Complementarity of Two Rice Mapping Approaches: Characterizing Strata Mapped by Hypertemporal MODIS and Rice Paddy Identification Using Multitemporal SAR

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.

Evolution of regional to global paddy rice mapping methods: A review

“…The free release of Landsat archive data by USGS/EROS provides an unprecedented data source for paddy rice mapping given its ideal spatial resolution and long records dating back to the 1970s. Synthetic Aperture Radar (SAR) is anticipated to play an important role in paddy rice mapping in tropical and subtropical regions given their frequent cloud coverage (Shao et al, 2001) and one study showed its even more promising capability in mapping rice area and transplanting dates (Asilo et al, 2014); however, its acquisition capability is still not as good as optical sensors.…”

In-Season Mapping of Crop Type with Optical and X-Band SAR Data: A Classification Tree Approach Using Synoptic Seasonal Features

“…The σ° was derived through three processing steps: (a) multi-temporal speckle filtering according to the approach developed by De Grandi et al [59,60,72], to balance differences in reflectivity between images at different times, (b) geocoding and radiometric calibration, using a Digital Elevation Model (SRTM DEM, at 90m equivalent ground resolution) and the radar equation, in which scattering area, antenna gain patterns and range spread loss were considered, and finally (c) normalization on local incidence angle, according to the cosine law. The interferometric coherence γ maps were produced using the complex data of image pairs of consecutive acquisitions [73], with a temporal baseline of 16 days (32 days for the 08 June-10 July 2014 pair).…”

“…CSK images were pre-processed with MAPscape-RICE software [59] for (i) mosaicking single frames into slant range continuous strips and (ii) co-registration of images using orbital information and automatic spatial matching based on cross-correlation.…”

“…Different proxies were used, sensitive to vegetation and soil conditions and able to characterize the dynamics of different crop types throughout the growing season: Spectral Indices (SIs) from optical data and/or backscatter and interferometric coherence information from X-band SAR data. Most of the literature using multi-temporal information for crop mapping focus on the use of temporal profiles of spectral indices derived from optical data [55][56][57][58], and only recently some authors successfully included SAR backscatter profiles for rice mapping [59,60]. The novelty of our work are the use of synoptic seasonal features integrating optical and SAR data, and the delivery of crop type mapping already during the early stage of growth; eventually, the transferability to a growing season different from the one used for developing the approach is tested.…”

In-season early mapping of rice area and flooding dynamics from optical and SAR satellite data

“…span), which are indicators of the presence of flooded conditions, rice biomass accumulation and rapid rice growth after flooding, respectively. As we specified above, TSD are computed over different time horizons to be diagnostic indicators of rice growing and/ or agronomic practices for the discrimination between rice and other crops (Asilo et al, 2014;Fontanelli et al, 2014;Villa et al, 2015) (Table 1). Both time horizons and the threshold values can be adapted and refined to different crop and environmental conditions.…”