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Seasonal Paddy Rice Production Estimation Derived from Copernicus Sentinel-1 Time Series

Leinenkugel, Patrick and Clauss, Kersten and Ottinger, Marco and Künzer, Claudia (2019) Seasonal Paddy Rice Production Estimation Derived from Copernicus Sentinel-1 Time Series. ESA Living Planet Symposium 2019, 13-17 May 2019, Milano, Italy.

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Rice is the primary food crop for more than half of the world’s population and responsible for over 50% of the daily calorie intake for most Asian people. Asia’s biggest rice producers are facing increasing pressure to ensure food security due the consequences of population and economic growth. At the same time demand for rice imports is increasing and agricultural areas are confronted with urban encroachment and the limits of yield increase. Paddy fields in Asia are often located in coastal lowlands and river deltas, which are vulnerable to sea level rise, floods and droughts, salt water intrusion and other hazards. The floodplains of coastal river deltas in Asia are among the most productive rice producing regions in the world and are experiencing a reduction in rice yield due to increased salinity in the ground water as a result of sea-level rise. Despite the importance of knowledge about rice production the countries official land cover products and rice production statistics are of varying quality and unable to provide timely information about the quantity of rice production. The growth of rice plants can be monitored with time series from Synthethic Aperture Radar (SAR) sensors. In regions with high rice production the rice plants are commonly grown under agronomic flooding, where rice is transplanted or seeded into flooded fields, followed by the emergence of the plant through the water surface and a rapid increase in biomass. The application of SAR time series to monitor the growth of rice plants has been limited by a lack of sensor data. Since the launch of Sentinel-1A and 1B the data availability increased massively and the access to this free and open archive allows for the creation of temporally dense time series for the monitoring of rice growth in Asia. We present a study which used Sentinel-1 SAR time series to estimate rice production in the Mekong Delta, Vietnam, for multiple years and intra-annual rice growing seasons. For each growing season the paddy rice area was mapped by generating object based time series utilizing a superpixel segmentation algorithm and a phenology based decision tree classifier. The rice yield and production was estimated per object with a random forest regression model trained on per-season yield data. Yield reference data was collected by surveying 357 rice farms distributed over the Mekong Delta. The results show good agreement with the reference data and rice production data published by Vietnam’s Statistic Offices at the district level and point towards the potential of Sentinel-1 time series for transparent and timely, post-harvest, rice production estimation.

Item URL in elib:https://elib.dlr.de/127596/
Document Type:Conference or Workshop Item (Speech)
Title:Seasonal Paddy Rice Production Estimation Derived from Copernicus Sentinel-1 Time Series
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Ottinger, MarcoUNSPECIFIEDhttps://orcid.org/0000-0002-7336-1283UNSPECIFIED
Refereed publication:No
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Keywords:Mekong Delta, Rice Production, Sentinel-1, Time Series, Random Forest Regression
Event Title:ESA Living Planet Symposium 2019
Event Location:Milano, Italy
Event Type:international Conference
Event Dates:13-17 May 2019
Organizer:European Aerospace Agency
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Remote Sensing and Geo Research
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center
Deposited By: Leinenkugel, Patrick
Deposited On:19 Jun 2019 09:31
Last Modified:29 Mar 2023 00:41

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