In semi-arid regions of India, reliable rainfall predictions are vital for sustainable crop yields. As climate change disrupts rainfall patterns, farmers face increased uncertainty. My research leverages remote sensing to utilize vegetation status as a rainfall indicator, helping farmers in data-scarce areas make timely irrigation decisions and conserve precious water resources.
Rain is crucial for keeping crops healthy in semi-arid and arid regions, especially during critical growth stages when soil moisture can make or break yields. Historically, traditional farming methods were more sustainable in their water use. However, the rise of modern agricultural practices has increased water demands, creating new challenges in regions already struggling with limited water resources.
For farmers who rely on a mix of rainfall and minimal irrigation, timing is everything. Yet, as climate change makes rainfall harder to predict, especially in agriculture-dependent countries in the Global South, farmers face greater uncertainty. This is particularly true in areas lacking reliable weather data. To fill this data gap, three main strategies are available. The first is to set up dense networks of weather stations, which involves installing a large number of weather stations in a given area to provide comprehensive and real-time weather data. However, this can be prohibitively expensive for developing nations. The second is to use satellite-based remote sensing to track rainfall, though its resolution is often too broad to capture smaller, local rainfall events. The third approach is to assess vegetation health, which reflects past rainfall patterns.
My doctoral research, Climate Change and Water Availability in Data-Scarce Regions: Advancing the Nexus of Soil, Water, and Atmosphere through Remote Sensing and Modeling, focuses on this third approach. By analyzing plant health as a rainfall indicator, my work aims to support climate adaptation measures and help farmers make informed irrigation decisions even in data-scarce regions.
Blooming deserts as indicators of rainfall
In this study, vegetation health acts as a natural indicator of soil moisture levels. Using remote sensing, we can measure vegetation through spectral indices, which often correlate closely with soil moisture levels. By utilizing these remote sensing data of vegetation as input to the crop simulation model, we aim to estimate rainfall timing and quantity at the field level.
Using Localized Weather Data to Conserve Water
Farmers in Tamil Nadu’s semi-arid regions face daily challenges from unpredictable weather patterns and limited water resources. With weather stations spread unevenly, local weather data is scarce, making accurate forecasts difficult—especially on a daily scale. This can lead to costly decisions, like irrigating on a rainy day, which wastes water in an area where every drop counts.
Seasonal forecasts are typically more reliable than daily ones, but rainfall can vary dramatically from one field to another. To address this, our study aims to enhance weather models to provide field-level rainfall estimates, starting with areas as small as 250 square meters. By refining these models, we hope to boost daily forecast accuracy, helping farmers make smarter irrigation choices. Historical data gathered through this method will also help fine-tune irrigation strategies using the Deficit Irrigation Toolbox, a tool designed by Schuetze et al. (2019) to optimize water use.