Abstract
Monitoring crop status at plot scale in agricultural areas is essential for crop and irrigation management and yield optimization. The Vegetation Optical Depth (VOD) of canopy is directly related to the canopy water content, and thus, it represents an effective tool for crop health monitoring. Currently, VOD is provided at low spatial resolution which makes these estimations useless for vegetation monitoring at plot scale. Therefore, the aim of this study is to provide the first approach to estimate VOD at plot scale for non-irrigated plots from C-band Sentinel-1 (S1) Synthetic Aperture Radar (SAR) data. The proposed approach was tested on a study site of 50 km × 50 km located in Catalonia, Spain. VOD estimates were provided for two crop growth cycles of non-irrigated crop types (barley, fallow, oat, wheat, and rapeseed). The relevance of VOD estimates was investigated for both growth cycles using temporal profiles of the Normalized Difference Vegetation Index (NDVI). It is shown that the temporal dynamics of VOD values computed from VV polarization fits that of NDVI with a medium to good coefficient of determination (R2 ranging from 0.39 to 0.61 for barley, fallow, oat, and wheat respectively). However, during the beginning of the senescence period in both cycles (mainly in May for winter crops), VOD decreases with the decrease in Vegetation Water Content (VWC) while NDVI keeps increasing as photosynthetic activity continues developing. This illustrates the importance of VOD in crop water loss (stress and/or transpiration) monitoring. The potential of VOD to spot water loss in vegetation is also demonstrated as the evening (18h00) VOD values are lower than those of morning (06h00) due to high daytime temperature that reduces water content in vegetation. Finally, it is shown that VOD values computed from VH polarization are not correlated with NDVI.
Highlights
According to United Nations, the world population will reach 9.7 billion by 2050
Similar results were obtained for the second crop growth cycle where the temporal dynamics of Vegetation Optical Depth (VOD)-VV match well to that of NDVIav
An approach for Vegetation Optical Depth (VOD) mapping at plot scale has been proposed. This approach is based on retrievals from times series of Sentinel-1 (S1) images using the Water Cloud Model (WCM)
Summary
According to United Nations, the world population will reach 9.7 billion by 2050. As global air temperature is rising and water scarcity is pushing, maintaining food security for the generations is certainly a challenge. Tools and solutions should be developed to adapt agricultural practices to future climate and water conditions, which are likely to not be favorable for having enough food production and to precisely predict the impact of agricultural practices and weather changes on crop yield. Several agronomic models have been developed in order predict the yield, based on several parameters including, but not limited to, weather conditions and agricultural practices. Such models can predict potential stress and can aid decision makers to take actions. As in-situ sensors are limited to crop or field scale, remote sensing, on the other hand, is an effective tool in providing observations on larger scales, with high temporal resolution (6 days of revisit time for Sentinel). In addition to feeding crop models, remote sensing observations can save yield by early detecting the presence of crop diseases and could assist farmers for better interventions
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