Crop Growth Monitor System with Coupling of AVHRR and VGT Data 1

Crop Growth Monitor System with Coupling of AVHRR and VGT Data 1 Wu Bingfng and Liu Chenglin Remote Sensing for Agriculture and Environment Institute of Remote Sensing Application P.O. Box 9718, Beijing 100101 wubf@irsa.irsa.ac.cn Abstract This paper describes the Crop Growth Monitor System (CGMS) developed and operated by Agriculture and Environment Sector, Institute of Remote Sensing Application, Chinese Academy of Sciences. The CGMS is a quantitative, operational system for monitoring crop conditions throughout China farming land using NOAA AVHRR data and SPOT VGT data. A customized GIS interface allows users to view, analyze and compare changing crop conditions by region, province and county. AVHRR data are used in a real-time basis and VGT data are used for analysis. In agriculture, monitoring of crop growth and development, and early estimates of the final production to be expected are of general interest. Traditionally, the monitoring of crop growth and yield forecasts are made on the basis of samples by field visits or written inquiries. Problems encountered concern subjectivity in responses, respondent differences and non-response. On national scale, the processing of these sample data is an expensive and time-consuming procedure. In general, there is a need for an objective, standardized and possibly cheaper and faster methodology for crop growth monitoring and yield forecasts. China started to use remote sensing to monitor crop growth and to forecast production in as early as 1983, but failed to put into operation. In 1997, Chinese Academy of Sciences with the support from State Development Planning Commission initiated Crop Monitoring project with its purposes to realize operational crop monitoring on wheat, rice, corn and bean over the whole country. This paper describes this operational methodology by using AVHRR and VGT data. Remote sensing is able to supply the user community with an update on crop conditions over a large geographic area using a series of coarse resolution satellites (Brown, et.al 1982, Rasmussan, 1997, Wu, 1999). Daily, the entire earth's surface is imaged by NOAA series of satellites carrying the Advanced Very High Resolution Radiometer (AVHRR), a five channel scanning sensor which images in the visible, near infrared and thermal infrared wavelength bands, and by SPOT VGT, a 4 channel scanning sensor which images in the visible, near infrared and short-wave infrared wavelength bands. Vegetation monitoring using the red and near infrared AVHRR channels has been one of the most widely used indices. The Normalized Difference Vegetation Index (NDVI) correlates closely with green biomass and the leaf area index. Despite the spatial resolution of 1.1 km at nadir, there are many scientific publications documenting the usefulness of AVHRR data as a means of monitoring vegetation conditions on a near real-time basis (Philipson and Teng, 1988; Bullock, 1992; Quarmby et al., 1993). Chinese Program on Crop Monitoring with Remote Sensing In 1997, the program of Crop Monitoring with Remote Sensing(CMRS) was initiated as a key project of Chinese Academy of Sciences (CAS) with support from State Development Planning Commission. Ministry of Agriculture, State Statistics Bureau and State Grain Storage Administration are invited as project users. The CMRS was initiated because China had more than 14 years research experience on crop monitoring with remote sensing, but failure to realize operational, and traditional procedures for reporting crop conditions and forecasting production in 1 Funded by Chinese Academy of Sciences, KZ951-A1-302-02 and KZ95T-03-02

China relied primarily upon meteorological data and ground survey. There exist different channels to collect ground survey information in China. Frequently they are not inconsistent., and they are time and cost consuming. CMRS program has three main components: Crop Growth Monitoring System (CGMS), Crop Planting Area Estimation and Crop Yield Forecasting. Crop Growth Monitoring System In 1998, the Crop Growth Monitoring System (CGMS) was developed and implemented by Institute of Remote Sensing Applications (IRSA) of CAS. The CGMS sent at first report at first dekad of August 1998. At that time, China was suffering heavy flood disaster. The report and its successor did provide in-time information to the decision maker on how flood affected crops. The CGMS consists of a meteorological satellite receiving station, data processing unit and GIS Interface. In 1998 and most of 1999, we only use AVHRR data. Due to the wide span of China territory, we can not cover the whole country by using our own receiving station located in Beijing. The data for western part of China had to be purchased from China Meteorological Bureau. Delivery period is a key problem. Thanks to its global coverage and good data processing, SPOT VGT data become a supplementary data. Again due to the delivery period, SPOT VGT data can not be used for real time monitoring at present, which we continue to use AVHRR data, instead VGT data is used for quantities analysis due to its high quality. We purchased SPOT VGT data from SPOT Imagery who delivered in CD, but the delivery period can not be accepted either. Thanks to the framework of the Share-cost action on the improvement of the VEGETATION mission, we established a cooperation relationship with the Joint Research Centre of the European Commission. Through this relationship, we obtain the VGT NDVI data from JRC SAI site, later NDWI. It is quite efficient, although we have to pay a lot to Internet provider in China. The CGMS is a ten-day report of crop growth. The report is sent in both hard and digital copy to more than 20 state authorities. These reports were supplementary to ground survey conducted by different authority, for example, Ministry of Agriculture and State Statistics Bureau have their own ground survey system, and provided information on crop development throughout the growing season over the most part of country. The benefits of monitoring vegetation conditions across the farming land were immediately evident during the flood year of 1998. The success further reinforced the role of remote sensing for crop monitoring. Data Processing As part of the CGMS, NOAA AVHRR data for the most part of China are collected daily throughout the crop-growing season (March-October) at the satellite receiving station, sited on the top of IRSA building. These data are transferred to the processing unit where processing is performed using the PCI image processing system. The red and near-infrared bands of the AVHRR data were calibrated to reflectance and the two thermal bands were calibrated to surface temperature. Geometrical rectification was done using orbit information from the TLE data allowing a registration accuracy at the sub pixel level. Daily images are used to produce a ten-day, cloud free composite image for NDVI with maximum value composite (MVC) method. Although the ten-day composite substantially reduces cloud cover problems inherent with daily images, the process does not always produce a composite image that is completely free of cloud influence. Cloud influence contained in a composite must be addressed to increase the accuracy and usefulness. Clouds and noises were detected and removed interactively.

