## 1 Introduction  
Planting and harvesting times drive the phenologies of crops. Complex networks of factors influence when farmers plant particular crops and when they harvest them, including: recent and forecast weather; commodity prices, situation of local markets, and foreign crop conditions; crop insurance and governmental policies and subsidies; agronomic practices; and the cold hardness, drought tolerance, or differential maturities of planted crop varieties. Most of these factors cannot be observed directly through remote sensing technologies. However, the remote sensing of land surface phenology (LSP) can reliably reveal the seasonality of vegetation in croplands in an ordinal sequence of broad phases: green-up onset (start-of-season, SOS), maturity onset, senescence onset, and dormancy onset (end-of-season, EOS). The dates or days of the year of the SOS and EOS are commonly used as phenological metrics (or phenometrics) to study the land surface phenology of croplands in relation to climatic and hydrological variability and, to some extent, the variation in land management [1]. Cropland phenology is not equivalent to crop phenology. The phenologies of crops describe particular observable life history events in the specific crop species. Implicit in crop phenology is a human observing the crop at sufficiently close distance to distinguish the plant parts relevant to the phenological phases (or phenophases). In contrast, cropland phenologies describe the land surface phenologies of croplands captured through remote sensing. These may include a mixture of multiple crop types with non-crop elements such as roads, vegetated ditches, hedgerows, wooddots, and rural buildings. Crop phenologies can be quite detailed; cropland phenologies offer relatively few phenophases. Cropland phenologies are useful for models of land surface, weather, and climate. Crop phenologies are useful for models of crop growth and yield and field-scale hydrological and biogeochemical processes. Few remote sensing data products are used to derive explicit phenometrics, and each of these datasets uses the VNIR (visible-to-near-infrared) spectrum [2]. Moreover, each of the phenologically explicit LSP products uses different methods to derive phenological metrics, resulting in different metrics for SOS and EOS [2]. This discrepancy is also partly due to the challenge of equating satellite derived LSP metrics to in situ observations of plant phenology, and the spatial and temporal compositing of the VNIR satellite data to minimize data gaps that result from cloud cover, atmospheric aerosols, and orbital and solar illumination constraints [3; 4]. Microwave remote sensing is based on the dielectric property of materials, which is largely governed by the material's water content. Thus, microwave measurements are sensitive to vegetation and soil water content and to changes in plant biomass and soil moisture. The frequencies of microwaves emitted by the land surface are much lower than VNIR light. These longer wavelengths are less attenuated by the atmosphere, and the earth's surface constantly emits microwaves. Satellite active and passive microwave data have been successfully applied in many LSP studies such as vegetation phenology assessment [5; 6; 7; 8], vegetation drought response [9; 10], and growing season variability [11; 12]. Limitations of microwave LSP monitoring include very coarse spatial resolution (25 km in this study) due to the faint microwave signal emissions of land surface materials [1; 13], sensitivity to radio frequency interference (RFI; [14; 15]), and signal degradation or loss due to snow cover and frozen ground [5; 6]. Vegetation optical depth (VOD) is a measure of aboveground vegetation canopy thickness [16] that is less sensitive to dense biomass saturation, less sensitive to atmospheric contamination and solar illumination constraints, and it can have high temporal resolution [1; 6; 17]. VOD is directly proportional to the vegetation dielectric constant and canopy water content.