Remote Sensing Derived Phenological Metrics to Assess the Spatio-Temporal Growth Variability in Cropping Fields

• 作者: Bertram Ostendorf Greg Lyle Megan Lewis Sofanit Araya
• 来源期刊: 遥感技术进展(英文)
• 学科: 医学
• 关键词: Remote Sensing CROP PHENOLOGY MULTI-TEMPORAL Images NDVI Precision Agriculture SPATIO-TEMPORAL VARIABILITY
• 年，卷（期）: 2017,（3）
• 页码范围: 212-228
• 页数: 17页
• 分类号: R73

摘要

Precision Agriculture (PA) recognizes and manages intra-field spatial variability to increase profitability and reduced environmental impact. Site Specific Crop Management (SSCM), a form of PA, subdivides a cropping field into uniformly manageable zones, based on quantitative measurement of yield limiting factors. In Mediterranean environments, the spatial and temporal yield variability of rain-fed cropping system is strongly influenced by the spatial variability of Plant Available Water-holding Capacity (PAWC) and its strong interaction with temporally variable seasonal rainfall. The successful adoption of SSCM depends on the understanding of both spatial and temporal variabilities in cropping fields. Remote sensing phenological metrics provide information about the biophysical growth conditions of crops across fields. In this paper, we examine the potential of phenological metrics to assess the spatial and temporal crop yield variability across a wheat cropping field at Minnipa, South Australia. The Minnipa field was classified into three management zones using prolonged observations including soil assessment and multiple year yield data. The main analytical steps followed in this study were: calculation of the phenological metrics using time series NDVI data from Moderate Resolution Imaging Spectroscope (MODIS) for 15 years (2001-2015);producing spatial trend and temporal variability maps of phenological metrics;and finally, assessment of association between the spatial patterns and temporal variability of the metrics with management zones of the cropping field. The spatial trend of the seasonal peak NDVI metric showed significant association with the management zone pattern. In terms of temporal variability, Time-integrated NDVI (TINDVI) showed higher variability in the “good” zone compared with the “poor” zone. This indicates that the magnitude of the seasonal peak is more sensitive to soil related factors across the field, whereas TINDVI is more sensitive to seasonal variability. The interpretation