Monitoring Drought and Predicting Rice Supply with GIS, Satellite Data, and Python

Presented at FOSS4G North America 2026 in Sacramento, California.
This talk explores how GIS, drought data, and satellite observations can be combined to monitor California rice production and identify early signals of supply change.

The workflow combines USDA NASS agricultural statistics, California DWR rice-field polygons, U.S. Drought Monitor data, Sentinel-2 optical indicators, and Sentinel-1 SAR observations processed with Google Earth Engine and Python.

A 2021–2023 case study highlights the sharp reduction in California rice acreage and production in 2022, while satellite indicators suggest that the rice fields actually planted followed broadly similar seasonal crop-condition patterns. The analysis motivates a framework that combines water availability, planted acreage, and in-season satellite signals for future GeoAI and machine-learning applications.

Event: FOSS4G North America 2026
Location: Sacramento, California
Tools: Python, GeoPandas, Google Earth Engine, Sentinel-1, Sentinel-2, USDA NASS, California DWR, U.S. Drought Monitor

Transcript

1. Monitoring Drought and Predicting Rice Supply with GIS, Satellite Data, and Python Takeo Shibata 0
2. - Born and raised in Aichi, Japan - Background in Geophysics - Over 30 years of experience in software and information systems - Currently working on data science and AI-related projects - Based in Southern California Areas of Interest - GIS & Remote Sensing - GeoAI / Machine Learning - Earth Observation & Satellite Data - Python for Geospatial Analysis - Open Data & Open Source GIS - Environmental, agricultural, and disaster-related applications Outside of GIS - Hiking & camping - Cycling - Train travel - Robotics & electronics projects @takeofuture dev.to/takeofuture www.linkedin.com/in/takeofuture/
3. OUTLINE ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ Why Rice — and Why Sacramento? California Rice: Long-Term Trends From Macro to Micro: Data and Workflow Monitoring Drought Impacts Satellite Data and Indicators Used Satellite Indicators for Early Signals From Monitoring to Early Supply Signals Next Steps: GeoAI / ML Takeaways References Appendix
4. Why Rice — and Why Sacramento? Why Sacramento? The Sacramento Valley is the heart of California rice production • About 500,000 acres of rice are grown in the Sacramento Valley. • Nearly all of California’s rice is grown within 100 miles of the State Capitol. • California produces virtually all of the nation’s sushi rice. America’s Sushi Rice Starts Here! Sources: California Rice Commission; UC Agriculture and Natural Resources https://www.calrice.org/rice-101 https://www.calrice.org/rice-benefits https://rice.ucanr.edu/files/217622.pdf A California Rice Story 1909 First recorded experimental rice plantings near Butte Creek in the Sacramento Valley ↓ 1912 Rice became an established commercial crop in California ↓ 1948 Calrose, California’s foundational medium-grain rice, was released ↓ Today Calrose is a trade name for California medium-grain rice and is widely associated with sushi and other Asian-style rice dishes.
5. California Rice: Long-Term Trends Production Sharp decline in 2022 Acreage Planted and harvested acreage also dropped sharply in 2022 2022: unusually low planted and harvested acreage Data source: United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS), Quick Stats API
6. From Macro to Micro: Data and Workflow Key message: Move from statewide anomalies to field-level evidence. • • • USDA NASS U.S. Department of Agriculture — National Agricultural Statistics Service DWR California Department of Water Resources USDM U.S. Drought Monitor — NDMC / USDA / NOAA
7. Monitoring Drought Impacts Observation: Severe drought affected rice-growing areas in both 2021 and 2022, but acreage and production dropped much more sharply in 2022. Literature context: Multi-year drought, depleted water supplies, and irrigation restrictions had already constrained 2022 planting, leading to widespread fallowing. Data sources: USDA NASS Quick Stats; California DWR Crop Mapping; U.S. Drought Monitor (NDMC/USDA/NOAA) Literature / context: UCCE Rice Leaf, Nov. 2022; USDA ERS Rice Outlook, Sep. 2022; U.S. Bureau of Reclamation, CVP Water Supply Allocation, Feb. 23, 2022
8. Satellite Data and Indicators Used Data & Processing Google Earth Engine • Cloud-based processing; raster imagery was processed in GEE • Representative August monthly median composite • Same processing and display scales for 2021– 2023 Sentinel-2 SR Harmonized — Optical • True Color: B4 / B3 / B2 = Red / Green / Blue • NDVI: (B8 − B4) / (B8 + B4) • NDMI: (B8 − B11) / (B8 + B11) • MNDWI: (B3 − B11) / (B3 + B11) • Cloud filtering with Sentinel-2 Cloud Probability Sentinel-1 GRD — SAR • IW mode, dual polarization VV + VH • Example shown: VH backscatter • Radar provides observations independent of daylight and is less affected by cloud cover Study period: 2021–2023 Snapshot month: August Spatial focus: California rice-growing areas from DWR crop maps
