Abstract:
Water is the most precious gift of nature. The need for
proper planning in development, management and optimal utilisation of this vital resource is paramount for the economic development of the country. India's water resources are finite and greatly diversified in space and time. Agriculture is often at the mercy of weather gods, added to these, remorseless floods and droughts, low efficiency of irrigation systems, waterlogging and salinity in command areas, accelerating land degradation, alarming rate of
reservoir sedimentation, deteriorating water quality and
environment etc. are some of our problems concerned with
management and monitoring of water resources. All these are
required to be tackled carefully through systematic approaches involving judicious mix of conventional methods and new efforts like remote sensing for optimum productivity and use.
Various hydrologic data and water resources information
in our country, including data on soil and land resources required for various activities in water resources are not being collected and maintained at one place or available with one organisation. For remote and inaccessible areas, the existing system of water resources information generation is tedious, time consuming and difficult, Under the circumstances, remote sensing technology can be gainfully used for surveying and monitoring of water resources.
It is contemplated that there is ample scope for the
application of remote sensing in the assessment of various
components of hydrologic cycle. In the fields of snow hydrology,
watershed conservation, command areas planning, groundwater
exploration, flood estimation and forecasting, water quality
monitoring etc., through fairly reliable, reasonably accurate,
incredibly faster and near-real time data acquisition, remote
3.en,,it-g with conventional data would te able to provide best
management practices and facilitate efficient monitoring.
However, remote sensing is not an immediate panacea to all
problems in water resources. It is complementary to conventional
data.