Zheng Yang, Wu Bingfang, Zhang Miao, Zeng Hongwei
Key Laboratory of Digital Earth, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China.
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2016 Dec 10;16(12):2099. 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.
及时且高效地以高空间分辨率监测作物物候对于农业的精准有效管理至关重要。近年来，诸如归一化植被指数（NDVI）等卫星衍生植被指数已被广泛用于陆地生态系统的物候检测。本文提出了一个框架，用于利用从SPOT5（对地观测卫星系统5）和中分辨率成像光谱仪（MODIS）图像融合得到的高时空分辨率数据来检测作物物候。该框架包括一种数据融合方法，以生成具有SPOT5空间分辨率和MODIS时间分辨率的合成NDVI数据集，以及一种基于NDVI时间序列分析的物候提取算法。我们的物候检测方法的可行性在中国山东省的县级尺度上进行了评估。结果表明：（1）时空自适应反射率融合模型（STARFM）算法能够准确融合SPOT5和MODIS的NDVI，预测数据与参考数据之间的决定系数大于0.69，均方根误差（RMSE）小于0.11；（2）估计的物候参数，如季节开始和结束时间（SOS和EOS），与实地观测数据密切相关，SOS的决定系数在0.68至0.86之间，EOS的决定系数在0.72至0.79之间。我们的研究为农田高度碎片化地区的作物物候制图提供了一种可靠方法，这对精准农业的实施具有重要意义。
