comparison of remote sensing soil moisture dataset across a range of spatial resolution...

Jornada PATT ‘Validació de dades teletransportades en camp’ ICGC, Barcelona, 3rd June 2016 | 1

Comparison of Remote Sensing and Simulated Soil Moisture Datasets across a range of spatial resolution in Mediterranean Landscapes !Maria Jose Escorihuela (1) Pere Quintana-Seguí(2) ) and Olivier Merlin(2) (1) isardSAT, (2) Observatori de l’Ebre, (3) CESBIO

H2020-MSCA-RISE-2014, 2015- 2019

Grant agreement no: 645642


CONTEXT

Soil moisture is a critical variable in many physical processes related to agriculture, hydrology, meteorology or climatology The Mediterranean region is one of the most sensitive areas to climate change Agriculture uses up to 80% of water available in Mediterranean countries IPCC predictions, the change in precipitation patterns descend the productivity of rainfed agriculture down to the 50% of its present value towards 2020 Irrigation performance assessments at the field scale show a mismatch between irrigation requirements and the amount of irrigation water that is actually applied. Optimize on-farm irrigation management.


Context

MicroWave Thermal MicroWave

Fluorescence NDVI


How to measure SM globally?

MW •  active •  passive

high sensitivity to dielectric constant in MW domain

1.4 GHz


How to measure SM globally?

SMOS mission provides for the first time global mapping of soil moisture from L- band (1.4 GHz) MW radiometry

•  soil moisture accuracy 0.04 m3m-3

•  biomass ≤ 5 kg m-2 •  spatial resolution better

than 50 km •  revisit frequency of 2 to

3 days •  crossing time over the

equator 6 am - 6 pm


Passive MW L-band

November 2 2009

2017

January 31 2015 2020

before 2020 2025

SMOS

SMAP

WCOM


OBJECTIVE

Evaluate the state of art of soil moisture remote sensing products in a Mediterranean area

•  ASCAT L2 Soil Moisture Index TU-Wien (5.2 GHz) •  AMSR-E LPRM (6.9 Ghz) •  SMOS L2 OP (1.4 GHz)

Focus on regional and local applications in the context of agriculture and water management


REC OBJECTIVE

Evaluate the state of art of soil moisture remote sensing products in a Mediterranean area

•  ASCAT L2 Soil Moisture Index TU-Wien (5.2 GHz) •  AMSR-E LPRM (6.9 Ghz) •  SMOS L2 OP (1.4 GHz)

Focus on regional and local applications in the context of agriculture and water management

REC aims at develop an operational remote sensing algorithm dedicated to root zone soil moisture monitoring at the parcel scale. These estimates will be integrated in an irrigation management system that will be used to trigger irrigation.


Disaggregation Methodology

20 cm

0 -‐ 5 cm

L-band Passive MW (40 km, 2/3 d) + Medium Resolution OT (1 km, 1 d) + High Resolution OT (100 m, 16 d)

SM (1 km, 2/3 d) SM (100 m, 16 d)

DI SPATCH


Urgell Field Campaign

Soil texture, stone percentage, wilting point and field capacity Once a month 2011 gravimetric SSM (0 – 5 cm) measurements:


Urgell Field Campaign

DoY 146: 26 May 2011 DoY 244: 1 September 2011


Validation 1km


DisPATCh 100m

DoY 229: 17 August 2011


DisPATCh 100m

DoY 277: 3 October 2011


Validation 100m

O. Merlin, M. J. Escorihuela, M. Aran-Mayoral, O. Hagolle, A. Al Bitar, and Y. Kerr ''Self-calibrated evaporation-based disaggregation of SMOS soil moisture: an evaluation study at 3 km and 100 m resolution in Catalunya, Spain'' Remote Sensing of Environment, 130, pp 25–38, doi:10.1016/j.rse.2012.11.008, 2013


Catalonia NE Iberian Peninsula Mediterranean climate altitudes over 3,000 m wide range of bioclimatic habitats.


Land-surface simulations

Model: SURFEX •  Developed at Météo-France. •  SURFEX is modular, we used the ISBA

3L scheme. Spatial resolution: 5 km. Time resolution: we decided to work with daily series Variable of interest: •  we focused on the surface layer soil

wetness. •  Satellite soil moisture products are

sensitive to the first few centimeters of soil.

Simulation period: 1/9/2009 – 31/12/2011 (1 year spin-up) Period of comparison with satellite data: •  1 year: 2011 •  Therefore, initial conditions are not an

issue.

Masson et al., “The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes,” Geosci. Model Dev., vol. 6, no. 4, pp. 929–960, 2013.


Land-surface simulations

The SAFRAN meteorological analysis system was used to force the LSM. Optimal interpolation. First guess: HIRLAM HNR 5km. Operational NWP model at AEMET. Observed data: All available AEMET meteorological stations. SAFRAN analyses temperature, relative humidity, precipitation, wind and cloudiness and simulates downward short and long wave radiation. Temporal resolution: 6h (interpolated to hourly series). Spatial resolution: 5 km.

Quintana-Seguí et al. “Analysis of Near-Surface Atmospheric Variables: Validation of the SAFRAN Analysis over France,” J. Appl. Meteorol. Climatol., vol. 47, pp. 92–107, 2008.


Methodology

SM products in different units (m3m3, %) and each refer to different depths -> data normalised A moving average was applied to ZV or ZV35 to enhance soil moisture memory and detect seasonal effects, two windows of 10 and 30 days were used Correlations calculated for each grid point and agregated per cover class Temporal series analysed over selected points representative of Mediterranean landscapes: rainfed crops, irrigated crops, grass-shrubs and forest


Cover Class


ZV SM Correlation with LSM


Correlation per Cover Class

With no moving average ASCAT correlates better with LSM With the 10 or 30-day average results depend on the method


URGELL Dryland Crops rain

veg rough


Irrigated Crops

Urgell

Delta

Fields inundated May - Nov


Forest

Solsonès

Cerdanya

counter-phased


Forest

Solsonès

Cerdanya

counter-phased

Escorihuela and Quintana-‐Seguí 2016 Comparison of remote sensing and simulated soil moisture datasets in Mediterranean landscapes, RSE


Summary and Conclusions

•  Soil Moisture products evaluated across different Mediterranean landscapes

•  ASCAT correlates best with LSM, AMSR-E when seasonal behavior enhanced

•  Over crops, ASCAT soil moisture increases from July to Novembre probably due to increase of roughness (ploughing or increase of penetration depth)

•  AMSR-E shows very high values of correlation but seems to be correlated with vegetation cycle

•  SMOS is the only to see irrigation •  Enhanced resolution (DisPATCh) is needed in Mediterranean

landscapes


THANK YOU ! QUESTIONS?

comparison of remote sensing soil moisture dataset across a range of spatial resolution...

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