proximal and remote sensing approaches for quantitative ... · proximal and remote sensing...
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Proximal and remote sensing approaches for quantitative soil analysis and precision agriculture
Matteo Poggio and Joseph Pollacco
Precision Agriculture Mission – Montpellier, France
22nd October 2018
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Plan
• Introducing Manaaki Whenua Landcare Research , New Zealand,
• Mapping farm landuse by using remote sensing (Joe),
• Airborne sensors for land-use management (Matteo),
• Wireless sensing networks for precision agriculture (Matteo),
• Soil spectroscopy analysis (Matteo),
• Soil mapping: A system to quantify soil spatial variability and generate
land-use impact assessments (Joe),
• Deriving hydraulic parameters cost effectively for agro-hydrological
models (Joe)./fast/accurately hydraulic parameters for each management zone
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Plan
• Introducing Manaaki Whenua Landcare Research , New Zealand,
• Mapping farm landuse by using remote sensing (Joe),
• Airborne sensors for land-use management (Matteo),
• Wireless sensing networks for precision agriculture (Matteo),
• Soil spectroscopy analysis (Matteo),
• Soil mapping: A system to quantify soil spatial variability and generate
land-use impact assessments (Joe),
• Deriving hydraulic parameters cost effectively for agro-hydrological
models (Joe)./fast/accurately hydraulic parameters for each management zone
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Manaaki Whenua Landcare Research
Science for our land and future Kō te pūtaiao mō tō tātou whenua, mō āpōpō
• OUR PURPOSE
Kia matomato te tupu a Tāne, a Rongo, a Haumia-Tiketike
• OUR VISION
Let it be that the land and all its fruits may flourish
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•Characterising Land Biota,
• Enhancing Biodiversity,
•Managing Invasives,
• Supporting Business and Policy,
•Mitigating Greenhouse Gases,
•Characterising Land Resources,
•Managing Land and Water,
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Our Research Portfolios:
• Leader provider of biodiversity, biosecurity, land & soils research,
• Partner to Māori to sustainably develop land,
• Advisor to the primary sector on sustainable land use,
• Custodian to 1/3rd of the National Collections & Databases,
• Public sector-focused (90% of clients are government).
How the external world sees us
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Plan
• Introducing Manaaki Whenua Landcare Research , New Zealand,
• Mapping farm landuse by using remote sensing,
• Airborne sensors for land-use management,
• Wireless sensing networks for precision agriculture,
• Soil spectroscopy analysis
• Soil mapping: A system to quantify soil spatial variability and generate
land-use impact assessments,
• Deriving hydraulic parameters cost effectively for agro-hydrological
models./fast/accurately hydraulic parameters for each management zone
Sentinel 2a, 2b
Deriving vegetation cover by using Sentinel-2 & Landsat-8
Potatoes
Potatoes, turnips, pasture,
clover and lucerne
Remote Sensing M
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Use of time-series
satellite imagery to
track each paddock
through a season
Viewing time series data M
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Plan
• Introducing Manaaki Whenua Landcare Research , New Zealand,
• Mapping farm landuse by using remote sensing,
• Airborne sensors for land-use management,
• Wireless sensing networks for precision agriculture,
• Soil spectroscopy analysis
• Soil mapping: A system to quantify soil spatial variability and generate
land-use impact assessments,
• Deriving hydraulic parameters cost effectively for agro-hydrological
models./fast/accurately hydraulic parameters for each management zone
RPAS/UAV Research Thermal Camera
Fixed Wing RPAS
DJI Phantom 4 Advanced
Hyperspectral Camera +
Hexacopter
LiDAR Multispectral Camera
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Courtesy of Andrew McMillian and Ben Jolly
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Manuka Kanuka
M K
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ManuKa-
Kanuka flower
Index
Distinguishing Manuka from Kanuka trees
The “streakiness” index
Courtesy of Andrew McMillian and Ben Jolly
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Plan
• Introducing Manaaki Whenua Landcare Research , New Zealand,
• Mapping farm landuse by using remote sensing,
• Airborne sensors for land-use management,
• Wireless sensing networks for precision agriculture,
• Soil spectroscopy analysis
• Soil mapping: A system to quantify soil spatial variability and generate
land-use impact assessments,
• Deriving hydraulic parameters cost effectively for agro-hydrological
