Abstract
In tropical/subtropical monsoon regions, accurate rice mapping is hampered by the following factors: (1) The frequent occurrence of clouds in such areas during the rice-growing season interferes strongly with optical remote sensing observations; (2) The agro-landscape in such regions is fragmented and scattered. Rice maps produced using low spatial resolution data cannot well delineate the detailed distribution of rice, while pixel-based mapping using medium and high resolutions has significant salt-and-pepper noise. (3) The cropping system is complex, and rice has a rotation schedule with other crops. Therefore, the Phenology-, Object- and Double Source-based (PODS) paddy rice mapping algorithm is implemented, which consists of three steps: (1) object extraction from multi-temporal 10-m Sentinel-2 images where the extracted objects (fields) are the basic classification units; (2) specifying the phenological stage of transplanting from Savitzky–Golay filtered enhanced vegetation index (EVI) time series using the PhenoRice algorithm; and (3) the identification of rice objects based on flood signal detection from time-series microwave and optical signals of the Sentinel-1/2. This study evaluated the potential of the combined use of the Sentinel-1/2 mission on paddy rice mapping in monsoon regions with the Hangzhou-Jiaxin-Huzhou (HJH) plain in China as the case study. A cloud computing approach was used to process the available Sentinel-1/2 imagery from 2019 and MODIS images from 2018 to 2020 in the HJH plain on the Google Earth Engine (GEE) platform. An accuracy assessment showed that the resultant object-based paddy rice map has a high accuracy with a producer (user) accuracy of 0.937 (0.926). The resultant 10-m paddy rice map is expected to provide unprecedented detail, spatial distribution, and landscape patterns for paddy rice fields in monsoon regions.
Highlights
Rice feeds more than half of the global human population [1] with more than 90%of rice production being in Asia [2]
The cadastral the intensity of salt-and-pepper effects that result from data of different spatial resoludata provided by land administration in Jiashan County is used as the reference boundary tions, astowell as theour advantages of object-based classification over pixel-based approaches dataset validate field boundary delineation
We developed a systematic method to map paddy rice called the PODS algorithm using MODIS and Sentinel-1/2 time series data
Summary
Rice feeds more than half of the global human population [1] with more than 90%. Of rice production being in Asia [2]. Obtaining information on the rice location and distribution is important for food security and water use. Field survey-based agricultural statistical methods, which is the conventional approach to determine rice planting areas, is time- and labor-intensive. Tabular data obtained from this method lacks explicit spatial distribution information, and the data may be altered in regions where agriculture subsidies are provided. Remote sensing methods are an efficient and reliable approach to obtain spatially explicit and objective data. The main data sources are optical and synthetic aperture radar (SAR). Optical data provide spectral information of the surface and reflects the biochemical characteristics
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