## 1 Introduction  
Drought-induced reduction of vegetation production threatens both the global ecological balance and food security. The frequency and intensity of drought is increasing under the warmer temperatures resulting from global climate change [1; 2]. From 2000 to 2009, global terrestrial net primary production (NPP) declined by 0.55 Pg of carbon because of drought [3]. Hence, the timely monitoring and precise assessment of large-scale drought are important for food security and for understanding vegetation responses to climate change. However, drought is very complex and difficult to monitor for two reasons. First, drought is not universally defined. In general, drought can be categorized into meteorological, agricultural, hydrological, and socioeconomic droughs [4], and thus it is hard to define what a \"real drought\" is. Second, the onset and end point of a drought are indistinct [5; 6]. Thus, drought is hard to detect in its early stages. Drought indices, such as the Palmer drought severity index (PDSI), which is derived considering both precipitation and temperature [7], and the standard precipitation index (SPI), which is directly calculated from the precipitation over a certain historical period [8; 9], have been proposed to simplify the evaluation of drought conditions. Because drought indices are generally calculated from meteorological data, they are able to monitor meteorological drought effectively [10]. However, for terrestrial plants, drought conditions might be affected by non-meteorological factors such as underground water storage and agricultural irrigation with the result that drought indices alone are insufficient to monitor agricultural drought. Remotely-sensed satellite data, providing spatiotemporally continuous observations on vegetation, have been employed to evaluate drought-related vegetation conditions. Reflectance-based vegetation indices (VIs), such as the normalized difference vegetation index (NDVI), can indicate the greenness of terrestrial plants. Other water-sensitive VIs, such as the normalized difference water index (NDWI) [11], or other drought-oriented advanced indices, such as the vegetation condition index (VCI) [12] and the scaled drought condition index (SDCI) [10], are also widely used to study agricultural drought. Because VIs and drought indices are considered as proxies for green biomass and meteorological drought, respectively, and there is a close relationship between vegetation vigor and available soil moisture, especially in unirrigated arid regions, the relationships between vegetation and drought indices directly indicate the response of vegetation to drought and provide a simple method to study meteorologically-induced agricultural drought (e.g., [6; 10; 13]). Although previous studies of drought have shown the effectiveness of satellite data, remotely-sensed data have intrinsic limitations for drought monitoring and assessment. One major shortcoming is that the lag in the response of satellite-measured VIs to drought is significant. This is because the spectral characteristics of the vegetation canopy do not change immediately when water stress occurs. For example, the NDVI can still remain high when plants are heavily stressed by short-term drought. Generally, the lag for the NDVI to response precipitation is about one to two months [14; 15], while for central North America, this lag is more significant in early summer than in late summer [16]. Despite the improved performance of other VIs (e.g., NDWI, VCI, and SCDI) in monitoring drought, these reflectance-based indices fail to indicate rapid changes in drought stress because they have no direct link to photosynthetic functioning beyond their sensitivity to canopy structure and pigment concentration changes [17]. This makes it difficult to monitor drought in a timely fashion from space when using VIs. Moreover, these satellite products cannot indicate primary production precisely because they are related to potential rather than actual plant photosynthesis.