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.

The objective of this study is to provide complete information on the dynamic relationship between X-band (3.11 cm) backscattering intensity (σ°) and rice crop’s leaf area index (LAI) at all growth phases. Though the relationship between X-band σ° and LAI has been previously explored, details on the relationship at the reproductive phase remain unstudied. LAI at the reproductive phase is important particularly at the heading stage where LAI reaches its maximum as it is closely related to grain yield, and at flowering stage where the total leaf area affects the amount of photosynthates. Therefore, this study examined the relationship of increasing LAI (vegetative to reproductive phase) and decreasing LAI (ripening phase) with TerraSAR-X (TSX) ScanSAR (3.11 cm) σ° at HH polarisation and 45° incidence angle. The results showed a statistically significant (R² = 0.51, p value < 0.001) non-linear relationship of LAI with σ° at the vegetative to reproductive phase while no significant linear relationship was found at the ripening phase. This study completes the response curve of X-band σ° to LAI by filling in the information on the reproductive phase which more accurately characterises the dynamic relationship between the rice crop’s LAI and X-band’s σ°. This contributes to improved knowledge on the use of X-band data for estimating LAI for the whole crop cycle which is essential for the modelling of crop growth and estimation of yield.

Tropical cyclones (TCs) significantly impact the agricultural sector, particularly rice production. Anticipatory action aims to reduce the impact by triggering early actions based on an impact-based forecast. We present a machine-learning framework to predict rice losses at the municipal level in the Philippines, using data from 51 TCs between 2006 and 2020. The target variable is the percentage of rice area loss, and the features cover hazard, exposure, and vulnerability. We developed regression and binary classification models using Random Forest (RF) and XGBoost (XGB) and integrated them into a two-step model for operational use. In this two-step model, the classification identifies the municipalities to be selected for anticipatory action, while the subsequent regression supports the decision-making on the magnitude of the early actions for the selected municipalities, as it estimates the extent of rice loss. The RF models outperformed the benchmarks and the XGB models in classification and regression tasks. The hazard features—particularly maximum sustained windspeed, rainfall, and minimum track distance—were the most influential predictors, whereas the exposure features and the vulnerability feature hampered model performance. Two methods for extracting rainfall features were compared: a landfall-based approach and an approach that delineates time frames according to the TC’s spatial extent. The landfall-based method had comparable performance and was simpler to understand and implement. While further enhancements in data collection and modelling are encouraged, our two-step modelling approach can be used to trigger anticipatory actions for rice losses from TCs.