Description of the Instruments and Algorithm Approach

Similar documents
SMAP Hands-On. ARSET Applied Remote Sensing Training. Jul. 20,

SMAP Overview. Ron Weaver Slides li0ed from Barry Weiss and Jennifer Cruz at JPL Barry Weiss. Jet Propulsion Laboratory

SMAP. The SMAP Combined Instrument Surface Soil Moisture Product. Soil Moisture Active Passive Mission

Introduction to Radar

RADAR (RAdio Detection And Ranging)

Soil Moisture Active Passive (SMAP) Mission Applications Plan

High-Resolution Enhanced Product Based on SMAP Active-Passive Approach using Sentinel 1A and 1B SAR Data

Aquarius/SAC-D and Soil Moisture

Earth Remote Sensing using Surface-Reflected GNSS Signals (Part II)

Introduction to Remote Sensing Fundamentals of Satellite Remote Sensing. Mads Olander Rasmussen

Sub-Mesoscale Imaging of the Ionosphere with SMAP

Estimation of soil moisture using radar and optical images over Grassland areas

Thomas Meissner, Frank Wentz, Kyle Hilburn Remote Sensing Systems

SMAP Calibration Requirements and Level 1 Processing

Aquarius/SAC-D Mission Mission Simulators - Gary Lagerloef 6 th Science Meeting; Seattle, WA, USA July 2010

Sea surface temperature observation through clouds by the Advanced Microwave Scanning Radiometer 2

Microwave Remote Sensing (1)

Soil moisture retrieval using ALOS PALSAR

ACTIVE SENSORS RADAR

Synthetic aperture RADAR (SAR) principles/instruments October 31, 2018

ACTIVE MICROWAVE REMOTE SENSING OF LAND SURFACE HYDROLOGY

Introduction Active microwave Radar

Passive Microwave Sensors LIDAR Remote Sensing Laser Altimetry. 28 April 2003

Scatterometer Algorithm

Fundamentals of Remote Sensing

Remote Sensing 1 Principles of visible and radar remote sensing & sensors

AGRON / E E / MTEOR 518: Microwave Remote Sensing

Earth Exploration-Satellite Service (EESS) - Passive Spaceborne Remote Sensing

Microwave Remote Sensing

EE 529 Remote Sensing Techniques. Introduction

MODULE 9 LECTURE NOTES 1 PASSIVE MICROWAVE REMOTE SENSING

9/12/2011. Training Course Remote Sensing Basic Theory & Image Processing Methods September 2011

Global 25 m Resolution PALSAR-2/PALSAR Mosaic. and Forest/Non-Forest Map (FNF) Dataset Description

Global 25 m Resolution PALSAR-2/PALSAR Mosaic. and Forest/Non-Forest Map (FNF) Dataset Description

Remote Sensing for Rangeland Applications

Remote Sensing. Ch. 3 Microwaves (Part 1 of 2)

Recent developments in Deep Blue satellite aerosol data products from NASA GSFC

Radar Imaging Wavelengths

Synthetic Aperture Radar for Rapid Flood Extent Mapping

Algorithm Theoretical Basis Document

Assessment of instrument STability and Retrieval Algorithms for SMOS data (ASTRA)

The Moderate Resolution Imaging Spectroradiometer (MODIS): Potential Applications for Climate Change and Modeling Studies

SATELLITE OCEANOGRAPHY

Introduction to RADAR Remote Sensing for Vegetation Mapping and Monitoring. Wayne Walker, Ph.D.

The Global Imager (GLI)

NON-PHOTOGRAPHIC SYSTEMS: Multispectral Scanners Medium and coarse resolution sensor comparisons: Landsat, SPOT, AVHRR and MODIS

Are Radiometers and Scatterometers Seeing the Same Wind Speed?

