Integrating top-down remote sensing and bottom-up participatory approaches to model water productivity in marginal farming communities in Maharashtra, India

Water productivity, defined as yield per unit consumptive water use, remains low in many marginal farming communities with landholdings of 1 ha or less, as defined by the Government of India. This persists despite the wide availability of field-scale weather forecasts, seasonal climate outlooks, and remote-sensing-based crop condition and soil moisture products, along with irrigation advisories. The challenge is not only limited information, but also the weak connection between top-down climate and crop data and bottom-up day-to-day irrigation and crop management decisions made by marginal farmers. This study develops an integrated framework to model water productivity for climate-resilient agriculture under high climate variability. In such conditions, rainfall and temperature vary strongly across seasons and years, creating uncertainty about when and how much to irrigate. This increases the risk of crop water stress or over-irrigation. Water productivity becomes critical under these conditions because it reflects how efficiently limited and uncertain water supplies are converted into yield, rather than focusing only on the volume of water applied. The research focuses on marginal farmers in semi-arid villages of Nashik District, Maharashtra, India. Top- down remote sensing data from MODIS and Sentinel-2 are used to derive evapotranspiration and NDVI, CHIRPS is used for rainfall, and ERA5 for temperature to generate initial local-scale estimates of water productivity. These estimates are then interpreted and refined using bottom-up field data. Bottom-up data collected from household surveys, focus group discussions, and participatory need assessment mapping capture farmer irrigation practices, perceived stress periods, soil moisture conditions, and decision rules. Seasonal and sub-seasonal patterns of water productivity are analysed and related to rainfall variability, temperature stress, irrigation timing, and NDVI-based crop growth dynamics. NDVI and temperature time- series fields are used to identify short stress windows and link fluctuations in water productivity to irrigation timing and crop growth stages, without overstating final yield outcomes. The framework links remote-sensing-based water productivity estimates with farmer-reported irrigation timing, irrigation method, and perceived stress, allowing fields to be grouped into short-term stress categories and relative performance classes that directly inform irrigation decisions. Comparison of satellite observations with farmer responses shows that mismatches between satellite-derived signals and farm-level outcomes arise mainly in small and fragmented plots, during short irrigation decision windows, and when advisory information lacks local relevance or trust. Results show strong variation in water productivity within small areas, driven by differences in irrigation decisions and access to usable information rather than by total consumptive water use. The study provides an integrated framework that reformulates water productivity based on remote sensing into indicators that are suitable for decision-making and are influenced by farmer participation. The framework demonstrates how combining top-down climate data with bottom-up participation can support more adaptive and equitable water use under increasing climate variability.