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Special Issue "Remote Sensing for Precision Nitrogen Management"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 31 December 2019

Special Issue Editors

Guest Editor
Dr. Yuxin Miao

Precision Agriculture Center, University of Minnesota, 1991 Upper Buford Circle, St. Paul, MN 55108, USA
Website | E-Mail
Interests: precision agriculture; remote sensing-based precision nitrogen management; combining crop growth modeling and remote sensing for precision crop management; food security and sustainable development
Guest Editor
Dr. Raj Khosla

Department of Soil and Crop Sciences, Colorado State University, 307 University Ave., Fort Collins, CO 80523, USA
Website | E-Mail
Interests: precision agriculture; management zone; precision nitrogen management
Guest Editor
Dr. David J. Mulla

Precision Agriculture Center, University of Minnesota, 1991 Upper Buford Circle, St. Paul, MN 55108, USA
Website | E-Mail
Interests: precision agriculture; geospatial modeling and analysis; remote sensing; precision conservation

Special Issue Information

Dear Colleagues,

Nitrogen is the most widely used macro nutrient in the world. Agriculture is a major source of N2O emissions in the biosphere. Precision nitrogen management is an important area of advanced nutrient management as well as precision agriculture for solving problems in food and environmental security for sustainable agricultural and social development. Precision nitrogen management aims to match nitrogen supply with crop N demand in both space and time to ensure high crop yield while increasing N use efficiency and protecting the environment. It is a research area that involves management zone delineation, proximal and remote sensing, crop growth modeling, spatial statistics, variable rate technology, agronomy, soil science, meteorology, plant nutrition and greenhouse gas emission mitigation, etc.

Remote sensing is one of the key supporting technologies for precision agriculture, and advances of proximal and remote sensing technologies have greatly contributed to the development of precision nitrogen management. To help readers keep up with the progresses on the applications of proximal canopy sensors, UAV-based remote sensing, aerial remote sensing and satellite remote sensing in precision nitrogen management of cereal crops, vegetables and fruit trees, etc., we would like to invite you to submit research and review papers on the following topics:

  • Proximal and remote sensing-based non-destructive diagnosis of crop nitrogen status
  • Proximal and remote sensing-based in-season variable rate nitrogen recommendation algorithms and precision management strategies
  • Remote sensing-based site-specific management zone delineation and evaluation for precision nitrogen management
  • Simultaneous diagnosis of crop nitrogen stress and other stress factors (other nutrients, water, disease, insect damage, etc.)
  • Combining remote sensing and crop growth modeling for precision nitrogen management
  • Data fusion of sensing and other related data for precision nitrogen management
  • Integration of sensing technology-based precision nitrogen management with other high yield crop management technologies
  • Evaluation of sensing technology-based precision nitrogen and crop management strategies using field experiments and on-farm trials
  • New sensing technologies for precision nitrogen management

Dr. Yuxin Miao
Dr. Raj Khosla
Dr. David J. Mulla
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Precision nitrogen management
  • Active canopy sensing
  • UAV remote sensing
  • Aerial and satellite remote sensing
  • In-season nitrogen status diagnosis
  • In-season site-specific nitrogen management
  • Nitrogen use efficiency
  • Crop growth modeling
  • Food security
  • Sustainable development
  • Precision agriculture
  • Management zone

Published Papers (5 papers)

