2. Data and Methods
2.1. Study Area and Data
- Terra MODIS 8-day surface reflectance data (MOD09). The MOD09 products provide an estimate of surface reflectance at 500 m spatial resolution during an 8-day period based on the best possible observations;
- Terra Land Surface Temperature/Emissivity 8-Day L3 Global 1km (MOD11). MOD11 data products contain day and nighttime land surface temperatures at 1 km spatial resolution, as well as emissivity. Daytime land surface temperature data were used in this study;
- Terra/Aqua MODIS land cover type data (MCD12). MCD12 products provide data on global land cover type assessments and quality control information at 500 m spatial resolution.
2.2. Data Processing and Analysis
2.2.1. Land Cover Types
2.2.2. MODIS Vegetation Indices
- For each 8-day period during year 2000–2017, use the MODIS Reprojection Tool (MRT) to reproject and resample MODIS surface reflectance data to the study area at 500 m spatial resolution, and then calculate the NDVI based on Equation (1);
- For each 8-day period during years 2000–2017, use the MODIS Reprojection Tool (MRT) to reproject and resample MODIS land surface temperature data (LST) to the study area at 500 m resolution;
- For the study area, use the 2001–2010 10-year data to calculate the maximum and minimum values of NDVI (NDVImax and NDVImin) and the maximum and minimum values of land surface temperature (LSTmax and LSTmin) for each pixel and each 8-day period;
- For each 8-day period during years 2000–2017, calculate the VCI using Equation (2);
- For each 8-day period during years 2000–2017, calculate the TCI using Equation (3);
- Calculate the VHI using Equation (4) with VCI and TCI data;
- Use the 10 years from 2001 to 2010 as normal to get VCI, TCI, and VHI climatology for each of the 8-day periods;
- Generate 8-day VCI anomaly, TCI anomaly, and VHI anomaly data at 1 km spatial resolution and 8-day temporal resolution by subtracting VCI, TCI, and VHI data from corresponding climatology;
- In addition, reproject and sample MODIS land cover type data over the study area at the same resolution (500 m) with the VCI, TCI, and VHI. The land cover type data are used to generate a spatial mask of the agricultural area for analysis of the VCI, TCI, and VHI;
- Subset VCI, TCI, and VHI anomaly data using the mask for the agricultural area generated in step 9, and then conduct a spatial and temporal analysis.
2.2.3. Rainfall Data
- Generate a spatial mask at 0.25° spatial resolution for HOA;
- Subset the TRMM 3B43 monthly rainfall data from years 1998 and 2017 to the HOA area;
- Generate precipitation climatology using the data from years 2001–2010, and then get monthly anomalies of precipitations in the HOA;
- Analyze the spatial patterns and temporal trends of precipitation during crop growth seasons.
3.1. The 2015–2016 Extreme Drought Event in the HOA
3.2. Temporal Trends of Rainfall and Vegetation Indices in Crop Growing Season
3.3. Rainfall and Crop Status in the Growth Season
Conflicts of Interest
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|Month||Trend||Trend Confidence Interval (95% Level)||p-Value|
|June||−0.0415 mm/day/year||[−0.0630, −0.0201] mm/day/year||7.1941 × 10−4|
|July||−0.0435 mm/day/year||[−0.0698, −0.0172] mm/day/year||0.0027|
|August||−0.0328 mm/day/year||[−0.0600, −0.0055] mm/day/year||0.0211|
|Index Anomaly||Trend||Trend Confidence Interval (95% Level)||p-Value|
|VCI Anomaly||−0.2266/year||[−0.3559, −0.0973]/year||0.0006|
|TCI Anomaly||−0.2315/year||[−0.4325, −0.0306]/year||0.0240|
|VHI Anomaly||−0.2364/year||[−0.3810, −0.0918]/year||0.0014|
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