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

233 Symposium on Remote Sensing for Resources Development and Environmental Management / Enschede / August 1986 TURTLE and HARE, two detailed crop reflection models J.A.den Dulk Department of Theoretical Production Ecology, Agricultural University, Wageningen, Netherlands ABSTRACT: Yield prediction can be done by means of dynamic simulation, based on crop properties and global environmental conditions. The use of remote-sensing data, interpreted by means of a crop reflection model will improve the quality of the prediction. Two crop reflection models are presented: both models can be used to predict the reflection of a wide range of crops, even when crop properties vary with crop height. One of them (TURTLE) consumes much computertime and yields a complete flux profile within the canopy, whereas the other one (HARE) is much faster, but produces only the reflection properties of the complete crop. RESUME: Le rendement des cultures arables peut être prédit par simulation dynamique, basé à les caractéristiques de la culture et les conditions environnementales. L'usage des données obtenues par télédétection et analysées au moyen d'un modèle de rèflection du couvert végétale, peut amélorier la qualité de prédiction. Deux modèles de ce type sont ici présentés. Tous les deux peut être utilisé pour prédire la rèflection du couvert. L'un deux (TURTLE), exigeant un temps prolongé d'exécution sur ordinateur, produit un profil complet des flux à 1'intérieur du couvert végétal, alors que l'autre (HARE) exige un temps d'exécution beaucoup plus court, mais ne produit les caractéristiques de reflection que pour la culture entière. 1 YIELD PREDICTION BY DYNAMIC SIMULATION At the Department of Theoretical Production Ecology of the Wageningen University, and in cooperation with the Centre for Agrobiological Research and other institutes, a number of dynamic simulation models for crop growth have been developed during the last decennia (Penning de Vries & van Laar, 1982). Some of these models are especially meant for detailed simulation of the processes in a single plant during one diurnal cycle, whereas other models are used to simulate growth and development of a complete plant stand during a complete growing season. If the modelled crop grows under well-known circumstances, as in a green house or in a region where weather conditions are fairly stable, then a good correspondence between the real crop and the model results is achieved. When however the knowledge about the actual growing conditions (weather, soil, nutrients) is poor, serious deviations between model results and measured data can occur. This means that the quality of crop yield simulation based on mean environmental conditions hardly exceeds the quality of a prediction that is based on intelligent extrapolation of the yields as obtained in the past under comparable circumstances. 2 GENERAL CROP PRODUCTION THEORY Light interception is an important factor in biomass production rate, and on the other hand the coverage of a crop is directly related to the intercepted radiation. In the first phase of the growth of a crop, when the crop does not cover the soil totally, interception, and therefore also growth rate, increases with biomass production. This phase is called the phase of exponential growth. During this phase there is a strong positive feedback of biomass on biomass production rate. Therefore relatively small differences between model assumptions and reality will cause serious errors in the simulation. In the second phase of the growth, when the canopy becomes more and more closed, light interception does not increase further with biomass production. This phase is called the linear phase, where feedback is less important than in the first phase. But during this phase stress factors like water or nutrient shortage or pests and diseases may play their role In the growth of the crop, so also in this phase the simulation can show serious deviations between the modelled situation at harvest time and the real crop. 3 ENHANCING THE QUALITY OF YIELD PREDICTION The purpose of this work was to enhance the quality of yield prediction obtained by dynamic simulation by including data gathered during the growing season from the crop itself. Combination of the actual weather data (in stead of mean climatological knowledge) with the deviations between the actual and the predicted state of the crop caused by stress factors will improve yield prediction. Data collection on the ground can be a problem. A good way to observe the crops themselves seems to be the use of remote sensing techniques and among these, multiband spectral scanning has been proven to be an applicable technique for the detection of crop properties (Bunnik, 1978). 4 DYNAMIC GROWTH MODELS Before continuing on the topic of the interpretation of reflection data to adjust simulations carried out with dynamic growth models, we will give a brief explanation of the technique of dynamic simulation. A dynamic crop simulation model can be considered as a set of simultaneous ordinary or partial differential equations, in which each equation represents the in- or decrease in one quantity of interest, like leaf weight or dry matter stored in the kernels. The state of the system at the start of the simulation (for instance the beginning of the growing season) defines the initial values, whereas the environmental conditions such as rainfall, radiation and temperature are the boundary conditions

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