Integration of SAR multi-frequency and optical data for the retrieval of soil moisture and vegetation water content

Integration of SAR multi-frequency and optical data for the retrieval of soil moisture and vegetation water content A. Padovano 1,2, F. Greifeneder 1, R. Colombo 2, G. Cuozzo 1, C. Notarnicola 1 1 - Eurac Research Institute for Earth Observation, Bolzano, Italy 2 - Department of Environmental Science, University of Milano Bicocca, Milano, Italy

INTRODUCTION The soil moisture retrieval from SAR data is a challenging topic, especially in vegetated areas. The radar signal can be of difficult interpretation as the total radar backscatter is a complex sum of the backscatter from vegetation and soil, making it complicated to determine which contribution comes from soil and which contribution comes from vegetation. Optical and SAR images integration by the use of data driven techniques, such as Support Vector Regression (SVR), can lead to the separation of soil and vegetation contribution. Separation of soil and vegetation contribution offers the opportunity to compute both soil moisture and vegetation water content. The main aim of this work is to: Understand the contributions of different bands (optical and radar) for the retrieval of Soil Moisture Content (SMC) and Vegetation Water Content (VWC). Develop an algorithm that integrates multi-frequency SAR data and optical data to estimate SMC and VWC in vegetated areas Integration of SAR multi-frequency and optical data for the retrieval of soil moisture and vegetation water content 2

OUTLINE Test sites and data set Algorithm description 1.SVR SMC Soil Moisture retrieval 2.Compute Bare Soil Backscattering with physical based models 3.SVR VWC Vegetation water content retrieval Results and discussion Conclusions and future steps Integration of SAR multi-frequency and optical data for the retrieval of soil moisture and vegetation water content 3

Test site & Data set SMEX 02 - IOWA To develop the algorithm a controlled data set will be used, the data-set that was chosen is the SOIL MOISTURE EXPERIMENTS IN 2002 (SMEX02) IOWA, USA. Data set for the algorithm development AIRSAR data have been geocoded and projected in ground-range Ground Soil Moisture SMEX02 Iowa Regional Ground Soil Moisture Data (on Corn and Soybean fields) Aircraft Remote Sensing SMEX02 Airborne Synthetic Aperture Radar Data (AIRSAR, Band P,L and C spatial resolution of 7.5 m) Data Satellite Remote Sensing SMEX02 Iowa Satellite Vegetation and Water Index (NDVI and NDWI) Data Derived from Landsat 5 and 7 Thematic Mapper Imagery (resolution of 30m) Vegetation SMEX02 Vegetation Water Content (on Corn and Soybean fields), Iowa Regional and Walnut Creek Watershed Integration of SAR multi-frequency and optical data for the retrieval of soil moisture and vegetation water content 4

Test site & Data set Mazia Valley The study area is Mazia Valley, it is a small valley located at Nord-West of Alto Adige/Südtirol (Italy). It covers an area of about 100 Km 2 with an height ranging between 920m and 3738m and it is mainly composed by meadows and pastures. Field campaigns have been performed during 2016 Summer and new field campaigns have been planned for 2017and 2018. The final version of the algorithm will be run by using this data-set Sentinel 1 C band (20m) ALOS 2 L band (10m) Sentinel 2 (20m) in situ Soil Moisture 16/06/2016 15/06/2016 16/06/2016 22/07/2016 21/06/2016 22/07/2016 27/08/2016 29/08/2016 27/08/2016 29/08/2016 20/09/2016 21/09/2016 19/09/2016 21/09/2016 Integration of SAR multi-frequency and optical data for the retrieval of soil moisture and vegetation water content 5

Algorithm for soil moisture and vegetation water content retrieval Satellite SAR data Satellite Optical data DEM data Feature Extraction SVR_SMC Training and Test SVR_SMC Model The measured backscattering from SAR images contains both soil and vegetation contributions σ image = f(vegetation, soil parameters) ; SAR backscattering coefficient Maps Optical Surface Reflectance Maps DEM Maps AVG of Roughness Satellite Optical Data Satellite SAR data SVR_SMC Model Application Electromagnetic Model Application Feature Extraction Computed SMC Map Best Fit Roughness Parameters SVR_VWC Training and Test Computed Bare Soil Backscattering SVR_VWC Model The main concept is to develop an approach able to disentangle these two contributions: Optical and SAR data integration, by the mean of machine learning techniques (such as SVR), is used to compute SMC maps; By using the retrieved SMC maps and a physical model, bare soil backscattering is simulated σ soil = f(soil parameters) ; SAR backscattering coefficient Maps Optical Surface Reflectance Maps SVR_VWC Model Application Vegetation Water Content Map A second SVR is trained providing as input reflectance of optical bands, SAR satellite backscattering and also bare soil simulated backscattering, to compute VWC maps. Computed Soil Backscattering Maps Integration of SAR multi-frequency and optical data for the retrieval of soil moisture and vegetation water content 6

