scholarly journals Multispectral remote sensing for site-specific nitrogen fertilizer management

2013 ◽  
Vol 48 (10) ◽  
pp. 1394-1401 ◽  
Author(s):  
Nikrooz Bagheri ◽  
Hojjat Ahmadi ◽  
Seyed Kazem Alavipanah ◽  
Mahmoud Omid
Keyword(s):  
Remote Sensing ◽  
Nitrogen Content ◽  
Nitrogen Fertilizer ◽  
Vegetation Index ◽  
Fertilizer Management ◽  
Site Specific ◽  
Multispectral Remote Sensing ◽  
Index 2 ◽  
Canopy Nitrogen ◽  
Corn Canopy

The objective of this work was to evaluate the use of multispectral remote sensing for site-specific nitrogen fertilizer management. Satellite imagery from the advanced spaceborne thermal emission and reflection radiometer (Aster) was acquired in a 23 ha corn-planted area in Iran. For the collection of field samples, a total of 53 pixels were selected by systematic randomized sampling. The total nitrogen content in corn leaf tissues in these pixels was evaluated. To predict corn canopy nitrogen content, different vegetation indices, such as normalized difference vegetation index (NDVI), soil-adjusted vegetation index (Savi), optimized soil-adjusted vegetation index (Osavi), modified chlorophyll absorption ratio index 2 (MCARI2), and modified triangle vegetation index 2 (MTVI2), were investigated. The supervised classification technique using the spectral angle mapper classifier (SAM) was performed to generate a nitrogen fertilization map. The MTVI2 presented the highest correlation (R²=0.87) and is a good predictor of corn canopy nitrogen content in the V13 stage, at 60 days after cultivating. Aster imagery can be used to predict nitrogen status in corn canopy. Classification results indicate three levels of required nitrogen per pixel: low (0-2.5 kg), medium (2.5-3 kg), and high (3-3.3 kg).

scholarly journals Remote sensing of LAI, chlorophyll and leaf nitrogen pools of crop- and grasslands in five European landscapes

2012 ◽  
Vol 9 (8) ◽  
pp. 10149-10205 ◽  
Author(s):  
E. Boegh ◽  
R. Houborg ◽  
J. Bienkowski ◽  
C. F. Braban ◽  
T. Dalgaard ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Nitrogen Content ◽  
Field Data ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Leaf Nitrogen ◽  
Nitrogen Pools ◽  
Canopy Nitrogen ◽  
Use Categories

Abstract. Leaf nitrogen and leaf surface area influence the exchange of gases between terrestrial ecosystems and the atmosphere, and they play a significant role in the global cycles of carbon, nitrogen and water. Remote sensing data from satellites can be used to estimate leaf area index (LAI), leaf chlorophyll (CHLl) and leaf nitrogen density (Nl). However, methods are often developed using plot scale data and not verified over extended regions that represent a variety of soil spectral properties and canopy structures. In this paper, field measurements and high spatial resolution (10–20 m) remote sensing images acquired from the HRG and HRVIR sensors aboard the SPOT satellites were used to assess the predictability of LAI, CHLl and Nl. Five spectral vegetation indices (SVIs) were used (the Normalized Difference Vegetation index, the Simple Ratio, the Enhanced Vegetation Index-2, the Green Normalized Difference Vegetation Index, and the green Chlorophyll Index) together with the image-based inverse canopy radiative transfer modelling system, REGFLEC (REGularized canopy reFLECtance). While the SVIs require field data for empirical model building, REGFLEC can be applied without calibration. Field data measured in 93 fields within crop- and grasslands of five European landscapes showed strong vertical CHLl gradient profiles in 20% of fields. This affected the predictability of SVIs and REGFLEC. However, selecting only homogeneous canopies with uniform CHLl distributions as reference data for statistical evaluation, significant (p < 0.05) predictions were achieved for all landscapes, by all methods. The best performance was achieved by REGFLEC for LAI (r2=0.7; rmse = 0.73), canopy chlorophyll content (r2=0.51; rmse = 439 mg m−2) and canopy nitrogen content (r2 = 0.53; rmse = 2.21 g m−2). Predictabilities of SVIs and REGFLEC simulations generally improved when constrained to single land use categories (wheat, maize, barley, grass) across the European landscapes, reflecting sensitivity to canopy structures. Predictability further improved when constrained to local (10 × 10 km2) landscapes, thereby reflecting sensitivity to local environmental conditions. All methods showed different predictabilities for land use categories and landscapes. Combining the best methods, LAI, canopy chlorophyll content (CHLc) and canopy nitrogen content (CHLc) for the five landscapes could be predicted with improved accuracy (LAI rmse = 0.59; CHLc rmse = 346 g m−2; Ncrmse = 1.49 g m−2). Remote sensing-based results showed that the vegetation nitrogen pools of the five agricultural landscapes varied from 0.6 to 4.0 t km−2. Differences in nitrogen pools were attributed to seasonal variations, extents of agricultural area, species variations, and spatial variations in nutrient availability. Information on Nl and total Nc pools within the landscapes is important for the spatial evaluation of nitrogen and carbon cycling processes. The upcoming Sentinel-2 satellite mission will provide new multiple narrow-band data opportunities at high spatio-temporal resolution which are expected to further improve remote sensing predictabilities of LAI, CHLl and Nl.

