New Publication: Rapid Satellite-Based Estimates of Flood and Drought Damage to Rice in the Philippines
Embedded Files
When floods and drought hit rice farms: a faster way to estimate damage using satellites
After a big flood or a long drought, one urgent question always comes up: How much rice was affected—and how much did we lose? These numbers matter because they trigger real-world support: calamity assistance, recovery programs, and even insurance processes. But on-the-ground reporting can take time, and estimates can vary depending on who collects them and how.
A published paper in the International Journal of Disaster Risk Reduction offers a practical solution: an Earth Observation (EO)–based workflow that can rapidly estimate rice losses from floods and droughts, from pixel-level maps all the way up to municipal and provincial summaries.
The study is co-authored by Arnan Araza (IMPACT R&D) and Elmer Alosnos (Philippine Rice Research Institute), combining satellite-derived hazard information with crop-stage sensitivity and uncertainty analysis to support faster and more verifiable post-disaster estimates.
What the researchers did (in simple terms)
Think of it this way: instead of waiting weeks for reports to be compiled and validated, the team used satellite data to generate answers much sooner—and in a way that can be checked and repeated. The workflow starts by identifying where the flood or drought actually occurred, then overlays that hazard information with rice-growing areas to see which fields were exposed. Crucially, it also accounts for timing: exposure during the seedling stage does not have the same impact as exposure during the reproductive stage, so crop stage becomes a key part of estimating damage.
From there, the approach translates exposure into likely yield loss and corresponding peso value, while also reporting how uncertain those estimates are—so decision-makers can see not just a single number, but a range they can trust and interpret.
The researchers tested this workflow in Iloilo Province using two real events: flooding linked to Typhoon Odette (Rai) and the 2024 drought. They also added a future-looking 1:100-year flood scenario to illustrate how severe losses could become under extreme flooding conditions.
Key findings that stand out
The results show that the method can generate rapid loss estimates while still being transparent about uncertainty—reporting a value together with a confidence bound rather than presenting a single “exact” figure. In Iloilo, the estimated losses were PHP 270.9 ± 0.8 million for the Typhoon Odette (Rai) flood case and PHP 390.0 ± 0.3 million for the 2024 drought event, giving decision-makers not just a point estimate but a sense of how stable the estimate is.
The maps also suggest that drought affected a much broader share of rice areas than flooding did within the case study coverage. The flood impacted about 9.3% of rice area, while severe drought conditions were observed over roughly 37.6%, based on the study’s severity classification—highlighting how drought can be widespread even when impacts are less visually dramatic than floodwaters.
When the authors compared Earth Observation (EO)–derived estimates with consolidated provincial figures, the EO values were lower in both events (flood: PHP 270.9M vs PHP 692.3M; drought: PHP 390.0M vs PHP 706.6M). The paper doesn’t frame this as a simple case of one being “right” and the other “wrong.” Instead, it underscores why EO-based estimates are useful as a verification and triangulation tool, especially when assistance and recovery decisions depend on consistent, spatially specific evidence of where impacts occurred and how severe they were.
The study also looks beyond observed events to illustrate what could happen under more extreme conditions. In a modeled 1:100-year flood scenario, potential losses rose sharply to PHP 1,694.2 ± 1.8 million (about PHP 1.694B), largely because extreme floods can inundate far wider areas than a single typhoon event.
Finally, the analysis points to an important planning insight: some municipalities emerge as multi-hazard hotspots, where rice areas overlap with both flood exposure and severe drought exposure. Identifying these places ahead of time can help prioritize resilience investments, target adaptation programs, and pre-position response resources before disasters hit.
Why this matters for everyday Filipinos
Rice isn’t just a crop—it’s food on the table and income for farming families. When disasters hit, delays or inconsistencies in damage estimates can slow down assistance, send support to the wrong places, and leave local governments and communities planning recovery with too much uncertainty.
EO-based approaches help fill that gap by providing faster situational awareness right after an event, pairing estimates with transparent uncertainty bounds instead of pretending the numbers are perfect, and producing actionable maps that show where impacts are concentrated and which areas should be prioritized. In that sense, the study supports a shift toward impact-based services: linking hazards to crop exposure and crop-stage sensitivity, then translating those signals into likely yield effects—so decisions can be more timely, consistent, and evidence-based.
Read the full paper
Araza, A., & Alosnos, E. (2025). Advancing Earth Observation-based methods for rapid mapping and estimation of flood and drought impacts on rice production in the Philippines. International Journal of Disaster Risk Reduction.
Google Sites
Report abuse