Advancing Rice Yield Forecasting and Crop Assessment in Kazakhstan’s Kyzylorda Region: A Multisource Satellite Data Approach

¹ National Center for Space Research and Technology, No. 15 Shevchenko, Almaty 050010, Kazakhstan,
² Al-Farabi Kazakh National University, Faculty of Geography and Environmental, Department of Cartography and Geoinformatics, Almaty, Kazakhstan, asset@spaceres.kz, sin.asset@gmail.com
³ Department of Irrigation and Agricultural Mechanization Engineers, Tashkent Institute of Irrigation and Agricultural Mechanization Engineers, National Research University, Uzbekistan

^* Corresponding author: sin.asset@gmail.com

Abstract

The monitoring and prediction of rice yields are essential across various domains, including agriculture, environmental conservation, ecology, agricultural insurance, and land resource management. The central districts of Kazakhstan’s Kyzylorda region, encompassing 70% of the total rice cultivation area, are of particular importance for ensuring national food security. This study focuses on the central Kyzylorda region, specifically the Syrdarya, Zhalagash, and Karmakshi districts, which serve as the primary hub for rice cultivation in Kazakhstan. The primary objective is to advance remote sensing techniques tailored to evaluate crop conditions and forecast rice yields, accounting for the region’s unique soil and climatic attributes. The research methodology involves comprehensive analysis of Sentinel-2 and Landsat-8 satellite data, harmonized with ground-truth information. This analysis encompasses the latest rice crop data for 2020 and precise sowing dates. Extensive field surveys were conducted to gather crucial data on growth, development, crop health, and productivity under specific agrometeorological conditions. The study provides a detailed flowchart outlining the sequential processing of remote sensing data and ground-based information, with a primary focus on forecasting rice yields in the central Kyzylorda districts for 2020. Rigorous satellite data analysis established robust correlations between the Normalized Difference Vegetation Index (NDVI) and various crop conditions, directly linked to rice yield, with high precision (R2 values ranging from 0.82 to 0.91). Validation exercises, cross-referencing satellite data with real-time field data, further enhanced accuracy. The research outcomes have extensive applications, benefiting agriculture, environmental preservation, ecology, insurance, statistics, and land management. This study underscores the significance of addressing critical challenges across various sectors through multifaceted findings.