1. Estimating Soil Moisture Using Satellite Observations By RamonVasquez

2. Contents

3. 1.Introduction 2.Some characteristics of the selected region 3.Ground weather stations 4.The algorithm to estimate volumetric soil moisture 5.Partial results 6.instrumentation

4. Introduction

5. The soil moisture is an important parameter in climate modeling, its high variability occur on the firsts centimeters of top layer of soil surface.

6. About the selected region

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

8. An aerial photo showing points of ground weather stations

9. Vegetation types Detailed vegetation types information

10. Combining vegetation, soil types, and elevation maps by use ERDAS software

11. Soil types For this work will be useful obtain the map with the same sand and clay contents mainly. This work part was done initially digitalizing in ArcView software. Was performed for this proposal by joining equal classes

12. Very detailed soil type information Less detailed soil type information Resampling type soils

13. The algorithm to estimate volumetric soil moisture

14. 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

15. Brightness Temperature The possible data sources to use are Band 3, 4 or 5 from NOAA satellite or L-band of SAR This temperature is obtained by considering that the radiance perceived by the sensor is coming from blackbody.

16. Brightness Temperature Brightness temperature from channel 3, NOAA satellite, this was achieved by use the Matlab software

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

18. Surface Temperature surface temperature image, from channel 3 NOAA satellite, this was achieved by use the Matlab software

19. Classified soil surface temperature This figure shows an image classified (unsupervised, ERDASsoftware) of an image of a thermal band of NOAA satellite. It shows levels of land surface temperature

20. 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. A difficult with this parameter is the little amount of data. For that it will be estimate by some empirical methods 1 and 2, this work consider the first method.

21. Soil Temperature Method 1: –Assuming some degrees less than surface temperature –In presence of dense vegetation the surface and deep temperature 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 considered for research

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

23. Effective soil temperature 2.80.802±0.006 6.00.667±0.008 11.00.480±0.010 21.00.246±0.009 Wavelength (cm)C 49.00.084±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.

24. Effective soil temperature This image (effective soil surface temperature) is generated in Matlab software using surface temperature and depth soil temperature (depth temperature is estimate by method 1 mentioned before), actually colors do not represent the real value.

25. Vegetation Correction This process is required to determine the initial radiation emitted by the soil surface which depending of transmisivity, there are more than two ways to determine the transmisivity, the simplest and practical way is mentioned here, As first way to determine the transmisitivity is:

26. 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):

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

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

29. 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 loses through signal trajectory:

30. 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.

31. 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:

32. Compute the soil moisture For soil moisture less than wt:

33. Compute the soil moisture For soil moisture greater than wt:

34. Partial results

35. The algorithm was performed in Matlab software.

36. loacationtowndepthSandclayBulk density Monte del Estadomaricao8-2531.4421.5 Monte Guillarteadjuntas0-1010.357.71.09 Bosque SecoGuanica0-1025551.5 combateCabo rojo0-1281.811.91.59 The table bellow shows the quantitative characteristics of diferent places where the stations provide the data

37. station% moisture(from station) %moisture (from algorithm) Monte del Estado Monte Guillarte Bosque Seco2.40.540 Combate2.30.2537 The following is the values of soil moisture for different locations, given by the station and algorithm

38. instrumentation

39. Theta prove ML2x This devise is a sensor to estimate volumetric soil moisture with ±1% accuracy

40. Data logger HH2 This devise is used to store information of sampling red by theta probe