Microwave remote sensing. Rudi Gens Alaska Satellite Facility Remote Sensing Support Center

9 Moisture Monitoring

CEGEG046 / GEOG3051 Principles & Practice of Remote Sensing (PPRS) 8: RADAR 1

Review. Guoqing Sun Department of Geography, University of Maryland ABrief

A Coherent Bistatic Vegetation Model for SoOp Land Applications: Preliminary Simulation Results

ESA Radar Remote Sensing Course ESA Radar Remote Sensing Course Radar, SAR, InSAR; a first introduction

SAR Remote Sensing. Introduction into SAR. Data characteristics, challenges, and applications.

VIIRS Cloud-Free Compositing For Nighttime Lights

Lecture 13: Remotely Sensed Geospatial Data

MODULE 9 LECTURE NOTES 2 ACTIVE MICROWAVE REMOTE SENSING

The studies began when the Tiros satellites (1960) provided man s first synoptic view of the Earth s weather systems.

Satellite Imagery and Remote Sensing. DeeDee Whitaker SW Guilford High EES & Chemistry

Specificities of Near Nadir Ka-band Interferometric SAR Imagery

Lecture 6: Multispectral Earth Resource Satellites. The University at Albany Fall 2018 Geography and Planning

SMAP Calibrated, Time-Ordered Brightness Temperatures L1B_TB Data Product

Introduction to Remote Sensing

COMPATIBILITY AND INTEGRATION OF NDVI DATA OBTAINED FROM AVHRR/NOAA AND SEVIRI/MSG SENSORS

Sentinel-2 Products and Algorithms

Data Sources. The computer is used to assist the role of photointerpretation.

Improvement of Himawari-8 observation data quality

Theoretical Simulations of GNSS Reflections from Bare and Vegetated Soils

MEaSUREs Northern Hemisphere Polar EASE-Grid 2.0 Daily 6 km Land Freeze/Thaw Status from AMSR-E and AMSR2. Table of Contents

Architecture, implementation and application of soil moisture in-situ sensor

APCAS/10/21 April 2010 ASIA AND PACIFIC COMMISSION ON AGRICULTURAL STATISTICS TWENTY-THIRD SESSION. Siem Reap, Cambodia, April 2010

SAR Multi-Temporal Applications

Contributions of the Remote Sensing by Earth Observation Satellites on Engineering Geology

SAR Remote Sensing (Microwave Remote Sensing)

Enhanced Noise Removal Technique Based on Window Size for SAR Data

REMOTE SENSING INTERPRETATION

Configuration, Capabilities, Limitations, and Examples

An Introduction to Remote Sensing & GIS. Introduction

Aquarius Satellite Salinity Measurements. Simon Yueh Post Launch Cal/Val team Lead Jet Propulsion Laboratory California Institute of Technology

SMAP Level 1 Radar Data Products

Int n r t o r d o u d c u ti t on o n to t o Remote Sensing

RADAR REMOTE SENSING

Sentinel-2 : A New Perspective for Research and Operational Applications in the Areas of Agriculture and Environment

SEN3APP Stakeholder Workshop, Helsinki Yrjö Rauste/VTT Kaj Andersson/VTT Eija Parmes/VTT

Active and Passive Microwave Remote Sensing

University of Texas at San Antonio EES 5053 Term Project CORRELATION BETWEEN NDVI AND SURFACE TEMPERATURES USING LANDSAT ETM + IMAGERY NEWFEL MAZARI

Principles of Remote Sensing. Shuttle Radar Topography Mission S R T M. Michiel Damen. Dept. Earth Systems Analysis

SPATIAL MAPPING OF SOIL MOISTURE USING RADARSAT-1 DATA INTRODUCTION

MERIS instrument. Muriel Simon, Serco c/o ESA

Data Requirements Definition and Data Services Options for RAPP

Remote sensing radio applications/ systems for environmental monitoring

All rights reserved. ENVI, IDL and Jagwire are trademarks of Exelis, Inc. All other marks are the property of their respective owners.

