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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004
that no interpretation procedure provides ‘new’ information; at
best it reveals information that was hidden in the (collected and/or
estimated) basic data and does so with varying accuracy. In this
research we will try to (partly) alleviate this drawback by making
use of multisensor satellite observations.
1.2 Objectives
The overall aim of this research is to develop knowledge and
technology for crop production monitoring based on multi-sensor
satellite data. The objective is to assess if an improvement in a
crop production estimate can be obtained when the temporal
resolution of parameter values are increased by combining data
from satellites of different nature, and to see if this enhances the
detection of periods when the crop is subjected to stress.
2. METHODOLOGY: CROP GROWTH SIMULATION
WITH RS-DATA
Analytical models of biophysical production potential of annual
food and fiber crops have been built and tested in The
Netherlands and elsewhere since the 1960's (De Wit and Penning
de Vries, 1985). These models account for the dynamics of crop
growth by dividing the crop cycle in successive (short) time
intervals during which processes are assumed to take place at
steady rates. ‘State variables” such as leaf, root, stem and storage
organ masses indicate the state of the system during a particular
interval; their values are updated after each cycle of interval
calculations. The relative simplicity and low data needs of these
production situation analyses allow to accurately quantifying
reference yield (i.e. the harvested produce) and production (i.e.
total dry plant mass) levels, but for regional applications adequate
basic data availability is a concern. As an adaptation from
algorithms documented by Driessen and Konijn (1992), the crop
growth simulation model (PSn) programmed for this research
follows a similar line of reasoning but tries to improve upon its
regional applicability by incorporating satellite derived parameter
values.
2.1 Crop growth simulation
As a minimum configuration, known as ‘Production Situation 1'
(PS-1), the model represents a simplified Land Use System in
which production and yield are solely determined by the available
light, the temperature and the photosynthetic mechanism of the
crop:
PS-1: P,Y = filight, temperature, C3/C4)
(Eq.1)
The levels of crop production and yield calculated for PS-1 are
not the actual production and yield but potentials that are
normally only realized at experiment stations where even the last
weed plant or bug is mercilessly eliminated, irrespective of cost.
In many regions, water availability to the crop is the main
constraint to crop growth. Water is needed in great quantity (in
dry regions a maize crop may well transpire 1 cm of water on a
clear sunny day, equivalent to 100,000 1 ha''d''). Irrigation (and/or
drainage) requires expensive infrastructure and skilled labour to
restrict losses to the minimum and prevent soil degradation, e.g.
caused by accumulation of soluble salts in the root zone. It has
therefore been tried tó extend the model with a water budget
routine that matches actual consumptive water use with the crop's
water requirement, i.e. with the theoretical transpiration rate of a
constraint-free crop. The so-defined ‘Production Situation 2° (PS-
2) calculates the ‘water-limited production potential’ of the crop
as a function of available light, temperature, photosynthetic
mechanism and available water:
213
PS-2: PY = flight, temperature, C3/C4, water) (Eq.2)
In production environments where the crop's consumptive water
needs are met at all times, the water-limited production potential
is equal to the biophysical production potential because actual
crop transpiration is equal to the theoretical maximum rate. If
water uptake by the roots is less than required to meet the
maximum transpiration needs, actual transpiration is limited to the
actual water uptake rate. In this case the ‘water sufficiency
coefficient’ (¢fH20) assumes a value <1.0 and assimilation and
growth are less than in Production Situation 1 due to water stress.
2.2 Crop stress and canopy heating
Incident radiation heats the canopy whereas transpiration cools it
(Barros 1997; Kalluri and Townshed 1998). The fraction of the
incoming radiation that is available for heating the canopy is set
equal to the net intercepted radiation minus the energy needed for
assimilation and for the vaporization of water lost in actual
transpiration. The sensible heat component of the energy balance
equation is approximated from the instantaneous temperature
difference between air temperature and canopy temperature of a
crop surface. More rigorous considerations of the momentum flux
theory are provided by, inter alia, Bastiaanssen (1998) and Parodi
(2000); isolated terms of the energy balance equation essential for
this research are detailed below. The energy balance equation is
given by the form:
INTER = INRAD + TRLOSS + MISCLOSS (Eq.3)
Where:
INTER - net radiation intercepted by the canopy,
INRAD - sensible heat exchangeable crop canopy and air,
TRLOSS - latent heat flux to the air due to canopy transpiration,
MISCLOSS = miscellaneous energy transfer components.
Assuming /NRAD can be measured or modelled within the error
margin of satellite-derived estimates, and that the components
represented by MISCLOSS are comparatively small (and can be
ignored from the equation) the energy balance equation takes the
following form: (Rosenberg 1983; Driessen and Konijn,1992).
INTER = INRAD + TRLOSS (Eq.4)
The latent heat flux can be isolated from the energy balance
equation using a similar formulation as used by Soer (1980):
(Eq.5)
*VHEATCAP
TRLOSS = (INTER) - [sumus
AERODR
Where:
INTER = intercepted radiation /J m* d'],
AT = temperature difference between canopy and air /K7,
VHEATCAP - volumetric heat capacity /J m^ K /]
Quantifying aerodynamic resistance to heat transport is far from
easy. A semi-empirical equation that maintains its integrity at low
wind speed (Jackson et al 1988) is:
AERODR = 4.72{In[z-d)/z0]k}2 / (1 + 0.54U) (Eq.6)
For non-neutral conditions (i.e. ‘measurable 47 *)), AERODR
varies non-linearly with temperature; approximate values are
obtained with iterative methods.
TRLOSS represents the energy needed to vaporize the water lost
in actual transpiration by the crop:
TRLOSS = TR, * LATHEAT (Eq.7)