Figure 1. Crop Growth Monitor with AVHRR SPOT VGT data are well-processed data with high geometrical and radiometric accuracy. Only processing is to convert the data to local projection system.in order to focus on crop growth information, only farming land pixels were kept. This was done by combining land cover database at a resolution of 1km, gridded from vector maps at a scale of 1:100,000. The real time monitoring uses AVHRR differential image, which is a color image by assigning red, yellow, green, cyan and blue to five categories together with statistic table(wu 1999). For crop growth analysis and assessment, SPOT VGT data are used. The data are put into a GIS. The mean VGT NDVI curve by selected administration can be viewed, analyzed and compared to other years within the archive. This should be explained in the term of crop growth and at the same time, the reason as well as the recommendation should be addressed too, with ancillary information, such as the majority crop, the crop phonology, and the climate data. Geographic Information System Interface CGMS provided the users with ten-days reports on crop conditions. These reports were available both in paper and digital format. Paper format reports were delivered by mail to users. Digital format reports are put into internal government website. Authorized users can access the website with password.

Figure 2: GIS for Crop Monitoring with SPOT VGT data At the same time, we developed a GIS to integrated VGT NDVI, crop phonological data, landcover, administrative data, and meteorological data together. This integrated system provides value-added processing, ease of access and program flexibility. Users can view several types of image and map products, statistical data, and NDVI curves, all of which are updated dekadly. Image products show vegetation conditions on a pixel by pixel basis for the entire country. Map products illustrate the predominant vegetation condition. A detailed, quantitative and statistic analysis within the GIS system is accomplished by calculating the percentages of 5 categories, as well as the mean, maximum and minimum of the NDVI value, on a dekad basis, for crop masks, for each of the 31 provincial-level administrative zone. Pixels influenced by cloud are excluded from the calculation of the mean NDVI statistics. Users have the flexibility to choose the comparison years and can electronically export the data or the NDVI curves into reports or presentations. It includes: 1) comparison of any dekad of the current growing season with the previous dekad of the same growing season; 2) the current dekad with the same dekad of the previous growing season; 3) the current dekad with the same dekad of the normal; and 4) percent comparison of the current dekad with the maximum NDVI value within the normal. The normal is an average of the VGT NDVI channel for the crop years from 1998 to 1999, or to the year previous to the present growing season. Years of drought/flood and record production are included in the calculation of the normal. Using the GIS interface, users can view agricultural areas of importance for grains. This type of qualitative analysis allows users to quickly assess how much and where conditions have either deteriorated, remained unchanged or improved. Water bodies, rivers, roads, provincial boundaries are overlaid onto the image to aid in area location. Meteorological data are integrated and supplement to the analysis. Conclusions and Recommendations The CGMS is an operational system that provides information about crop across the whole

country in a timely and reliable manner. These early assessments are invaluable to decision makers and analysts within government agencies for better management of agricultural prices and distribution. It is recommended that further research are needed: Atmospheric correction for AVHRR and aerosol and BRDF correction for VGT data Incorporating VGT NDVI curves into crop yield models Web-based CGMS development Fusion of VGT and AVHRR data NDVI normal data development References: Brown, R.J., M. Bernier, G. Fedosejevs and L. Skretkowicz. 1982. NOAA - AVHRR Crop Condition Monitoring. Canadian Journal of Remote Sensing, 56(10) pp. 1359-1365. Bullock, P.R.. 1992. Operational Estimates of Western Canadian Grain Production Using NOAA AVHRR LAC Data. Canadian Journal of Remote Sensing, 18(4), pp. 23-28. Cihlar, J. and F. Huang. 1994. Effect of Atmospheric Correction and Viewing Angle Restriction on AVHRR Data Composites. Canadian Journal of Remote Sensing, 20(2), pp.132-137. Leprieur, C., Y.H. Kerr, and J.M. Pichon. 1996. Critical Assessment of Vegetation Indices From AVHRR in a Semi-arid Environment. International Journal of Remote Sensing, 17(13), pp. 2549-2563. Philipson, W.R., and W.L.Teng. 1988. Operational Interpretation of AVHRR Vegetation Indices for World Crop Information. Photogrammetric Engineering and Remote Sensing, 54(1), pp. 55-59. Quarmby, N.A., M. Milnes, T.L. Hindle, and N. Silleos. 1993. The Use of Multi-temporal NDVI Measurements from AVHRR Data for Crop Yield Estimation and Prediction. International Journal of Remote Sensing, 14(2), pp. 199-210. Rasmussen, M.S.. 1997. Operational Yield Forecast Using AVHRR NDVI Data: Reduction of Environmental and Inter-annual variability. International Journal of Remote Sensing, 18(5), pp. 1059-1077. Wu Bingfang, 1999, Crop Monitoring of China with AVHRR, Presented at Science Symposium on Space Technology for Improving Quality of Life in Developing Countries: A Perspective for the Next Millenium, Nov. 15-17, 1999, Delhi, India Wu Bingfang and Liu Haiyan, 1997, A simple method for accurate geometric correction of NOAA AVHRR 1B format Data, International Journal of Remote Sensing,, 18(8)