Soil-line vegetation indices for corn nitrogen content prediction

2012 ◽  
Vol 26 (2) ◽  
pp. 103-108 ◽  
Author(s):  
N. Bagheri ◽  
H. Ahmadi ◽  
S. Alavipanah ◽  
M. Omid
Keyword(s):  
Nitrogen Content ◽  
Vegetation Indices ◽  
Polynomial Equation ◽  
Measured Data ◽  
Second Order ◽  
Soil Line ◽  
Order Polynomial ◽  
Canopy Nitrogen ◽  
Order Polynomial Equation ◽  
Corn Canopy

Soil-line vegetation indices for corn nitrogen content prediction The soil-line vegetation indices for prediction of corn canopy nitrogen content were investigated. Results indicated that the vegetation indices applied were correlated with corn canopy nitrogen content and the wavelengths between 630-860 nm are suitable for nitrogen diagnosis. The second-order polynomial equation was the best model for nitrogen content prediction among different regression types. Analyses based on both predicted and measured data were carried out to compare the performance of existing vegetation indices.

scholarly journals Determining nitrogen deficiencies for maize using various remote sensing indices

2022 ◽  
Author(s):  
Brayden W. Burns ◽  
V. Steven Green ◽  
Ahmed A. Hashem ◽  
Joseph H. Massey ◽  
Aaron M. Shew ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Grain Yield ◽  
Nitrogen Fertilizer ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Nitrogen Deficiency ◽  
Maize Grain Yield ◽  
Nitrogen Inputs ◽  
Chlorophyll Index ◽  
Maize Grain

AbstractDetermining a precise nitrogen fertilizer requirement for maize in a particular field and year has proven to be a challenge due to the complexity of the nitrogen inputs, transformations and outputs in the nitrogen cycle. Remote sensing of maize nitrogen deficiency may be one way to move nitrogen fertilizer applications closer to the specific nitrogen requirement. Six vegetation indices [normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), red-edge normalized difference vegetation index (RENDVI), triangle greenness index (TGI), normalized area vegetation index (NAVI) and chlorophyll index-green (CIgreen)] were evaluated for their ability to detect nitrogen deficiency and predict grain maize grain yield. Strip trials were established at two locations in Arkansas, USA, with nitrogen rate as the primary treatment. Remote sensing data was collected weekly with an unmanned aerial system (UAS) equipped with a multispectral and thermal sensor. Relationships among index value, nitrogen fertilizer rate and maize growth stage were evaluated. Green NDVI, RENDVI and CIgreen had the strongest relationship with nitrogen fertilizer treatment. Chlorophyll Index-green and GNDVI were the best predictors of maize grain yield early in the growing season when the application of additional nitrogen was still agronomically feasible. However, the logistics of late season nitrogen application must be considered.

scholarly journals Estimation of Leaf Nitrogen Content in Wheat Using New Hyperspectral Indices and a Random Forest Regression Algorithm

2018 ◽  
Vol 10 (12) ◽  
pp. 1940 ◽  
Author(s):  
Liang Liang ◽  
Liping Di ◽  
Ting Huang ◽  
Jiahui Wang ◽  
Li Lin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Nitrogen Content ◽  
Vegetation Index ◽  
Leaf Nitrogen ◽  
Support Vector ◽  
Leaf Nitrogen Content ◽  
Random Forest Regression ◽  
First Derivative ◽  
Remote Sensing Mapping

