IAPRS & SIS, Vol.34, Part 7, “Resource and Environmental Monitoring”, Hyderabad, India,2002
INVERSION OF PHYSICAL RADIATIVE TRANSFER MODELS USING
MULTISPECTRAL REMOTE SENSING DATA AND GROUND CONTROL
INFORMATION FOR PRECISION FARMING
Franz Kurz
Technical University Munich, Chair for Photogrammetry and Remote Sensing
ArcisstraBe 21, 80333 München, Germany, email: franz.kurz@bv.tum.de
Commission VII, WG VII/2
Keywords: Agriculture, Multispectral, Estimation, Accuracy, Radiation Model
ABSTRACT:
We propose a general framework to estimate vegetation parameters from multispectral remote sensing data by inversion of combined
physical radiative transfer models and by using a moderate amount of ground control information. This framework has been
exemplarily demonstrated for different winter wheat fields imaged by a Daedalus ATM multispectral scanner in the last two years.
The focus lies on the variations of vegetation parameters within single fields, which are used to derive information about soil
heterogeneities for precision farming. For the estimation of vegetation parameters, we use physical radiative transfer models, e.g.
SAIL and PROSPECT, combined with a linear empirical model. Results show the invertibility of the models for leaf area index,
chlorophyll content, specific dry matter, and specific water content. À strategy for the use of ground control data is proposed to
receive high accuracies of the estimated vegetation parameters with a minimum of necessary ground measurements.
1. INTRODUCTION
‘
Remote sensing techniques play an important role in precision
farming by providing continuous and contactlessly acquired
data of agricultural crops. Remote sensors image vegetation,
which is growing on different soil types with different water
availability, substrate, impact of cultivation, and relief. These
differences influence the state of the plants and cause
heterogeneous regions within single fields. Hence, the
heterogeneous vegetation acts as an interface between soil and
remote sensing information, because vegetation parameters
describing the state of the plants can be deduced from remote
sensing imagery.
In this context, a framework for the estimation of vegetation
parameters from multispectral imagery is proposed. The focus
of our approach lies on the variation of the vegetation
parameters within single fields assuming that field borders and
vegetation type are given. This framework applies both a
physical and an empirical model to derive the functional
relationship between vegetation parameters and measured image
grey values. The physical model is used to estimate selected
vegetation parameters by an inversion process, whereas the
empirical model fits the physical model to local characteristics
and sensor specifics.
This technique has been exemplarily tested for several sites with
winter wheat imaged by a Daedalus ATM multispectral scanner
from DLR (German Aerospace Center) Results show the
attained accuracies for the estimated vegetation parameters with
respect to the amount of ground control points.
2. RELATED WORK
The estimation of vegetation parameters using physical models
is based on the description of radiative transfer in the canopy by
means of an analytical reflectance model. In the last 30 years,
various models describing radiative transfer in canopy, soil and
leaves have been published. These models provide the
relationship between the radiation incoming from the sun and —
according to the bidirectional reflectance distribution function
(BRDF) - to the observer scattered radiation. Inputs of these
models are the structural and spectral parameters of the
vegetation/soil medium. Models describing the complete
vegetation/soil medium are called canopy transfer models, e.g.
the SAIL model (VERHOEF 1984), the Nilson-Kuusk canopy
reflectance model (NILsON and Kuusk 1989), and the LCM2
model (GANAPOL et al. 1999). In these models, the leaves are
considered as the only components of the vegetation canopy
characterised by their reflectance and transmittance. The
spectral properties of the leaves are mainly influenced by the
chemical consistency of the leaves, which can be modelled by
so called leaf optical physical models, e.g. PROSPECT
(JACQUEMOUD and BARET 1990), LEAFMOD (GANAPOL et al.
1998), and SLOPE (MAIER 2000).
Generally, these models were set up in the forward mode. This
means output parameters are the reflectance on top of the
canopy for given parameters of the vegetation/soil medium. The
solution of the resulting inverse problem was subject of many
investigations during the last years. Depending on the applied
sensors two main methods can be distinguished, inversion with
multidirectional and with multispectral data. Independent of the
method the inversion of the physical model is conducted using
different mathematical algorithms such as look up table
(KNYAZIKHIN et al. 1998), iterative optimisation (JACQUEMOUD
et al. 1995), and neural networks (BUELGASIM et al. 1998).
These algorithms adjust the model input parameters in such a
way that the model-predicted values closely match the measured
values. A comparison of these methods (PRAGNERE et al. 1999)
gives slight advantages to the neural networks technique that is
most robust for different sensors and canopy types. Up to now,
the inversion studies are performed with simulated reflectance
or field spectrometer measurements. In practical applications
with airborne or space-borne sensor data, a variety of empirical
tools, such as vegetation indices and spectral mixture models,
are widely used to derive biophysical parameters of the
vegetation. Our approach combines strict inversion of physical
models with empirical elements to estimate biophysical
parameters from airborne sensor data. In previous work (Kurz
and HELLWICH 2000), we describe our inversion method, the
investigation of invertibility and the selection of relevant
biophysical parameters in more detail. We chose four
parameters for a partial inversion of the applied physical
models: leaf area index, chlorophyll content, specific dry
matter, and specific water content. The inversion is conducted
using simulated annealing followed by a least squares
adjustment.
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