Estimation of transplanting and harvest dates of rice crops in the Philippines using Sentinel-1 data

IF 3.8 Q2 ENVIRONMENTAL SCIENCES
Arturo G. Cauba , Roshanak Darvishzadeh , Michael Schlund , Andrew Nelson , Alice Laborte
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引用次数: 0

Abstract

Rice is a staple crop in the Philippines, thus, identifying the ideal window to carry out crop management activities is valuable for efficient monitoring and resource allocation. This study used Sentinel-1A and 1B Synthetic Aperture Radar (SAR) data to estimate the transplanting and harvesting dates of paddy rice under dry and wet seasons and varying climatic conditions. A total of 99 rice fields in three provinces with distinct climatic patterns were considered in this study.
From Sentinel-1, we extracted the mean backscatter coefficients in VV, VH, and VH/VV polarizations for each field to generate time series curves with a temporal resolution of 6 days. To mitigate noise, locally weighted scatterplot smoothing (LOWESS) was applied. Periodogram analysis and the Breusch-Godfrey test were used to identify repetitive patterns and their statistical significance. Local extrema and corresponding dates suggest potential transplanting and harvesting dates. The identified dates were then compared with field data from farmer interviews. The root mean squared difference (RMSD) for transplanting ranged from 9 to 16 days and 14–29 days for dry and wet seasons, respectively. Harvest estimates followed similar trends with generally less scattered RMSD during the dry season (16–17.5 days) compared to the wet season values (8–22 days). Results show that VH and VV polarizations are promising for estimating transplanting and harvest dates during the dry season, whereas, VH/VV polarization were better during the wet season. The study emphasized the importance of SAR data for monitoring crop management strategies which are important for the agricultural sector.
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来源期刊
CiteScore
8.00
自引率
8.50%
发文量
204
审稿时长
65 days
期刊介绍: The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems
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