Abstract
Many challenges prevail in cropland mapping over large areas, including dealing with massive volumes of datasets and computing capabilities. Accordingly, new opportunities have been opened at a breakneck pace with the launch of new satellites, the continuous improvements in data retrieval technology, and the upsurge of cloud computing solutions such as Google Earth Engine (GEE). Therefore, the present work is an attempt to automate the extraction of multi-year (2016–2020) cropland phenological metrics on GEE and use them as inputs with environmental covariates in a trained machine-learning model to generate high-resolution cropland and crop field-probabilities maps in Morocco. The comparison of our phenological retrievals against the MODIS phenology product shows very close agreement, implying that the suggested approach accurately captures crop phenology dynamics, which allows better cropland classification. The entire country is mapped using a large volume of reference samples collected and labelled with a visual interpretation of high-resolution imagery on Collect-Earth-Online, an online platform for systematically collecting geospatial data. The cropland classification product for the nominal year 2019–2020 showed an overall accuracy of 97.86% with a Kappa of 0.95. When compared to Morocco’s utilized agricultural land (SAU) areas, the cropland probabilities maps demonstrated the ability to accurately estimate sub-national SAU areas with an R-value of 0.9. Furthermore, analyzing cropland dynamics reveals a dramatic decrease in the 2019–2020 season by 2% since the 2018–2019 season and by 5% between 2016 and 2020, which is partly driven by climate conditions, but even more so by the novel coronavirus disease 2019 (COVID-19) that impacted the planting and managing of crops due to government measures taken at the national level, like complete lockdown. Such a result proves how much these methods and associated maps are critical for scientific studies and decision-making related to food security and agriculture.
Highlights
IntroductionIn any accurately monitoring related to agriculture, whether directly or indirectly (e.g., crop inventory, crop status assessment, and yield forecasting), an in-depth understanding of the detailed spatial patterns and the temporal dynamics of the cropland areas is highly needed [2]
The three phenological metrics derived appear to be generally spatially smooth and visually appealing. Their spatial patterns vary depending on the metric type and growing season’s properties
A point to consider is the patterns in the timing of midgreenup and midgreendown, as well as the integral of the EVI2 curve (Figure 7) show in general strong geographic variation related to climate forcing, land cover, and associated environmental variables
Summary
In any accurately monitoring related to agriculture, whether directly or indirectly (e.g., crop inventory, crop status assessment, and yield forecasting), an in-depth understanding of the detailed spatial patterns and the temporal dynamics of the cropland areas is highly needed [2]. In this regard, remote sensing provides the best opportunity to accurately and repeatedly map this relevant information, as it offers timeliness, global coverage, and objective observation. The task of mapping cropland areas is not a straightforward and simple matter since it requires specific data and particular analysis techniques, meant to extract the exact required information, especially over highly heterogeneous and fragmented regions [3]. A reliable cropland monitoring system requires satellite datasets with both high spatial and temporal resolutions [4]
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