The work in this report consists of three parts: 1) a joint image fusion algorithm from mis-registered image pairs, 2) a land cover mapping algorithms algorithm for mis-registered image datasets and 3) rice cultivation date estimation using cloudy multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS). In a joint image fusion algorithm from misregistered image pairs, a new method for fusion and registration of low spatial and high spectral resolution image with high spatial and low spectral resolution image in a single step is developed. In the usual procedure, fusion is an independent process separated from registration. However, both image registration and fusion can be formulated as estimation problems. Hence, the registration parameters can be automatically tuned so that both fusion and registration can be optimized simultaneously. Here, we concentrate on the relationship between low-resolution multispectral and high-resolution panchromatic imagery. The proposed technique is based on a statistical model. It employs the maximum a posteriori (MAP) estimator to solve the fusion problem, and applies the Metropolis algorithm to solve the joint optimization problem. A closed-form solution to find the fused high spatial and spectral resolution image with correcting registration is also derived here. We examine our algorithm into datasets. In a land cover mapping algorithms algorithm for mis-registered image datasets, we proposed a joint classification and registration technique based on a Markov random field (MRF) model to simultaneously align two or more images and obtain a land cover map (LCM) of the scene. The expectation maximization (EM) algorithm is employed to solve the joint image classification and registration problem by iteratively estimating the map parameters and approximate posterior probabilities. Then, the maximum a posteriori (MAP) criterion is used to produce an optimum land cover map. We conducted experiments on a set of four simulated images and one pair of remotely sensed images to investigate the effectiveness and robustness of the proposed algorithm. Our results show that, with proper selection of a critical MRF parameter, the resulting LCMs derived from an unregistered image pair can achieve an accuracy as high as when images are perfectly aligned. Furthermore, the registration error can be greatly reduced In our last work, we developed a rice cultivation date estimation based on remote sensing data, since rice is critical information to evaluate the damages in rice fields from natural disasters. In this study, the 8-day composite normalized difference vegetation index (NDVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) data was modeled as a triply modulated cosine function, and the extended Kalman filter (EKF) is used to estimate the mean, amplitude and phase parameters of the cosine function. The cultivation dates are estimated as the date where the seasonal variation derived from the EKF is greater than a threshold after its minimum. From the experimental results, the estimated cultivation dates derived from the proposed algorithm agree with rice cultivation information from the National Rice Department. The 73.3 percentages of the estimated cultivation dates is within 16 days for the rain-fed rice areas, and more than 80 percentages of the estimated data is within 16 days for irrigated areas with two crop cycles per year