◽  
Crop Monitoring ◽  
Nitrogen Contents

Spectra of reflectance (Sr) and fluorescence (Sf) are significant for crop monitoring and ecological environment research, and can be used to indicate the leaf nitrogen content (LNC) of crops indirectly. The aim of this work is to use the Sr-Sf features obtained with hyperspectral and laser-induced fluorescence LiDAR (HSL, LIFL) systems to construct novel combined spectral indices (NCIH-F) for multi-year rice LNC estimation. The NCIH-F is in a form of FWs* Φ + GSIs* Φ , where Φ is the Sr-Sf features, and FWs and GSIs are the feature weights and global sensitive indices for each characteristic band. In this study, the characteristic bands were chosen in different ways. Firstly, the Sr-Sf characteristics which can be the intensity or derivative variables of spectra in 685 and 740 nm, have been assigned as the Φ value in NCIH-F formula. Simultaneously, the photochemical reflectance index (PRI) formed with 531 and 570 nm was modified based on a variant spectral index, called PRIfraction, with the Sf intensity in 740 nm, and then compared its potential with NCIH-F on LNC estimation. During the above analysis, both NCIH-F and PRIfraction values were utilized to model rice LNC based on the artificial neural networks (ANNs) method. Subsequently, four prior bands were selected, respectively, with high FW and GSI values as the ANNs inputs for rice LNC estimation. Results show that FW- and GSI-based NCIH-F are closely related to rice LNC, and the performance of previous spectral indices used for LNC estimation can be greatly improved by multiplying their FWs and GSIs. Thus, it can be included that the FW- and GSI-based NCIH-F constitutes an efficient and reliable constructed form combining HSL (Sr) and LIFL (Sf) data together for rice LNC estimation.

scholarly journals Estimating the leaf nitrogen content of paddy rice by using the combined reflectance and laser-induced fluorescence spectra

2016 ◽  
Vol 24 (17) ◽  
pp. 19354 ◽  
Author(s):  
Jian Yang ◽  
Lin Du ◽  
Jia Sun ◽  
Zhenbing Zhang ◽  
Biwu Chen ◽  
...  
Keyword(s):  
Nitrogen Content ◽  
Fluorescence Spectra ◽  
Laser Induced Fluorescence ◽  
Leaf Nitrogen ◽  
Paddy Rice ◽  
Leaf Nitrogen Content

Partitioning of Nitrogen Between and Within Leaves Grown Under Different Irradiances

1989 ◽  
Vol 16 (6) ◽  
pp. 533 ◽  
Author(s):  
JR Evans
Keyword(s):  
Nitrogen Content ◽  
Photosynthetic Rate ◽  
Protein Complexes ◽  
Leaf Nitrogen ◽  
High Irradiance ◽  
Leaf Nitrogen Content ◽  
Unit Leaf ◽  
Growth Irradiance ◽  
Pigment Protein Complexes ◽  
Nitrogen Contents

The distribution of nitrogen between leaves on individual plants of Phaseolus vulgaris and Cucumis sativus which were grown under different irradiances was examined. For Phaseolus, shading treatments were imposed on individual leaflets when they had reached one-third of full expansion. Adjacent leaflets were either grown under the same irradiance or had different irradiances imposed on them. The nitrogen content of leaves depended on their growth irradiance and not on the growth irradiance of adjacent leaflets, with more nitrogen being found in leaves grown under higher irradiance compared to those grown in shade. For Cucumis, the nitrogen contents of the leaves changed following the imposition of shading treatments. The experiment was repeated four times with different nitrate nutrient treatments, twice in combination with a pretreatment growth irradiance of 40% sunlight. The relative changes in leaf nitrogen content for each irradiance treatment were independent of changes to the leaf nitrogen content of the plant and of the growth irradiance prior to the shading treatments. Again, nitrogen contents were highest in leaves grown at high irradiance. Acclimation of individual leaves to their irradiance treatment was seen for both Phaseolus and Cucumis. Growth under shade resulted in lower rates of oxygen evolution per unit of chlorophyll, when measured at high irradiance, and increased partitioning of nitrogen into pigment-protein complexes. These two changes working in opposition to each other meant that for Cucumis, the relationship between photosynthetic capacity and nitrogen content was similar between irradiance treatments. For Phaseolus, the increased partitioning of nitrogen into pigment-protein complexes at low irradiance was not as great as the reduction in photosynthetic rate per unit of chlorophyll, so that the photosynthetic rate per unit leaf nitrogen was less for leaves grown under low irradiance compared to those grown under high irradiance. It is shown that acclimation to lower irradiance can increase the potential daily photosynthesis for a given leaf nitrogen content.

