Rice growth monitoring using multi-temporal GF-1 images

Abstract

With China Remote Sensing career advancement, a large number of independent researches and development satellites have launched. Among a new generation of high-resolution satellites, GaoFen-1 (GF-1) stands out. It sets high spatial resolution (2 m-16 m), multi-spectral and high temporal resolution (4-day) with 60 km-800 km swath in a fusion technology with strategic significance. In order to explore the adaptability of Chinese GF-1 images in rice growth monitoring, aboveground biomass (AGB) was considered as plant growth indicator. Multi-temporal GF-1 WFV images of Xinghua City, Jiangsu Province were selected for rice growth parameter retrieval. An extensive field campaign was carried out during the rice growing season in 2015. Six rice sample plots with areas larger than 200 × 200 m2 in Xinghua City were randomly chosen in order to measure the vegetation characteristics. Only cloud-free images were selected for AGB modeling. Using Savitzky-Golay filters, daily vegetation indices (VIs) time series were created from all the GF-1 images. For modeling of AGB from GF-1, there were 42 matching AGB sample sites. The matched cumulative VIs were calculated from 10-day composite data and were adopted for the estimation of AGB. Five traditional regression equations (linear, exponential, power, logarithmic, and quadratic polynomial regression) were applied in model construction. The leave-one-out cross-validation method was implemented to test the prediction capability of the models. The cumulative NDVI-based quadratic polynomial fit function was adopted for the prediction of AGB at all stages. In this paper, the application provided an important reference of field management and decision-making information. Indicated that GF-1 satellite's high time resolution provides chances to get cloudless data, and high spatial and spectral resolution features can replace the traditional medium resolution remote sensing of agricultural growth monitoring data to a certain extent, but a lot of ground survey data still needed to improve model and monitoring accuracy. This research shows that GF-1 WFV is an important data source and the data's application in other areas of agriculture is the focus of future research.