Estimating the Rice Yield and Determining Water Productivity of Paddy Fields with Remote Sensing and Lysimeter Data (The Studied Case: North of Sari)
2021
Jafari Sayadi, Fatemeh | Gholami Sefidkouhi, Mohammad Ali | Pirdashti, Hemmatollah | Khoshravesh, Mojtaba
Due to the key role of rice crops in food security and employment in Iran, access to on-time information of productivity and water productivity in paddy fields can provide important strategies for planning activities such as harvesting, storage, marketing, and management of resources and inputs. This study aimed to estimate the yield and determine water productivity of paddy fields in the north of Sari city using Landsat 8 satellite data and N type lysimeter. For this purpose, NDVI, SAVI, and RGVI indices were extracted from the images. Using these indices, a suitable regression relationship was created with rice yield. With continuous monitoring of paddy fields and installation of type N lysimeter, water consumption and evapotranspiration of rice data were measured. Finally, the study area's rice water productivity map was obtained by incorporating remote sensing data (yield) and field data (water consumption and evapotranspiration). The results showed that plant indices in the tillering stage have the highest correlation with rice crop yield, and if yield estimation using remote sensing data is considered, plant indices in tillering stage should be used. Among the plant indices, the SAVI index had the best correlation (r=0.94) with yield, and the yield map obtained from this plant index was used to prepare a water productivity map based on water consumption and rice evapotranspiration. Evapotranspiration-based water productivity map provided more realistic data than water consumption-based productivity map, so the SAVI index average productivity was 0.63 kg/m3, and the average measured productivity was 0.68 kg/m3. Findings showed that remote sensing provides useful information for mapping crop yield and water productivity in paddy fields and has good potential for precision and smart agriculture.
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