Estimation of biophysical parameters in wheat crops in Golestan province using ultra-high resolution images

ABSTRACT Due to various factors which impress wheat growth variability, we need short–term monitoring of biophysical parameters using ultra–high resolution images. These provide an ability to monitor crops at the individual plant level. Two flight missions were carried out at altitude of 40 m with a commercial quad copter and a commercial camera. The images were taken before and after tillage over an 8.8 ha field. The 2 cm orthoimages and surface models were generated using photogrammetric software. Then, the image variables including ratio of the blue and green band, ratio of the red and blue band,ratio of the red and green band, plant height were extracted from orthoimages and surface models. Field measurement included the leaf area index, plant height and biomass for 15 plots each of area 1 m × 1 m. Due to a linear relationship between the biophysical parameters and image variables, it was used a multivariate regression model for modelling. The model using the image variables resulted in coefficient of determination (R2) of 0.95 and the lowest error measures (RMSE = 0.24). The results show that ultra–high resolution images can be used for monitoring of biophysical parameters in wheat crops but it is limited for large area.

[1]  Manabu Watanabe,et al.  Forest Structure Dependency of the Relation Between L-Band$sigma^0$and Biophysical Parameters , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Huazhong Ren,et al.  A new narrow band vegetation index for characterizing the degree of vegetation stress due to copper: the copper stress vegetation index (CSVI) , 2017 .

[3]  Z. Niu,et al.  Identifying crown areas in an undulating area planted with eucalyptus using unmanned aerial vehicle near-infrared imagery , 2016 .

[4]  Tomislav Hengl,et al.  Finding the right pixel size , 2006, Comput. Geosci..

[5]  A. Bolten,et al.  MULTI-TEMPORAL CROP SURFACE MODELS COMBINED WITH THE RGB VEGETATION INDEX FROM UAV-BASED IMAGES FOR FORAGE MONITORING IN GRASSLAND , 2016 .

[6]  R. Kent Crookston,et al.  A top 10 list of developments and issues impacting crop management and ecology during the past 50 years , 2006 .

[7]  Y. Miao,et al.  Quantifying spatial variability of indigenous nitrogen supply for precision nitrogen management in small scale farming , 2012, Precision Agriculture.

[8]  D. Ehlert,et al.  Suitability of a laser rangefinder to characterize winter wheat , 2010, Precision Agriculture.

[9]  J. Eitel,et al.  LiDAR based biomass and crop nitrogen estimates for rapid, non-destructive assessment of wheat nitrogen status , 2014 .

[10]  D. Mulla.  Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps , 2013 .

[11]  Y. Cohen,et al.  Use of thermal and visible imagery for estimating crop water status of irrigated grapevine. , 2006, Journal of experimental botany.

[12]  W. Mirschel,et al.  Crop growth modelling on different spatial scales—a wide spectrum of approaches , 2004 .

[13]  Alireza Sharifi,et al.  Comparison the accuracies of different spectral indices for estimation of vegetation cover fraction in sparse vegetated areas , 2011 .

[14]  I. M. Scotford,et al.  Estimating Tiller Density and Leaf Area Index of Winter Wheat using Spectral Reflectance and Ultrasonic Sensing Techniques , 2004 .

[15]  R. Houborg,et al.  Remote sensing of LAI, chlorophyll and leaf nitrogen pools of crop and grasslands in five European landscapes , 2012 .

[16]  Frédéric Baret,et al.  Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots , 2008, Sensors.

[17]  Kazuaki Yoshida,et al.  Spectral Index for Quantifying Leaf Area Index of Winter Wheat by Field Hyperspectral Measurements: A Case Study in Gifu Prefecture, Central Japan , 2015, Remote. Sens..

[18]  Martin K. van Ittersum,et al.  Scale changes and model linking methods for integrated assessment of agri-environmental systems , 2011 .

[19]  Simon Bennertz,et al.  Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging , 2014, Remote. Sens..

[20]  Ryutaro Tateishi,et al.  Estimation of Forest Biomass Using Multivariate Relevance Vector Regression , 2016 .

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

[22]  Weixing Cao,et al.  Analysis of common canopy vegetation indices for indicating leaf nitrogen accumulations in wheat and rice , 2008, Int. J. Appl. Earth Obs. Geoinformation.

[23]  Luis Miguel Contreras-Medina,et al.  A Review of Methods for Sensing the Nitrogen Status in Plants: Advantages, Disadvantages and Recent Advances , 2013, Sensors.

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

[25]  Chaoqing Yu,et al.  Exploring the correlations between ten monthly climatic variables and the vegetation index of four different crop types at the global scale , 2017 .

[26]  Heikki Saari,et al.  Processing and Assessment of Spectrometric, Stereoscopic Imagery Collected Using a Lightweight UAV Spectral Camera for Precision Agriculture , 2013, Remote. Sens..

[27]  Detlef Ehlert,et al.  Biomass related nitrogen fertilization with a crop sensor. , 2010 .

[28]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  Juliane Bendig,et al.  UAV-based Imaging for Multi-Temporal, very high Resolution Crop Surface Models to monitor Crop Growth Variability , 2013 .

[30]  Alessandro Matese,et al.  A flexible unmanned aerial vehicle for precision agriculture , 2012, Precision Agriculture.