Crop Phenology Detection Using High Spatio-Temporal Resolution Data Fused from SPOT5 and MODIS Products

• Published in: Sensors (Basel, Switzerland) Switzerland), 2016-12, Vol.16 (12), p.2099-2099
• Main Authors: Zheng, Yang, Wu, Bingfang, Zhang, Miao, Zeng, Hongwei
• Format: Article
• Language: English
• Subjects: Adaptive algorithms; Agricultural land; Agricultural management; Agriculture; Algorithms; Crops; data fusion; Data integration; Mathematical models; MODIS; Multisensor fusion; Normalized difference vegetative index; Parameter estimation; Phenology; phenology detection; Reflectance; Root-mean-square errors; Spatial resolution; SPOT5; STARFM; Temporal resolution; Time series; Vegetation
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s16122099

Timely and efficient monitoring of crop phenology at a high spatial resolution are crucial for the precise and effective management of agriculture. Recently, satellite-derived vegetation indices (VIs), such as the Normalized Difference Vegetation Index (NDVI), have been widely used for the phenology detection of terrestrial ecosystems. In this paper, a framework is proposed to detect crop phenology using high spatio-temporal resolution data fused from Systeme Probatoire d'Observation de la Tarre5 (SPOT5) and Moderate Resolution Imaging Spectroradiometer (MODIS) images. The framework consists of a data fusion method to produce a synthetic NDVI dataset at SPOT5's spatial resolution and at MODIS's temporal resolution and a phenology extraction algorithm based on NDVI time-series analysis. The feasibility of our phenology detection approach was evaluated at the county scale in Shandong Province, China. The results show that (1) the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm can accurately blend SPOT5 and MODIS NDVI, with an ² of greater than 0.69 and an root mean square error (RMSE) of less than 0.11 between the predicted and referenced data; and that (2) the estimated phenology parameters, such as the start and end of season (SOS and EOS), were closely correlated with the field-observed data with an ² of the SOS ranging from 0.68 to 0.86 and with an ² of the EOS ranging from 0.72 to 0.79. Our research provides a reliable approach for crop phenology mapping in areas with high fragmented farmland, which is meaningful for the implementation of precision agriculture.