The AATSR LST retrieval: State of knowledge and current developments

Soil Moisture Observation Utilizing Reflected GNSS Signals

The studies began when the Tiros satellites (1960) provided man s first synoptic view of the Earth s weather systems.

An intercomparison of SMAP, SMOS, AMSR2, FY3B and ESA CCI soil moisture products at different spatial scales over two dense network regions

GNSS-R for Land Bio-Geophysical Parameters Monitoring: the LEiMON Project

Introduction of Satellite Remote Sensing

Pro s and Con s of using remote sensing in fire research

Outline. Introduction. Introduction: Film Emulsions. Sensor Systems. Types of Remote Sensing. A/Prof Linlin Ge. Photographic systems (cf(

Transcription:

Description of the Instruments and Algorithm Approach

Passive and Active Remote Sensing SMAP uses active and passive sensors to measure soil moisture National Aeronautics and Space Administration Applied Remote Sensing Training Program 28

Microwave Remote Sensing With Visible and Infrared sensors the soil is masked by clouds and vegetation. Optical sensors operate by measuring scattered sunlight and are daytime only. Microwaves can penetrate through clouds and vegetation, operate day and night, and are highly sensitive to the water in the soil due to the change in the soil microwave dielectric properties. National Aeronautics and Space Administration Applied Remote Sensing Training Program 29

Advantages of L-Band Vegetation attenuation increases as frequency increases 1.4 GHz 6.0 GHz 10.0 GHz National Aeronautics and Space Administration Applied Remote Sensing Training Program 30

Land Surface Dielectric: Surface Freeze/Thaw State As the land surface transitions from frozen to thawed, there is a large change in dielectric producing a notable increase in radar backscatter, on the order of 3 db. National Aeronautics and Space Administration Applied Remote Sensing Training Program 31

Relation Between Brightness Temperature and Soil Moisture [Jackson and O Neill, IEEE TGARS, GE-25, 1987.] National Aeronautics and Space Administration Applied Remote Sensing Training Program 32

Measurement Approach p = H, V (radiometer) y pq = VV, HH, HV (radar) Contributions from the: soil, vegetation, and soil-vegetation interaction t T Bp Emission = T s Bp L p + T v sv Bp + T Bp t σ pq Backscatter s 2 v sv = σ pq L pq + σ pq + σ pq Soil moisture is the dominant contributor to the signal Soil moisture measurements are corrected for the effects of vegetation, surface roughness and temperature National Aeronautics and Space Administration Applied Remote Sensing Training Program 33

Ancillary Data Sources Ancillary data are used to estimate the key unknown parameters: surface temperature ( surface air temp. at 6 am), vegetation opacity, surface roughness and soil texture Parameter Surface air meteorology Vegetation opacity Surface topography Soil texture Land/water boundaries Description/Sources - Data assimilation (GEOS/DAO) - Forecast models (NCEP and ECMWF) - Vis/IR satellite-derived NDVI, LAI, landcover (MODIS, IGBP-DIS) - Historical phenology (AVHRR) - Digital elevation models (USGS and SRTM) - Soils databases (Global, NGDC; US, STATSGO) - Coastal boundaries and inland water bodies (NGDC) National Aeronautics and Space Administration Applied Remote Sensing Training Program 34

Radar and Radiometer Operation Feed Horn/ OMT Rotating 6 meter Antenna Output to Radiometer H-Pol Channel H-Pol Diplexer Transmit Polarization Switch V-Pol Diplexer Output to Radiometer V-Pol Channel Spun De-Spun H-Pol Receiver Transmitter V-Pol Receiver National Aeronautics and Space Administration Applied Remote Sensing Training Program 35

