scholarly journals DROUGHT MONITORING OVER PADDY FIELD AREA IN INDRAMAYU DISTRICT, WEST JAVA USING REMOTELY SENSED INDICES

Author(s):  
Parwati ◽  
Miao Jungang ◽  
Orbita Roswintiarti

In this research, several meteorological and agricultural drought indices based on remote sensing data are built for drought monitoring over paddy area in Indramayu District, West Java, Indonesia. The meteorological drought index of Standardized Precipitation Index (SPI) is developed from monthly Outgoing Long Wave Radiation (OLR) data from 1980 to 2005. The SPI represents the deficient of precipitation. Meanwhile, the agricultural drought of Vegetation Health Index (VHI) was developed from daily Moderate-resolution ImagingSpectroradiometer (MODIS) data during dry season (May-August) 2003-2006. The VHI was designed to monitoring vegetation health, soil moisture, and thermal conditions. The result shows that the agricultural drought occurate in Indramayu District, especially in the northern and southern part during the dry season in 2003 and 2004. It is found that there is a strong correlation between VHI and soil moisture measured in the field (r=0.84). Key words:Agricultural drought, Meteorological drought, Standardized Precipitation Index, Temperature Condition Index, Vegetation Condition Index.

2021 ◽  
Author(s):  
Trupti Satapathy ◽  
Meenu Ramadas ◽  
Jörg Dietrich

<p>Among natural hazards, droughts are known to be very complex and disastrous owing to their creeping nature and widespread impacts. Specifically, the occurrence of agricultural droughts poses a threat to the productivity and socio-economic development of countries such as India. In this study, we propose a novel framework for agricultural drought monitoring integrating the different indicators of vegetation health, crop water stress and soil moisture, that are derived from remote sensing satellite data. The drought monitoring is performed over Odisha, India, for the period 2000-2019. Soil moisture and land surface temperature datasets from GLDAS Noah Land Surface Model and surface reflectance data from MODIS (MOD09GA) are used in this study. We compared the utility of popular indices: (i) soil moisture condition index, soil moisture deficit index and soil wetness deficit index to represent the soil moisture level; (ii) temperature condition index, vegetation condition index and normalised difference water index to indicate vegetation health; (iii) short wave infrared water stress to represent crop water stress condition. Correlation analyses between these indices and the seasonal crop yields are performed, and suitable indicators are chosen. The popular entropy weight method is then used to integrate the indices and develop the proposed composite drought index. The index is then used for monitoring the agricultural drought condition over the study area in drought periods. The proposed framework for week- to month-scale monitoring have potential applications in identification of agricultural drought hotspots, analysis of trends in drought severity, and drought early warning for agricultural water management.</p>


2015 ◽  
Vol 16 (3) ◽  
pp. 1397-1408 ◽  
Author(s):  
Hongshuo Wang ◽  
Jeffrey C. Rogers ◽  
Darla K. Munroe

Abstract Soil moisture shortages adversely affecting agriculture are significantly associated with meteorological drought. Because of limited soil moisture observations with which to monitor agricultural drought, characterizing soil moisture using drought indices is of great significance. The relationship between commonly used drought indices and soil moisture is examined here using Chinese surface weather data and calculated station-based drought indices. Outside of northeastern China, surface soil moisture is more affected by drought indices having shorter time scales while deep-layer soil moisture is more related on longer index time scales. Multiscalar drought indices work better than drought indices from two-layer bucket models. The standardized precipitation evapotranspiration index (SPEI) works similarly or better than the standardized precipitation index (SPI) in characterizing soil moisture at different soil layers. In most stations in China, the Z index has a higher correlation with soil moisture at 0–5 cm than the Palmer drought severity index (PDSI), which in turn has a higher correlation with soil moisture at 90–100-cm depth than the Z index. Soil bulk density and soil organic carbon density are the two main soil properties affecting the spatial variations of the soil moisture–drought indices relationship. The study may facilitate agriculture drought monitoring with commonly used drought indices calculated from weather station data.


2014 ◽  
Vol 12 (3) ◽  
pp. 253-264 ◽  
Author(s):  
Mladen Milanovic ◽  
Milan Gocic ◽  
Slavisa Trajkovic

Drought represents a combined heat-precipitation extreme and has become an increasingly frequent phenomenon in recent years. In order to access the entire analysis of drought, it is necessary to include the analysis of several types of drought. In this paper, impacts of meteorological and agricultural drought were analyzed across the Standardized Precipitation Index (SPI) and Agricultural Rainfall Index (ARI) on the territory of Serbia for the period from 1980 to 2010. For both types of drought, year 2000 is notable as the year when most of the observed stations had the highest drought intensity. It was found that meteorological drought for year 2000 has a higher intensity in the central and southeastern parts of the country, as well as in the north. Of all the stations, the highest intensity of meteorological drought was observed at Loznica station in 1989. Agricultural drought in 2000 had the lowest intensity in western Serbia.


