Mr. Somdeep Kundu will present his first APS as per the details below:
Date: Wednesday, 24th September 2025
Time: 1000 - 1100 hrs.
Venue: RuDRA Lab, C-TARA
Title: Multisensor Remote Sensing for Crop Water Need
Supervisor: Prof. Pennan Chinnasamy
Abstract:
Optimization of crop water resource use is one of the biggest challenges of the 21st century. Approximately seven out of every ten liters of freshwater withdrawn globally is used for agriculture. India, a country with 16% of the world's population, has only 4% of the world's freshwater resources, and agriculture occupies more than half of its total land, with a cropping intensity of over 155%. Traditional observed data collection, such as walking through fields or taking manual measurements, is time-consuming, labor-intensive, and expensive. It is nearly impossible to get a complete picture of large agricultural areas this way, especially in a country like India with its vast, fragmented farms.
Remote sensing plays a pivotal role in comprehending this vast expanse systematically and holistically. With cloud computing, analyzing agricultural spectroscopic spatial big data is easier than ever, where the scale of observation crafts the level of abstraction we need to model this spatiotemporal change. Drones provide very high-spatial-resolution data, which is essential for India's complex landscape of small, fragmented, and multi-cropping farms. Satellites, with their radiometers, sample a larger spectrum beyond the human visiable range, including the infrared (IR) and thermal regions, to get a sense of crop-land ecodynamics. This has become more crucial as the shortwave infrared (SWIR) thermal characteristics of plant water stress are visible near the 1450nm and 1950nm bands. This data is pre-processed, analyzed using models, and integrated with other data streams.
Combining satellite and drone imagery with government data on water releases and irrigation schedules and using robust crop hydrological models can create a powerful tool for managing future scenarios. An effective decision support system for water management requires integrating multiple data streams.