Methodology for combining optical and microwave remote sensing in agricultural crop monitoring : the sugar beet crop as special case

Summary

Accurate and up-to-date information on agricultural production is a vital component in running present market economies. In Europe considerable differences between c es in their agricultural production have led to a complex system of rules and subsidies which all rely on a certain level of accuracy regarding agricultural statistics (such as acreage and yield). At national level and regional level, such statistics have been collected so far by using conventional methods, which are mostly based on knowledge and experience from the past. Before using this information on a European level, there is a growing need for combining new information techniques and present knowledge to provide realistic estimates of crop yield and production on a lower scale level.

Yield prediction is an important tool for industry, fanners and policy makers, facilitating logistic planning of transportation and production, storage and sale at national level and planning at farm level. In this thesis, the study is concentrated on the application of observation or remote sensing (RS) techniques to crop growth monitoring of agricultural crops in the Netherlands. A common crop in the Netherlands is sugar beet crop and this crop served as a perfect illustration for validation of the developed methodology in this study. The objective of this study is to understand how optical and microwave remote sensing may be used in a synergetic way in order to develop a methodology, that can be used to monitor crop growth and predict crop yield together with existing knowledge.

More specifically, the study presented in this thesis aims to reveal (1) how useful information on biophysical properties of agricultural crops estimated with airborne remote sensing is for crop growth monitoring and yield prediction, (2) how successful this information can be utilized in the developed methodology for combining crop growth and remote sensing and (3) whether there are possibilities to apply this methodology for operational crop growth monitoring and yield prediction procedures by using airborne and to some lesser extent the current available spaceborne sensors.

The thesis work is subdivided in three parts. Part I outlines the theory and background supporting the thesis methodology and the combination methodology itself In Part IL the test data are presented and, for the case study, the synergy of the combination of information is studied, especially for the multi-sensor airborne campaign MAC Europe 1991. Here the research questions I and 2 are being studied. The application of the methodology (research question 3) described in this thesis is evaluated in Part 111, accompanied by concluding remarks and recommendations.

In Chapter 2, an inventory of the information estimated with RS is made in order to prepare the development of a methodology to monitor growth and production of agricultural crops with RS techniques. The major objective of this study is the investigation of the possibilities of a synergistic use of both optical and microwave RS data. Therefore, a review of the state of the art in modelling in the reflective optical and microwave region of the electromagnetic (EM) spectrum is performed. Furthermore, the most suitable models are selected and validated with the data from campaigns held in the Flevoland Province of the Netherlands as good as possible. It appeared that semi-empirical RS models, describing the observation of crops in a simplified physical way, could be calibrated and validated better than the complex radiative transfer models. The CLAIR model in the optical region has proven to be applicable over the different growing seasons, while the semi-empirical Cloud model in the microwave region revealed an unstable behaviour. Both models are calibrated with campaign data and were applied under strict conditions in this study in order to supply actual crop status information on respectively leaf area index (LAI) from the optical and biomass in the microwave model by inversion. From sensitivity analysis of the more complex radiative transfer (RS) models canopy structure appeared to be another important factor in the observation of crops as well in the optical as in the microwave region. Canopy structure information is not clearly incorporated in the semi-empirical RS models and therefore difficult to estimate. Changes in canopy structure have been recognised as specific features in time series of RS observation of the crop during the growing season, especially in microwave RS observations. The sugar beet crop revealed some characteristic features during the growing season, but not as clear as the vertically structured cereal crops, like winter wheat. Crop development related to changes in canopy structure in the case of winter wheat showed more potential for detection in RS time series as for sugar beet.

In Chapter 3, a general methodology is proposed for combining the information (RS data, field data and models) of different sources in order to monitor crop growth and predict the yield. The underlying physiological processes of crop growth are studied for linkage of crop growth models with RS information. The SUCROS-type of crop growth model for the sugar beet crop from the School of de Wit from Wageningen appeared to be very suitable for this study, because of its detailed description of crop growth modelling and its status of being well initialised for crop growth conditions for sugar beet in the Flevoland Province. In this chapter, different methods were developed to calibrate the crop growth model with the actual information estimated by RS. The combination methods are:
· Direct modelling method: Calibration of crop growth model with a forward RS model. By comparing the simulated RS signal with the observed RS data optimization of the most important variables of the crop growth model is performed.
· Inverse modelling method: Calibration of crop growth model with an inverse RS model. In this method crop variables estimated with an inverse RS model are compared with crop variables of the crop growth model and used for optimization of the most important crop growth variables of the crop growth model.
· Feature modelling method: Calibration of crop growth model by using characteristic information from RS time-series, which is mostly related to a change in structure of the canopy owing to changes in development stage of the crop.

