**6. Web-GIS-based spatial decision support system development**

An interactive web-GIS-based spatial decision support system (SDSS) is being developed to cater to various requirements of IMD in the field of agriculture, hydrology, weather forecasting, pest and disease forecasting. This software has the facility for digitisation, editing, interpolation and advisory generation and dissemination. The system has the following modules:


**5.3. Soil temperature and its anomaly at different depths**

**Figure 5.** Realised soil moisture on a pan India mode.

**Figure 4.** Daily spatial rainfall.

200 Multi-purposeful Application of Geospatial Data

**Figure 6.** Forecast soil moisture on a pan India mode.

Presently, 153 stations are recording soil temperature at different depths at 07 LMT and 14 LMT. Soil temperatures recorded at these stations are used for spatial spread on a pan India mode using GIS software at different depths (5, 10 and 20 cm) which are depicted in **Figure 7**.

**d.** agromet advisories preparation.

The details of the modules are mentioned as follows:

**a.** Spatial database generation

This module has tools for


This module includes tools for


**c.** Hydrological analysis

This module includes tools for

**d.** Agromet advisory preparation

This module includes tools for

respect to geographic locations,

**Figure 9.** An example of an interactive web-GIS portal.

• importing point data such as rainfall, temperature, relative humidity, and so on, with

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• interpolation of point data to generate surface map of rainfall, temperature, and so on, • generation of isoline maps: this tool is used to generate isolines of different weather

• Field data entry: a form is designed to facilitate field station user to enter field data such

• Map generation for parameters like soil type, soil moisture, soil temperature, air temperature, humidity, rainfall, wind, cloud, bright hours of sunshine, crop condition (biotic and abiotic stress) and pest-disease infestation status, drought and aridity along

• Generation of real-time agromet products (crop and location specific) like pest forecasting, irrigation scheduling, fertiliser application, and so on, using requisite meteorological

parameters like temperature (isotherm), and so on as shown in **Figure 8**.

as crop, crop condition, soil moisture, location, and so on.

• Importing field data from the data entry form.

with textual and data information.

**Figure 8.** Annual rainfall map (with Isohyet) of Krishna Basin.

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**Figure 9.** An example of an interactive web-GIS portal.

**c.** Hydrological analysis

• digitisation of point, line and polygon feature,

• entry of attribute (non-spatial) information,

parameters like rainfall, temperature, and so on,

• linking of GTS (global telecommunication satellite) data with GIS.

• generation of forecast maps based on forecasts/warnings issued for various weather

• generation of weather anomaly maps (pressure, wind, temperature (maximum, minimum and dew point) and extreme meteorological event maps like heat wave, cold wave,

• generation of climatological maps of rainfall, maximum temperature and minimum

• linking of external non-spatial data,

**b.** Weather information map preparation

This module includes tools for

temperature all over India.

**Figure 8.** Annual rainfall map (with Isohyet) of Krishna Basin.

• editing of digitised features,

202 Multi-purposeful Application of Geospatial Data

• data import/export,

etc.),

This module includes tools for


This module includes tools for


data, crop data and soil data. Preparation of agromet advisories (crop and location specific) for different agro-climatic zones (a total of 127) in the country addressing to intrazonal variability at the district level (about 640).

	- Accuracy check: the accuracy of the individual map will be assessed in terms of topology, digitisation error and attribution.
	- Map projection: all collected maps will be brought into a standard projection system as per the SRS.
	- Mosaicking and edge matching: the individual map with a desired accuracy will be processed further for edge matching and mosaicking to prepare seamless datasets.
	- Data attribution: each feature of the digitised map will be linked with its attribute data.

the same in terms of crop and soil moisture status will help the experts to frame the advisories in a better way and ultimately improve the quality of the advisories. In order to extend the support of the ongoing operational AAS, the generation of satellite products for the generation of

Multi-purposeful Application of Geospatial Data http://dx.doi.org/10.5772/intechopen.74217 205

Besides, the remote-sensing technology helps to provide information for monitoring the pest and disease, drought and flood conditions along with the remedies to the farming community before the situation turns into a disaster affecting the crop yield and productivity. The potential zone for agriculture activities can be identified by studying the climatic variability, and localised services may be provided to the farmers to increase productivity. The yield productivity can be estimated using the remote-sensing technology where information can be further used for the crop insurance services. Remote-sensing methods can be integrated with crop growth models and statistical models to estimate better result in a spatial format. The great challenges would be to the meteorologists/agrometeorologists and space scientists to develop bias-free meteorological/agrometeorological information at its proper application at a local scale to further increase agricultural production to the huge population of the country in the coming years. In order to provide high-resolution information at a ground level, it is almost mandatory to use the remote-sensing data into the GIS hub under the advanced operational Agromet Advisory Services. At present, utilising the information of geostationary and polar satellite, a number of information like soil moisture index, land surface temperature (LST), Normalised Difference of Vegetation index and vegetative condition (VCI) are used to gener-

location-specific agromet advisories is required to meet the end-user requirement.

**Figure 10.** Current Indian geostationary meteorological satellites.

ate a number of information particularly when the plants are in stress conditions.

ture in the soil.

All information produced by the satellite is elaborated for the extraction of the desired information. There are many methods, algorithms and procedures to derive fundamental data for agrometeorological application from remote-sensing. Among the existing indices, the most extensively used are the land surface temperature (LST) which is a good indicator of climatic and microclimatic conditions prevailing close to the surface, as well as the frost or the mois-

**Figure 9** depicts an example of an interactive Web-GIS portal for operational agrometeorology.
