Sentinel-1 SAR Time Series-Based Assessment of the Impact of Severe Salinity Intrusion Events on Spatiotemporal Changes in Distribution of Rice Planting Areas in Coastal Provinces of the Mekong Delta, Vietnam

Abstract

Food security has become a key global issue due to rapid population growth, extensive conversion of arable lands, and declining overall productivity in some areas because of the effects of floods, water shortage, salinity intrusion, and plant diseases. In this study, we analyzed the relationship between the pattern of salinity intrusion and the spatiotemporal distribution of rice cultivation in the winter–spring crops of 2015, 2016, 2019 and 2020 in coastal provinces of the Vietnamese Mekong Delta. Sentinel-1 (S-1) data were used to extract the spatial distribution information of six rice growth stages based on a rice age algorithm. The classification accuracy of rice crop growth stages was found to have an overall accuracy of 85% and a Kappa coefficient of 0.80 (n = 373). For evaluating salinity intrusion effects, salinity isolines (4 g/L) were used to determine the percentage of rice areas affected. Results show that in the years observed to have severe salinity intrusion such as 2016 and 2020, a strong shift in planting calendar was identified to avoid salinity intrusion, with some areas being sown or transplanted 10–30 days earlier than normal planting. In addition, the lack of irrigation water and salinity intrusion limits rice cultivation in the dry season of coastal areas. Further analysis from the S-1 data confirms that the spatiotemporal distribution of rice cultivation is related to the change in government policy/recommendation affected by salinity intrusion. These findings demonstrate the potential and feasibility of using S-1 data to develop an operational rice crop adaptation framework on the delta scale.

Application of multi-temporal Sentinel-1 SAR data for yield estimation of rice crops in An Giang, Vietnam

“…The study evaluated the ability to estimate rice yield in the 2018 crops by analyzing VH polarized Sentinel-1 data with in-situ data as shown in Figure 2. Radar images were collected during one rice crop, then go through the following steps: a) Pre-processing [26,27]; b) Rice mapping: applying threshold method for multi-temporal images acquired during the crop season [14,28]; c) Extracting radar backscatter values according to rice age at the sample fields [29,30]; d) Regression analysis and mapping estimated rice yield [24]. WS SA AW Figure 2.…”

Multi-Scale Drivers of Land-Use Changes at Farm Level I: Conceptual Framework and Application in the Highly Flooded Zone of the Vietnamese Mekong Delta

“…With the development of remote sensing and geographic information system (GIS) techniques in Vietnam in the 2000s, scientists became able to spatially quantify land-use and land-cover changes in more detail over vaster geographical areas [25,26] and identify spatial correlations between land uses, and land-use changes and hydrological factors [2,5,27,28]. However, a spatial approach cannot capture all drivers of change because many drivers, like village and household socioeconomic characteristics, are difficult to observe on the land surface [29].…”

Rice Mapping in a Subtropical Hilly Region Based on Sentinel-1 Time Series Feature Analysis and the Dual Branch BiLSTM Model

“…Currently, multitemporal Sentinel-1 data are the primary data source for these evaluations. First, features to discriminate rice from non-rice crops are defined by analyzing the characteristics of each phase and the variations in the backscattering coefficients during the rice growth cycle [14,15]. For example, empirical methods are used to extract phenological indicators [16,17], or mathematical equations are established to fit time series curves [18,19].…”