Mapping a Paddy Rice Area in a Cloudy and Rainy Region Using Spatiotemporal Data Fusion and a Phenology-Based Algorithm

Abstract

The timely and accurate mapping of paddy rice is important to ensure food security and to protect the environment for sustainable development. Existing paddy rice mapping methods are often remote sensing technologies based on optical images. However, the availability of high-quality remotely sensed paddy rice growing area data is limited due to frequent cloud cover and rain over the southwest China. In order to overcome these limitations, we propose a paddy rice field mapping method by combining a spatiotemporal fusion algorithm and a phenology-based algorithm. First, a modified neighborhood similar pixel interpolator (MNSPI) time series approach was used to remove clouds on Sentinel-2 and Landsat 8 OLI images in 2020. A flexible spatiotemporal data fusion (FSDAF) model was used to fuse Sentinel-2 data and MODIS data to obtain multi-temporal Sentinel-2 images. Then, the fused remote sensing data were used to construct fusion time series data to produce time series vegetation indices (NDVI\LSWI) having a high spatiotemporal resolution (10 m and ≤16 days). On this basis, the unique physical characteristics of paddy rice during the transplanting period and other auxiliary data were combined to map paddy rice in Yongchuan District, Chongqing, China. Our results were validated by field survey data and showed a high accuracy of the proposed method indicated by an overall accuracy of 93% and the Kappa coefficient of 0.85. The paddy rice planting area map was also consistent with the official data of the third national land survey; at the town level, the correlation between official survey data and paddy rice area was 92.5%. The results show that this method can effectively map paddy rice fields in a cloudy and rainy area.

High-Resolution Ratoon Rice Monitoring under Cloudy Conditions with Fused Time-Series Optical Dataset and Threshold Model

“…This threshold was determined by counting the characteristics of the training samples: Firstly, the flooding signal was determined. As shown by the red circle in Figure 4, around DOY 100, a flooding signal, LSWI > NDVI, appeared, and at least one time flooding signal was observed during the following periods [20]:…”

“…Existing methods for remote-sensing monitoring of paddy rice include machine-learning models [13], time-series matching [14], vegetation index feature methods [15], and object-based classification mapping [16]. In recent years, the phenological method has become a relatively popular method of rice mapping, which belongs to the category of vegetation index characteristic methods [17][18][19][20][21][22]. Xiao et al [18,23] discovered the characteristics of vegetation indices in key phenological periods and carried out large-scale rice-extraction studies in regions such as South Asia.…”

Quantifying the Impacts of the 2020 Flood on Crop Production and Food Security in the Middle Reaches of the Yangtze River, China

“…Food security and related issues are always the PRC's highest priority and are among the most sensitive of topics. Recently, several techniques have been developed to map cropland patterns and dynamics from RS observations, including traditional satellite-based LULC mapping and UAV imagery applications [49][50][51][52][53]. These studies focus on the damage assessment of high-value crops in smaller areas.…”

Estimating fractional cover of saltmarsh vegetation species in coastal wetlands in the Yellow River Delta, China using ensemble learning model

“…Compared to the number of algorithms that have been developed, the number of applications are limited. A few studies in recent years have applied cloud removal as preprocessing step for phenological metrics derivation (Tian et Zhu et al, 2021), paddy rice mapping (Zhao et al, 2021) or vegetation cover estimation . Zhu et al (2021) built time-series cloud-free Landsat imagery by reconstructing cloud-contaminated imagery using NSPI algorithm and then derived dry-season phenology in tropical forest.…”

“…Their study found that cloud removal could help better characterize the phenological features. Zhao et al (2021) applied mNSPI to remove cloud from Landsat imagery, and then extracted phenological features from the time series imagery for paddy rice mapping. They mentioned that the mNSPI could not accurately restore the small and continuous boundaries on the image under the clouds.…”