# Mapping Irrigated and Rainfed Wheat Areas Using Multi-Temporal Satellite Data
## Materials and Methods
### Methods
#### 3.2.2 Estimation of the Wheat Growing Period Based on Satellite Data  
_In situ_ observations cannot capture large-scale spatiotemporal changes in phenology. Remote sensing enables low-cost monitoring of vegetative multi-temporal dynamics over the entire region and therefore is a powerful tool to examine phenological changes on a regional scale [38; 39].  
NDVI represents seasonal and annual variations in crop growth and development (_i.e._, cyclical changes from planting, emergence, heading to maturity and harvest). Estimation of plant phenology using NDVI time series has been widely carried out during recent decades [40; 41; 42; 43]. Many phenological parameters can be derived from remote sensing time series data, such as average NDVI, maximum NDVI, TIN (the integrated NDVI from sowing to maturity, defined as the area of the region between the fitted function and the zero level [44]), date of maximum NDVI and length of the growing season. The classification accuracy of remote sensing imagery can be improved by combining these phenological parameters.  
NDVI time series reflecting vegetation growth processes are usually continuous and smooth curves. During the acquisition process, however, satellite data are subject to various interferences, such as solar and observation angles, clouds, water vapor and aerosols, resulting in fluctuations in time series and changes in seasonal trends, which may affect the estimation of vegetation phenology. Therefore, it is necessary to smooth time series of original vegetation index data to reduce noise. In this study, double logistic functions were used to smooth the raw NDVI time series [44; 45; 46].  
Usually, the start of vegetation growth derived from satellite remote sensing is later than the time of the first leaf unfolding or budburst [47]. It does not represent the beginning of the growing season when individual leaves turn green, but when canopy greenness reaches a specific value that can be recognized by a satellite sensor. Earlier studies have used dynamic threshold methods to estimate the start and end of the growing season for natural vegetation based on NDVI time series data [48; 49; 43]. However, for cropland, there is usually a period after crop sowing when crops cannot be recognized with remote sensing, which means that the sowing date cannot be directly detected. [PERSON] _et al._ (2013) [50] defined the green-up date as the earliest time at which vegetation growth can be reliably detected with satellite sensors. They found that the date should be offset from the actual sowing date by a fixed number of days. They compared satellite-based sowing dates and ground-based observations in Punjab and found that the average difference between these dates is two days, indicating that satellite remote sensing is capable of realistically estimating the sowing date of wheat.  
In this study, double logistic functions were fit to each NDVI time series on a pixel-by-pixel basis to estimate green-up and maturity dates using Timesat [44]. Then, the wheat sowing date was obtained based on the relationship between the green-up and sowing dates presented by [PERSON] _et al._ (2013) [50].