Assessing the Effect of Real Spatial Resolution of In Situ UAV Multispectral Images on Seedling Rapeseed Growth Monitoring

[1]  Jonathan P. Dash,et al.  UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health , 2018, Remote. Sens..

[2]  A. Huete,et al.  A review of vegetation indices , 1995 .

[3]  Andreas Burkart,et al.  Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance , 2015 .

[4]  Jürgen Schellberg,et al.  Distinguishing Intensity Levels of Grassland Fertilization Using Vegetation Indices , 2017, Remote. Sens..

[5]  Shanyu Huang,et al.  Active canopy sensor-based precision N management strategy for rice , 2012, Agronomy for Sustainable Development.

[6]  D. Lu.  The potential and challenge of remote sensing‐based biomass estimation , 2006 .

[7]  R. Downey.  Agricultural and genetic potentials of cruciferous oilseed crops , 1971 .

[8]  B. Nelson,et al.  Allometric regressions for improved estimate of secondary forest biomass in the central Amazon , 1999 .

[9]  F. Baret,et al.  Green area index from an unmanned aerial system over wheat and rapeseed crops , 2014 .

[10]  Yanjie Wang,et al.  Estimation of Winter Wheat Above-Ground Biomass Using Unmanned Aerial Vehicle-Based Snapshot Hyperspectral Sensor and Crop Height Improved Models , 2017, Remote. Sens..

[11]  G. Meyer,et al.  Color indices for weed identification under various soil, residue, and lighting conditions , 1994 .

[12]  Weixing Cao,et al.  Detection of rice phenology through time series analysis of ground-based spectral index data , 2016 .

[13]  Jiali Shang,et al.  Assessment of red-edge vegetation indices for crop leaf area index estimation , 2019, Remote Sensing of Environment.

[14]  Frédéric Baret,et al.  Estimating leaf chlorophyll content in sugar beet canopies using millimeter- to centimeter-scale reflectance imagery , 2017 .

[15]  Jianyu Yang,et al.  Modified ALV for selecting the optimal spatial resolution and its scale effect on image classification accuracy , 2011, Math. Comput. Model..

[16]  Dong-Wook Kim,et al.  Modeling and Testing of Growth Status for Chinese Cabbage and White Radish with UAV-Based RGB Imagery , 2018, Remote. Sens..

[17]  Rajesh Govindan,et al.  Optimisation of the energy, water, and food nexus for food security scenarios , 2019, Comput. Chem. Eng..

[18]  A. Gitelson,et al.  Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation , 1994 .

[19]  Masatoshi Okutomi,et al.  Single-Sensor RGB-NIR Imaging: High-Quality System Design and Prototype Implementation , 2019, IEEE Sensors Journal.

[20]  Wang Zongming,et al.  Spatial variability and pattern analysis of soil properties in Dehui city, Jilin province , 2004 .

[21]  Frédéric Baret,et al.  Review of methods for in situ leaf area index determination Part I. Theories, sensors and hemispherical photography , 2004 .

[22]  Chunhua Zhang,et al.  The application of small unmanned aerial systems for precision agriculture: a review , 2012, Precision Agriculture.

[23]  L. You,et al.  Evaluation of a UAV-mounted consumer grade camera with different spectral modifications and two handheld spectral sensors for rapeseed growth monitoring: performance and influencing factors , 2020, Precision Agriculture.

[24]  Jonathan T. Barron,et al.  Burst photography for high dynamic range and low-light imaging on mobile cameras , 2016, ACM Trans. Graph..

[25]  H. Pleijel,et al.  Evaluating the relationship between leaf chlorophyll concentration and SPAD-502 chlorophyll meter readings , 2007, Photosynthesis Research.

[26]  Satoshi Tsuyuki,et al.  Digital Aerial Photogrammetry for Uneven-Aged Forest Management: Assessing the Potential to Reconstruct Canopy Structure and Estimate Living Biomass , 2019, Remote. Sens..

[27]  Yi Peng,et al.  Remote prediction of yield based on LAI estimation in oilseed rape under different planting methods and nitrogen fertilizer applications , 2019, Agricultural and Forest Meteorology.

