Remote Sensing Information and Digital Earth Center, School of Computer Science and Technology, Qingdao University, Qingdao, 266071, China.
School of Business, Qingdao University, Qingdao, 266071, China.
Environ Sci Pollut Res Int. 2024 Jan;31(3):3598-3613. doi: 10.1007/s11356-023-31345-3. Epub 2023 Dec 12.
Monitoring agricultural drought across a large area is challenging, especially in regions with limited data availability, like the Peshawar Valley, which holds great agricultural significance in Pakistan. Although remote sensing provides biophysical variables such as precipitation (P), land surface temperature (LST), normalized difference vegetation index (NDVI), and relative soil moisture (RSM) to assess drought conditions at various spatiotemporal scales, these variables have limited capacity to capture the complex nature of agricultural drought and associated crop responses. Here, we developed a composite drought index named "Temperature Vegetation ET Dryness Index" (TVEDI) by modifying the Temperature Vegetation Precipitation Dryness Index (TVPDI) and integrating NDVI, LST, and remotely sensed evapotranspiration (ET) using 3D space and Euclidean distance. Several statistical techniques were employed to examine TVPDI and TVEDI trends and relationships with other commonly used drought indices such as the standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), and standardized soil moisture index (SSI), as well as crop yield, to better understand how these indices captured the spatial and temporal distribution of agricultural drought in the Peshawar valley between 1986 and 2018. Results indicated that while the temporal patterns of the 3-month SPI, SPEI, and SSI generally align with those of TVEDI and TVPDI, TVEDI was more strongly correlated with these indices (e.g., correlation coefficient, r = 0.78-0.84 from TVEDI and r = 0.73-0.79 from TVPDI). Moreover, the crop yield, a measure of crop response to agricultural drought, demonstrated a significant positive correlation with TVEDI (r = 0.60-0.80), much higher than its correlation with TVPDI (r = 0.30-0.48). These outcomes indicate that the inclusion of ET in TVEDI effectively captured changes in soil moisture, crop water status, and their impact on crop yield. Overall, TVEDI exhibited enhanced capability to identify drought impacts compared to TVPDI, showing its potential for characterizing agricultural drought in regions with limited data availability.
大面积农业干旱监测具有挑战性，特别是在像巴基斯坦白沙瓦谷这样数据有限的地区，该地区在农业方面具有重要意义。尽管遥感提供了降水（P）、地表温度（LST）、归一化差异植被指数（NDVI）和相对土壤湿度（RSM）等生物物理变量，可用于在各种时空尺度评估干旱状况，但这些变量在捕捉农业干旱的复杂性及其与作物响应的关系方面能力有限。在这里，我们通过修改温度植被降水干燥指数（TVPDI）并整合 NDVI、LST 和遥感蒸散量（ET），利用三维空间和欧几里得距离，开发了一种名为“温度植被 ET 干燥指数”（TVEDI）的综合干旱指数。采用多种统计技术研究了 TVPDI 和 TVEDI 的趋势及其与其他常用干旱指数（如标准化降水指数（SPI）、标准化降水蒸散指数（SPEI）和标准化土壤湿度指数（SSI））以及作物产量的关系，以更好地了解这些指数如何捕捉 1986 年至 2018 年间白沙瓦山谷的农业干旱的时空分布。结果表明，尽管 3 个月 SPI、SPEI 和 SSI 的时间模式通常与 TVEDI 和 TVPDI 的时间模式一致，但 TVEDI 与这些指数的相关性更强（例如，TVEDI 的相关系数为 0.78-0.84，而 TVPDI 的相关系数为 0.73-0.79）。此外，作物产量作为对农业干旱的作物响应的度量，与 TVEDI 呈显著正相关（r=0.60-0.80），远高于与 TVPDI 的相关性（r=0.30-0.48）。这些结果表明，在 TVEDI 中纳入 ET 有效地捕捉了土壤水分变化、作物水分状况及其对作物产量的影响。总体而言，与 TVPDI 相比，TVEDI 显示出识别干旱影响的增强能力，表明其在数据有限的地区描述农业干旱的潜力。
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