研究目的
Investigating the use of high-resolution, 0.5, 1, and 4 km2 38-year NOAA operational polar-orbiting satellite data for early drought detection, monitoring its features (intensity, duration, area, origination, impacts), and prediction of agricultural losses, which are used to assess global and regional food security.
研究成果
The Vegetation Health (VH) method has proven to be perfect for operational drought management as a separate tool and as well as in combination with weather data. It provides numerical classification of vegetation conditions, which distinguishes drought from non-drought and assesses drought start/end, intensity, area, duration, origination, and impacts. The new generation of operational satellite technology that started at the end of 2011 has considerably improved with the development of a new generation of operational satellite technology.
研究不足
The study acknowledges the limitations of meteorological, vegetation, and soil moisture methods, including sparseness of weather station networks, especially in climate, ecosystem, and population marginal areas; selection of the right period of data aggregation to reflect cumulative drought feature; the identification of drought start; the estimation of contribution of pre-drought conditions into the current drought; and the absence of drought validation.
1：Experimental Design and Method Selection:
The study utilizes the Vegetation Health (VH) method, which is based on a combination of the VIS (0.58–0.68 μm) and NIR (0.72–1.1 μm) radiances (channels), converted to NDVI, and infrared (10.3–11.3 μm) channel measurements, converted to brightness temperature (BT). These data were pre- and post-launch calibrated, daily and weekly-composite maps were created, and high-frequency noise was completely removed from weekly NDVI and BT time series, converting them to Smoothed NDVI (SMN) and Smoothed BT (SMT).
2：58–68 μm) and NIR (72–1 μm) radiances (channels), converted to NDVI, and infrared (3–3 μm) channel measurements, converted to brightness temperature (BT). These data were pre- and post-launch calibrated, daily and weekly-composite maps were created, and high-frequency noise was completely removed from weekly NDVI and BT time series, converting them to Smoothed NDVI (SMN) and Smoothed BT (SMT).
Sample Selection and Data Sources:
2. Sample Selection and Data Sources: The study uses operational NOAA/AVHRR 4 km2 resolution global area coverage (GAC), SNPP/VIIRS, and NOAA-20/VIIRS data sets. The AVHRR global VH is produced from 1981 through present for each 4 and 16 km2 land pixels between 75° N and 55° S and between 180° W and 180° E.
3：List of Experimental Equipment and Materials:
Advanced Very High Resolution Radiometer (AVHRR), Visible Infrared Imaging Radiometer Suite (VIIRS), National Polar-orbiting Operational Satellite System (NPOESS) Suomi Preparatory Platform (SNPP).
4：Experimental Procedures and Operational Workflow:
The VH method is built on the three basic environmental laws: Law of Minimum (LOM), Law of Tolerance (LOT), and the Principle of Carrying Capacity (CC), which defined the last 37-year (since 1981) environmental limits that resources of habitats can support. Following these laws, climatology in each pixel was developed. The ecosystems were stratified and high-to-low frequency vegetation fluctuations associated with weekly weather variations were identified.
5：Data Analysis Methods:
The three indices characterizing moisture (VCI = 100*((SMN–SMNmin)/(SMNmax–SMNmin)), thermal (TCI = 100*((SMTmax–SMT)/(SMTmax–SMTmin)), and vegetation health (VTI = a*VCI + (1?a)*TCI) conditions were approximated, where SMN, SMNmax, and SMNmin are no noise weekly NDVI, 36-year absolute maximum and minimum, respectively; SMT, SMTmax, and SMTmin are no noise data characterizing brightness temperature; a is a coefficient that quantifies a share of VCI and TCI contribution to the total Vegetation Health.
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