The first three rows in equation control the estimates of soil moisture from the regression equation assuring that the estimated soil moisture content will fall within the interval defined by the wilting point and the field capacity. The fourth row of equation estimates the soil moisture that falls between the field capacity and the saturation point. The last row of equation 5 estimates the soil moisture for the cases where the material over the surface is not a vegetable. Data from seventeen stations were studied to identify the linear relationship between the dependent variable (soil moisture) and the independent variables which includes rainfall, texture, vegetation index, air temperature, elevation and surface slope. The data used for building a model includes a mixture of in-situ observations and remote sensing data. Remote sensing and statistical techniques to estimate soil moisture over tropical areas 1 Nazario D. Ramirez Beltran, 2 Christian H. Calderón Arteaga, 3 Ramon Vasquez, 4 Eric Harmsen, 3 Carlos R. Rios Mora 1 Industrial Engineering, University of Puerto Rico – Mayagüez 2 Mechanical Engineering, University of Puerto Rico – Mayagüez 3 Electrical and Computer Engineering, University of Puerto Rico – Mayagüez 4 Agricultural and Biological Engineering, University of Puerto Rico – Mayagüez Abstract. Estimation of soil moisture is a complex process since it is the result of the interactions of many variables. In this study, a small number of the most relevant variables, which control soil moisture dynamics, were selected based on results from several field experiments. The selected empirical model used in this study was a piecewise regression model that expresses the monthly soil moisture at a 1 km resolution. The proposed algorithm was successfully implemented and validated for Puerto Rico climate conditions. The model was used to estimate the surface soil moisture content over the 8,701 km2 area of Puerto Rico for each month between January and June, Results from the model could potentially be useful to create soil moisture initial conditions required by atmospheric and hydrological models, for instance, the Regional Atmospheric Modeling System (RAMS), the Mesoscale Model (MM5) and the WASH123D hydrological model. Introduction. Soil moisture affects numerous climatologically phenomena and is a critical parameter to model the water cycle. It is well established that performances of atmospheric numerical models are very sensitive to initial and boundary conditions. The soil moisture over land is a fundamental component of the surface water and energy budget. The soil moisture regulates the partition of latent and sensible heat fluxes at the surface, affecting the boundary layer. Several studies have investigated the influence of soil moisture on the atmospheric boundary layer and have provided insights into the importance of soil moisture in controlling the feedbacks between land surface and atmosphere that influence climate. Therefore the main purpose of this paper is to develop a reliable algorithm for estimating at 1 km resolution the soil moisture over tropical areas. The temporal and spatial variability are modeled by an empirical equation that relates the major factors that affect soil moisture. Methodology. The soil moisture is a climatologically variable and is affected by atmospheric phenomena like the rain or temperature. And also is affected by physics phenomena like the water transport in the soil and the soil textural characteristics. Having in account these, three main points are the wilting point, the field capacity and porosity. This three points are estimated if the texture of the soil are known. The water contained from the field capacity up to the saturation point is gravitational water and exhibits relatively rapid drainage. Thus, after the rain stops the soil moisture content decays in an exponential form until it reaches the field capacity, which may occur within a couple of days of the rainfall event. The amount of water contained from the wilting point up to the field capacity is the available water and the water contained below the permanent wilting point is known as the hydroscopic water. Conclusions. A diagnostic soil moisture equation is derived from the piecewise linear multiple regression. The soil moisture average is mainly influenced by the rain phenomena, and the soil texture is a factor very important during the dry months. The multiple regression analysis is a good tool for preliminary studies and to know the potential behavior of the soil moisture. Experimental results show that the critical variables to estimate soil moisture are: rainfall, soil texture, and vegetation index. These variables were included in all derived models. In addition the secondary variables were also included in the model to complement the empirical relation. The secondary variables are: the gradient air temperature, elevation, and average slope, which contribute in a smaller proportion to explain the soil moisture. Acknowledgments The research has been supported by NASA-EPSCOR program with grant: NCC5-595, NOAA – CREST grant NA17AE1625, and also by the University of Puerto Rico at Mayagüez. Authors want to appreciate and recognize the funding support from these institutions. The remote sensing data are download from MODIS and NEXRAD, this data has been interpolated for 1Km of resolution. The topography and textural data was provided for NRCS and USGS. All data has been interpolated for 1Km of spatial resolution. Difference between day temperature and night temperature for June Normalized difference vegetation index (NDVI) for June Percentage of Clay for Puerto Rico. Percentage of Sand for Puerto Rico. Soil Moisture estimation for June 2005 Accumulated Rain for June 2005 We are proposing a piecewise regression model to represents the monthly soil moisture behavior over densely vegetated areas. This equations related the three points described above with the textural characteristics of the soil We are proposing a piecewise regression model to represent the monthly soil moisture behavior over densely vegetated areas: Cross Validation Leave-one-out cross-validation technique was used to asses the performance of the proposed empirical models. The empirical models were validated using six months of the available observations from January to June The cross-validation was implemented by each month and consists of dropping one observation at a time and comparing the estimated versus the observed value. Each time that an observation was dropped the parameters of the model were estimated. The deviation between the estimated soil moisture and the actual observation is called the validation error. The validation error was computed for each observation and for each month. Future Work. The Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) instrument on the NASA EOS Aqua satellite provides global passive microwave measurements of terrestrial, oceanic, and atmospheric variables for the investigation of water and energy cycles. Ancillary data include time, geolocation, and quality assessment. Input brightness temperature data, corresponding to a 56 km mean spatial resolution, are resample to a global cylindrical 25 km Equal-Area Scalable Earth Grid (EASE-Grid) cell spacing. Data are stored in HDF-EOS format. Advanced Microwave Scanning Radiometer (AMSR-E) January 1, 2005; 6.9 GHz; footprint 56 km. Brightness temperature. Spatial Variability An area of 250 x 750 meters was selected to perform an analysis of soil moisture variability. The objective is to know moisture changes in a given area. The average was 37.7% water content. The number of observations was 70. In 97.7% of the measurements there was a variability of + - 7% in moisture content.