[28]  Jinhui Lan,et al.  A Multiscale Hierarchical Model for Sparse Hyperspectral Unmixing , 2019, Remote. Sens..

[29]  R. Gupta,et al.  Crop sensors for efficient nitrogen management in sugarcane: potential and constraints , 2006, Sugar Tech.

[30]  G. Rondeaux,et al.  Optimization of soil-adjusted vegetation indices , 1996 .

[31]  Anne M. Smith,et al.  Radiometric and spectral comparison of inexpensive camera systems used for remote sensing , 2018 .

[32]  D. Raes,et al.  The effect of tillage, crop rotation and residue management on maize and wheat growth and development evaluated with an optical sensor , 2011 .

[33]  A. Gitelson,et al.  Use of a green channel in remote sensing of global vegetation from EOS- MODIS , 1996 .

[34]  Holly Croft,et al.  Modelling leaf chlorophyll content in broadleaf and needle leaf canopies from ground, CASI, Landsat TM 5 and MERIS reflectance data , 2013 .

[35]  Xiaojun Liu,et al.  Combining Color Indices and Textures of UAV-Based Digital Imagery for Rice LAI Estimation , 2019, Remote. Sens..

[36]  C. Daughtry,et al.  Evaluation of Digital Photography from Model Aircraft for Remote Sensing of Crop Biomass and Nitrogen Status , 2005, Precision Agriculture.

[37]  Thomas Jarmer,et al.  High-Resolution UAV-Based Hyperspectral Imagery for LAI and Chlorophyll Estimations from Wheat for Yield Prediction , 2018, Remote. Sens..

[38]  Frédéric Baret,et al.  Exploiting the centimeter resolution of UAV multispectral imagery to improve remote-sensing estimates of canopy structure and biochemistry in sugar beet crops , 2019, Remote Sensing of Environment.

[39]  J. Everitt,et al.  Using spectral vegetation indices to estimate rangeland productivity , 1992 .

[40]  L. Deng,et al.  UAV-based multispectral remote sensing for precision agriculture: A comparison between different cameras , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[41]  Michael Marshall,et al.  Developing in situ Non-Destructive Estimates of Crop Biomass to Address Issues of Scale in Remote Sensing , 2015, Remote. Sens..

[42]  N. Coops,et al.  Monitoring plant condition and phenology using infrared sensitive consumer grade digital cameras , 2014 .

[43]  Z. Niu,et al.  Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system , 2016 .

[44]  C. M. Parihar,et al.  Comparison of Vegetation Indices from Two Ground Based Sensors , 2018, Journal of the Indian Society of Remote Sensing.

[45]  Weixing Cao,et al.  Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery , 2017 .

[46]  Ni Wang,et al.  Estimation of Wheat LAI at Middle to High Levels Using Unmanned Aerial Vehicle Narrowband Multispectral Imagery , 2017, Remote. Sens..

[47]  Chenghai Yang,et al.  Rapeseed Seedling Stand Counting and Seeding Performance Evaluation at Two Early Growth Stages Based on Unmanned Aerial Vehicle Imagery , 2018, Front. Plant Sci..

[48]  W. Raun,et al.  In-Season Prediction of Corn Grain Yield Potential Using Normalized Difference Vegetation Index , 2006 .

[49]  Jianwei Lu,et al.  Diagnosis of nitrogen status in winter oilseed rape (Brassica napus L.) using in-situ hyperspectral data and unmanned aerial vehicle (UAV) multispectral images , 2018, Comput. Electron. Agric..

[50]  P. Levy,et al.  Direct and indirect measurements of LAI in millet and fallow vegetation in HAPEX-Sahel , 1999 .

[51]  Simon Bennertz,et al.  Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[52]  G. Meyer,et al.  Verification of color vegetation indices for automated crop imaging applications , 2008 .

[53]  Bodo Bookhagen,et al.  3D hyperspectral point cloud generation: Fusing airborne laser scanning and hyperspectral imaging sensors for improved object-based information extraction , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[54]  Weixing Cao,et al.  Assessing the Impact of Spatial Resolution on the Estimation of Leaf Nitrogen Concentration Over the Full Season of Paddy Rice Using Near-Surface Imaging Spectroscopy Data , 2018, Front. Plant Sci..