Mapping Paddy Rice with Sentinel-1/2 and Phenology-, Object-Based Algorithm—A Implementation in Hangjiahu Plain in China Using GEE Platform

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.

Waterbody loss due to urban expansion of large Chinese cities in last three decades

“…This analysis provides a valuable argument for protecting of surface waterbodies in urbanization; however, a challenge remains in evaluating encroached waterbodies because of the difficulty of eliminating the influence of natural conditions such as climate on waterbodies 46 . To negate this effect, we used the 3-year waterbody frequency index to describe surface waterbodies.…”

Monitoring migratory birds of India's largest shallow saline Ramsar site (Sambhar Lake) using geospatial data for wetland restoration

“…Each Sentinel-2 product set (zip archive), and contains multiple granules, which are individual assets in GEE (Xulu et al 2021 ). Sentinel-2 carries the format as COPERNI-CUS/S2/20211005T002653_ 20211231T102149_T56MNN as a GEE asset (Xiao et al 2021 ). The first numeric part represents the data acquisition date and time, the second part signifies the product generation date and time, and the final six-character string represents unique granule identifier, showing its UTM grid reference.…”

Cropping Patterns of Annual Crops: A Remote Sensing Review

“…High temporal resolution microwave sensors such as RADARSAT-2, Sentinel-1, ERS and SkyMed have been used to map single cropping both independently and integrated with optical sensors [70][71][72][73][74][75][76][77][78]. Sentinel-1 sensors have also been used for regional mapping of paddy rice in Asia [79][80][81], while Ramadhani et al [82] integrated microwave data with Sentinel-2 and MODIS MOD13Q1 to map rice in Africa regionally.…”