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

Abstract

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 growth variability across a cropping field. The study was conducted at a wheat cropping field in Minnipa, South Australia. The 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 Spectroradiometer (MODIS) for 15 years (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(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 of the association between phenological metrics and the management zone site conditions was discussed in relation to soil-climate interaction. The results demonstrate the potential of the phenological metrics to assess the spatial and temporal growth variability across cropping fields and to understand the soil-climate interaction. The approach presented in this paper provides a pathway to utilize phenological metrics for precision agricultural management application.

Real-Time Monitoring of Crop Phenology in the Midwestern United States Using VIIRS Observations

“…On the other hand, satellite based phenological metrics could provide information about growth variability in crop fields and thus support agricultural management. This is because the spatial trend of maximum greenness is significantly associated with the management zone pattern [59].…”

Spatial structure as a factor of stability of bioproduction functioning of steppe geosystems (on the example of Aituar steppe, the Southern Urals)

“…В автономных и трансэлювиальных типичностепных и петрофитно-степных урочищах связь между зеленой фитомассой и NDVI положительная и практически линейная. Это подтверждает рекомендацию, отраженную в литературе, что для использования NDVI как суррогата зеленой фитомассы необходимо предварительно строго устанавливать диапазон условий, для которых существует статистически значимая связь, а также учитывать межгодовое варьирование тесноты связи Araya et al, 2017;.…”

Spatial and Temporal Biomass and Growth for Grain Crops Using NDVI Time Series

“…Estimation of the AGB from satellite time series dates back to the early 1980s (e.g., [1,2]). Since then, the body of work utilizing remote sensing in crop studies has developed, encompassing approaches such as the development of empirical relationships [3][4][5], integration with crop and biophysical models [6][7][8], and the use of crop phenology metrics [9,10].…”

“…At a broad scale, the summed NDVI has been used to represent regional productivity in animal and crop systems [17,18], as well as grassland systems [19], over multiple years. Araya et al [9] used summed NDVI to examine the crop growth variability across paddocks using MODIS imagery. They found the summed NDVI to be sensitive to seasonal variability.…”