Early detection of canopy nitrogen deficiency in winter wheat (<i>Triticum aestivum</i>L.) based on hyperspectral measurement of canopy chlorophyll status

Zhao, Chunjiang; Wang, Z; Wang, J; Huang, Wenjiang; Guo, Tao

doi:10.1080/01140671.2011.588713

Cited by 11 publications

(6 citation statements)

References 29 publications

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“…The LUT-based retrieval method used in this study had the lowest LNC estimation accuracy of the three approaches tried, in contrast to previous research results [27,58]. Indeed, as some variables (e.g., LAI and chlorophyll content) could be retrieved directly from the PROSAIL model while LNC was generated indirectly from the empirical relationship between LNC and LCC [59,60], estimation accuracy was influenced by retrieval equation accuracy. We also note that the LUT-based retrieval method has a number of drawbacks, including the need for too many input parameters, large data size, and long processing times, and the fact that only parameters inherent to the model can be retrieved.…”

Section: Discussioncontrasting

confidence: 67%

A Comparative Assessment of Different Modeling Algorithms for Estimating Leaf Nitrogen Content in Winter Wheat Using Multispectral Images from an Unmanned Aerial Vehicle

Zheng

Jiang

et al. 2018

Remote Sensing

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Unmanned aerial vehicle (UAV)-based remote sensing (RS) possesses the significant advantage of being able to efficiently collect images for precision agricultural applications. Although numerous methods have been proposed to monitor crop nitrogen (N) status in recent decades, just how to utilize an appropriate modeling algorithm to estimate crop leaf N content (LNC) remains poorly understood, especially based on UAV multispectral imagery. A comparative assessment of different modeling algorithms (i.e., simple and non-parametric modeling algorithms alongside the physical model retrieval method) for winter wheat LNC estimation is presented in this study. Experiments were conducted over two consecutive years and involved different winter wheat varieties, N rates, and planting densities. A five-band multispectral camera (i.e., 490 nm, 550 nm, 671 nm, 700 nm, and 800 nm) was mounted on a UAV to acquire canopy images across five critical growth stages. The results of this study showed that the best-performing vegetation index (VI) was the modified renormalized difference VI (RDVI), which had a determination coefficient (R2) of 0.73 and a root mean square error (RMSE) of 0.38. This method was also characterized by a high processing speed (0.03 s) for model calibration and validation. Among the 13 non-parametric modeling algorithms evaluated here, the random forest (RF) approach performed best, characterized by R2 and RMSE values of 0.79 and 0.33, respectively. This method also had the advantage of full optical spectrum utilization and enabled flexible, non-linear fitting with a fast processing speed (2.3 s). Compared to the other two methods assessed here, the use of a look up table (LUT)-based radiative transfer model (RTM) remained challenging with regard to LNC estimation because of low prediction accuracy (i.e., an R2 value of 0.62 and an RMSE value of 0.46) and slow processing speed. The RF approach is a fast and accurate technique for N estimation based on UAV multispectral imagery.

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Section: Discussioncontrasting

confidence: 67%

A Comparative Assessment of Different Modeling Algorithms for Estimating Leaf Nitrogen Content in Winter Wheat Using Multispectral Images from an Unmanned Aerial Vehicle

Zheng

Jiang

et al. 2018

Remote Sensing

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“…The obtained 'combined index' was a powerful tool to predict canopy N. It was able to give prediction from Zadoks 14-37 with an R 2 of 0.97 and root mean square error of 0.65 g N m −2 , when dry weight biomass by area was also considered. Recently, Zhao et al (2011) developed a method based on the relationship between winter wheat canopy vertical chlorophyll distribution and canopy reflectance. The canopy chlorophyll density (i.e.…”

Section: Advantages and Drawbacks Of Hyperspectral Proximal Sensingmentioning

confidence: 99%

Precision nitrogen management of wheat. A review

Diacono

Rubino

Montemurro³

2012

Agron. Sustain. Dev.

