## 1 Introduction  
Several decades of recent climate change are mainly driven by anthropogenic influences in a global scale. Even with the significant societal impact of the drought, it still remains challenging to define its causes and effects of the drought in the context of hydrological and agricultural practices. Recent studies demonstrate that drought can be classified into various types based on the theoretical definitions [1; 2; 3; 4; 5]. Agricultural drought is one of the main drought classifications. In this agricultural drought, short-term rainfall deficit decreases soil moisture (hereafter SM) and affects vegetation activity [6; 7]. Most previous studies estimated the drought index using SM or the vegetation index (hereafter VI), to analyze agricultural drought [6; 7; 8; 9; 10; 11; 12; 13; 14]. For example, [PERSON] derived the vegetation health index (VHI) and the vegetation condition index (hereafter VCI) from the VI [8]. [PERSON] et al. derived the soil wetness deficit index (hereafter SWDI) from SM [11]. Most of the recent studies used remote sensing data as the alternative to indirectly estimate SM and VI. Among the agricultural drought-related factors, VIs can be determined by optical sensor products. Among the VIs, the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) provided by the moderate resolution imaging spectroradiometer (MODIS) are widely used [15; 16; 17; 18]. Generally, satellite-based soil moisture is commonly used as detected by passive microwave sensors. Soil moisture calculated in the frequency range of L-, X-, and C-bands of microwave sensors has been applied in many studies [19; 20; 21; 22; 23]. SM based on passive microwaves has high accuracy, while spatial resolution is relatively coarse. These SM products are suitable for analyzing large areas, such as a continent, but not catchments or smaller regions. In order to overcome spatial limitations and pursue high-resolution data of smaller regions, an SM estimation method that uses optical sensors has been explored [24; 25; 26; 27]. This optics-based method is based on the relationship between land surface temperature (hereafter LST) and land surface albedo. With this approach, the SM at 0.5-km spatial resolution can be calculated in accordance with LST and albedo. In addition, most studies related to agricultural drought have derived drought indices using SM and VI and by monitoring and analyzing agricultural drought status or analyzed the relationship between vegetation activity and photosynthesis [28; 29; 30]. However, few studies have analyzed the inter-relationship among SM, VI, and photosynthesis. This study aims to analyze the relationship between a surface flux and a vegetation phenology, under extreme climates such as drought. Based on data available in the literature, we hypothesize that prolonged periods of agricultural drought cause vegetation to become inactive and thus hamper photosynthesis. This in turn increases the amount of CO\({}_{2}\) in the atmosphere. As part of the research objectives, we provide evidence for this hypothesis and validate it. To show the evidence of the CO\({}_{2}\) increase due to the recent agricultural drought, the main parts of this study are as follows: (a) to evaluate the applicability of optical sensor-derived SM by comparing the MODIS data-derived SM (an optical multispectral sensor) with the SM observed in a flux tower; (b) to examine the possibility of remote sensing-based carbon monitoring by estimating CO\({}_{2}\) flux through gross primary productivity (hereafter GPP), a MODIS product, and compare it with the results observed in the flux tower; and, (c) to verify the hypothesis that prolonged periods of agricultural drought reduce SM, hinder vegetation growth, hamper photosynthesis and thus increase CO\({}_{2}\) concentrations. In particular, in order to analyse the effects of soil moisture, vegetation, and CO2 under drought condition, lag correlation analysis was performed between factors synthesized as a monthly scale.