Feasibility assessment of multi-spectral satellite sensors in monitoring and discriminating wheat diseases and insects

Abstract Monitoring and differentiating multiple crop diseases and insects over vast areas are of practical importance in guiding differential treatment. Despite the hyperspectral remote sensing had shown effectiveness in discriminating diseases and insects, the scarcity of the hyperspectral data limited its application. Recently, a number of high resolution multi-spectral satellite sensors became available around the world, which provided routine observations and thus offered an important chance for remotely sensed monitoring of crop diseases and insects. To assess the suitability of channel settings of these broad-band satellite sensors in monitoring and discriminating crop diseases and insects, a feasibility analysis was conducted relying on hyperspectral spectral experiment and a simulation procedure based on channels’ relative spectral response (RSR) function. Taking three typical diseases and insect of winter wheat as an example, this study analyzed the suitability of channels’ reflectance and some classic vegetation indexes (VIs) of seven high-resolution satellite sensors. For each channel/VI of all sensors, the capability in differentiating healthy and infected samples was assessed by an independent t -test, whereas the capability in discriminating diseases and insect was evaluated by an analysis of variance (ANOVA). The results showed that the response of the channels/VIs to diseases and insect reflected the corresponding spectral changes and were highly consistent among sensors. Based on the selected channels and VIs, a discrimination model was established. The model produced a satisfactory overall accuracy of 0.74, which suggested the feasibility of using high resolution multi-spectral satellite sensors in monitoring and discriminating crop diseases and insects.

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

[2]  L. Plümer,et al.  Original paper: Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance , 2010 .

[3]  Armando Apan,et al.  Detecting sugarcane ‘orange rust’ disease using EO-1 Hyperion hyperspectral imagery , 2004 .

[4]  Ruiliang Pu,et al.  Mapping forest leaf area index using reflectance and textural information derived from WorldView-2 imagery in a mixed natural forest area in Florida, US , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[5]  Wenjiang Huang,et al.  Analysis of winter wheat stripe rust characteristic spectrum and establishing of inversion models , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[6]  A. Huete.  A soil-adjusted vegetation index (SAVI) , 1988 .

[7]  Minghua Zhang,et al.  Spectral prediction of Phytophthora infestans infection on tomatoes using artificial neural network (ANN) , 2008, International Journal of Remote Sensing.

[8]  S. Ustin,et al.  Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing , 2003 .

[9]  John R. Miller,et al.  Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture , 2004 .

[10]  Z. Niu,et al.  Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging , 2007, Precision Agriculture.

[11]  U. Steiner,et al.  Spectral signatures of sugar beet leaves for the detection and differentiation of diseases , 2010, Precision Agriculture.

[12]  O. Mutanga,et al.  Using WorldView-2 bands and indices to predict bronze bug (Thaumastocoris peregrinus) damage in plantation forests , 2013 .

[13]  John A. Gamon,et al.  Assessing leaf pigment content and activity with a reflectometer , 1999 .

[14]  F. J. Pierce,et al.  The potential of spectral reflectance technique for the detection of Grapevine leafroll-associated virus-3 in two red-berried wine grape cultivars , 2009 .

[15]  L. Plümer,et al.  Development of spectral indices for detecting and identifying plant diseases , 2013 .

[16]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[17]  Rong-Kuen Chen,et al.  Changes in spectral characteristics of rice canopy infested with brown planthopper and leaffolder , 2007 .

[18]  Pablo J. Zarco-Tejada,et al.  High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices , 2013 .

[19]  G. McLachlan.  Discriminant Analysis and Statistical Pattern Recognition , 1992 .

[20]  Johanna Link,et al.  Identification of powdery mildew (Erysiphe graminis sp. tritici) and take-all disease (Gaeumannomyces graminis sp. tritici) in wheat (Triticum aestivum L.) by means of leaf reflectance measurements , 2006, Central European Journal of Biology.

[21]  N. M. Kelly,et al.  Spectral absorption features as indicators of water status in coast live oak ( Quercus agrifolia ) leaves , 2003 .

[22]  D. Sims,et al.  Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages , 2002 .

[23]  Ruiliang Pu,et al.  Using in-situ hyperspectral data for detecting and discriminating yellow rust disease from nutrient stresses , 2012 .

[24]  A. Gitelson,et al.  Novel algorithms for remote estimation of vegetation fraction , 2002 .

[25]  J. Peñuelas,et al.  The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. , 1994 .

[26]  Wenjiang Huang,et al.  Development, evaluation and application of a spectral knowledge base to detect yellow rust in winter wheat , 2011, Precision Agriculture.

[27]  Ruiliang Pu,et al.  Detecting powdery mildew of winter wheat using leaf level hyperspectral measurements , 2012 .

[28]  Wenjiang Huang,et al.  Detecting Aphid Density of Winter Wheat Leaf Using Hyperspectral Measurements , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[29]  Dionysis Bochtis,et al.  Intelligent multi-sensor system for the detection and treatment of fungal diseases in arable crops , 2011 .

[30]  N. Broge,et al.  Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density , 2001 .

[31]  Gunter Menz,et al.  Multi-temporal wheat disease detection by multi-spectral remote sensing , 2007, Precision Agriculture.

[32]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[33]  Jingcheng Zhang,et al.  Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects , 2014 .