scholarly journals Estimating the Leaf Nitrogen Content with a New Feature Extracted from the Ultra-High Spectral and Spatial Resolution Images in Wheat

2021 ◽  
Vol 13 (4) ◽  
pp. 739
Author(s):  
Jiale Jiang ◽  
Jie Zhu ◽  
Xue Wang ◽  
Tao Cheng ◽  
Yongchao Tian ◽  
...  
Keyword(s):  
Nitrogen Content ◽  
Precision Agriculture ◽  
Field Experiments ◽  
Mean Squared Error ◽  
Growing Season ◽  
Leaf Nitrogen ◽  
Textural Analysis ◽  
Hyperspectral Images ◽  
Leaf Nitrogen Content ◽  
New Feature

Real-time and accurate monitoring of nitrogen content in crops is crucial for precision agriculture. Proximal sensing is the most common technique for monitoring crop traits, but it is often influenced by soil background and shadow effects. However, few studies have investigated the classification of different components of crop canopy, and the performance of spectral and textural indices from different components on estimating leaf nitrogen content (LNC) of wheat remains unexplored. This study aims to investigate a new feature extracted from near-ground hyperspectral imaging data to estimate precisely the LNC of wheat. In field experiments conducted over two years, we collected hyperspectral images at different rates of nitrogen and planting densities for several varieties of wheat throughout the growing season. We used traditional methods of classification (one unsupervised and one supervised method), spectral analysis (SA), textural analysis (TA), and integrated spectral and textural analysis (S-TA) to classify the images obtained as those of soil, panicles, sunlit leaves (SL), and shadowed leaves (SHL). The results show that the S-TA can provide a reasonable compromise between accuracy and efficiency (overall accuracy = 97.8%, Kappa coefficient = 0.971, and run time = 14 min), so the comparative results from S-TA were used to generate four target objects: the whole image (WI), all leaves (AL), SL, and SHL. Then, those objects were used to determine the relationships between the LNC and three types of indices: spectral indices (SIs), textural indices (TIs), and spectral and textural indices (STIs). All AL-derived indices achieved more stable relationships with the LNC than the WI-, SL-, and SHL-derived indices, and the AL-derived STI was the best index for estimating the LNC in terms of both calibration (Rc2 = 0.78, relative root mean-squared error (RRMSEc) = 13.5%) and validation (Rv2 = 0.83, RRMSEv = 10.9%). It suggests that extracting the spectral and textural features of all leaves from near-ground hyperspectral images can precisely estimate the LNC of wheat throughout the growing season. The workflow is promising for the LNC estimation of other crops and could be helpful for precision agriculture.

scholarly journals Leaf traits and gas exchange in saplings of native tree species in the Central Amazon

2010 ◽  
Vol 67 (6) ◽  
pp. 624-632 ◽  
Author(s):  
Keila Rego Mendes ◽  
Ricardo Antonio Marenco
Keyword(s):  
Gas Exchange ◽  
Nitrogen Content ◽  
Leaf Area ◽  
Tree Species ◽  
Leaf Traits ◽  
Leaf Nitrogen ◽  
Dry Season ◽  
Leaf Thickness ◽  
Rainfall Regime ◽  
Leaf Nitrogen Content

Global climate models predict changes on the length of the dry season in the Amazon which may affect tree physiology. The aims of this work were to determine the effect of the rainfall regime and fraction of sky visible (FSV) at the forest understory on leaf traits and gas exchange of ten rainforest tree species in the Central Amazon, Brazil. We also examined the relationship between specific leaf area (SLA), leaf thickness (LT), and leaf nitrogen content on photosynthetic parameters. Data were collected in January (rainy season) and August (dry season) of 2008. A diurnal pattern was observed for light saturated photosynthesis (Amax) and stomatal conductance (g s), and irrespective of species, Amax was lower in the dry season. However, no effect of the rainfall regime was observed on g s nor on the photosynthetic capacity (Apot, measured at saturating [CO2]). Apot and leaf thickness increased with FSV, the converse was true for the FSV-SLA relationship. Also, a positive relationship was observed between Apot per unit leaf area and leaf nitrogen content, and between Apot per unit mass and SLA. Although the rainfall regime only slightly affects soil moisture, photosynthetic traits seem to be responsive to rainfall-related environmental factors, which eventually lead to an effect on Amax. Finally, we report that little variation in FSV seems to affect leaf physiology (Apot) and leaf anatomy (leaf thickness).

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