Drought Monitoring Using the Sentinel-3-Based Multiyear Vegetation Temperature Condition Index in the Guanzhong Plain, China

Abstract

The vegetation temperature condition index (VTCI) has been shown to perform well for drought monitoring using multiyear advanced very high-resolution radiometer (AVHRR) and moderate resolution imaging spectroradiometer (MODIS) data. Compared to single-year VTCI, the VTCI calculated by multiyear data can quantitatively reflect the degree of drought and precipitation in a region. Sentinel-3 is a recently launched remote sensing satellite with high temporal resolution and is similar to the satellites carrying AVHRR and MODIS sensors. One year of Sentinel-3 data is available for calculating the VTCI, and given the need for developing quantitatively drought monitoring capabilities, the aim of this study is to investigate the methods used to calculate the potential multiyear Sentinel-3 VTCI for quantitative drought monitoring. This is based on a comparison with multiyear Terra MODIS VTCI and a correlation analysis with cumulative precipitation data. The analysis results indicate that the potential multiyear Sentinel-3 VTCI can be accurately calculated from the single-year Sentinel-3 VTCI based on the linear correlation between the single-year VTCI and multiyear VTCI derived from Terra MODIS, which do not exhibit obvious systematic deviations from the multiyear Terra MODIS VTCI (the absolute value of the average bias <0.04). The multiyear Sentinel-3 VTCIs can quantitatively reflect the drought levels, which are significantly correlated with the recent cumulative precipitation throughout the study period (R 2 = 0.731, P < 0.001). Therefore, it is proposed that Sentinel-3 can successfully inherit the VTCI-based drought monitoring tasks from MODIS.

Monitoring and Assessment of Agricultural Drought Based on Solar-Induced Chlorophyll Fluorescence During Growing Season in North China Plain

“…where slope is the value of drought tends in different months, xi is the scaled SIF index value, n is the length of the study period (n=18), and ti is the number of the year from 2000 to 2017 (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18). A positive slope value represents that the scaled SIF increase and the agricultural drought abate.…”

“…Site-based measurements can provide long-term data, which is essential for drought monitoring [10]. However, in regions and local scales with limited sampling gauges, stationbased data are not sufficient to characterize drought [11]- [14]. Remote sensing can obtain real-time and large-scale data, which provides information about soil and vegetation, this overcomes the shortcomings of site-based observations [11], [14].…”

“…However, in regions and local scales with limited sampling gauges, stationbased data are not sufficient to characterize drought [11]- [14]. Remote sensing can obtain real-time and large-scale data, which provides information about soil and vegetation, this overcomes the shortcomings of site-based observations [11], [14]. Remotely sensed reflectance-based vegetation indices (VIs)，such as the Normalized Difference Vegetation Index (NDVI), have been successfully applied to global and regional agricultural drought monitoring [15]- [18].…”

Establishment of a Comprehensive Drought Monitoring Index Based on Multisource Remote Sensing Data and Agricultural Drought Monitoring

“…The site-based data can be used to obtain long-term drought monitoring distribution data. However, some stations are sparsely distributed at the regional scale, and observing the information of soil and vegetation using site-based data is difficult [16]- [18]. Remote sensing can obtain real-time data with information coming from the atmosphere, soil, and vegetation.…”

“…Remote sensing can obtain real-time data with information coming from the atmosphere, soil, and vegetation. The agricultural drought indexes established based on remote sensing methods are greatly suitable for long-term drought monitoring [18]. The drought indexes established based on a single data source include following: the normalized difference vegetation index (NDVI) [19], [20], vegetation condition index (VCI) [21]- [23], temperature condition index (TCI) based on land surface temperature (LST) [21]- [23], precipitation condition index (PCI) based on microwave precipitation data [24], soil moisture condition index (SMCI) based on soil moisture [25], and others.…”