Abstract
The NDVI dataset with high temporal and spatial resolution (HTSN) is significant for extracting information about the phenological change of vegetation in regions with a complex earth surface. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) has been successfully applied to synthesize the HTSN by fusing the data with different characteristics. Based on the model, there are two different schemes for synthesizing the HTSN. One scheme is that red reflectance and near-infrared (NIR) reflectance are synthesized, respectively, and the HTSN is then obtained through algebraic operation (Scheme 1); the other scheme is that the red and NIR reflectance are used to calculate NDVI, which is directly taken as input data to synthesize the HTSN (Scheme 2). In this paper, taking the hill areas in eastern Sichuan China as a case, the two schemes were compared with each other. Seven Landsat images and time-series MOD13Q1 datasets spanning from October 2001 to February 2003 were used as the test data. The results showed the prediction accuracies of both derived HTSNs by the two different schemes were generally in good agreement, and Scheme 2 was slightly superior to Scheme 1 (R2: 0.14 < Scheme 1 < 0.53; 0.15 < Scheme 2 < 0.53). Although the two HTSNs showed high temporal and spatial consistence, the small spatiotemporal difference between them had a different influence on different applications. The coincidence rate of cropping intensity extracted from two derived HTSNs was fairly high, reaching up to 93.86%, while the coincidence rate of crop peak dates (i.e., the emerging dates of peaks in an annual time-series NDVI curve) was only 70.95%. Therefore, it is deemed that Scheme 2 can replace Scheme 1 in the application of extracting cropping intensity, so that more calculation time and memory space can be saved. For extracting more quantitative crop phenological information like crop peak dates, more tests are still needed in order to compare the absolute accuracy for both schemes.
Highlights
The NDVI dataset with high temporal and spatial resolution (HTSN) is significant for extracting the phenology of vegetation or crops in regions with a complex earth surface
Some other scholars synthesized the dataset of red band and near-infrared (NIR) band with high temporal and spatial resolutions firstly based on the reflectance fusion model (e.g., Spatial and Temporal Adaptive Reflectance Fusion Model, STARFM [9]), and calculated the corresponding HTSN
The fusion model based on the unmixing theory often requires a land use/cover map with a high spatial resolution as auxiliary data [6,10], while the STARFM algorithm does not need other auxiliary data and it is more practical and has become the most widely applied algorithm for synthesizing reflectance or an NDVI dataset with high spatial and temporal resolutions [11,12,13], since it is easier to realize [14]
Summary
The NDVI dataset with high temporal and spatial resolution (HTSN) is significant for extracting the phenology of vegetation or crops in regions with a complex earth surface. The fusion model based on the unmixing theory often requires a land use/cover map with a high spatial resolution as auxiliary data [6,10], while the STARFM algorithm does not need other auxiliary data and it is more practical and has become the most widely applied algorithm for synthesizing reflectance or an NDVI dataset with high spatial and temporal resolutions [11,12,13], since it is easier to realize [14]. Hilker et al [15] proved that the HTSN synthesized by the STARFM algorithm can reflect the law of vicissitude of different vegetation in a one-year term well; Bhandari et al [16] obtained the reflectance dataset at an eight-day interval and with a 30 m spatial resolution by fusing Landsat TM and MODIS Nadir BRDF Adjusted Reflectance (NBAR) data along with use of the STARFM algorithm, and constructed the HTSN
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