SVR SMC Soil Moisture retrieval Satellite SAR data SVR is a data driven machine learning technique. Satellite Optical data DEM data SAR backscattering coefficient Maps Feature Extraction SVR_SMC Training and Test SVR_SMC Model The regressor is trained to create a model that is able to derive soil moisture values measured in field by providing as input reflectance and backscattering values (in correspondence of the measured data). Optical Surface Reflectance Maps DEM Maps SVR_SMC Model Application Computed SMC Map Other additional parameters are tested to checked whether the accuracy of the result can be improved (Digital Elevation Model, Local Incidence Angle, Topographic Wetness Index, etc.). The obtained model can finally be applied providing as input optical and SAR images to get SMC maps. Integration of SAR multi-frequency and optical data for the retrieval of soil moisture and vegetation water content 7

Compute Bare Soil Backscattering Computed SMC Map AVG of Roughness Electromagnetic Model Application Best Fit Roughness Parameters Computed Bare Soil Backscattering Dielectric constant computation from soil moisture maps has been done by using the empirical relation of Hallikainen, Ulaby (1985) The bare-soil simulated backscattering has been computed using three different models: Integral Equation Model (Fung et al. 1992) Semi-empirical Oh method (Oh et al. 1992, Oh 2002) Semi-empirical Dubois method (Dubois et al. 1995) All the methods provide HH polarized back-scattering and VV polarized backscattering. The enhanced semi-empirical Oh method (2002) provide also HV polarized backscattering. Integration of SAR multi-frequency and optical data for the retrieval of soil moisture and vegetation water content 8

SVR VWC Vegetation Water Content retrieval Computed Bare Soil Backscattering A second SVR is then applied; Satellite Optical Data Satellite SAR data SAR backscattering coefficient Maps Optical Surface Reflectance Maps Computed Soil Backscattering Maps Feature Extraction SVR_VWC Model Application SVR_VWC Training and Test Vegetation Water Content Map SVR_VWC Model The regressor is trained to create a model that is able to derive VWC values by providing as input reflectance and real and simulated backscattering values (in correspondence of the measured data); The obtained model can finally be applied providing in input optical and SAR real and simulated maps to obtain VWC maps. Integration of SAR multi-frequency and optical data for the retrieval of soil moisture and vegetation water content 9

SVR SMC Soil Moisture retrieval - Results SMEX 02 SVR_SMC The SVR performances prove that providing only SAR backscattering information in a vegetated area is not enough to model SMC; input: L HH, L VV, L HV, L VH, C HH, C VV, C HV and C VH RMSE = 0.07 R= 0.28 Integration of SAR multi-frequency and optical data for the retrieval of soil moisture and vegetation water content 10

SVR SMC Soil Moisture retrieval - Results SMEX 02 SVR_SMC The SVR performances prove that providing only SAR backscattering information in a vegetated area is not enough to model SMC; input: B1 B2 B3 B4 B5 B7 NDVI NDWI LHH LVV LHV LVH L band backscattering and optical reflectance combined information leads to a stable and reliable model (0.7<R<0.9 0.04[m 3 /m 3 ]<RMSE<0.05[m 3 /m 3 ]); RMSE = 0.04 R= 0.89 Integration of SAR multi-frequency and optical data for the retrieval of soil moisture and vegetation water content 11

SVR SMC Soil Moisture retrieval - Results SMEX 02 SVR_SMC The SVR performances prove that providing only SAR backscattering information in a vegetated area is not enough to model SMC; input: B1 B2 B3 B4 B5 B7 NDVI NDWI LHH LVV LHV LVH DEM L band backscattering and optical reflectance combined information leads to a stable and reliable model (0.7<R<0.9 0.04[m 3 /m 3 ]<RMSE<0.05[m 3 /m 3 ]); In a flat area information about elevation, aspect and slope does not improve SVR performances; RMSE = 0.04 R= 0.81 Integration of SAR multi-frequency and optical data for the retrieval of soil moisture and vegetation water content 12

