Document Type

Article

Publication Date

12-2015

Keywords

HJ CCD, GF-1 WFV, STDFA, phenology, time series high spatiotemporal resolution remote sensing

Description

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 R2 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.

Publication Title

Remote Sensing

Volume

7

Issue

12

First Page

16293

Last Page

16314

DOI of Published Version

10.3390/rs71215826

Pages

22

Format

application/pdf

Language

en

Publisher

MDPI

Rights

Copyright © 2015 the authors

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Comments

This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license.

Supplemental materials include seven PNG files.

remotesensing-07-15826-g001.png (1936 kB)
Locations of the study areas.

remotesensing-07-15826-g002.png (1780 kB)
The scatter plot comparisons of Landsat Normalized Different Vegetation Index (NDVI) data, Gaofen satellite no. 1 wide field-of-view camera (GF-1 WFV) NDVI, and Huanjing satellite (HJ) NDVI data (a) before and (b) after sensor calibration.

remotesensing-07-15826-g003a.png (3728 kB)
Comparison of Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI, HJ NDVI, GF-1 NDVI, and synthetic NDVI generated by the Spatial and Temporal Data Fusion Approach (STDFA): (a) MODIS NDVI; (b) HJ NDVI; (c) Synthetic NDVI; and (d) difference map between HJ NDVI and synthetic NDVI in (1) HJ-MODIS fusion result comparison on April 26, 2013; (a) MODIS NDVI; (b) GF-1 NDVI; (c) Synthetic NDVI; and (d) difference map between GF-1 NDVI and synthetic NDVI in (2) GF-MODIS fusion result comparison on 14 July 2013.

remotesensing-07-15826-g003b.png (4959 kB)
Comparison of Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI, HJ NDVI, GF-1 NDVI, and synthetic NDVI generated by the Spatial and Temporal Data Fusion Approach (STDFA): (a) MODIS NDVI; (b) HJ NDVI; (c) Synthetic NDVI; and (d) difference map between HJ NDVI and synthetic NDVI in (1) HJ-MODIS fusion result comparison on April 26, 2013; (a) MODIS NDVI; (b) GF-1 NDVI; (c) Synthetic NDVI; and (d) difference map between GF-1 NDVI and synthetic NDVI in (2) GF-MODIS fusion result comparison on 14 July 2013.

remotesensing-07-15826-g004.png (3122 kB)
Results of model performance quantitative evaluation: (a) coefficient of determination (R2) in (1) Luntai and (2) Bole; and (b) root mean square error (RMSE) in (1) Luntai and (2) Bole.

remotesensing-07-15826-g005.png (1793 kB)
Coefficient of efficiency (E) values in (a) Luntai and (b) Bole.

remotesensing-07-15826-g006.png (232 kB)
Simulated phenology for all land cover types in (a) Bole and (b) Luntai.

remotesensing-07-15826-g007.png (6211 kB)
Calculated onset date of greenness increase (OGI) and greenness decrease (OGD) in (a) Bole and (b) Luntai.

remotesensing-07-15826-g008.png (3560 kB)
Land cover types in (a) Bole and (b) Luntai.

remotesensing-07-15826-g009.png (252 kB)
Phenology for different land cover types extracted from actual MODIS NDVI data in (a) Bole and (b) Luntai.

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