321

170

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Conventional farming has led to extensive use of chemicals and, in turn, to negative environmental impacts such as soil erosion, groundwater pollution and atmosphere contamination. Farming systems should be more sustainable to reach economical and social profitability as well as environmental preservation. A possible solution is to adopt precision agriculture, a win-win option for sustaining food production without degrading the environment. Precision technologies are used for gathering information about spatial and temporal differences within the field in order to match inputs to site-specific field conditions. Here we review reports on the precision N management of wheat crop. The aims are to perform an investigation both on approaches and results of site-specific N management of wheat and to analyse performance and sustainability of this agricultural practice. In this context, we analysed literature of the last 10-15 years. The major conclusions are: (a) before making N management decisions, both the measurement and understanding of soil spatial variability and the wheat N status are needed. Complementary use of different sensors has improved soil properties assessment at relatively low cost; (b) results show the usefulness of airborne images, remote and proximal sensing for predicting crop N status by responsive in-season management approaches; (c) red edge and near-infrared bands can penetrate into higher vegetation fraction of the canopy. These narrowbands better estimated grain yield, crop N and water status, with R 2 higher than 0.70. In addition, different hyperspectral vegetation indices accounted for a high variability of 40-75 % of wheat N status; (d) various diagnostic tools and procedures have been developed in order to help wheat farmers for planning variable N rates. In-season adjustments in N fertilizer management can account for the specific climatic conditions and yield potential since less than 30 % of spatial variance could show temporal stability; (e) field studies in which sensor-based N management systems were compared with common farmer practices showed high increases in the N use efficiency of up to 368 %. These systems saved N fertilizers, from 10 % to about 80 % less N, and reduced residual N in the soil by 30-50 %, without either reducing yields or influencing grain quality; (f) precision N management based on real-time sensing and fertilization had the highest profitability of about $5-60 ha −1 compared to undifferentiated applications.

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“…The chlorophyll content and distribution in leaves is an important index in estimation of plant nutrition information [149], which is an important indicator of vegetation health. Research on chlorophyll inversion was widely performed in winter wheat [150], rice [151], [152], and cucumber [153].…”

Section: E Vegetation Information Extractionmentioning

confidence: 99%

Progress in Hyperspectral Remote Sensing Science and Technology in China Over the Past Three Decades

Tong

Xue

Zhang

2014

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

187

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This paper reviews progress in hyperspectral remote sensing (HRS) in China, focusing on the past three decades. China has made great achievements since starting in this promising field in the early 1980s. A series of advanced hyperspectral imaging systems ranging from ground to airborne and satellite platforms have been designed, built, and operated. These include the field imaging spectrometer system (FISS), the Modular Airborne Imaging Spectrometer (MAIS), and the Chang'E-I Interferometer Spectrometer (IIM). In addition to developing sensors, Chinese scientists have proposed various novel image processing techniques. Applications of hyperspectral imaging in China have been also performed including mineral exploration in the Qilian Mountains and oil exploration in Xinjiang province. To promote the development of HRS, many generic and professional software tools have been developed. These tools such as the Hyperspectral Image Processing and Analysis System (HIPAS) incorporate a number of special algorithms and features designed to take advantage of the wealth of information contained in HRS data, allowing them to meet the demands of both common users and researchers in the scientific community.

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Early detection of canopy nitrogen deficiency in winter wheat (Triticum aestivumL.) based on hyperspectral measurement of canopy chlorophyll status

Cited by 11 publications

References 29 publications

A Comparative Assessment of Different Modeling Algorithms for Estimating Leaf Nitrogen Content in Winter Wheat Using Multispectral Images from an Unmanned Aerial Vehicle

A Comparative Assessment of Different Modeling Algorithms for Estimating Leaf Nitrogen Content in Winter Wheat Using Multispectral Images from an Unmanned Aerial Vehicle

Precision nitrogen management of wheat. A review

Progress in Hyperspectral Remote Sensing Science and Technology in China Over the Past Three Decades

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