Erratic rainfall with varying intensity and duration has raised the risks of crop failure in semi-arid areas of south and south-east Asia. In subsistence irrigation cropping systems often it’s difficult to schedule the irrigation, i.e. when and how much water to irrigate. Therefore there is a need for a regional real / near real-time updated database on vegetation greening and browning to facilitate the irrigation scheduling decisions. With the advent of open archives of remote sensing from United States Geological Survey (USGS) and European Space Agency (ESA) have proven a unique set of long-term historical and near real-time observations. In this study, an attempt has been made to understand the vegetation greening and browning patterns using time series of remote sensing observations for irrigation water management. The main objective is to study the greening and browning of natural vegetation (i.e., grasslands and forests) and agricultural areas of Indian sub-continent for understanding the breaks in the rainfall spells and integrated approach for irrigation scheduling. The time series of vegetation indices have been extracted for predefined grid locations from Sentinel 2 remote sensing sensor. Further, an algorithm based on time series analysis were evaluated for estimating the vegetation growth stages. The estimated vegetation growth stages was compared with the agro-climatic zones. A methodology for subsistence irrigation scheduling has been proposed based on regional vegetation growth stages (i.e. onset, peak and end of the season). The estimated vegetation growth stages showed poor alignment with the agro-climatic zones. The integrated approach based on vegetation growth stages is promising for scheduling subsistence irrigation. The proposed methodology for vegetation growth stage identification has potential applications in drought risk assessment and in establishing key indicators for agro-climatic zones.
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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