## 1 Introduction  
The past two decades have seen an increasing demand for timely, transparent and accurate information on global agricultural monitoring for enhancing food security at global, national and sub-national scales. Information about the condition of crops in early crop-growing stages (before harvest) can help indicate potential food surpluses and shortages and support related decision-making [1]. It is essential to facilitate food price stability for agriculture importers and exporters, especially when production shortfalls are anticipated [2]. Several countries and organizations currently employ crop monitoring systems to monitor their own countries' or regional and global crop production [3,4]. In the United States, the US Department of Agriculture (USDA) Foreign Agricultural Service (FAS) provides crop monitoring as part of its Global Agricultural Monitoring (GLAM) program [3]. The European Commission operates its AGRI4 CAST program implemented by the European Commission Joint Research Center under the Monitoring Agricultural Resources (MARS) program [5, 6, 7, 8, 9]. FAO has established the Global Information and Early-Warning System (GIEWS) [10], which focuses on food and agriculture at the global scale. The CropWatch system developed by the Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences (CAS), is designed specifically to use remote sensing data to assess national and global crop production and related indicators [4]. The U.S. Agency for International Development (USAID) Famine Early Warning System Network (FEWS-NET) collaborates with international, regional and national partners to provide timely and rigorous early warning and vulnerability information on emerging and evolving food security issues [11]. India operates the Crop Acreage and Production Estimation (CAPE) system [12]. Canada has the Crop Condition Assessment Program (CCAP) [13], and Brazil has the Geosafras program [14]. While a variety of methods are employed, crop condition monitoring is a key component for all these systems [15]. It is particularly important to obtain the crop condition information at an early stage of the crop-growing season, especially for major crop producing and exporting countries [16]. Crop condition in this paper represents the growth status of crops compared with that in a certain reference period [17,18]. In most cases, the crop condition is assessed by using various satellite-derived indices. The normalized difference vegetation index (NDVI), an indicator of the level of photosynthetic activity, reflecting whether the vegetation is stressed or not, is one of the most commonly used indices [1,3,4,11,19,20,21,22,23]. Furthermore, the leaf area index (LAI), the fraction of absorbed photosynthetically active radiation (FAPAR), the temperature condition index (TCI), the vegetation condition index (VCI) and the normalized difference water index (NDWI) have been tested for their suitability and validity for crop condition and crop stress monitoring under different environmental conditions around the world [9, 24, 25, 26]. Based on a multi-year comparison of those various remote sensing-based indices, analysts have combined both current snapshot and seasonal dynamics to evaluate crop conditions and provide information for early crop production evaluation [1, 3, 4, 22]. However, the inter-annual variability of phenology and crop rotation are a major source of uncertainty for crop condition assessment. The result is that information on crop condition variation is mixed with variation in crop phenophase and crop types [27]. Due to the inter-annual variability of crop phenology, crop rotation and cropping area, crop condition monitoring will generate misleading information in the presence of a large change in crop phenology, crop rotation and/or cropping area from the base year to the monitoring year. This is the main source of uncertainty for crop condition monitoring.