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Research

Open AccessArticle Remote Sensing of Leaf and Canopy Nitrogen Status in Winter Wheat (Triticum aestivum L.) Based on N-PROSAIL Model
Remote Sens. 2018, 10(9), 1463; https://doi.org/10.3390/rs10091463
Received: 26 June 2018 / Revised: 6 September 2018 / Accepted: 10 September 2018 / Published: 13 September 2018
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Abstract
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
[...] Read more.
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. Full article
(This article belongs to the Special Issue Remote Sensing for Precision Nitrogen Management)
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Open AccessArticle Analyzing the Effect of Fluorescence Characteristics on Leaf Nitrogen Concentration Estimation
Remote Sens. 2018, 10(9), 1402; https://doi.org/10.3390/rs10091402
Received: 24 July 2018 / Revised: 23 August 2018 / Accepted: 30 August 2018 / Published: 3 September 2018
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Abstract
Leaf nitrogen concentration (LNC) is a significant indicator of crops growth status, which is related to crop yield and photosynthetic efficiency. Laser-induced fluorescence is a promising technology for LNC estimation and has been widely used in remote sensing. The accuracy of LNC monitoring
[...] Read more.
Leaf nitrogen concentration (LNC) is a significant indicator of crops growth status, which is related to crop yield and photosynthetic efficiency. Laser-induced fluorescence is a promising technology for LNC estimation and has been widely used in remote sensing. The accuracy of LNC monitoring relies greatly on the selection of fluorescence characteristics and the number of fluorescence characteristics. It would be useful to analyze the performance of fluorescence intensity and ratio characteristics at different wavelengths for LNC estimation. In this study, the fluorescence spectra of paddy rice excited by different excitation light wavelengths (355 nm, 460 nm, and 556 nm) were acquired. The performance of the fluorescence intensity and fluorescence ratio of each band were analyzed in detail based on back-propagation neural network (BPNN) for LNC estimation. At 355 nm and 460 nm excitation wavelengths, the fluorescence characteristics related to LNC were mainly located in the far-red region, and at 556 nm excitation wavelength, the red region being an optimal band. Additionally, the effect of the number of fluorescence characteristics on the accuracy of LNC estimation was analyzed by using principal component analysis combined with BPNN. Results demonstrate that at least two fluorescence spectral features should be selected in the red and far-red regions to estimate LNC and efficiently improve the accuracy of LNC estimation. Full article
(This article belongs to the Special Issue Remote Sensing for Precision Nitrogen Management)
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Open AccessArticle Difference and Potential of the Upward and Downward Sun-Induced Chlorophyll Fluorescence on Detecting Leaf Nitrogen Concentration in Wheat
Remote Sens. 2018, 10(8), 1315; https://doi.org/10.3390/rs10081315
Received: 9 June 2018 / Revised: 30 July 2018 / Accepted: 10 August 2018 / Published: 20 August 2018
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Abstract
Precise detection of leaf nitrogen concentration (LNC) is helpful for nutrient diagnosis and fertilization guidance in farm crops. Numerous researchers have estimated LNC with techniques based on reflectance spectra or active chlorophyll fluorescence, which have limitations of low accuracy or small scale in
[...] Read more.
Precise detection of leaf nitrogen concentration (LNC) is helpful for nutrient diagnosis and fertilization guidance in farm crops. Numerous researchers have estimated LNC with techniques based on reflectance spectra or active chlorophyll fluorescence, which have limitations of low accuracy or small scale in the field. Given the correlation between chlorophyll and nitrogen contents, the response of sun-induced chlorophyll fluorescence (SIF) to chlorophyll (Chl) content reported in a few papers suggests the feasibility of quantifying LNC using SIF. Few studies have investigated the difference and power of the upward and downward SIF components on monitoring LNC in winter wheat. We conducted two field experiments to evaluate the capacity of SIF to monitor the LNC of winter wheat during the entire growth season and compare the differences of the upward and downward SIF for LNC detection. A FluoWat leaf clip coupled with a ASD spectrometer was used to measure the upward and downward SIF under sunlight. It was found that three (↓FY687, ↑FY687/↑FY739, and ↓FY687/↓FY739) out of the six SIF yield (FY) indices examined were significantly correlated to the LNC (R2 = 0.6, 0.51, 0.75, respectively). The downward SIF yield indices exhibited better performance than the upward FY indices in monitoring the LNC with the ↓FY687/↓FY739 being the best FY index. Moreover, the LNC models based on the three SIF yield indices are insensitive to the chlorophyll content and the leaf mass per area (LMA). These findings suggest the downward SIF should not be neglected for monitoring crop LNC at the leaf scale, although it is more difficult to measure with current instruments. The downward SIF could play an increasingly important role in understanding of the SIF emission for LNC detection at different scales. These results could provide a solid foundation for elucidating the mechanism of SIF for LNC estimation at the canopy scale. Full article