SVR SMC Soil Moisture retrieval - Results SMEX 02 SVR_SMC input: B1 B2 B3 B4 B5 B7 NDVI NDWI L HH L VV L HV C HH C VV C HV The SVR performances prove that providing only SAR backscattering information in a vegetated area is not enough to model SMC; L band backscattering and optical reflectance combined information leads to a stable and reliable model (0.7<R<0.9 0.04[m 3 /m 3 ]<RMSE<0.05[m 3 /m 3 ]); In a flat area information about elevation, aspect and slope doesn t improve SVR performances; Providing information about C band to the SVR worsen the performances of the model, this is mainly due to the fact that especially in corn fields the radar signal is not able to reach the ground. RMSE = 0.04 R= 0.67 Integration of SAR multi-frequency and optical data for the retrieval of soil moisture and vegetation water content 13

SVR SMC Soil Moisture retrieval Results 1 July 2002 input: B1 B2 B3 B4 B5 B7 NDVI NDWI LHH LVV LHV SMEX 02 Integration of SAR multi-frequency and optical data for the retrieval of soil moisture and vegetation water content 14

SVR SMC Soil Moisture retrieval Results 5 July 2002 input: B1 B2 B3 B4 B5 B7 NDVI NDWI LHH LVV LHV SMEX 02 Integration of SAR multi-frequency and optical data for the retrieval of soil moisture and vegetation water content 15

SVR SMC Soil Moisture retrieval Results 7 July 2002 input: B1 B2 B3 B4 B5 B7 NDVI NDWI LHH LVV LHV SMEX 02 Integration of SAR multi-frequency and optical data for the retrieval of soil moisture and vegetation water content 16

SVR SMC Soil Moisture retrieval Results 8 July 2002 input: B1 B2 B3 B4 B5 B7 NDVI NDWI LHH LVV LHV SMEX 02 Integration of SAR multi-frequency and optical data for the retrieval of soil moisture and vegetation water content 17

SVR SMC Soil Moisture retrieval Results 9 July 2002 input: B1 B2 B3 B4 B5 B7 NDVI NDWI LHH LVV LHV SMEX 02 Integration of SAR multi-frequency and optical data for the retrieval of soil moisture and vegetation water content 18

SVR SMC Soil Moisture retrieval - Results Mazia Valley SVR_SMC The SVR performances prove that providing only SAR backscattering gives good results to model SMC if we provide also information about Local Incidence angle (R= 0.76 RMSE= 0.06[m 3 /m 3 ]); input: Sentinel 1, Sentinel 2, Alos 2, DEM In an area with a complex topography elevation, aspect and slope information are relevant to improve SVR performances; L and C band backscattering plus optical reflectance information leads to a stable and reliable model (R=0.9 RMSE=0.04[m 3 /m 3 ]); L and C band combined information, in an area mainly composed by pastures and meadows, improve the SVR performances. RMSE = 0.04 R= 0.91 Integration of SAR multi-frequency and optical data for the retrieval of soil moisture and vegetation water content 21

SVR SMC Soil Moisture retrieval Results 21 September 2016 input: Sentinel 1, Sentinel 2, Alos 2, DEM Mazia Valley [0-1] Integration of SAR multi-frequency and optical data for the retrieval of soil moisture and vegetation water content 22

SVR VWC Vegetation water content retrieval Results SMEX 02 SVR_VWC Corn and Soybean VWC values for soybean and corn fall into two different clusters, this brings to a bias, then both corn and all fields analysis have been performed (there were not enough data about soybean to perform a specific SVR); Integration of SAR multi-frequency and optical data for the retrieval of soil moisture and vegetation water content 23

SVR VWC Vegetation water content retrieval Results SMEX 02 SVR_VWC Input: LHH LVV LHH bare-soil LVV bare-soil Corn and Soybean VWC values for soybean and corn fall into two different clusters, this brings to a bias, then both corn and all fields analysis have been performed (there were not enough data about soybean to perform a specific SVR); Providing original L band SAR backscattering and simulated SAR backscattering to train the SVR leads to a good model for computing VWC maps (R=0.93 - RMSE=0.65[Kg/m 2 ]; Corn R=0.74 - RMSE=0.57[Kg/m 2 ]); Corn RMSE = 0.65 R= 0.93 RMSE = 0.57 R= 0.74 Integration of SAR multi-frequency and optical data for the retrieval of soil moisture and vegetation water content 24

