Assesment of Era Sar Data for Tropical Acricultural Crop Monitoring
J. Aschbacher1, A Pongsrihadulchai2, S. Karchanasutham2, D.R. Paudyal3, E. Nezry1, M.Wooding4
1European Commission, Joint Research Centre (JRC),
TP 441, I-21020 Ispra (VA), Italy
Tel : +39-332-78- 5425, Fax : +39-332-78-5461;
E-mail : Josef.aschbacher@jrc.i
2OAE, Phaholyothin Road, KU Campus, Bangkok 10900, Thailand
3Forestry Building, Indooroopilly, Brisbane QLD 4068, Australia
4RSAC, Mansfield Park, Medstead, Alton, Hamsphire GU34 PZ, UK
Abstract
The current study discusses the potential of radar remote sensing for tropical agriculture crop monitoring. Theoretical considerations about radar backscattering of agriculture crops are presented and discusses. The theoretical considerations are complemented by actual case studies carried on different agricultural crops such as rice, sugarcane, rubber plantations, and other crops. Temporal radar signatures are presented for different tropical crop types and compared with those of temperate crops. The presentation also compares different approaches regarding the analysis of SAR data, such as field based versus pixel based approaches, or data processing methods including speckle filtering, texture analysis and segmentation. Results obtained via different mean are compared and discussed.
Among the case studies particular emphasis is given to the monitoring of rice using radar data. It can be shown that, for example, the use of radar data allows to retrieve rice area as well as accurate information about the plant's growth from ERS SAR data.
1. Introduction
The 1990's have seen major developments in the use of satellite remote sensing for agricultural monitoring and production forecasting. The `Monitoring Agriculture by Remote Sensing' (MARS) project of the European Union, for example, is a major initiative using satellite-based techniques for the collection of crop statistical information. Also in tropical countries the use of remote sensing has become more important in recent years. At regional and local levels, there is increasing use of remote sensing as a source of information on changes in agricultural cropping and for production forecasting.
Agricultural applications of remote sensing are time critical. The accurate identification of crop types depends on the availability of images acquired within specific time windows through the crop growing season, when there are marked differences in the appearance of particular crop types on remote sensing images. Equally, there is a need for images acquired at particular key times or yield prediction purposes. Despite the progress which has been made towards operational applications, experience shows that high Despite the progress which has been towards to wards operational applications, experience shows that high resolution visible and infra-red satellite sensors cannot always provide the desired information due to constraints related to cloud cover and revisit schedules.
*the work presented in this paper was carried out within the farm work of the `EC-ASEAN ERS-1 Radar remote sensing Project (ALA/ASN//28)'. The project is funded by the European Commission (EC) with support from the European Space Agency (ESA) and the Association of South-East Asian Nations (ASEAN). In addition, the current project is supported by the Office of Agricultural Statistics & Economics (OAE) of the Thai Ministry of Agriculture and the National Research Council of Thailand (NRCT).
Radar satellites like ERS-1 and ERS-1 overcome the problem of cloud cover. Synthetic Aperture Radar (SAR) systems transmit microwave energy down to the earth's surface and record the variable strength and phase of the `backscattered' return signal. Images are obtained independently of cloud coverage of daylight conditions and contain information on roughness and dielectric properties of the surface. Radar is sensitive to the structure and moisture content of vegetation canopies, and to soil roughness and moisture content.
The use of radar remote sensing for tropical agricultural monitoring
There is a great variety of tropical agricultural crops, examples of which are rice, sugarcane, maize, tapioca, coffee, tea, rubber and fruit tree plantations. However, in the paper emphasis is put on rice, being the crop with the largest economic and social importance for many countries. The example of rice monitoring and mapping also highlights in an exemplary manner the potential of radar remote sensing and is applicable in principle to many other crops. A short discussion on agricultural plantations and other agricultural crops (e.g. sugar cane, maize complements the assessment of radar for rice mapping and monitoring.
2. Comparison of Different Data Analysis Techeniques
Paudyal (1994) has investigated also other land cover categories at the Thailand study area, where, apart from rice fields, also large plots of sugarcane are present, intermixed with bushes, intermixed with
bushes, shrubs, water and urban areas.
Knowledge based classification methods, or unfiltered versus speckle filtered and/or texture analysesd images. The latter methods was developed making use of pre-assumptions about the rice growth cycle based on temporal profiles of s [dB]. These results were compared with a Landsat TM image and ground measurements for accuracy assessment. A tabular overview of the classification results is given in Table 1.
Table 1 : Classification accuracy for different classification methods based on five radar dates. The observed land cover categories in the Thailand area (Kanchanaburi) are rice, sugarcane, bush, shrubs, water and urban areas (from Paudyal, 1994).
| No. |
Classification method |
Input data |
Overall Accuracy (%) |
Rice Accuracy (%) |
Sugarcane Accuracy (%) |
| 1. |
Max. likelihood |
Unfiltered |
65.3 |
58.4 |
70.8 |
| 2. |
Max. likelihood |
MAP speckle filtered |
69.9 |
72.4 |
73.0 |
| 3. |
Max. likelihood |
Lee speckle filtered (2 iterations) |
75.2 |
78.0 |
87.5 |
| 4. |
Max. likelihood |
Texture (angular second moment plus contrast) |
69.9 |
63.9 |
73.1 |
| 5. |
Max. likelihood |
Texture(ASM+ IDM) plus speckle filtered (Lee) |
79.7 |
77.8 |
87.7 |
| 6. |
knowledge based segmentation |
MAP filtered |
79.9 |
91.8 |
71.0 |
The results of the supervised classification based on five dates (MAP-filtered, Nezry et al., 1991) has given an overall classification accuracy of 69.9%, while the knowledge-based method gave 79.9%, which is a clear improvement. The same accuracy was obtained when combining speckle and texture analysed images as input for a maximum likelihood classification. As an example, the classification accuracy matrix was extracted for the agricultural crops rice and sugarcane only, and compared with the overall accuracy including all six land cover categories. It is worth noting that the accuracy of rice alone has increased from72.4% for the MAP-filtered classification to 91.8% for the knowledge-based segmentation method. For sugarcane, however, the combined speckle filtered and texture analysed image are the best source for further classification. The accuracy reaches 87.7% in this case.
As can be seen from Table 1 there is no general method superior to another if all land-use categories are considered. However, the more sophisticated methods combine speckle filtered and texture analysed data are clearly superior to a classification using only unfiltered or speckle filtered images. The knowledge-based method was adapted to discriminate rice fields from other categories and performs best for the category of rice. A further description of the methodology can be obtained from Paudyal (1994).