models./fast/accurately hydraulic parameters for each management zone
Gamma-rays EM
1. Mapping → Soil variability
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Precision Irrigation & Wireless Sensing Network
Courtesy of Ahmed El-Naggar, Carolyn Hedley, Pierre Roudier, Jaggath Ekanayake and
Ben Jolly
2. Monitoring → soil moisture & crop stress
Precision Irrigation & Wireless Sensing Network
Rain
gauge
s
Soil matric potential and
water content
Radiometers for
canopy
temperature
Courtesy of Ahmed El-Naggar, Carolyn Hedley, Pierre Roudier, Jaggath Ekanayake and
Ben Jolly
3. Improved management
Cellular Gateway
Cloud Database
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Precision Irrigation & Wireless Sensing Network
Courtesy of Ahmed El-Naggar, Carolyn Hedley, Pierre Roudier, Jaggath Ekanayake and
Ben Jolly
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Plan
• Introducing Manaaki Whenua Landcare Research , New Zealand,
• Mapping farm landuse by using remote sensing,
• Airborne sensors for land-use management,
• Wireless sensing networks for precision agriculture,
• Soil spectroscopy analysis
• Soil mapping: A system to quantify soil spatial variability and generate
land-use impact assessments,
• Deriving hydraulic parameters cost effectively for agro-hydrological
models./fast/accurately hydraulic parameters for each management zone
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Soil Spectroscopy: Vis-NIR and MIR
Vis-NIR for soil water prediction
Courtesy of Michael Blaschek and Pierre Roudier
Prediction Models based on
Laboratory Database:
oMineralizable N
o Particle Size Distribution
o Carbon fractions
o….
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SCANS (Soil Condition Analysis System)
VisNIR + γ-rays + CCD camera on intact core
Depth [mm]
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Plan
• Introducing Manaaki Whenua Landcare Research , New Zealand,
• Mapping farm landuse by using remote sensing,
• Airborne sensors for land-use management,
• Wireless sensing networks for precision agriculture,
• Soil spectroscopy analysis
• Soil mapping: A system to quantify soil spatial variability and generate
land-use impact assessments,
• Deriving hydraulic parameters cost effectively for agro-hydrological
models./fast/accurately hydraulic parameters for each management zone
http://smap.landcareresearch.co.nz Total coverage = 26%
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Courtesy of David Medyckyj-Scott and Gerard Grealish
Underlying
Data
Soil process &
management
research
Spatial soil data and morphology
attributes
Information
modelling
engine
Joint ARC / WRC Franklin update Feb2018
National soils data
repository (NSDR)
- Lab measured analytical
data
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S-map models and
information generator
S-map information system
National soils data repository (NSDR)
Courtesy of David Medyckyj-Scott, Gerard Grealish and Pierre
Roudier
Land use interpretations
•N leaching vulnerability,
•P leaching vulnerability,
•Runoff vulnerability,
•Bypass flow vulnerability (microbial leaching),
•Pugging + compaction vulnerability,
• Irrigation,
•Erodibility,
•Land use capability,
•Hydraulic characterization.
S-map was designed to derive soil hydraulic properties
Stone content Texture of
fines
Structure
size
Consistence
Non-stony
Stony (S) Sandy (A)
Loamy (L)
Clayey (Y)
Coarse (C)
Fine (F)
Weak (w)
Slightly firm
(s)
Firm (f)
Very stony (V)
Extremely stony (X)
Loose (l)
Compact (c)
Dense (d)
SYCf = subsoil, stony (S), clayey (Y), coarse structure (C), firm (f)
Pedotransfer functions
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Plan
• Introducing Manaaki Whenua Landcare Research , New Zealand,
• Mapping farm landuse by using remote sensing,
• Airborne sensors for land-use management,
• Wireless sensing networks for precision agriculture,
• Soil spectroscopy analysis
• Soil mapping: A system to quantify soil spatial variability and generate
land-use impact assessments,
• Deriving hydraulic parameters cost effectively for agro-hydrological
models./fast/accurately hydraulic parameters for each management zone
Deriving hydraulic parameters for each management zones, fast, accurately and cheaply
Collaboration University of Granada & CNRS of Grenoble
Hydraulic parameters change with time depending on agricultural practices M
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Beerkan 3D method: estimation of hydraulic parameters
steady
Water movement driven
by sorptivity
Input Data: • Initial soil moisture;
• Infiltration test;
• Bulk density;
• Optional: Derive PSD from laser;
Output required to run hydrological models:
• θ(h)
• K(θ)
transit
2D axisymmetric
domain
Water
movement
driven by
gravity