SVR VWC Vegetation water content retrieval Results SMEX 02 SVR_VWC Input: CHH CVV LHH LVV LHH bare-soil LVV bare-soil Corn and Soybean VWC values for soybean and corn fall into two different clusters, this brings to a bias, then both corn and all fields analysis have been performed (there were not enough data about soybean to perform a specific SVR); Providing original L band SAR backscattering and simulated SAR backscattering to train the SVR leads to a good model for computing VWC maps (R=0.93 - RMSE=0.65[Kg/m 2 ]; Corn R=0.74 - RMSE=0.57[Kg/m 2 ]); Adding information about C band brings to a worsening of the model (R=0.87 - RMSE=0.86[Kg/m 2 ]; Corn R=0.70 - RMSE=0.6[Kg/m 2 ]); Corn RMSE = 0.86 R= 0.87 RMSE = 0.6 R= 0.7 Integration of SAR multi-frequency and optical data for the retrieval of soil moisture and vegetation water content 25

SVR VWC Vegetation water content retrieval Results SMEX 02 SVR_VWC Input: NDWI LHH LVV LHH bare-soil LVV bare-soil Corn and Soybean VWC values for soybean and corn fall into two different clusters, this brings to a bias, then both corn and all fields analysis have been performed (there were not enough data about soybean to perform a specific SVR); Providing original L band SAR backscattering and simulated SAR backscattering to train the SVR leads to a good model for computing VWC maps (R=0.93 - RMSE=0.65[Kg/m 2 ]; Corn R=0.74 - RMSE=0.57[Kg/m 2 ]); Adding information about C band brings to a worsening of the model (R=0.87 - RMSE=0.86[Kg/m 2 ]; Corn R=0.70 - RMSE=0.6[Kg/m 2 ]); Providing optical information by the mean of NDWI index, improves SVR performances (R=0.93 - RMSE=0.63[Kg/m 2 ]; Corn R=0.83 - RMSE=0.48[Kg/m 2 ]). Corn RMSE = 0.63 R= 0.93 RMSE = 0.48 R= 0.83 Integration of SAR multi-frequency and optical data for the retrieval of soil moisture and vegetation water content 26

SVR VWC Vegetation Water Content retrieval Results 1 July 2002 Input: NDWI LHH LVV LHH bare-soil LVV bare-soil SMEX 02 Integration of SAR multi-frequency and optical data for the retrieval of soil moisture and vegetation water content 27

SVR VWC Vegetation Water Content retrieval Results 5 July 2002 Input: NDWI LHH LVV LHH bare-soil LVV bare-soil SMEX 02 Integration of SAR multi-frequency and optical data for the retrieval of soil moisture and vegetation water content 28

SVR VWC Vegetation Water Content retrieval Results 7 July 2002 Input: NDWI LHH LVV LHH bare-soil LVV bare-soil SMEX 02 Integration of SAR multi-frequency and optical data for the retrieval of soil moisture and vegetation water content 29

SVR VWC Vegetation Water Content retrieval Results 8 July 2002 Input: NDWI LHH LVV LHH bare-soil LVV bare-soil SMEX 02 Integration of SAR multi-frequency and optical data for the retrieval of soil moisture and vegetation water content 30

SVR VWC Vegetation Water Content retrieval Results 9 July 2002 Input: NDWI LHH LVV LHH bare-soil LVV bare-soil SMEX 02 Integration of SAR multi-frequency and optical data for the retrieval of soil moisture and vegetation water content 31

SVR VWC Soil Moisture retrieval Results Mazia Valley SVR_VWC Input: LHH LHV LHH bare-soil LHV bare-soil Alos 2 L band backscattering combined with L band simulated backscattering, leads to a reliable model to compute VWC, without any optical information. RMSE = 0.07 R= 0.81 Integration of SAR multi-frequency and optical data for the retrieval of soil moisture and vegetation water content 32

SVR VWC Vegetation Water Content retrieval Results 21 September 2016 Input: LHH LHV LHH bare-soil LHV bare-soil Mazia Valley (preliminary results*) Kg/m 2 Integration of SAR multi-frequency and optical data for the retrieval of soil moisture and vegetation water content 33

Conclusions and next steps The integration of optical and SAR data by the mean of machine learning techniques lead to an accurate retrieval of soil moisture (<RMSE> = 0.04); In mountain areas, it is important to provide additional information about topography and SAR Satellite local incidence angle in order to obtain accurate results; The simulations of theoretical bare soil backscattering can help in providing information for a discrimination of water content in the soil and in the vegetation; Results about Mazia Valley are still preliminary results; further validation is needed with ground data. New ground data acquisition for soil moisture and vegetation water content are planned for 2017-2018 for the area of Val Mazia; Next step is to evaluate the propagation of the error in the algorithm to check the accuracy on the retrieved parameters Integration of SAR multi-frequency and optical data for the retrieval of soil moisture and vegetation water content 34

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