Paper Info
Title
Reconstruction of Daily 30 m Data from HJ CCD, GF-1 WFV, Landsat, and MODIS Data for Crop Monitoring
Abstract
With the recent launch of new satellites and the developments of spatiotemporal data fusion methods, we are entering an era of high spatiotemporal resolution remote-sensing analysis. This study proposed a method to reconstruct daily 30 m remote-sensing data for monitoring crop types and phenology in two study areas located in Xinjiang Province, China. First, the Spatial and Temporal Data Fusion Approach (STDFA) was used to reconstruct the time series high spatiotemporal resolution data from the Huanjing satellite charge coupled device (HJ CCD), Gaofen satellite no. 1 wide field-of-view camera (GF-1 WFV), Landsat, and Moderate Resolution Imaging Spectroradiometer (MODIS) data. Then, the reconstructed time series were applied to extract crop phenology using a Hybrid Piecewise Logistic Model (HPLM). In addition, the onset date of greenness increase (OGI) and greenness decrease (OGD) were also calculated using the simulated phenology. Finally, crop types were mapped using the phenology information. The results show that the reconstructed high spatiotemporal data had a high quality with a proportion of good observations (PGQ) higher than 0.95 and the HPLM approach can simulate time series Normalized Different Vegetation Index (NDVI) very well with R-2 ranging from 0.635 to 0.952 in Luntai and 0.719 to 0.991 in Bole, respectively. The reconstructed high spatiotemporal data were able to extract crop phenology in single crop fields, which provided a very detailed pattern relative to that from time series MODIS data. Moreover, the crop types can be classified using the reconstructed time series high spatiotemporal data with overall accuracy equal to 0.91 in Luntai and 0.95 in Bole, which is 0.028 and 0.046 higher than those obtained by using multi-temporal Landsat NDVI data.
Year
DOI
Venue
2015
10.3390/rs71215826
REMOTE SENSING
Keywords
Field
DocType
HJ CCD,GF-1 WFV,STDFA,phenology,time series high spatiotemporal resolution remote sensing
Satellite,Moderate-resolution imaging spectroradiometer,Normalization (statistics),Remote sensing,Sensor fusion,Temporal database,Normalized Difference Vegetation Index,Geology,Charge-coupled device,Phenology
Journal
Volume
Issue
ISSN
7
12
2072-4292
Citations 
PageRank 
References 
11
0.69
8
Authors
7
Name
Order
Citations
PageRank
Mingquan Wu116918.49
Xiaoyang Zhang2235.37
Wenjiang Huang317951.84
Zheng Niu414422.34
Changyao Wang5908.63
wang li6468.53
pengyu hao7603.75