preprints.org > environmental and earth sciences > remote sensing > doi: 10.20944/preprints202410.1683.v1

Preprint Article Version 1 This version is not peer-reviewed

Version 1 : Received: 4 October 2024 / Approved: 18 October 2024 / Online: 22 October 2024 (11:44:23 CEST)

The main theme of this work is to present the results and conclusions of a study conducted to explore the feasibility and efficiency of the crop area and crop production estimations in the context of small farming systems for food security, assisted by Remote Sensing (RS) technology. The study carried out in the three selected Greek prefectures of Ileia, Larisa and Imathia (NUTS-3) and focused on the development and testing of RS methods and classification techniques used in the production of land and crop cover maps. The purpose was to unveil the role of small farming plots (less than 5 ha) in a food security context and determine their contribution in estimating the crop area, the production, and the spatial distribution, factors which remain unclear, mainly because the official statistical offices rarely include them in the surveys, particularly in the non-developed countries. The efficiency of using and combining Sentinel satellite images acquired during the spring-summer season of 2017 with field surveys implemented on stratified samples of square segments for crop area estimations was assessed. The produced results show good classification accuracies for several key-crops under small scale farming systems with various environmental and territorial conditions. Noteworthy, the satellite data and derived products can be effectively used for stratification purposes and a posteriori correction of crop area estimates from ground observations. The knowledge of the unbiased crop area estimation is a key element for the estimation of the total crop production and, therefore, the management of crop products. The unbiased crop area computation and the crop production estimates was performed only for the highly accurate key-crop products (FScore > 75\%). Then, the key-crop production in each region was determined by using the estimated self-reported crop yields multiplied by the corresponding key-crop area of the small farming plots. The derived results indicate that small farming plots make an important contribution in the integration of the key-crop production of the selected crops with the official statistical data. Finally, potential changes occurred in the cultivation of small plots from the previous cultivation year in the key-crop areas of the same key-crop products of the three regions considered were also estimated (and mapped) by the Land Parcel Identification System (LPIS) of the Greek Integrated Administration and Control System (IACS) and agree with those reported by the official statistics.

Remote Sensing Algorithms; Earth Observations; Regional Food Systems, Food Security; Assessment, Small Farms, Land Use Land Cover Surveys, Crop Map Classification; Crop Area Estimation; Yield Estimation; Crop Productivity

Environmental and Earth Sciences, Remote Sensing

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