9. Satellite Indicators for Early Signals • • • • NDVI / EVI → Crop growth / vegetation vigor NDMI → Canopy / vegetation moisture NDWI / MNDWI → Surface-water / flooding-related signal SAR VV / VH → Complementary radar signal for surface/canopy structure and moisture Early season: NDWI / MNDWI can help indicate flooded or surface-water conditions. Growing season: NDVI / EVI track crop development, while NDMI provides a moisture-related vegetation signal. Key Findings • Growth: Similar seasonal patterns across all three years. • Water: No comparable collapse in water-related signals in 2022. • Moisture: Planted rice showed broadly similar seasonal recovery. Working Hypothesis 2022 supply loss was driven primarily by reduced planted acreage—not widespread deterioration of the rice that was planted.
10. From Monitoring to Early Supply Signals 2022 suggests that acreage reduction was the dominant supply signal, while satellite indicators helped monitor conditions within planted rice fields.
11. Next Steps: GeoAI / ML Move from a three-year case study to a field-level spatiotemporal dataset that can support continuously updated rice-supply estimates.
12. Takeaways 2022 was an exceptional supply year: California rice acreage and production dropped sharply during a multi-year drought. Water constraints affected planting decisions: literature indicates that limited water supplies and irrigation restrictions led to widespread fallowing. Planted rice remained comparatively resilient: satellite vegetation and water-related indicators showed broadly similar seasonal patterns across 2021–2023. Early supply monitoring needs both signals: acreage / water availability tells us how much can be planted, while satellite indicators tell us how the planted crop is developing. ★ Less acreage can reduce supply even when the crop that is planted develops relatively normally. GitHub: Colab-ready notebooks available publicly. https://github.com/takeofuture/foss4g_na_2026 Slides: Available on Speaker Deck https://speakerdeck.com/takeofuture/monitoring-drought-and-predicting-rice-supply-with-gis-satellite-data-and-python Note: Google Earth Engine access is required for automated satellite retrieval/processing, or equivalent satellite data can be supplied manually.
13. References — 1/2: Data & California Rice Context • • • • • • • • California Department of Water Resources. (2021–2023). Statewide crop mapping [Data sets]. California Natural Resources Agency Open Data. https://data.cnra.ca.gov/dataset/statewide-crop-mapping California Rice Commission. (n.d.). Rice 101: How rice grows in California; Rice benefits. Retrieved September 22, 2026. https://www.calrice.org/rice-101 https://www.calrice.org/rice-benefits Childs, N., & LeBeau, B. (2022). Rice outlook: September 2022 (RCS-22H). U.S. Department of Agriculture, Economic Research Service. https://ers.usda.gov/publications/104742 National Drought Mitigation Center, U.S. Department of Agriculture, & National Oceanic and Atmospheric Administration. (n.d.). U.S. Drought Monitor GIS data [Data set]. Retrieved September 22, 2026. https://droughtmonitor.unl.edu/DmData/GISData.aspx Rutger, J. N., & Lehman, W. F. (1977). Rice introduction and germplasm development. California Agriculture, 31(9), 28–29. https://californiaagriculture.org/article/111630-rice-introduction-and-germplasm-development U.S. Bureau of Reclamation. (2022, February 23). Reclamation outlines initial 2022 water allocations for Central Valley Project contractors. https://www.usbr.gov/newsroom/news-release/4104 U.S. Department of Agriculture, National Agricultural Statistics Service. (n.d.). Quick Stats [Database]. Retrieved September 22, 2026. https://www.nass.usda.gov/Quick_Stats/ University of California Cooperative Extension. (2022, November). Rice Leaf [Newsletter]. University of California Agriculture and Natural Resources. https://ucanr.edu/sites/default/files/2026-04/November%202022.pdf
14. References — Satellite Data & Remote Sensing • • • • • • • • • European Union/ESA/Copernicus. (n.d.). Sentinel-2 MSI: MultiSpectral Instrument, Level-2A surface reflectance Harmonized [Data set]. Google Earth Engine Data Catalog. Retrieved September 22, 2026. https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED European Union/ESA/Copernicus. (n.d.). Sentinel-1 SAR GRD: C-band Synthetic Aperture Radar Ground Range Detected [Data set]. Google Earth Engine Data Catalog. Retrieved September 22, 2026. https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S1_GRD European Union/ESA/Copernicus/Sentinel Hub. (n.d.). Sentinel-2: Cloud Probability [Data set]. Google Earth Engine Data Catalog. Retrieved September 22, 2026. https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_CLOUD_PROBABILITY Gao, B.-C. (1996). NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257–266. https://doi.org/10.1016/S0034-4257(96)00067-3 Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031 Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1–2), 195–213. https://doi.org/10.1016/S0034-4257(02)00096-2 McFeeters, S. K. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425–1432. https://doi.org/10.1080/01431169608948714 Rouse, J. W., Jr., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. In Third Earth Resources Technology Satellite-1 Symposium (Vol. 1, pp. 309–317). NASA. https://ntrs.nasa.gov/citations/19740022614 Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open-water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025–3033. https://doi.org/10.1080/01431160600589179