Remote sensing for resources development and environmental management: Remote sensing for resources development and environmental management

Damen, M. C. J.

Bibliographic data

Multivolume work

Persistent identifier:: 856342815

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856342815

Language:: English

Additional Notes:: Volume 1-3 erschienen von 1986-1988

Editor:: Damen, M. C. J.

Document type:: Multivolume work

Volume

Persistent identifier:: 856343064

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Scope:: XV, 547 Seiten

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856343064

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(26,7,1)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Damen, M. C. J.

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: 3 Spectral signatures of objects. Chairman: G. Guyot, Liaison: N. J. J. Bunnik

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: TURTLE and HARE, two detailed crop reflection models. J. A. den Dulk

Document type:: Multivolume work

Structure type:: Chapter

234 of the system. In vector notation: at a each moment t the rate vector R(t) is described as a function of the state of the system S(t) and the environmental conditions E(t) at the same time: R(t) = f (S(t), E(t)) (1) to five minutes or less, for a complete growing season of 100 - 150 days, generally one day is a good choice for AT. Phenomena that show a large amplitude during one timestep (for instance incoming radiation during one day) must be averaged or totalised over each step. The state of the system S(t) is calculated by integration of R(t), starting with S(0), the initial 5 COUPLING REMOTE SENSING DATA AND GROWTH MODELS situation: T S(T) = / R(t).dt + S(0) (2) 0 The environmental conditions are not affected by the changes in the system itself, so they can be written as a function of time only: E(t) = g (t) (3) The influence of the states S on the rates R, as expressed in a general way by equation 1, will cause feedback so that the rates R are not a function of time only. -The most common feedback loop is the one [biomass -> leaf area index (LAI) -> growth -> biomass]. In figure 1, this loop is drawn by arrows. These arrows represent flows of information (dashed in the figure). The last one is a flow of material, closing the loop by an integration (solid arrow). Figure 1 serves only as an example, it is obvious that the dynamic models that can be applied for yield prediction are much more complicated than this one. state variables rate variables sources and sinks auxiliary variables flow of material flow of information boundary conditions Figure 1. Some relations in a dynamic model for crop growth (simplified). simulation An important decision is to be made on the boundaries of the system: they depend on the total simulation time and on the desired level of detail. For instance, soil water content is fairly constant over one day, so in a simulation that only concerns one diurnal cycle it may be considered to be constant. When the soil water content in a porous sandy soil is mainly a function of human interventions in the level in surrounding ditches, it is a function of time and at last, when the water uptake by the plants plays an important role in the soil water content, soil water must probably be taken In the state vector S of the model and the changes in it in the rate vector R. All relations in the models are defined as mathematical expressions, as tabular functions or as combinations of both. The complexity of the relations between S, E and R prohibits generally the application of an analytical solution of integral S. so only a numerical solution can be applied. Because of the discontinuities in E, Euler's integration method is generally used to solve expression (2). This means that this expression is rewritten to: S(T+6T) = S(T) + AT * R(T) (4) where AT is the integration time step. For simulations that concern one diurnal cycle, AT is set A problem in the incorporation of remote sensing data in simulation models is the difference between the type of information that is used in the models like biomass or LAI and the type of data as collected with remote sensing techniques. It is obvious that a coupling mechanism must be applied. Roughly spoken three types of coupling mechanisms are possible: 1. Statistics: from a wide range of crops growing under different circumstances and in different stages of development, the reflective behaviour must be available. The measured reflection is compared to the data set of known reflections. This can probably give the information which we are interested in, but it requires a tremendous data collection in advance. 2. Direct calculation of the crop state from the measured reflection. This means that it must be possible to invert the set of functions that describes the relation between crop properties and reflection. There exists no unique relation between reflection and crop status. Therefore both the first and the second method will give ambiguous results. 3. Starting with the simulated crop, the reflection of this crop is estimated and compared with the measured data. When differences are detected between these two, the most likely parameters in the growth simulation are changed and a new simulation run is made. This process is repeated until a good correspondence between measured and estimated reflection is achieved. In this work, the choice is made for the third method, because it takes into account additional knowledge from ground truth and about relations between parameters concerning crop and soil. Therefore a model is needed to calculate the reflection of a crop from from its optical properties and leaf density distribution. A model that can serve for this purpose must fulfil two conflicting requirements: 1. The model must be complicated in view of generality, because it must be possible to calculate the reflection of a crop in any arbitrary direction as a function of crop properties, soil reflection and the spatial distribution of the incoming radiation. Too many limitations of the model cause the computation results to be a function of the model restrictions rather than a function of the crop properties. 2. The model must be simple in view of its frequent iterative application, so one run with the program may not exceed an acceptable level of use of computer resources. 6 SOME EXISTING MODELS Several models published before are investigated on these needs. All are rejected on their limitations. The Suits-model (Suits, 1972) is based on a very simplified crop geometry. Especially for off-nadir observations or in the situation where the sun's direction deviates from the zenith, the model results show only a qualitative relation with experimental data. A second model that is considered is the model published by de Wit (1965), which is enhanced later by Goudriaan (1977). These models are developed to estimate the absorption of incoming radiation. Therefore, these models are based on a simplified leaf reflection submodel and on aggregating functions for reflection by crop layers. Although the overall

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