SMAP Products

Data Product Short Name Description Grid Resolution Granule Extent L1A_Radar Parsed Radar Instrument Telemetry Half Orbit L1A_Radiometer Parsed Radiometer Instrument Telemetry Half Orbit L1B_S0_LoRes Low Resolution Radar σ o in Time Order 5x30 km (10 slices) Half Orbit L1C_S0_HiRes High Resolution Radar σ o on Swath Grid 1 km Half Orbit L1B_TB Radiometer T B in Time Order 39x47 km Half Orbit L1C_TB Radiometer T B 36 km Half Orbit L2_SM_A Radar Soil Moisture ( includes Freeze-Thaw ) 3 km Half Orbit L2_SM_P Radiometer Soil Moisture 36 km Half Orbit L2_SM_AP Active-Passive Soil Moisture 9 km Half Orbit L3_FT_A Daily Global Composite Freeze/Thaw State 3 km North of 45 N L3_SM_A Daily Global Composite Radar Soil Moisture 3 km Global L3_SM_P Daily Global Composite Radiometer Soil Moisture 36 km Global L3_SM_AP Daily Global Composite Active-Passive Soil Moisture 9 km Global L4_SM Surface & Root Zone Soil Moisture 9 km Global L4_C Carbon Net Ecosystem Exchange 9 km North of 45 N National Aeronautics and Space Administration Applied Remote Sensing Training Program 37

Data Product Design All products are in HDF5 format Each SMAP HDF5 file contains the primary data parameters (e.g., soil moisture, freeze/thaw, sensor data) and all data used in the production of those primary parameters. These files also include metadata, geolocation information, quality flags, etc. Projection: EASE-Grid 2.0 Equal-area projection Level 2, 3, 4, and radiometer L1C are in this projection Values Radiometer data (brightness temperature) is in Kelvin Radar data is in sigma naught Soil moisture is a volumetric measurement expressed as cm 3 /cm 3 Freeze/thaw is a binary measurement, either frozen or thawed Net ecosystem exchange is in grams of carbon/square meter per day National Aeronautics and Space Administration Applied Remote Sensing Training Program 38

Radiometer Data Level 1C National Aeronautics and Space Administration Applied Remote Sensing Training Program 39

Soil Moisture Derived from the Radiometer- Level 3 National Aeronautics and Space Administration Applied Remote Sensing Training Program 40

Surface and Root Zone Soil Moisture- Level 4 Root zone soil moisture [m 3 m -3 ] 26 Apr 2015 at 00:00 UTC National Aeronautics and Space Administration Applied Remote Sensing Training Program 41

Net Ecosystem Carbon Exchange- Level 4 NEE (g C m -2 d -1 ) 4 2 0-2 -4 ENLF Tower Site (CA-OJP, 53.92 N, 104.7 W) L4_C Tower r = 0.56; RMSE = 0.65 g C m -2 d -1 2004 2005 Year L4_C NEE (DOY 196, g C m -2 d -1 ) National Aeronautics and Space Administration Applied Remote Sensing Training Program 42

SMAP Enhanced Active-Passive Product Using Sentinel Source: Narendra Das National Aeronautics and Space Administration Applied Remote Sensing Training Program 43

Soil Moisture Retrieval Map Retrievable Mask (Black Colored Pixels) Prepared with Following Specifications: a) Urban Fraction < 1 b) Water Fraction < 0.5 c) DEM Slope Standard Deviation < 5 deg National Aeronautics and Space Administration Applied Remote Sensing Training Program 44

Soil Moisture Expected Accuracy Retrieval expected quality mask (black colored pixels indicate good quality) with following specifications: a) Vegetation water content 5 kg/m 2; b) Urban fraction 0.25 c) Water fraction 0.1; d) DEM slope standard deviation 3 deg National Aeronautics and Space Administration Applied Remote Sensing Training Program 45

Access to SMAP Data: NSIDC http://nsidc.org/data/smap/ National Aeronautics and Space Administration Applied Remote Sensing Training Program 46

Access to SMAP Data: ASF https://www.asf.alaska.edu/smap National Aeronautics and Space Administration Applied Remote Sensing Training Program 47