Comparison and Evaluation of Remote Sensing Indices for Agricultural Drought Monitoring Over Kazakhstan

Sun, Baozhen; Qian, Jing; Chen, X.; Zhou, Qiming

doi:10.5194/isprs-archives-xliii-b3-2020-899-2020

Cited by 8 publications

(5 citation statements)

References 5 publications

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“…Following studies on the remote sensing monitoring of agricultural drought [19,27,32,33], seven widely used remote sensing indices were selected. These indexes were calculated using the MOD09A1 and MOD11A2 products.…”

Section: Methods 231 Selection Of Remote Sensing Indicesmentioning

confidence: 99%

“…Due to the complexity of the earth-atmosphere system, the diversity of organisms, and drought variability, the applicability and accuracy of remote sensing indices for drought monitoring may be affected by many factors, such as time and space, farmland environment, crop type, and different developmental stages of crops [32]. Previous studies have indicated that remote sensing indices vary significantly in different regions, and a single index may not reflect the intensity and severity of drought in different regions [33][34][35].…”

Section: Introductionmentioning

confidence: 99%

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Assessing the Spatial–Temporal Pattern of Spring Maize Drought in Northeast China Using an Optimised Remote Sensing Index

Wang,

Wu,

et al. 2023

Remote Sensing

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Northeast China plays a pivotal role in producing commodity grains. The precipitation and temperature distribution during the growth season is impacted by geographical and climate factors, rendering the region vulnerable to drought. However, relying on a single index does not reflect the severity and extent of drought in different regions. This research utilised the random forest (RF) model for screening remote sensing indices. Relative soil moisture (RSM) was employed to compare seven commonly used indices: the temperature vegetation dryness index (TVDI), vegetation supply water index (VSWI), vegetation condition index (VCI), temperature condition index (TCI), vegetation health index (VHI), multi-band drought index (MBDI), and normalised difference drought index (NDDI). The effectiveness of these indices for monitoring drought during different developmental stages of spring maize was evaluated. Trend rates were employed to investigate the temporal changes in drought patterns of spring maize from 2003 to 2020, and the Sen + Mann–Kendall test was used to analyse spatial variations. The results showed the following: (1) The seven remote sensing indices could accurately track drought during critical growth stages with the TVDI demonstrating higher applicability than the other six indices. (2) The application periods of two TVDIs with different parameters differed for the drought monitoring of spring maize in different developmental periods. The consistency and accuracy of the normalised difference vegetation index (NDVI)-based TVDI (TVDIN) were 5.77% and 34.62% higher than those of the enhanced vegetation index (EVI)-based TVDI (TVDIE), respectively, in the early stage. In contrast, the TVDIE exhibited 13.46% higher consistency than the TVDIN in the middle stage, and the accuracy was the same. During the later stage, the TVDIE showed significantly higher consistency and accuracy than the TVDIN with consistency increases of 9.61% and 38.64%, respectively. (3) The drought trend in northeast China increased from 2003 to 2020, exhibiting severe spring drought and a weakening of the drought in summer. The southern, southwestern, and northwestern parts of northeast China showed an upward drought trend; the drought-affected areas accounted for 37.91% of the study area. This paper identified the most suitable remote sensing indices for monitoring drought in different developmental stages of spring maize. The results provide a comprehensive understanding of the spatial–temporal patterns of drought during the past 18 years. These findings can be used to develop a dynamic agricultural drought monitoring model to ensure food security.

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Section: Methods 231 Selection Of Remote Sensing Indicesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing the Spatial–Temporal Pattern of Spring Maize Drought in Northeast China Using an Optimised Remote Sensing Index

Wang,

Wu,

et al. 2023

Remote Sensing

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“…In the field of GIS, Google Earth Engine (GEE) is an advanced cloud-based platform to process all kind of spatial data in a quick time frame (Sun et al, 2020;. The GEE will work in JavaScript and Python streamline, likewise in this study all the data have been processed in GEE through JavaScript API.…”

Section: Earth Engine Data Processingmentioning

confidence: 99%

Google Earth Engine Based Agricultural Drought Monitoring in Kodavanar Watershed, Part of Amaravathi Basin, Tamil Nadu, India

Thilagaraj

Palanisamy

Venkatesh

et al. 2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The agricultural drought assessment and monitoring has become a prime concern in recent times as it impedes land capability and causes food scarcity. Therefore, the present study constructed a methodological framework through the Google Earth Engine (GEE) platform, which offers advanced and effective monitoring in a timely concern of the drought occurrences. The study has been carried out in the Kodavanar watershed, a part of the Amaravathi basin is noted with signs of drought such as insufficient rainfall and vegetation stress in the current situation. The remote sensing indices are utilised for the agriculture drought assessment including Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Temperature Condition Index (TCI), Vegetation Condition Index (VCI) and Vegetation Health Index (VHI). In particular, the VHI results show that the area of healthy vegetation and no drought category is rapidly decreased from 934.29 to 107.83 sq.km across the years and have been reached threatening condition as extreme drought category with extremely low vegetation cover has been increasing in a exponential proportion of over 5% in the year 2019 and 2020. However, the agriculture drought results compared through the meteorological drought indicator of Standardized Precipitation Index (SPI) reflects that the SPI and VHI are reflecting similar signs and indicating the dry condition of precipitation with moderate vegetation over the highlighted regions of northern tip and central eastern portions. This present work illustrates the effective use of the GEE platform in monitoring the agriculture drought and the highlighted portions of the study should be implemented with proper water resource management by the researchers, planners and policymakers in the Kodavanar watershed for reducing the vegetation stress.

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“…It describes the reduction of water volume in rivers or reservoirs due to insufficient surface or groundwater, and examples of this type of index are the SWSI [11] and SRI [12]. The above are single-element drought indices, which can only capture specific phases of drought and may not reflect the overall trend or internal mechanisms of the process [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Copula-Based Joint Drought Index Using Precipitation, NDVI, and Runoff and Its Application in the Yangtze River Basin, China

Wei,

Liu,

Hua

et al. 2023

Remote Sensing

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Drought monitoring ensures the Yangtze River Basin’s social economy and agricultural production. Developing a comprehensive index with high monitoring precision is essential to enhance the accuracy of drought management strategies. This study proposes the standardized comprehensive drought index (SCDI) using a novel approach that utilizes the joint distribution of C-vine copula to effectively combine three critical drought factors: precipitation, NDVI, and runoff. The study analyzes the reliability and effectiveness of the SCDI in detecting drought events through quantitative indicators and assesses its applicability in the Yangtze River Basin. The findings are as follows: (1) The SCDI is a highly reliable and applicable drought index. Compared to traditional indices like the SPI, VCI, and SRI, it has a consistency rate of over 67% and can detect drought events in more sensitive months by over 51%. It has a low false negative rate of only 2% and a false positive rate of 0%, making it highly accurate. The SCDI is also applicable to all the third-level sub-basins of the Yangtze River Basin, making it a valuable tool for regional drought monitoring. (2) The time lag effect of the NDVI can affect the sensitivity of the SCDI. When the NDVI time series data are shifted forward by one month, the sensitivity of the SCDI in detecting agricultural drought improves from 47.8% to 53%. (3) The SDCI can assist in monitoring drought patterns in the Yangtze River Basin. From 2001 to 2018, the basin saw fluctuations in drought intensity, with the worst in December 2008. The western region had less frequent but more intense and prolonged droughts, while the eastern part had more frequent yet less severe droughts.

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Comparison and Evaluation of Remote Sensing Indices for Agricultural Drought Monitoring Over Kazakhstan

Cited by 8 publications

References 5 publications

Assessing the Spatial–Temporal Pattern of Spring Maize Drought in Northeast China Using an Optimised Remote Sensing Index

Assessing the Spatial–Temporal Pattern of Spring Maize Drought in Northeast China Using an Optimised Remote Sensing Index

Google Earth Engine Based Agricultural Drought Monitoring in Kodavanar Watershed, Part of Amaravathi Basin, Tamil Nadu, India

Copula-Based Joint Drought Index Using Precipitation, NDVI, and Runoff and Its Application in the Yangtze River Basin, China

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