The direct model-based approach is only used for reference for the other methods and is developed in former research.

The inverse model-based approach combines LAI and biomass estimated by optical and microwave RS model inversion with the crop growth model. The crop growth model was calibrated with this information and their estimation accuracies by using the reciproke of the standard deviation, which reflects the 'state of the art' in the RS modelling.

The feature-based approach completes the methodology by detection of features in RS time series information on changes in canopy structure possibly related to crop development stages, which provide another source of information to calibrate the crop growth model as well. The overall methodology comprises the combination of the two approaches.

Chapter 4 comprises a brief overview of data sets from campaigns at the Flevoland test site held in the past. In order to study the effect of synergism of optical and microwave RS data, conditioned data sets were required and aspects of quality and quantity of data in campaigns were discussed. The criteria for the synergy study were best met by the data set of the MAC Europe 1991 campaign compared to the other available data sets. For testing the combination methodologies of Chapter 3, the data from the airborne MAC Europe 1991 campaign were selected for the synergy study. This campaign was held at the time of the thesis study, so specific additional measurements could be collected like measurements on canopy structure. For RS model calibration and validation as well as for crop growth model initialization the Agriscatt 1987 and 1988 campaigns proved particularly suitable, because of the highly detailed information on field measurements. The ROVE data set from the late seventies provided measurements of high temporal frequency and were used for study of the impact of canopy structure on microwave backscatter and with that to illustrate specific radar features. The spaceborne ERS-1 time series from 1992 and 1993 were selected in order to discuss the potential of microwave satellite RS for operational crop growth monitoring in the last chapter and were not explicitly used in the study. A total processing line and a database for RS data interpretation was set up to prepare the study.

In Chapter 5, the proposed combination methods of Chapter 3 were applied with contemporaneous (simultaneous) and non-contemporaneous recordings of airborne optical and microwave sensors of the MAC Europe 1991 campaign. The configuration of the airborne RS data was selected for this study on basis of the current optical (SPOT and Landsat) and microwave (ERS-1/2 and JERS-1) satellite configurations. The performance of the methods was measured by comparing the simulated yield as a result of the calibrated crop growth model and the actual measured yield figures at a specific harvest date.

The inverse method is tested on the selected data set. The inverse RS model estimates LAI and biomass with a certain accuracy. The accuracy depends on the success of calibration of the (direct) RS model. It appeared that estimation of LAI from the optical model 'CLAIR' is at least twice as good as estimation of LAI from microwave model 'Cloud'. The combination of the crop growth model with optical data only gave good results. The added value of microwave data to this is present when no optical data are available (e.g. bad weather conditions). Using the information from both the airborne optical and microwave sensors weighted with the reciproke of the standard deviations the combination methods yielded success especially when the RS data was acquired in the beginning of the growing season. In this period the LAI can be well estimated, especially with optical RS models. Later in the growing season other information was found in RS time-series. With special attention to microwave time-series information on changes in canopy structure has been found and validated with field measurements of leaf angle distributions with respect to sugar beet. In the case of sugar beet these changes in structure are not clearly related to development of the crop. However, this is more pronounced in the case of cereals (e.g. winter wheat). This is information is also a source of calibration of the crop growth model. However, the accuracy of the feature found in the time-series is not high enough to calibrate the already well initialized crop growth model. When the observation frequency is high enough (weekly) then this information could be used for estimating the moment of sowing by using the meteorological information during the growing season.

Chapter 6 discusses the practical application of the methodology. An important aspect is that the level of study is translated from field to regional level in order to find practical use for the method in conventional prediction strategies of the present (food) processing industry. The generalization step appeared to give new information. An example is that e.g. the minimum. in standard deviation in backscatter time-series from ERS-1 for all sugar beet fields in the Southern part of the Flevoland Province appeared to be related to a regional crop closure of the sugar beet for two different years (1992 and 1993). It is obvious that information estimated with RS models for each specific crop is valuable for crop growth monitoring when the moment and density of the RS measurement is well chosen during the growing season. This imposes high requirements to the present available satellite systems (ERS-1/2, JERS-1, Radarsat, SPOT, Landsat, etc.)

Chapter 7 presents the main conclusions and recommendations for further research. More research is needed to calibrate and validate RS models for application in crop growth monitoring. The present generation crop growth models evolve towards reliable tools for yield forecasting and impose high requirements on quality of the RS information in order to be valuable for operational purposes. The methods used in this study gave good results in the case for airborne RS data on field level. Airborne optical and microwave RS information appeared to give synergetic results when combining with a crop growth model. The step towards a more operational monitoring method is expected to be difficult. The latter should be studied into more detail by using a more simple crop growth model and regional information from RS.