Abstract
Different rice crop information can be derived from different remote sensing sources to provide information for decision making and policies related to agricultural production and food security. The objective of this study is to generate complementary and comprehensive rice crop information from hypertemporal optical and multitemporal high-resolution SAR imagery. We demonstrate the use of MODIS data for rice-based system characterization and X-band SAR data from TerraSAR-X and CosmoSkyMed for the identification and detailed mapping of rice areas and flooding/transplanting dates. MODIS was classified using ISODATA to generate cropping calendar, cropping intensity, cropping pattern and rice ecosystem information. Season and location specific thresholds from field observations were used to generate detailed maps of rice areas and flooding/transplanting dates from the SAR data. Error matrices were used for the accuracy assessment of the MODIS-derived rice characteristics map and the SAR-derived detailed rice area map, while Root Mean Square Error (RMSE) and linear correlation were used to assess the TSX-derived flooding/transplanting dates. Results showed that multitemporal high spatial resolution SAR data is effective for mapping rice areas and flooding/transplanting dates with an overall accuracy of 90% and a kappa of 0.72 and that hypertemporal moderate-resolution optical imagery is effective for the basic characterization of rice areas with an overall accuracy that ranged from 62% to 87% and a kappa of 0.52 to 0.72. This study has also provided the first assessment of the temporal variation in the backscatter of rice from CSK and TSX using large incidence angles covering all rice crop stages from pre-season until harvest. This complementarity in optical and SAR data can be further exploited in the near future with the increased availability of space-borne optical and SAR sensors. This new information can help improve the identification of rice areas.
Highlights
Rice is a staple food for more than half of the world’s population and the most important crop in low-income and lower-middle-income countries [1,2]
The 13 rice classes were merged into nine classes (Figure 4) based on similarity in cropping calendars
This study showed how hypertemporal MODIS and multitemporal, high-spatial-resolution Synthetic Aperture Radar (SAR)
Summary
Rice is a staple food for more than half of the world’s population and the most important crop in low-income and lower-middle-income countries [1,2]. Its contribution as a staple food and source of income and employment is crucial to food security [3]. In 2012, rice was cultivated on about 163 million hectares of land that produced approximately 719 million tons of rice with 90% of it originating from. The timely provision of accurate spatial information on the rice crop supports policies and decisions on agricultural production and food security in the region [5]. Remote sensing can provide spatial and temporal information to characterize rice agro-ecological attributes [6], assess rice growth [7] and productivity [8]. It is used to differentiate cropping patterns [9], detect cropping calendar [6] and the dynamic phenological stages of rice [10,11]
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