Novel hyperspectral indices, which are the first derivative normalized difference nitrogen index (FD-NDNI) and the first derivative ratio nitrogen vegetation index (FD-SRNI), were developed to estimate the leaf nitrogen content (LNC) of wheat. The field stress experiments were conducted with different nitrogen and water application rates across the growing season of wheat and 190 measurements were collected on canopy spectra and LNC under various treatments. The inversion models were constructed based on the dataset to evaluate the ability of various spectral indices to estimate LNC. A comparative analysis showed that the model accuracies of FD-NDNI and FD-SRNI were higher than those of other commonly used hyperspectral indices including mNDVI705, mSR, and NDVI705, which was indicated by higher R2 and lower root mean square error (RMSE) values. The least squares support vector regression (LS-SVR) and random forest regression (RFR) algorithms were then used to optimize the models constructed by FD-NDNI and FD-SRNI. The p-R2 values of the FD-NDNI_RFR and FD-SRNI_RFR models reached 0.874 and 0.872, respectively, which were higher than those of the exponential and SVR model and indicated that the RFR model was accurate. Using the RFR inversion model, remote sensing mapping for the Operative Modular Imaging Spectrometer (OMIS) image was accomplished. The remote sensing mapping of the OMIS image yielded an accuracy of R2 = 0.721 and RMSE = 0.540 for FD-NDNI and R2 = 0.720 and RMSE = 0.495 for FD-SRNI, which indicates that the similarity between the inversion value and the measured value was high. The results show that the new hyperspectral indices, i.e., FD-NDNI and FD-SRNI, are the optimal hyperspectral indices for estimating LNC and that the RFR algorithm is the preferred modeling method.

scholarly journals Study on the Quantitative Relationship Among Canopy Hyperspectral Reflectance, Vegetation Index and Cotton Leaf Nitrogen Content

2021 ◽  
Author(s):  
Caixia Yin ◽  
Jiao Lin ◽  
Lulu Ma ◽  
Ze Zhang ◽  
Tongyu Hou ◽  
...  
Keyword(s):  
Nitrogen Content ◽  
Nitrogen Fertilizer ◽  
Spectral Reflectance ◽  
Vegetation Index ◽  
Early Stage ◽  
Leaf Nitrogen ◽  
Nitrogen Fertilizers ◽  
Cotton Leaf ◽  
Leaf Nitrogen Content ◽  
Center Wavelength

AbstractStudy the response mechanism of Canopy spectral reflectance (CSR) to cotton nitrogen fertilizer, propose the sensitive band and center wavelength of cotton leaf nitrogen content (LNC), and compare the response characteristics of various vegetation indexes to LNC, propose a vegetation index that responds well to LNC and construct estimating model. This experiment sets five nitrogen fertilizer levels, namely N0 (control), N120 (120 kg/hm2), N240 (240 kg/hm2), N360 (360 kg/hm2), N480 (480 kg/hm2). Among them, referring to the conventional nitrogen fertilizer is applied by local farmers (N330, 330 kg/hm2). The results showed the following: (1) Visible light and near-infrared (NIR) can be used as two large ranges for precise monitoring of nitrogen, especially the CSR in the NIR range differs significantly under different nitrogen fertilizers. In the early stage of cotton growth, the CSR decreased with the nitrogen application rate increase, in a suitable nitrogen environment (360 kg/hm2), and beyond N360, vice versa. In the later growth period, the CSR increases with the increase in nitrogen fertilizer. This trend is most evident in the short-wave NIR regions;(2) the range of 690–709 nm, 717–753 nm, and 940–958, which can be remote sensed by the spectral reflectance when cotton is affected in poor or rich nitrogen. The center wavelength corresponding to the nitrogen-sensitive band, respectively, are 697 nm, 735 nm, 953 nm, the band width can maintain 5–15 nm, generally not more than 20 nm;(3) compared with the ratio vegetation index, difference vegetation index, and normalized vegetation index, the combined vegetation index of more than two bands has a better effect on cotton LNC monitoring, of which the index (R560−R670)/(R560 + R670−R450), (R700−1.7 × R670 + 0.7 × R450)/(R700 + 2.3 × R670−1.3 × R450) are significantly related to LNC in this papers, and the correlation coefficients can reach, respectively, 0.935* and 0.936*. These findings help to estimate the model of LNC. The model is as follows: Y = 19.883 × x + 42.285, where x refers to the combined vegetation index (R700−1.7 × R670 + 0.7 × R450)/(R700 + 2.3 × R670−1.3 × R450), Y is LNC, but the model accuracy will be affected in the crop different phenological stage, and the model has the highest monitoring accuracy during the bud period.

scholarly journals Estimation of forest canopy nitrogen content based on remote sensing

2013 ◽  
Vol 31 (6) ◽  
pp. 536-543 ◽  
Author(s):  
Xi-Guang YANG ◽  
Ying YU ◽  
Hai-Jun HUANG ◽  
Wen-Yi FAN
Keyword(s):  
Remote Sensing ◽  
Nitrogen Content ◽  
Forest Canopy ◽  
Canopy Nitrogen
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