农业干旱,这是世界上大部分地区的普遍现象,是最具有挑战性的自然灾害之一,要进行有效监测。陆地地表水指数(LSWI),计算为近红外(NIR)和短波红外(SWIR)之间的归一化比率,对植被和土壤含水量敏感。这项研究检查了基于LSWI的潜力,干旱监测算法,用于评估俄克拉荷马州113个Mesonet站点的夏季干旱,这些站点包括俄克拉荷马州的各种土地覆盖和土壤类型。一年中的干旱持续时间由夏季(6月至8月)LSWI<0(DNLSWI)的天数决定。夏季降雨异常和LSWI异常遵循相似的季节动态,并在干旱年份(2001年,2006年,2011年和2012年)显示出强相关性(r2=0.62-0.73)。DNLSWI跟踪了俄克拉荷马州夏季降雨的东西梯度。干旱强度随着DNLSWI持续时间的增加而增加,当DNLSWI超过48天时,强度迅速增加。LSWI与美国干旱监测(USDM)之间的比较显示出强烈的线性负相关关系;即,较高的干旱强度往往具有较低的LSWI值,反之亦然。然而,基于LSWI的算法和USDM指标之间的一致性从32%(D2类,中度干旱)至77%(0级和D0级,无干旱)适用于不同的干旱强度等级,各地区的干旱强度范围从〜30%(俄克拉荷马州西部)到>80%(俄克拉荷马州东部)。我们的结果表明,可以通过计算DNLSWI(以天为单位)来建立干旱强度阈值,并在俄克拉荷马州半干旱和半湿润地区的几种干旱应用中用作简单的补充工具。然而,在俄克拉荷马州西部的干旱地区,USDM与基于LSWI的算法之间存在较大差异,这表明需要进一步调整该算法以将其应用于干旱地区。
Agricultural drought, a common phenomenon in most parts of the world, is one of the most challenging natural hazards to monitor effectively. Land surface water index (LSWI), calculated as a normalized ratio between near infrared (NIR) and short-wave infrared (SWIR), is sensitive to vegetation and soil water content. This study examined the potential of a LSWI-based, drought-monitoring algorithm to assess summer drought over 113
Oklahoma Mesonet stations comprising various land cover and soil types in
Oklahoma. Drought duration in a year was determined by the number of days with LSWI <0 (DNLSWI) during summer months (June-August). Summer rainfall anomalies and LSWI anomalies followed a similar seasonal dynamics and showed strong correlations (r 2 = 0.62-0.73) during drought years (2001, 2006, 2011, and 2012). The DNLSWI tracked the east-west gradient of summer rainfall in
Oklahoma. Drought intensity increased with increasing duration of DNLSWI, and the intensity increased rapidly when DNLSWI was more than 48 days. The comparison between LSWI and the US Drought Monitor (USDM) showed a strong linear negative relationship; i.e., higher drought intensity tends to have lower LSWI values and vice versa. However, the agreement between LSWI-based algorithm and USDM indicators varied substantially from 32 % (D 2 class, moderate drought) to 77 % (0 and D 0 class, no drought) for different drought intensity classes and varied from ∼30 % (western Oklahoma) to >80 % (eastern
Oklahoma) across regions. Our results illustrated that drought intensity thresholds can be established by counting DNLSWI (in days) and used as a simple complementary tool in several drought applications for semi-arid and semi-humid regions of Oklahoma. However, larger discrepancies between USDM and the LSWI-based algorithm in arid regions of western
Oklahoma suggest the requirement of further adjustment in the algorithm for its application in arid regions.