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Perspective of Chinese GF-1 high-resolution satellite data in agricultural remote sensing monitoring
- ZHOU, Qing-bo, YU, Qiang-yi, LIU, Jia, WU, Wen-bin, TANG, Hua-jun
- Journal of integrative agriculture 2017 v.16 no.2 pp. 242-251
- agricultural statistics, agronomic traits, algorithms, crop yield, disasters, habitat fragmentation, land cover, monitoring, planting, remote sensing, satellites, soil quality, spatial data, China
- High-resolution satellite data have been playing an important role in agricultural remote sensing monitoring. However, the major data sources of high-resolution images are not owned by China. The cost of large scale use of high resolution imagery data becomes prohibitive. In pace of the launch of the Chinese “High Resolution Earth Observation Systems”, China is able to receive superb high-resolution remotely sensed images (GF series) that equalizes or even surpasses foreign similar satellites in respect of spatial resolution, scanning width and revisit period. This paper provides a perspective of using high resolution remote sensing data from satellite GF-1 for agriculture monitoring. It also assesses the applicability of GF-1 data for agricultural monitoring, and identifies potential applications from regional to national scales. GF-1's high resolution (i.e., 2 m/8 m), high revisit cycle (i.e., 4 days), and its visible and near-infrared (VNIR) spectral bands enable a continuous, efficient and effective agricultural dynamics monitoring. Thus, it has gradually substituted the foreign data sources for mapping crop planting areas, monitoring crop growth, estimating crop yield, monitoring natural disasters, and supporting precision and facility agriculture in China agricultural remote sensing monitoring system (CHARMS). However, it is still at the initial stage of GF-1 data application in agricultural remote sensing monitoring. Advanced algorithms for estimating agronomic parameters and soil quality with GF-1 data need to be further investigated, especially for improving the performance of remote sensing monitoring in the fragmented landscapes. In addition, the thematic product series in terms of land cover, crop allocation, crop growth and production are required to be developed in association with other data sources at multiple spatial scales. Despite the advantages, the issues such as low spectrum resolution and image distortion associated with high spatial resolution and wide swath width, might pose challenges for GF-1 data applications and need to be addressed in future agricultural monitoring.