TY - JOUR
T1 - Using High Spatial Resolution Satellite Imagery for Mapping Powdery Mildew at a Regional Scale
AU - Yuan, Lin
AU - Pu, Ruiliang
AU - Zhang, Jingcheng
AU - Wang, Jihua
AU - Yang, Hao
PY - 2016/6/1
Y1 - 2016/6/1
N2 - Efficient crop protection management requires timely detection of diseases. The rapid development of remote sensing technology provides a possibility of spatial continuous monitoring of crop diseases over a large area. In this study, to monitor powdery mildew in winter wheat in an area where a severe disease infection occurred, the capability of high resolution (6 m) multi-spectral satellite imagery, SPOT-6, in disease mapping was assessed and validated using field survey data. Based on a rigorous feature selection process, five disease sensitive spectral features: green band, red band, normalized difference vegetation index, triangular vegetation index, and atmospherically-resistant vegetation index were selected from a group of candidate spectral features/variables. A spectral correction was processed on the selected features to eliminate possible baseline effect across different regions. Then, the disease mapping method was developed based on a spectral angle mapping technique. By validating against a set of field survey data, an overall mapping accuracy of 78 % and kappa coefficient of 0.55 were achieved. Such a moderate but practically acceptable accuracy suggests that the high resolution multi-spectral satellite image data would be of great potential in crop disease monitoring.
AB - Efficient crop protection management requires timely detection of diseases. The rapid development of remote sensing technology provides a possibility of spatial continuous monitoring of crop diseases over a large area. In this study, to monitor powdery mildew in winter wheat in an area where a severe disease infection occurred, the capability of high resolution (6 m) multi-spectral satellite imagery, SPOT-6, in disease mapping was assessed and validated using field survey data. Based on a rigorous feature selection process, five disease sensitive spectral features: green band, red band, normalized difference vegetation index, triangular vegetation index, and atmospherically-resistant vegetation index were selected from a group of candidate spectral features/variables. A spectral correction was processed on the selected features to eliminate possible baseline effect across different regions. Then, the disease mapping method was developed based on a spectral angle mapping technique. By validating against a set of field survey data, an overall mapping accuracy of 78 % and kappa coefficient of 0.55 were achieved. Such a moderate but practically acceptable accuracy suggests that the high resolution multi-spectral satellite image data would be of great potential in crop disease monitoring.
KW - Powdery mildew
KW - Winter wheat
KW - Spectral angle mapping (SAM)
KW - SPOT-6
UR - https://digitalcommons.usf.edu/geo_facpub/1350
UR - https://doi.org/10.1007/s11119-015-9421-x
U2 - 10.1007/s11119-015-9421-x
DO - 10.1007/s11119-015-9421-x
M3 - Article
VL - 17
JO - Precision Agriculture
JF - Precision Agriculture
ER -