Estimating Soil Moisture Using Satellite Observations in Puerto Rico By Harold Cruzado Advisor: Dr. Ramón Vásquez University of Puerto Rico - Mayagüez Campus

1.Introduction 2.Study area characteristics 3.Ground weather stations 4. Instrumentation 5.Algorithm to estimate volumetric soil moisture 6.Preliminary results Contents

Soil moisture is a key component in the land surface schemes in regional climate models in the tropics. An application of an algorithm for a selected area of Puerto Rico is presented. NOAA satellite observations produce the remote sensing data, which supply the input parameters for the algorithm. Satellite images with one (1) km resolution were used to implement the algorithm using Matlab software. Introduction

Characteristics of Selected Region and Vegetation Types Detailed vegetation types information

Topographic Map Combining vegetation, soil types and topographic maps using ERDAS software

Soil Types and Profiles The polygon arrays of the soil maps were digitalized, resulting in a complex soil surface. Each of these polygons represents a soil profile, some with more than one soil textural class and others with a single one. The depth of a complete profile is more than 2 meters for all the polygons.

Detailed and Generalized Soil Type Information

South-West map of Puerto Rico and its weather stations, visualized by Arcmap software

An aerial photo showing locations of ground weather stations Ground weather stations

Theta prove ML2x This device is a sensor to estimate volumetric soil moisture with ±1% accuracy Instrumentation Data logger HH2 This device is used to store information from the theta probe

Soil texture Soil Temperature Surface temperature Apparent emissivity Roughness correction Effective Temperature Inversion of Fresnel Equation Vegetation correction Brightness temperature Brightness temperature Vegetation Type (ndvi) Surface roughtness Compute Soil moisture Algorithm to estimate volumetric soil moisture

Brightness Temperature The radiating (or brightness) temperature is the apparent temperature of a blackbody. It can be measured by a remote sensing device such as a radiometer. The possible data sources used are Band 3, 4 or 5 from NOAA satellite or L-band of SAR.

Brightness Temperature Brightness temperature from channel 3, NOAA satellite, using Matlab software

Surface Temperature This parameter can be approximated from air temperature near the soil surface and may also be obtained from satellite images from NOAA, using channels 4 and 5

Surface Temperature Surface temperature image from channel 3, NOAA satellite, using Matlab software. The blue color indicates cloud presence

Classified Soil Surface Temperature Classified images (unsupervised, ERDAS software) of a thermal band of a NOAA satellite showing levels of land surface temperature.

Soil Temperature The algorithm requires soil temperature for 10 to 15 cm of depth. This is provided by experimental stations such as Maricao, Adjuntas, Guanica, and Cabo Rojo in the study area. Because of insufficient data from the stations other methods need to be considered.

Soil Temperature Method 1: –Assuming some degrees less than surface temperature –In presence of dense vegetation the surface and deep temperature are almost the same. Method 2: –By training an artificial neural network, whose inputs are the following variables: Vegetation type Soil type Elevation levels Satellite observations on thermal frequency range The second method is preferred for research.

Apparent Emissitivity e : apparent emissitivity R: apparent reflectivity Due to signal attenuation, the emissivity isn’t real before making the correction

Effective Soil Temperature ± ± ± ±0.009 Wavelength (cm)C ±0.005 For remote sensing applications there are a simple form to obtain this effective soil temperature, mean look up table for C constant for the wavelength being used The net intensity (called the effective temperature) at the soil surface is a superposition of intensities emitted at various depths within the soil.

Effective soil temperature This image (effective soil surface temperature) is generated in Matlab software using surface temperature and depth soil temperature (depth temperature is estimated by method 1 as mentioned before); actual colors do not represent the real value

Vegetation Correction This process is required to determine the initial radiation emitted by the soil surface which depends on transmisivity. There are more than two ways to determine the transmisivity. The simplest and practical way is mentioned here. The first way to determine the transmisivity is:

Vegetation Correction Another way, used for this work, more directly to obtain transsmisivity through vegetation is by considering NDVI too: To get an estimation of VWC, there was considered a function piecewise defined depending of vegetation index (NDVI):

Vegetation Correction Then, when the transmissivity is already estimate, the reflectivity is corrected by

Vegetation Correction This image (NDVI) is generated in Matlab software using channels 1 and 2 of NOAA satellite. Actual colors do not represent the real value

Apparent Emissitivity where e is the apparent emissitivity, and R is apparent reflectivity Due to signal attenuation, the emissivity isn’t real before making the correction, the following estimations for emissitivity and reflectivity are apparent, because its not considering the losses through signal trajectory:

Roughness Correction Where respectively Rs and Rr are reflectance of smooth and rough surface For this preliminary work, this parameter is estimate y considering the class of soil only, in each region with same soil characteristics.

Computing soil moisture The relationship between volumetric soil moisture and dielectric constant was comprised in two distinct parts separated at a transition soil moisture value wt, where the wp is an empirical approximation of the wilting point moisture given by:

Compute the soil moisture For soil moisture less than wt:

Compute the soil moisture For soil moisture greater than wt:

Preliminary Results The algorithm was performed in Matlab software. Soil moisture readings from satellites need to be validated with more experimental work. Point measurements using the soil probe are lower than the satellite readings, which is not unexpected. The term “soil moisture” may need to be refined. The term “surface moisture” seems to describe the conditions better from a remote sensing point of view.

loacationtowndepthSandclayBulk density Monte del Estadomaricao Monte Guillarteadjuntas Bosque SecoGuanica combateCabo rojo Table below shows the quantitative characteristics of different places where the stations provide the data

station% moisture(from station) %moisture (from algorithm) Monte del Estado Monte Guillarte Bosque Seco Combate The values of soil moisture for different locations, given by the station and algorithm are as follows: