NDVI anomaly for drought monitoring and its correlation with climate factors over Mongolia from 2000 to 2016

Nanzad, Lkhagvadorj; Zhang, Jiahua; Tuvdendorj, Battsetseg; Nabil, Mohsen; Zhang, Sha; Bai, Yun

doi:10.1016/j.jaridenv.2019.01.019

Cited by 151 publications

(90 citation statements)

References 34 publications

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“…Since an abnormal temperature directly leads to a dramatic increase in evapotranspiration, soil moisture becomes reduced by a large margin. Notably, precipitation varies more than other meteorological factors [33,57], which our results confirmed to a certain extent.…”

Section: Drought Characteristics and Causesupporting

confidence: 86%

Exceptional Drought across Southeastern Australia Caused by Extreme Lack of Precipitation and Its Impacts on NDVI and SIF in 2018

Feng

Liu

et al. 2019

Remote Sensing

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Increased drought frequency in Australia is a pressing concern for scholars. In 2018, a severe drought in eastern Australia was recorded by the Emergency Events Database (EM-DAT). To investigate the main causes and impacts of this drought across southeastern Australia, this work presents an overview of the drought mechanism and depicts its evolutionary process. The Standardized Precipitation Evapotranspiration Index (SPEI) from the Global Drought Monitor was used to identify the drought event and characterize its spatiotemporal distribution. The Normalized Difference Vegetation Index (NDVI) and the sun-induced chlorophyll fluorescence (SIF) were used to investigate the drought impacts on vegetation growth. In addition, the effects of drought response measures on Sustainable Development Goals (SDGs) were analyzed. Our results showed that the exceptional drought occurred across southeastern Australia from April to December, and it was most severe in July, owing to an extreme lack of precipitation and increase in temperature. Moreover, we identified profound and long-lasting impacts of the drought on NDVI and SIF levels, especially for cropland. Furthermore, we also found that SIF was superior to NDVI in detecting drought impacts. This study advised on how to formulate timely and effective drought-response measures and supports sustainable socioeconomic development in Australia.

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Section: Drought Characteristics and Causesupporting

confidence: 86%

Exceptional Drought across Southeastern Australia Caused by Extreme Lack of Precipitation and Its Impacts on NDVI and SIF in 2018

Feng

Liu

et al. 2019

Remote Sensing

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“…Due to the impacts of climate change, more extreme and continued droughts have occurred in many parts of Mongolia and have directly affected the vegetation and crop growth, biodiversity and socioeconomics in Mongolia [9]. Nanzad et al [10] found that about 41-57% of Mongolia has been ravaged by mild to severe droughts for many of the last 17 years. A consecutive severe drought in 2002,2005,2007,2010,2013, and 2015 lowered spring wheat production severely as shown in Figure 1 and spring wheat had to be imported as local production declined due to weather conditions [4].…”

Section: Introductionmentioning

confidence: 99%

Determination of Appropriate Remote Sensing Indices for Spring Wheat Yield Estimation in Mongolia

Tuvdendorj

Zeng

et al. 2019

Remote Sensing

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In Mongolia, the monitoring and estimation of spring wheat yield at the regional and national levels are key issues for the agricultural policy and food management as well as for the economy and society as a whole. The remote sensing data and technique have been widely used for the estimation of crop yield and production in the world. For the current research, nine remote sensing indices were tested that include normalized difference drought index (NDDI), normalized difference water index (NDWI), vegetation condition index (VCI), temperature condition index (TCI), vegetation health index (VHI), normalized multi-band drought index (NMDI), visible and shortwave infrared drought index (VSDI), and vegetation supply water index (VSWI). These nine indices derived from MODIS/Terra satellite have so far not been used for crop yield prediction in Mongolia. The primary objective of this study was to determine the best remote sensing indices in order to develop an estimation model for spring wheat yield using correlation and regression method. The spring wheat yield data from the ground measurements of eight meteorological stations in Darkhan and Selenge provinces from 2000 to 2017 have been used. The data were collected during the period of the growing season (June–August). Based on the analysis, we constructed six models for spring wheat yield estimation. The results showed that the range of the root-mean-square error (RMSE) values of estimated spring wheat yield was between 4.1 (100 kg ha−1) to 4.8 (100 kg ha−1), respectively. The range of the mean absolute error (MAE) values was between 3.3 to 3.8 and the index of agreement (d) values was between 0.74 to 0.84, respectively. The conclusion was that the best model would be (R2 = 0.55) based on NDWI, VSDI, and NDVI out of the nine indices and could serve as the most effective predictor and reliable remote sensing indices for monitoring the spring wheat yield in the northern part of Mongolia. Our results showed that the best timing of yield prediction for spring wheat was around the end of June and the beginning of July, which is the flowering stage of spring wheat in this study area. This means an accurate yield prediction for spring wheat can be achieved two months before the harvest time using the regression model.

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“…It includes parts of the Siberian taiga forest, the Central Asian steppe, and the Gobi Desert. The northern part is forest (~10%), the middle-eastern part is steppe grassland (~60%), and the south-western region is the Gobi Desert (~28%) [5,[23][24][25].…”

Section: Study Area and Vegetation Characteristicsmentioning

confidence: 99%

Vegetation Mapping by Using GPM/DPR over the Mongolian Land

et al. 2019

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Mongolian steppe is one of the largest and important ecosystems. The degradation of grassland and the expansion of desert are occurring due to drought and desertification processes. We attempted monitoring of the broad-scale vegetation in Mongolia by a space-borne precipitation radar, which may complement typical approaches of vegetation monitoring (such as NDVI). We utilized the Global Precipitation Mission’s (GPM) dual-frequency precipitation radar (DPR). We characterized backscatter (σ0) of GPM/DPR’s two microwave bands (Ku and Ka) with respect to the dominant vegetation zones (forest, grassland, desert). Both Ku and Ka radars’ σ0 values were investigated for incidence angle dependency and the seasonal variation. As a result, the use of multi-angle, multi-band observations of GPM/DPR could help to characterize the vegetation zones. Especially, the σ0 at incidence angles between 1° and 8° represented useful characteristics of vegetation. Based on it, by using unsupervised clustering, we produced annual maps describing vegetation zones from 2014 to 2018. The result indicated that Mongolia experienced extensive changes in grassland and desert areas during the study years.

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NDVI anomaly for drought monitoring and its correlation with climate factors over Mongolia from 2000 to 2016

Cited by 151 publications

References 34 publications

Exceptional Drought across Southeastern Australia Caused by Extreme Lack of Precipitation and Its Impacts on NDVI and SIF in 2018

Exceptional Drought across Southeastern Australia Caused by Extreme Lack of Precipitation and Its Impacts on NDVI and SIF in 2018

Determination of Appropriate Remote Sensing Indices for Spring Wheat Yield Estimation in Mongolia

Vegetation Mapping by Using GPM/DPR over the Mongolian Land

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