Abstract
Earth Observation has become a progressively important source of information for land use and land cover services over the past decades. At the same time, an increasing number of reconnaissance satellites have been set in orbit with ever increasing spatial, temporal, spectral, and radiometric resolutions. The available bulk of data, fostered by open access policies adopted by several agencies, is setting a new landscape in remote sensing in which timeliness and efficiency are important aspects of data processing. This study presents a fully automated workflow able to process a large collection of very high spatial resolution satellite images to produce actionable information in the application framework of smallholder farming. The workflow applies sequential image processing, extracts meaningful statistical information from agricultural parcels, and stores them in a crop spectrotemporal signature library. An important objective is to follow crop development through the season by analyzing multi—temporal and multi—sensor images. The workflow is based on free and open—source software, namely R, Python, Linux shell scripts, the Geospatial Data ion Library, custom FORTRAN, C++, and the GNU Make utilities. We tested and applied this workflow on a multi—sensor image archive of over 270 VHSR WorldView—2, —3, QuickBird, GeoEye, and RapidEye images acquired over five different study areas where smallholder agriculture prevails.
Highlights
The world’s population reached 7.3 billion people in mid-2015 [1] and is expected to increase to 9.6 billion by 2050 [2]
The requirements for agriculture-oriented remote sensing systems have long been outlined [5] as the frequency of coverage, high spatial resolution (5 m to 20 m), timeliness, and integration in models
We developed an automated processing chain that ingests Very High Spatial Resolution (VHSR) satellite images and derives spectral and textural information for each smallholder field known to the system
Summary
The world’s population reached 7.3 billion people in mid-2015 [1] and is expected to increase to 9.6 billion by 2050 [2]. Global demand for food by that year is predicted to double at least that of 2005, because of population growth, and because of a shift to nutrient-richer diets in especially developing nations [3]. The latter scenario calls for technical solutions that help to improve crop yield, provide accurate information to aid in-field management decisions, increase the efficiency of applications of farm inputs, and boost profit margins in the agricultural sector [4]. These requirements have been increasingly met by a fleet of reconnaissance satellites with advanced capabilities that allow cost-effective agricultural applications [6]
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