Spatial Crop Monitoring System for India using Direct-Broadcast Remote Sensing Data and Open Source Web-GIS technologies

Abstract

The paper describes design, development and application of a web enabled spatial decision support system (SDSS) for near real time crop monitoring at district level and making the information available to different stakeholders for building resilience of agriculture to climate/weather variability. The system uses direct-broadcast remote sensing data available in public domain and free web enabled open source software (FOSS) technologies for building spatial decision support application. The system uses multi-temporal remote sensing images received at IARI satellite ground station from Terra/Aqua MODIS sensor. Regular real-time satellite derived parameters of rainfall, day and night land surface temperature (LST), and crop vigour index of NDVI are generated for crop pixels and aggregated at district level for 579 districts of the country. Using historical values (2000-2014), Weekly anomaly indices of standardized precipitation index (SPI), Temperature Condition Index (TCI) and Crop Condition Index (CCI) are generated for each districts of India for current period. The historical and real-time basic parameters and anomaly indices are archived in a database and are made available on a web portal. Three-tier architecture is implemented on web-portal using open source Web GIS – the data is imported and stored in PostGIS/PostgreSQL in tabular form. The server-tier includes Apache web server, PHP and Geoserver. Open layer is used for visualization of geospatial data for client application. This Geoportal allows visualization of SPI, VCI and TCI as categorized maps for current period and over the crop season. Besides, for a selected district, it shows the temporal profile of parameters for current year and its comparison with that of previous year and long term average in graphical and tabular format. This prototype SDSS allow researchers, farmers, stakeholders and policy makers to explore and benefit from visualizing and analyzing current weather and agricultural situation. It is expected that such a system will help in managing the agricultural weather uncertainties at the level of decision-makers in federal and provincial government departments and also at farmer’s level, thus building resilience of agricultural systems to climate variability at multiple levels.