利用Sentinel-1数据估计菲律宾水稻作物的移栽和收获日期

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

摘要

水稻是菲律宾的主要作物,因此,确定开展作物管理活动的理想窗口对于有效监测和资源分配具有重要意义。本研究利用Sentinel-1A和1B合成孔径雷达(SAR)数据,估算了干湿季节和不同气候条件下水稻的移栽和收获日期。本研究共考虑了3个不同气候类型省份的99块稻田。利用Sentinel-1提取各场在VV、VH和VH/VV极化下的平均后向散射系数,生成时间分辨率为6天的时间序列曲线。为了消除噪声,采用局部加权散点图平滑(LOWESS)。使用周期图分析和Breusch-Godfrey检验来识别重复模式及其统计显著性。局部极值和相应的日期提示可能的移植和采收日期。然后将确定的日期与农民访谈的现场数据进行比较。旱季移栽的均方根差(RMSD)为9 ~ 16 d,雨季移栽的RMSD为14 ~ 29 d。收获估计也遵循类似的趋势,与雨季值(8-22天)相比,旱季(16-17.5天)的RMSD分散程度普遍较低。结果表明,VH和VV极化在旱季可用于预测插秧期和采收期,而VH/VV极化在丰水季较好。该研究强调了SAR数据对监测作物管理战略的重要性,这对农业部门很重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimation of transplanting and harvest dates of rice crops in the Philippines using Sentinel-1 data
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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