Abstract
Widespread and long-lasting drought disasters can aggravate environmental degradation. They can lead to significant economic losses and even affect social stability. The existing drought index mostly chose arid and semi-arid regions as study areas, because cloudy weather in humid and semi-humid regions hindered the satellite in its attempts to obtain the surface reflectivity. In order to solve this problem, a cloudy region drought index (CRDI) is proposed to estimate the drought of the clouded pixels. Due to the cumulative effect of drought, the antecedent drought index (ADI) has a certain impact on the calculation of the current drought. Furthermore, cloud is the only source of natural precipitation, and it also affects the evaporation and emission process on the ground. Therefore, based on the remote sensing drought index, ADI and cloud optical thickness (COT) are used to estimate the drought of pixels with missing data due to cloud occlusion. In this paper, a case study of the cloudy Guangdong, which is located in a humid area, is presented. First, we calculated the CRDI using Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2003 to 2017, and then discussed the effect of CRDI with the data from 2016 as examples. Through the analysis of the parameters of regression equation, filling efficiency, rationality of the estimated value, the continuity of CRDI and the rationality of CRDI spatial distribution results, it is concluded that CRDI can effectively estimate the drought severity of the cloud-covered pixels, and more comprehensive drought data can be obtained by using CRDI. The successful application of CRDI in Guangdong shows it is robust and flexible, suggesting high efficiency and great potential for further utilization.
Highlights
Drought, as a serious natural disaster, has a serious impact on social economy and people’s lives [1]
Drought monitoring based on vegetation coverage usually uses remote sensing data to establish a corresponding drought index; for example, the vegetation conditions index (VCI), the normalized difference vegetation index (NDVI) [16,17], the normalized difference water index (NDWI) [18,19], the normalized multi-band drought index (NMDI) [20,21], the anomaly vegetation index (AVI) and the vegetation temperature condition index (VTCI) [22,23]
The Estimation of Extremum Value Compared with the histogram of current cloudy region drought index (CRDI), original VCI and the same period CRDI (Figure 7), for theCComRDpaI roefd2w01it6h, tthhee hniusmtogbrearmofoefsctuimrraetnetdCpRiDxeIl,sodriigsitnriabluVteCdI abnedtwtheeensa4m0 eanpderi8o0dfoCrRtDotIa(lFaignudre 7), for the CRDI of 2016, the number of estimated pixels distributed between 40 and 80 for total and different land cover types was more than the original data and the previous data
Summary
As a serious natural disaster, has a serious impact on social economy and people’s lives [1]. The method of drought monitoring based on remote sensing technology became more and more popular. It has the following advantages: data acquisition is relatively easy; data revisit rate is high; and there is better continuity in space and time. Drought monitoring based on vegetation coverage usually uses remote sensing data to establish a corresponding drought index; for example, the vegetation conditions index (VCI), the normalized difference vegetation index (NDVI) [16,17], the normalized difference water index (NDWI) [18,19], the normalized multi-band drought index (NMDI) [20,21], the anomaly vegetation index (AVI) and the vegetation temperature condition index (VTCI) [22,23]. In drought monitoring based on remote sensing technology it is difficult to avoid data loss due to cloud cover, especially in a cloudy region. How to monitor the drought of clouded pixels has become an urgent problem to be solved in the study of drought in cloudy areas
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