Proximal and remote sensing approaches for quantitative ... · Proximal and remote sensing...

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

• 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

• 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),

land-use impact assessments (Joe),

models (Joe)./fast/accurately hydraulic parameters for each management zone

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

•Characterising Land Biota,

• Enhancing Biodiversity,

•Managing Invasives,

• Supporting Business and Policy,

•Mitigating Greenhouse Gases,

•Characterising Land Resources,

•Managing Land and Water,

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

• Mapping farm landuse by using remote sensing,

• Airborne sensors for land-use management,

• Wireless sensing networks for precision agriculture,

• Soil spectroscopy analysis

land-use impact assessments,

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

Use of time-series

satellite imagery to

track each paddock

through a season

Viewing time series data M

RPAS/UAV Research Thermal Camera

Fixed Wing RPAS

DJI Phantom 4 Advanced

Hyperspectral Camera +

Hexacopter

LiDAR Multispectral Camera

Courtesy of Andrew McMillian and Ben Jolly

Manuka Kanuka

ManuKa-

Kanuka flower

Distinguishing Manuka from Kanuka trees

The “streakiness” index

Courtesy of Andrew McMillian and Ben Jolly

Gamma-rays EM

1. Mapping → Soil variability

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

Soil matric potential and

water content

Radiometers for

canopy

temperature

Ben Jolly

3. Improved management

Cellular Gateway

Cloud Database

Ben Jolly

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

SCANS (Soil Condition Analysis System)

VisNIR + γ-rays + CCD camera on intact core

Depth [mm]

http://smap.landcareresearch.co.nz Total coverage = 26%

Courtesy of David Medyckyj-Scott and Gerard Grealish

Underlying

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

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

Structure

Consistence

Non-stony

Stony (S) Sandy (A)

Loamy (L)

Clayey (Y)

Coarse (C)

Fine (F)

Weak (w)

Slightly firm

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

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

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

movement

driven by

gravity

Manaaki Whenua Landcare Research

Thank you for your attention

pollaccoj@landcareresearch.co.nz PoggioM@landcareresearch.co.nz

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