Mapping paddy rice planting area in cold temperate climate region through analysis of time series Landsat 8 (OLI), Landsat 7 (ETM+) and MODIS imagery

Abstract

Accurate and timely rice paddy field maps with a fine spatial resolution would greatly improve our understanding of the effects of paddy rice agriculture on greenhouse gases emissions, food and water security, and human health. Rice paddy field maps were developed using optical images with high temporal resolution and coarse spatial resolution (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)) or low temporal resolution and high spatial resolution (e.g., Landsat TM/ETM+). In the past, the accuracy and efficiency for rice paddy field mapping at fine spatial resolutions were limited by the poor data availability and image-based algorithms. In this paper, time series MODIS and Landsat ETM+/OLI images, and the pixel- and phenology-based algorithm are used to map paddy rice planting area. The unique physical features of rice paddy fields during the flooding/open-canopy period are captured with the dynamics of vegetation indices, which are then used to identify rice paddy fields. The algorithm is tested in the Sanjiang Plain (path/row 114/27) in China in 2013. The overall accuracy of the resulted map of paddy rice planting area generated by both Landsat ETM+ and OLI is 97.3%, when evaluated with areas of interest (AOIs) derived from geo-referenced field photos. The paddy rice planting area map also agrees reasonably well with the official statistics at the level of state farms (R2 = 0.94). These results demonstrate that the combination of fine spatial resolution images and the phenology-based algorithm can provide a simple, robust, and automated approach to map the distribution of paddy rice agriculture in a year.

Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data

“…Crop types are separated in some other studies (e.g. Kumar et al 2016;Kontgis, Schneider, and Ozdogan 2015;Mariotto et al 2013;Marshall and Thenkabail 2015;Qin et al 2015). However, these studies used, one or more of the following: (a) high spatial resolution (e.g.…”

An Object-Based Paddy Rice Classification Using Multi-Spectral Data and Crop Phenology in Assam, Northeast India

“…The third group is phenology-and pixel-based paddy rice mapping (PPPM) [15,17], where rice is identified for individual pixels based on the flooding signals of the rice transplanting phase by evaluating the differences between the Enhanced Vegetation Index (EVI)/NDVI and Land Surface Water Index (LSWI). The first two groups often generate maps that are difficult to compare for different regions, working groups and years, primarily due to the spectral heterogeneity, training sample selection, post-classification processing and the capabilities of the image interpreter [68]. In comparison, the PPPM methods of the third category are less affected by these issues.…”

Evaluation of One-Class Support Vector Classification for Mapping the Paddy Rice Planting Area in Jiangsu Province of China from Landsat 8 OLI Imagery

“…Nowadays, a common way to map rice planting area is to use phenology-based algorithms since the rice crops in major production regions (e.g., Asia) often experience the unique flooding and transplanting phase in the growing season [17][18][19][20]. They are simple to operate but need many preprocessing steps, such as distinguishing the flooding and transplanting period and developing a series of masking conditions to eliminate irrelevant classes.…”

“…Due to the frequent cloud cover in rice growing season, this requirement can hardly be met by well-suited Landsat optical imagery for mapping rice fields in China. Data unavailability over the critical time window would be the main obstacle for continuous and stable rice mapping [18]. One way is to use SAR (synthetic aperture radar) imagery whose imaging principle is different from that of optical images.…”