Discrimination of wheat crop stage using CHRIS/PROBA multi-angle narrowband data

Multi-angular narrowband compact high-resolution imaging spectrometer (CHRIS) on-board the project for on-board autonomy (PROBA) data of 18 March 2008 were used in this study to discriminate three different growth stages of wheat crop grown in the Central State Farm of Suratgarh, Rajasthan, India. Results showed that the off-nadir view angles performed better than nadir viewing for crop stage discrimination. Among all the off-nadir viewing angles, −55.37° view angle (in the backward-scattering direction) had the highest normalized distance between the crop stage classes. Based on the analysis, the five best bands were identified as 630, 660, 674, 705 and 712 nm for separating wheat at different stages.

[1]  H.F.M. ten Berge,et al.  ORYZA2000 : modeling lowland rice , 2001 .

[2]  Sushma Panigrahy,et al.  Evaluation of hyperspectral indices for LAI estimation and discrimination of potato crop under different irrigation treatments , 2006 .

[3]  John R. Miller,et al.  Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture , 2002 .

[4]  L. Dini,et al.  Retrieval of Leaf Area Index from CHRIS/PROBA data: an analysis of the directional and spectral information content , 2008 .

[5]  Sushma Panigrahy,et al.  Use of hyperspectral data to assess the effects of different nitrogen applications on a potato crop , 2007, Precision Agriculture.

[6]  Bingfang Wu,et al.  Monitoring Crop Phenology with MERIS Data | A Case Study of Winter Wheat in North China Plain , 2009 .

[7]  Heather McNairn,et al.  Validation of a hyperspectral curve-fitting model for the estimation of plant water content of agricultural canopies , 2003 .

[8]  Philip H. Swain,et al.  Remote Sensing: The Quantitative Approach , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  M. A Cutter.  A low cost hyperspectral mission , 2004 .

[10]  M. Boschetti,et al.  Multi-year monitoring of rice crop phenology through time series analysis of MODIS images , 2009 .

[11]  Luis Alonso,et al.  Correction of systematic spatial noise in push-broom hyperspectral sensors: application to CHRIS/PROBA images. , 2008, Applied optics.

[12]  D. Roberts,et al.  View angle effects on the discrimination of soybean varieties and on the relationships between vegetation indices and yield using off-nadir Hyperion data , 2009 .

[13]  Prasad S. Thenkabail,et al.  Optimal hyperspectral narrowbands for discriminating agricultural crops , 2001 .

[14]  L. Guanter,et al.  Spectral calibration of hyperspectral imagery using atmospheric absorption features. , 2006, Applied optics.

[15]  D. Kimes.  Dynamics of directional reflectance factor distributions for vegetation canopies. , 1983, Applied optics.

[16]  J. Moreno,et al.  Retrieval of chlorophyll content and LAI of crops using hyperspectral techniques: application to PROBA/CHRIS data , 2008 .

[17]  Elizabeth Pattey,et al.  Impact of nitrogen and environmental conditions on corn as detected by hyperspectral reflectance , 2002 .

[18]  Philip Lewis,et al.  On the potential of CHRIS/PROBA for estimating vegetation canopy properties from space , 2000 .

[19]  Tristan Quaife,et al.  Mission Status of CHRIS/PROBA and its Utility for Sampling the Earth Surface BRDF. , 2001 .

[20]  M. Dingkuhn,et al.  Effect of drainage date on yield and dry matter partitioning in irrigated rice , 1996 .

[21]  J. Zadoks.  A decimal code for the growth stages of cereals , 1974 .

[22]  John S. Kimball,et al.  Satellite radar remote sensing of seasonal growing seasons for boreal and subalpine evergreen forests. , 2004 .

[23]  D S Kimes,et al.  Variation of directional reflectance factors with structural changes of a developing alfalfa canopy. , 1982, Applied optics.