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

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.