Mapping Early, Middle and Late Rice Extent Using Sentinel-1A and Landsat-8 Data in the Poyang Lake Plain, China

Abstract

Areas and spatial distribution information of paddy rice are important for managing food security, water use, and climate change. However, there are many difficulties in mapping paddy rice, especially mapping multi-season paddy rice in rainy regions, including differences in phenology, the influence of weather, and farmland fragmentation. To resolve these problems, a novel multi-season paddy rice mapping approach based on Sentinel-1A and Landsat-8 data is proposed. First, Sentinel-1A data were enhanced based on the fact that the backscattering coefficient of paddy rice varies according to its growth stage. Second, cropland information was enhanced based on the fact that the NDVI of cropland in winter is lower than that in the growing season. Then, paddy rice and cropland areas were extracted using a K-Means unsupervised classifier with enhanced images. Third, to further improve the paddy rice classification accuracy, cropland information was utilized to optimize distribution of paddy rice by the fact that paddy rice must be planted in cropland. Classification accuracy was validated based on ground-data from 25 field survey quadrats measuring 600 m × 600 m. The results show that: multi-season paddy rice planting areas effectively was extracted by the method and adjusted early rice area of 1630.84 km2, adjusted middle rice area of 556.21 km2, and adjusted late rice area of 3138.37 km2. The overall accuracy was 98.10%, with a kappa coefficient of 0.94.

Early Season Mapping of Sugarcane by Applying Machine Learning Algorithms to Sentinel-1A/2 Time Series Data: A Case Study in Zhanjiang City, China

“…Monitoring the stages of rice growth has been a key application of SAR in tropical regions since the 1990s, particularly in Asian countries. Most of this monitoring has focused on rice mapping by employing the European Remote Sensing satellites (ERS) 1 and 2 [16,17], Radarsat [18], Envisat ASAR (Advanced Synthetic Aperture Radar) [19], TerraSAR-X [20], COSMO-SkyMed [21] and recently Sentinel-1 (S1A) [22][23][24][25][26][27]. To date, however, there have been few studies that have utilized SAR data for sugarcane mapping.…”

“…To date, however, there have been few studies that have utilized SAR data for sugarcane mapping. Given that sugarcane fields and rice paddies are similar, a large number of rice identification methods [22][23][24][25][26][27] can be used as reference material in sugarcane identification.…”

Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm

“…For example, the Chinese government generally publishes this information at least six months after the crops have been harvested. Furthermore, the quality of the survey data tends to suffer from the influence of human errors [3].Remote sensing techniques provide a very effective means to map crops [2,11,16,17], due to their fast responses, periodic observations, wide field of view, and low cost [18][19][20]. However, there are still some challenges associated with large-scale (e.g., provincial-or national-scale) winter crop mapping by remote sensing.…”

“…Remote sensing techniques provide a very effective means to map crops [2,11,16,17], due to their fast responses, periodic observations, wide field of view, and low cost [18][19][20]. However, there are still some challenges associated with large-scale (e.g., provincial-or national-scale) winter crop mapping by remote sensing.…”

Long-Term Monitoring of Cropland Change near Dongting Lake, China, Using the LandTrendr Algorithm with Landsat Imagery

“…Selecting suitable vegetation indices is critical for successfully detecting cropland conversion in our study; different vegetation indices, such as Enhanced Vegetation Index (EVI) [20] and NDVI [32,45], have been used for this purpose. Research shows that NDVI distinguishes cropland change better than other indices [46,47], but there are still some limitations to applying the LandTrendr algorithm with NDVI. One example is the disturbance caused by other vegetation, such as the reeds in our study region that produced an NDVI signal similar to poplar and thus influenced the detection result.…”