(This article belongs to the Special Issue Remote Sensing for Precision Nitrogen Management)
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Open AccessArticle Integrating Airborne Hyperspectral, Topographic, and Soil Data for Estimating Pasture Quality Using Recursive Feature Elimination with Random Forest Regression
Remote Sens. 2018, 10(7), 1117; https://doi.org/10.3390/rs10071117
Received: 19 May 2018 / Revised: 11 July 2018 / Accepted: 11 July 2018 / Published: 13 July 2018
Cited by 1 | PDF Full-text (5815 KB) | HTML Full-text | XML Full-text
Abstract
Accurate and efficient monitoring of pasture quality on hill country farm systems is crucial for pasture management and optimizing production. Hyperspectral imaging is a promising tool for mapping a wide range of biophysical and biochemical properties of vegetation from leaf to canopy scale.
[...] Read more.
Accurate and efficient monitoring of pasture quality on hill country farm systems is crucial for pasture management and optimizing production. Hyperspectral imaging is a promising tool for mapping a wide range of biophysical and biochemical properties of vegetation from leaf to canopy scale. In this study, the potential of high spatial resolution and airborne hyperspectral imaging for predicting crude protein (CP) and metabolizable energy (ME) in heterogeneous hill country farm was investigated. Regression models were developed between measured pasture quality values and hyperspectral data using random forest regression (RF). The results proved that pasture quality could be predicted with hyperspectral data alone; however, accuracy was improved after combining the hyperspectral data with environmental data (elevation, slope angle, slope aspect, and soil type) where the prediction accuracy for CP was R2CV (cross-validated coefficient of determination) = 0.70, RMSECV (cross-validated root mean square error) = 2.06%, RPDCV (cross-validated ratio to prediction deviation) = 1.82 and ME: R2CV = 0.75, RMSECV = 0.65 MJ/kg DM, RPDCV = 2.11. Interestingly, the accuracy was further out-performed by considering important hyperspectral and environmental variables using RF combined with recursive feature elimination (RFE) (CP: R2CV = 0.80, RMSECV = 1.68%, RPDCV = 2.23; ME: R2CV = 0.78, RMSECV = 0.61 MJ/kg DM, RPDCV = 2.19). Similar performance trends were noticed with validation data. Utilizing the best model, spatial pasture quality maps were created across the farm. Overall, this study showed the potential of airborne hyperspectral data for producing accurate pasture quality maps, which will help farm managers to optimize decisions to improve environmental and economic benefits. Full article
(This article belongs to the Special Issue Remote Sensing for Precision Nitrogen Management)
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Open AccessArticle Evaluation of RGB, Color-Infrared and Multispectral Images Acquired from Unmanned Aerial Systems for the Estimation of Nitrogen Accumulation in Rice
Remote Sens. 2018, 10(6), 824; https://doi.org/10.3390/rs10060824
Received: 3 April 2018 / Revised: 19 May 2018 / Accepted: 23 May 2018 / Published: 25 May 2018
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Abstract
Unmanned aerial system (UAS)-based remote sensing is one promising technique for precision crop management, but few studies have reported the applications of such systems on nitrogen (N) estimation with multiple sensors in rice (Oryza sativa L.). This study aims to evaluate three
[...] Read more.
Unmanned aerial system (UAS)-based remote sensing is one promising technique for precision crop management, but few studies have reported the applications of such systems on nitrogen (N) estimation with multiple sensors in rice (Oryza sativa L.). This study aims to evaluate three sensors (RGB, color-infrared (CIR) and multispectral (MS) cameras) onboard UAS for the estimation of N status at individual stages and their combination with the field data collected from a two-year rice experiment. The experiments were conducted in 2015 and 2016, involving different N rates, planting densities and rice cultivars, with three replicates. An Oktokopter UAS was used to acquire aerial photography at early growth stages (from tillering to booting) and field samplings were taken at a near date. Two color indices (normalized excess green index (NExG), and normalized green red difference index (NGRDI)), two near infrared vegetation indices (green normalized difference vegetation index (GNDVI), and enhanced NDVI (ENDVI)) and two red edge vegetation indices (red edge chlorophyll index (CIred edge), and DATT) were used to evaluate the capability of these three sensors in estimating leaf nitrogen accumulation (LNA) and plant nitrogen accumulation (PNA) in rice. The results demonstrated that the red edge vegetation indices derived from MS images produced the highest estimation accuracy for LNA (R2: 0.79–0.81, root mean squared error (RMSE): 1.43–1.45 g m−2) and PNA (R2: 0.81–0.84, RMSE: 2.27–2.38 g m−2). The GNDVI from CIR images yielded a moderate estimation accuracy with an all-stage model. Color indices from RGB images exhibited satisfactory performance for the pooled dataset of the tillering and jointing stages. Compared with the counterpart indices from the RGB and CIR images, the indices from the MS images performed better in most cases. These results may set strong foundations for the development of UAS-based rice growth monitoring systems, providing useful information for the real-time decision making on crop N management. Full article
(This article belongs to the Special Issue Remote Sensing for Precision Nitrogen Management)
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