Abstract
This paper presents the analysis and a methodology for monitoring asparagus crops from remote sensing observations in a tropical region, where the local climatological conditions allow farmers to grow two production cycles per year. We used the freely available dual-polarisation GRD data provided by the Sentinel-1 satellite, temperature from a ground station and ground truth from January to August of 2019 to perform the analysis. We showed how particularly the VH polarisation can be used for monitoring the canopy formation, density and the growth rate, revealing connections with temperature. We also present a multi-output machine learning regression algorithm trained on a rich spatio-temporal dataset in which each output estimates the number of asparagus stems that are present in each of the pre-defined crop phenological stages. We tested several scenarios that evaluated the importance of each input data source and feature, with results that showed that the methodology was able to retrieve the number of asparagus stems in each crop stage when using information about starting date and temperature as predictors with coefficients of determination ( R 2 ) between 0.84 and 0.86 and root mean squared error (RMSE) between 2.9 and 2.7. For the multitemporal SAR scenario, results showed a maximum R 2 of 0.87 when using up to 5 images as input and an RMSE that maintains approximately the same values as the number of images increased. This suggests that for the conditions evaluated in this paper, the use of multitemporal SAR data only improved mildly the retrieval when the season start date and accumulated temperature are used to complement the backscatter.
Highlights
Due to the recent and future growth of freely available satellite remote sensing data, there is an opportunity to implement near real time agricultural monitoring systems to increase yield and crop management efficiency
Studies associated with the potential of space borne radar remote sensing concerning asparagus fields have been presented in [23,24,25,26,27], all of them focus on the crop type classification problem rather than in the analysis of individual crop stages as we present in this paper
We provided an interpretation of the synthetic aperture radar (SAR) backscatter response to asparagus crop growth and analysed the impact that temperature has on the canopy volume, its development rate, and the cultivation length
Summary
Due to the recent and future growth of freely available satellite remote sensing data, there is an opportunity to implement near real time agricultural monitoring systems to increase yield and crop management efficiency This is based on informed decision making with information derived fully or partially from satellite sensors. A distinctive operational characteristic in tropical and subtropical regions for several crop types is the uninterrupted production cycles, with cultivation of more than one cycle per year Each of these production cycles or campaigns may be under slightly different meteorological conditions due to a “soft seasonality”, e.g., mild winters, modifying to a certain extent the crop growth rate and structure (as will be shown later in this paper). Asparagus officinalis L. is a key crop for the country’s agricultural exports, being the largest exporter in the world, the second largest producer after China [2] and an important source of job [3]
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