Soo See Chai Department of Spatial Sciences, Curtin University of Technology

1. Backpropagation Neural Network for Soil Moisture Retrieval Using NAFE’05 Data : A Comparison Of Different Training Algorithms Soo See Chai Department of Spatial Sciences, Curtin University of Technology

2. CONTENT • Neural Network and Soil Moisture Retrieval • Backpropagation Neural Network • Training of Neural Network • Testing Results • Q and A

3. Neural Network For Soil Moisture Retrieval • Radiometric signatures of a vegetation-covered field reflect an integrated response of the soil and vegetation system to the observing microwave system • Surface parameters and radiometric signatures :

4. Backpropagation Neural Network

5. Different Backpropagation Training Algorithms • Several different training algorithms • have a variety of different computation and storage requirements • No one algorithm is best suited to all locations • MATLAB : 11 different training algorithms • Review : basic gradient descent and Levenberg-Marquardt(LM) algorithm • How about the other algorithms ?

6. Data Preparation • Roscommon area : 1/11, 8/11, 15/11 • Determine the area coordinate : • Roscommon : • Top latitude : -32.15380 • Bottom latitude : -32.18370 • Left longitude : 150.120 • Right longitude : 150.46900 • MATLAB : cut the area, extract the fields in the PLMR file • Copy the latitude, longitude, brightness temperature and altitude data into Excel • Extract the aircraft altitude of medium resolution mapping which is around 1050m to 1270m ASL

7. Roscommon 1/11 Roscommon 8/11 Roscommon 15/11

8. Example :

9. A bit of Statistics … • Find the minimum and maximum of average Tb for each data set • Next find the range (max-min) • Find the width for each class ( 3 classes : training, validation and testing ) • Range / 3 • Find starting and ending point for each class

10. We have now : • Group 1 of date 1/11, 8/11 and 15/11 (combined : GRP1) • Group 2 of date 1/11, 8/11, 15/11 (combined : GRP2) • Group 3 of date 1/11, 8/11, 15/11(combined : GRP3) • GRP 1 : randomly divide them into 3 groups : 60% for training, 30% for validation and 10% for testing • Same with GRP2 and GRP3 • All training data in one file , all validation data in one file, all testing data in one file

11. Training : K-Fold Cross Validation • No. of data set is small, to get a better accuracy result, K-fold validation is used. • Training data + validation data = 112 • 8-fold cross validation, each time 14 data will be used for validation, 98 data for training • To make sure the data is random enough, each time the data will be randomized. Eg: • First run : • Second run : validation training validation training

12. Training :NN Parameters determination • A series of experiments • trial and error • lowest RMSE • If yes, then save the input weight, layer weight and bias for the NN to be used for the other training algorithms • Fixed • Layer : 3 layers ( 1 input, 1 hidden, 1 output ) • Input : H polarized brightness temperature, TbH and physical soil temperature at 4cm • Hidden : sigmoid function • Output : linear function • Soil moisture (%v/v)

13. Experiments carried out : • Decision : • Learning rate, lr = 0.005 • Momentum, mc = 0.4 • Input Weight, iw = W2.mat • Layer weight, lw = LW2.mat • Bias, b = B2.mat • No. of hidden neuron = 4 • No. of epochs = 200

14. Testing Result I – Roscommon

15. Conclusions • Different types of training algorithms of backpropagation NN is giving different but similar accuracy result • The training data is representative of the testing data

16. Questions • Is the NN architecture transferable ? • Is number of data a factor contribute to the accuracy of the retrieval ? • Adding ancillary data (beside soil temperature) : vegetation water content and land cover information help ? • Adding V-polarized brightness temperature as an input ? • Adding these data directly or let the NN account for these data ?

17. THANK YOU !