Joint International Surface Working Group and Satellite Applications Facility on Land Surface Analysis Workshop, IPMA, Lisboa, June 2018

Transcription

1 Joint International Surface Working Group and Satellite Applications Facility on Land Surface Analysis Workshop, IPMA, Lisboa, June 2018

2 Introduction Soil moisture Evapotranspiration Future plan

3 1. Introduction COMS status KMA hydrological products

4 Located in E 4

5 - Combining Geo and Leo satellites 5

6 2. Hydrological Variables Soil Moisture Evapotranspiration

7 LST (COMS), NDVI (MODIS), Rainfall (AWS) LST spatial interpolation using Kriging method NDVI Low-peak correction Rainfall weighted average using Gaussian function Statistics analysis of In-situ Soil moisture NDVI Precipitation 7

8 CDF matching between AMSR-2 and GLDAS Comparisons AMSR2 and GLDAS to In-situ GCOM-W1/AMSR2 (local time is 13:30) GLDAS every 3h (0600 UTC) Collocated data using nearest neighborhood method AMSR2 : 7 days moving average 8

9 Linear Regression+CDF R: RMSE: Bias: In-situ GLDAS AMSR2 CDF Linear Regression R: RMSE: Bias: Time series Upper(Reg+CDF): similar variation with in-situ SM Lower(Reg): do not considered low SM domain(red arrow) Scatter plot Upper(Reg+CDF): Reasonable scatter, well matched one-to-one line Lower(Reg): overestimation < 0.2 m 3 /m 3, underestimation > 0.3 m 3 /m 3 9

10 Using R studio, H2O library Feed-forward network Supervised (using Truth) feedback propagation Hidden layer(3), Hidden node(100), Epochs(3000) Gaussian distribution (response variable distribution) Rectifier activation function 10

11 Grid search with early stopping Tolerance: (improvement < 0.5% stop training, Mean Squared Error) Rounds: 3(average of recent 3 rounds compared with average of prior 3 rounds) Hidden RMSE R 2 R Epochs Hidden RMSE R 2 R Epochs [600, 600, 600, 600] [400, 400, 400, 400, 400] [700, 700, 700, 700] [400, 400, 400] [700, 700, 700] [300, 300, 300, 300] [600, 600, 600] [500, 500, 500, 500] [700, 700, 700, 700, 700, 700, 700] [400, 400, 400, 400, 400, 400, 400] [600, 600, 600, 600, 600] [400, 400, 400, 400, 400, 400] [300, 300, 300, 300, 300] [500, 500, 500, 500, 500, 500] [600, 600, 600, 600, 600, 600] [600, 600, 600, 600, 600, 600, 600] [500, 500, 500] [300, 300, 300, 300, 300, 300] [400, 400, 400, 400] [500, 500, 500, 500, 500] [500, 500, 500, 500, 500, 500, 500] [300, 300, 300, 300, 300, 300, 300] [700, 700, 700, 700, 700, 700] [300, 300, 300] [700, 700, 700, 700, 700]

12 Youngcheon (R: 0.87/RMSE: 3.10/Bias: ) Deep learning SM Jincheon (R: 0.68/RMSE: 4.03/Bias: 0.376) DL GLDAS AMSR2 R RMSE Bias DL: relatively well matched with in-situ GLDAS: relatively high correlation with some bias AMSR2: different pattern with large bias 12

13 Based on the Surface Energy Equation COMS ET is characterized by simply simulated aerodynamic resistance, considering a variety of surface roughness length in the sensible heat flux (the so-called B-method of Jackson et al (1977)). Mapping Area : 20~50 N, 100~145 E (only clear skies, except on urban, desert area) Temporal/Spatial resolution : 1-day/1 km Estimation Period Z 0 Canopy height high: : April 1 ST 2011 ~ present Ecoclimap - Surface roughness (monthly) Z 0 Canopy height low: Z 0 =exp( NDVI) (Gupta et al., 2002) 13

14 2012 COMS ET MODIS ET GLDAS ET N.obs R RMSE Bias COMS ET MODIS GLDAS N. obs R RMSE Bias ( ) COMS daily ET MODIS AET MODIS PET GLDAS ET 14

15 R studio, H2O library/ Feed-forward network Supervised (using Truth) Error back propagation Hidden layer(3), Hidden node(72), Epochs(3000) Gaussian distribution (response variable distribution) Rectifier activation function 15

16 COMS ET Himawari-8 (Ch12) UTC 16

17 Drought index (%) = AET/PET (Nicolas Ghilain, Royal Meteorological Institute, 2014) Actual Evapotranspiration : COMS daily Evapotranspiration Potential Evapotranspiration: COMS priestly-taylor Evapotranspiration (using COMS Ta, ins) - Simplified input data for estimating of evapotranspiration - Reference evapotranspiration (in grass conditions) - Proposed for application in humid areas COMS AET 16days α: Priestley-Taylor coefficient (dimensionless) : slope of the saturation vapour pressure curve (kpa C -1 ) γ: Psychrometric constant (kpa C -1 ) Rn : Net radiation (MJ m -2 day -1 ) G : Soil heat flux (MJ m -2 day -1 ) λ : Latent heat of vaporization COMS PET 16days 17

18 Reference data : MODIS VHI (Vegetation Health index, 16days) Satellite based meteorological-agriculture drought index Reflect on the Vegetation Condition Index (VCI) and Temperature Condition Index (TCI) Fig.1. KMA Drought Index. COMS AET/PET ratio : 16 days MODIS VHI : 16 days Fig.2. KMA average temperature of temperature anomaly during 30years Fig.3. KMA average rainfall of Rainfall anomaly during 30years The Possibility of COMS evapotranspiration Plan to comparison of land state index (LST, NDVI, NDWI, etc) 18

19 3. Future plan

20 COMS-2A is scheduled to launch on November 2018 COMS-2A will have 52 baseline products including hydrological variables GK-2A Products - Soil moisture, Vegetation Health Index - Land surface temperature, - Vegetation (VI, FVC), Snow (snow, snow depth) - Precipitation (potential precipitation, rainfall) - Radiation (upward, downward solar radiation, longwave radiation) - Flood detection Soil moisture - MW based on soil moisture : Low orbital satellite (AMSR2, SMOS, ASCAT) - IR based soil moisture : Geostationary satellite (COMS) - Deep learning based soil moisture using Himawari -8 Evapotranspiration - IR based evapotranspiration : COMS - Deep learning based evapotranspiration using Himawari-8(plan) Vegetation Index Fraction Vegetation Cover VHI (drougt index) IR based soil moisture Flood detection 20

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