Dr. Suryakant Sawant Profile

Dr. Suryakant Sawant

at Tata Consultancy Services Ltd

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Author

Publications (2)

Proceedings Article | 21 October 2019 Presentation + Paper

Towards using vegetation greening and browning patterns obtained from time series of remote sensing observations for irrigation water management

Suryakant Sawant, Jayantrao Mohite, Priyanka Surapaneni, Ankur Pandit, Srinivasu Pappula

Proceedings Volume 11149, 1114911 (2019) https://doi.org/10.1117/12.2533412

KEYWORDS: Vegetation, Remote sensing, Databases, Sensors, Earth observing sensors, Landsat, MODIS, Time series analysis

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Proceedings Article | 21 October 2019 Presentation + Paper

Spatiotemporal surface water mapping using Sentinel-1 data for regional drought assessment

Jayantrao Mohite, Suryakant Sawant, Srinivasu Pappula

Proceedings Volume 11149, 1114905 (2019) https://doi.org/10.1117/12.2533410

KEYWORDS: Synthetic aperture radar, Agriculture, Satellites, Remote sensing, Associative arrays, Data modeling, Earth observing sensors, Sensors, Landsat, Floods

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Accurate and reliable information on spatio-temporal extent of surface water is critical for various agriculture/environmental applications such as drought, flood monitoring, and understanding the availability of surface water for irrigation. Remote sensing (Optical as well as SAR) datasets are extremely useful to monitor sur- face water at massive scale. In monsoon months the optical remote sensing observations over semi-arid Indian sub-continent are obstructed due to cloud cover. Synthetic Aperture Radar (SAR) is a useful alternative for year-round monitoring of the surface water bodies. Sentinel-1A and 1B are very useful to monitor the changes at very high spatial resolution and frequently due to its high spatiotemporal resolution. The main objective is to establish an operational methodology for estimation of spatiotemporal variations in the surface water availability using Sentinel-1A and 1B observations. The study has been carried out in four districts of Coastal Andhra Pradesh, India viz. Guntur, Krishna, East Godavari, and West Godavari. Training data for water vs. non-water (vegetation, forest, settlements, and barren lands) classes have been obtained from field visits and high-resolution Google Map overlay in Google Earth Engine. We divided the dataset into 70% data for model training and 30% for validation and evaluated the performance of tuned random forest classifier on the validation dataset. Results show the classification accuracy of 94.32%. Further, current and historical weather observations such as rainfall were used to assess the validity of spatiotemporal surface water layers. We found a good agreement between the rainfall and surface water availability. We observed the increase in the surface water area during July-August months due to rainfall as well as flooding in the rice fields during transplanting. We propose to use the crop area map, spatiotemporal surface water layers and weather observations for drought assessment i.e., historical drought events and areas prone to agricultural drought.

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