Rice is one of the world's most dominant staple foods, and hence rice farming plays a vital role in a nation's economy and food security. To examine the applicability of synthetic aperture radar (SAR) data for large areas, we propose an approach to determine rice age, date of planting (dop), and date of harvest (doh) using a time series of Sentinel-1 C-band in the entire Mekong Delta, Vietnam. The effect of the incidence angle of Sentinel-1 data on the backscatter pattern of paddy fields was reduced using the incidence angle normalization approach with an empirical model developed in this study. The time series was processed further to reduce noise with fast Fourier transform and smoothing filter. To evaluate and improve the accuracy of SAR data processing results, the classification outcomes were verified with field survey data through statistical metrics. The findings indicate that the Sentinel-1 images are particularly appropriate for rice age monitoring with R 2 ¼ 0.92 and root-mean-square error (RMSE) = 7.3 days (n ¼ 241) in comparison to in situ data. The proposed algorithm for estimating dop and doh also shows promising results with R 2 ¼ 0.92 and RMSE ¼ 6.2 days (n ¼ 153) and R 2 ¼ 0.70 and RMSE ¼ 5.7 days (n ¼ 88), respectively. The results have indicated the ability of using Sentinel-1 data to extract growth parameters involving rice age, planting and harvest dates. Information about rice age corresponding to the growth stages of rice fields is important for agricultural management and support the procurement and management of agricultural markets, limiting the negative effects on food security. The results showed that multitemporal Sentinel-1 data can be used to monitor the status of rice growth. Such monitoring system can assist many countries, especially in Asia, for managing agricultural land to ensure productivity.

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.

Rice production in Vietnam has been developing rapidly and sustainably in recent years, contributing to ensuring national food security. However, it is facing the effect of climate change, sea-level rise, salinity intrusion, drought, and flood which threatens food production, especially in the Vietnamese Mekong Delta. For this reason, building a tool that allows estimating rice yield is necessary. SAR (Synthetic Aperture Radar) remote sensing data from Sentinel-1 satellites is provided by European Space Agency (ESA) with no cost, large coverage, and high spatio-temporal resolution, which has the advantage of observation in cloudy, foggy, rainy weather and independent of solar radiation. Therefore, this data is suitable for rice monitoring in countries with tropical monsoon climate like Vietnam. This paper presents the results of estimating the Winter-Spring rice yield in 2018 by using multitemporal Sentinel-1 data with C-band. The estimated rice yield was compared with the in-situ yield, which shows that the average values of the samples of estimated and surveyed yield were equivalent with 6.5 ton/ha and 6.6 ton/ha respectively, and the standard deviation between the estimated and surveyed yield was 0.80 ton/ha. The results demonstrate the applicability of the multitemporal SAR Sentinel-1 data for estimating rice yield in the study area, An Giang province, the Vietnamese Mekong Delta.

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Different spatial resolutions of optical satellite imagery provide different details in land use/land cover (LULC) mapping. Therefore, it is essential to decide the appropriate classification system for specific spatial resolution. This study used the same classification system to extract land use/land cover for District 2 and District7 of Ho Chi Minh City, Vietnam in 2018 from two different spatial resolutions, Sentinel-2 (10 m) and VNREDSat-1 (2.5 m), images. Using the same set of ground truth data, the accuracy of the classified maps was assessed to select the right image for the study area corresponding to the given classification system. The outcomes revealed that both optical satellite images produced high overall accuracy (83% for Sentinel-2 images and 86% for VNREDSat-1 images) on the same classification system. It is easier to classify the cropland, grassland, roads, and industrial zones with VNREDSat-1 image whereas perennials, bare land, and aquaculture were easily classified with Sentinel-2 due to their homogeneity at a lower spatial resolution. This shows the importance of choosing the right spatial resolution following the requirements of the classification system.