Conceptual design

The author’s ambition has been to develop a satellite crop monitoring system for farmers. Therefore it was essential to conduct research on remote sensing data of agricultural crops throughout a complete crop growth cycle, to better understand the significant changes occurring in remote sensing data during that time frame. Of great interest was the relationship between the remote sensing data and plant parameters and crop yields under south-east Australian conditions. Hence the experiment was conceptually designed to collect satellite remote sensing data and ground observations/ sampling of crops in the same spatial extend at multiple points in time throughout the 1998 growth cycle and to study the relationship between the remote sensing and ground data. The gained insight was used to develop a concept of a satellite crop monitoring system for farmers and land managers.

The project was designed to study the “typical crop development” throughout the crop growth cycle for the five crop types under investigation: barley, canola, chickpeas, lentils and wheat. Therefore knowledge of the crop type on specific fields was needed; this information was supplied by farmers participating in the ALMIS project. It was investigated how the “typical” spectral properties of these crop fields in south east Australia appeared in the satellite data; these were a reference for further work, acting as a baseline for each crop type. A comparative study was conducted to determine “typical” spectral properties of the same crop types in a different year

(2001). Furthermore it was investigated how well the different crop types could be distinguished from each other at different acquisition dates, using the statistical method of discriminant function analysis.

Ground observations were collected during the 1998 crop cycle by the ALMIS team and included above ground green biomass collection, crop height measurements and soil moisture sampling. After laboratory work, dried above ground green biomass and crop plant water measures could be calculated. It was investigated how the corresponding satellite data and satellite data derived vegetation indices related to the ground samples of a given local sampling point. The Pearson Product Moment coefficient R was calculated at pairwise correlation of the ground observation data for plant height and each of the satellite bands and vegetation indices, respectively. Hereby the data of all dates were combined in one dataset and analysed separately for each crop type. The same procedure was repeated for the other field observations, namely above ground green biomass [g/m2], dried above ground green biomass [g/m2], plant water [g/m2], plant water content [%] and the soil parameters volumetric soil moisture 0-5 cm depth [%] and available soil water 0-100cm depth [mm]. For significant highly related parameters, linear regression equations were retrieved for empirical parameter estimation under south east Australian conditions.

Yield data acquired by precision farming yield monitors at harvest were used to relate yield with the satellite data. Homogeneous areas of interest in the yield maps were extracted and related to spatially corresponding satellite imagery and derived vegetation indices. The Pearson Product Moment coefficient was calculated at pairwise correlations for each single image acquisition date and for accumulated sums. Furthermore a stepwise analysis was conduced on all datasets and standard least square models were derived to investigate if results could be improved.

An early phase prototype crop monitoring system was designed and tested with the involvement of the end users (farmers). It was tested if the processed satellite imagery would assist in finding problem areas in the fields and if the information would result in modified management responses.

Farmers gave feedback in workshops and by questionnaires on the experiment and assisted in the development of an improved concept that considered the end-users requirements. The information gained from the different components of the experiment was then used to develop a concept for an improved satellite crop monitoring system.

Summary of project related tasks

In order to achieve the aim of the thesis, namely to develop a concept for a prototype crop monitoring system, numerous tasks needed to be executed. Table 4.1 gives an overview of the work completed by the author and a reference to where a detailed description can be found. When the research started there was no existing project to join. Therefore set-up tasks associated with the project were included in the task list:

Table 4.1: Task list to conduct ALMIS experiment

Task Reference

Finding a suitable test site (Project set-up)

Finding local farmers to cooperate with in the area (Project set-up)

Gaining support from satellite companies (for imagery) (Project set-up)

Acquire Images (Chapter 4)

In situ data collection by farmers and the ALMIS team (Chapter 4)

Determine routines for data pre-processing and quality

assurance (Chapter 4)

Extract typical crop signatures throughout vegetation growth

cycle for barley, canola, chickpeas, lentils and wheat (Chapter 5)

Determine accuracies for crop type discrimination through-out

the season (Chapter 5)

Analyse the satellite data in respect to in situ data such as plant height, above ground wet and dry green biomass, plant water,

soil moisture

Analyse the satellite data in respect to crop yield (Chapter 7)

Developing an early phase prototype crop monitoring system (Chapter 8)

Test which anomalies the farmers can identify in the field based

on information gained with the early phase prototype system (Chapter 8)

Evaluate farmers feedback on early phase prototype testing (Chapter 9)

Develop a concept for an improved crop monitoring system (Chapter 9)