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A rice yield estimation system was developed based on the crop growth model ORYZA and SAR-derived key information such as start of season (SOS) and leaf area growth rate. Results from study sites in South and South-east Asian countries suggest that incorporating remote sensing data, specifically Synthetic aperture radar (SAR), into a process-based crop model improves the spatial distribution of yield estimates. This article highlights the detailed methodology of SAR data incorporation into crop yield simulation and comprehensive validation of yield forecast and estimates in the Philippines, Vietnam, Cambodia, Thailand, and Tamil Nadu, India. Remote sensing data assimilation into a crop model effectively captures the responses of rice crops to environmental conditions over large spatial coverage, which otherwise is practically impossible to achieve. A process-based crop simulation model is used in the system to ensure that climate information is captured, and this provides the capacity to deliver a mid-season yield forecast for national planning and policy for rice. Good agreement between SAR-based yield and crop-cut-based yield and official yield statistics and ensuring efficiency of the processing suggest that the system is a promising solution for the needed timely information on rice yield for application in food security and policies, climate disaster management, and crop insurance programs. [ABSTRACT FROM AUTHOR]

Land-cover changes occur naturally in a progressive and gradual way, but they may happen rapidly and abruptly sometimes. Very high resolution remote sensed data acquired at different time intervals can help in analyzing the rate of changes and the causal factors. In this paper, we present an approach for detecting changes related to disasters such as an earthquake and for mapping of the impact zones. The approach is based on the pieces of information coming from SAR (Synthetic Aperture Radar) and on their combination. The case study is the 22 February 2011 Christchurch earthquake. The identification of damaged or destroyed buildings using SAR data is a challenging task. The approach proposed here consists in finding amplitude changes as well as coherence changes before and after the earthquake and then combining these changes in order to obtain richer and more robust information on the origin of various types of changes possibly induced by an earthquake. This approach does not need any specific knowledge source about the terrain, but if such sources are present, they can be easily integrated in the method as more specific descriptions of the possible classes. A special task in our approach is to develop a scheme that translates the obtained combinations of changes into ground information. Several algorithms are developed and validated using optical remote sensing images of the city two days after the earthquake, as well as our own ground-truth data. The obtained validation results show that the proposed approach is promising. [ABSTRACT FROM AUTHOR]