SPEI-Based Approach to Agricultural Drought Monitoring in Vojvodina Region

Abstract

This paper presents the standardized precipitation evapotranspiration index (SPEI)-based approach to agricultural drought monitoring (ADM-SPEI approach) combining well-known methods, expert’ critical opinions, and local agro-climatic specificities. The proposed approach has been described in detail in three phases. This allows its application in any region and modification according to different agro-climatic conditions. The application of the ADM-SPEI approach has resulted in obtaining a modified SPEI for different crops (agricultural drought SPEI (AD-SPEIcrop)) in the Vojvodina region. In the first phase of the proposed approach, analytical hierarchy process (AHP) was used to obtain an experts’ group decision regarding the most suitable method for calculating evapotranspiration for a particular analyzed region. In the second phase, SPEI was modified and adjusted to the conditions in Vojvodina, where ET0 was replaced by ETc. In the validation phase, the results of the application of AD-SPEIcrop were compared to crop yields and well-known indices and evaluated by the experts’ feedback. The statistically significant correlations were achieved between AD-SPEIcrop and crop yields. The highest correlations were achieved in the months when the analyzed crops were in the developmental stages when they are most sensitive to drought. The AD-SPEIcrop better correlates to the crop yields compared to SPEI. The comparison of AD-SPEIcrop to the standardized precipitation index (SPI), SPEI, and self-calibrated Palmer drought severity index (SC-PDSI) shows that it can successfully detect dry and wet periods. The results have indicated that the proposed approach can be successfully applied, and AD-SPEIcrop has shown a good performance for agricultural drought monitoring.

Remote Sensing Indices for Spatial Monitoring of Agricultural Drought in South Asian Countries

“…Remote Sens. 2021, 13,2059 of 25 VCI and TCI can be calculated using Equations ( 4) and ( 6) [41,43]. Previous studies [10,16] have also used TCI and VCI for drought monitoring.…”

“…In the case of VHI, May, June, and July indicated slight drought patterns in Punjab and agricultural land Remote Sens. 2021, 13,2059 of 25 along the Indus river of Sindh and Peshawar KPK. In the case of EVI, drought started to develop in April and the index values generally increased during May and June while the SAI was found as the least drought-sensitive index, which exposed slight drought only in May and June.…”

“…Drought monitoring based on satellite-derived products has been found appropriate and momentous [10]. A massive range of techniques has been developed to characterize agricultural drought based on remotely sensed data at regional and global scales, such as the normalized difference vegetation index (NDVI), developed first in the 1970s [11]; the vegetation condition index (VCI); temperature condition index (TCI); vegetation health index (VHI) [12]; standardized precipitation evapotranspiration index (SPEI) [13]; Palmer drought severity index (PDSI); and U.S. drought monitor (USDM) [12,14]. Previous studies [15][16][17] have used NDVI to monitor agricultural drought stress.…”

A Multi-Index Evaluation of Drought Characteristics in the Yarlung Zangbo River Basin of Tibetan Plateau, Southwest China

“…Effective quantification of agricultural drought impacts can mitigate crop losses and ameliorate adverse effects Baik et al, 2019). However, SPEI ignoring effects of soil moisture could not capture the characteristics of agricultural drought (Bezdan et al, 2019;Teweldebirhan Tsige et al, 2019). Among numerous drought indices calculated based on soil water content, the Soil Water Deficit Index (SWDI) is a more mature indicator to judge crop drought on the basis of wilting point content and field water capacity, which has been widely used in agricultural drought detection (Wagner et al, 2013;Martínez-Fernández et al, 2015;Bai et al, 2018).…”

A Novel Framework for Selecting Informative Meteorological Stations Using Monte Carlo Feature Selection (MCFS) Algorithm

“…However, the estimation of standardized drought indices (SDIs) and other novel approaches of drought indices on all meteorological stations make a chaotic situation for regional forecasting and early warning management policies. e use of the SDI has been found in many applications [25][26][27]. SDIs are useful for drought characterization and comparing meteorological stations having different climatological characteristics [28].…”