2020 ◽  
Author(s):  
Maria Jose Escorihuela ◽  
Pere Quintana Quintana-Seguí ◽  
Vivien Stefan ◽  
Jaime Gaona

<p>Drought is a major climatic risk resulting from complex interactions between the atmosphere, the continental surface and water resources management. Droughts have large socioeconomic impacts and recent studies show that drought is increasing in frequency and severity due to the changing climate.</p><p>Drought is a complex phenomenon and there is not a common understanding about drought definition. In fact, there is a range of definitions for drought. In increasing order of severity, we can talk about: meteorological drought is associated to a lack of precipitation, agricultural drought, hydrological drought and socio-economic drought is when some supply of some goods and services such as energy, food and drinking water are reduced or threatened by changes in meteorological and hydrological conditions. 
</p><p>A number of different indices have been developed to quantify drought, each with its own strengths and weaknesses. The most commonly used are based on precipitation such as the precipitation standardized precipitation index (SPI; McKee et al., 1993, 1995), on precipitation and temperature like the Palmer drought severity index (PDSI; Palmer 1965), others rely on vegetation status like the crop moisture index (CMI; Palmer, 1968) or the vegetation condition index (VCI; Liu and Kogan, 1996). Drought indices can also be derived from climate prediction models outputs. Drought indices base on remote sensing based have traditionally been limited to vegetation indices, notably due to the difficulty in accurately quantifying precipitation from remote sensing data. The main drawback in assessing drought through vegetation indices is that the drought is monitored when effects are already causing vegetation damage. In order to address drought in their early stages, we need to monitor it from the moment the lack of precipitation occurs.</p><p>Thanks to recent technological advances, L-band (21 cm, 1.4 GHz) radiometers are providing soil moisture fields among other key variables such as sea surface salinity or thin sea ice thickness. Three missions have been launched: the ESA’s SMOS was the first in 2009 followed by Aquarius in 2011 and SMAP in 2015.</p><p>A wealth of applications and science topics have emerged from those missions, many being of operational value (Kerr et al. 2016, Muñoz-Sabater et al. 2016, Mecklenburg et al. 2016). Those applications have been shown to be key to monitor the water and carbon cycles. Over land, soil moisture measurements have enabled to get access to root zone soil moisture, yield forecasts, fire and flood risks, drought monitoring, improvement of rainfall estimates, etc.</p><p>The advent of soil moisture dedicated missions (SMOS, SMAP) paves the way for drought monitoring based on soil moisture data. Initial assessment of a drought index based on SMOS soil moisture data has shown to be able to precede drought indices based on vegetation by 1 month (Albitar et al. 2013).</p><p>In this presentation we will be analysing different drought episodes in the Ebro basin using both soil moisture and vegetation based indices to compare their different performances and test the hypothesis that soil moisture based indices are earlier indicators of drought than vegetation ones.</p>


2021 ◽  
Vol 13 (19) ◽  
pp. 3907
Author(s):  
Simon Kloos ◽  
Ye Yuan ◽  
Mariapina Castelli ◽  
Annette Menzel

Droughts during the growing season are projected to increase in frequency and severity in Central Europe in the future. Thus, area-wide monitoring of agricultural drought in this region is becoming more and more important. In this context, it is essential to know where and when vegetation growth is primarily water-limited and whether remote sensing-based drought indices can detect agricultural drought in these areas. To answer these questions, we conducted a correlation analysis between the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) within the growing season from 2001 to 2020 in Bavaria (Germany) and investigated the relationship with land cover and altitude. In the second step, we applied the drought indices Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Vegetation Health Index (VHI) to primarily water-limited areas and evaluated them with soil moisture and agricultural yield anomalies. We found that, especially in the summer months (July and August), on agricultural land and grassland and below 800 m, NDVI and LST are negatively correlated and thus, water is the primary limiting factor for vegetation growth here. Within these areas and periods, the TCI and VHI correlate strongly with soil moisture and agricultural yield anomalies, suggesting that both indices have the potential to detect agricultural drought in Bavaria.


2021 ◽  
Vol 21 (2) ◽  
pp. 182-187
Author(s):  
DEEPA B. KAMBLE ◽  
SHWETA GAUTAM ◽  
HIMANI BISHT ◽  
SHRADDHA RAWAT ◽  
ARNAB KUNDU

The monthly weather data for 31 years from 1985-2015 was used to analyze the extent of meteorological drought using standardized precipitation index (SPI) over Allahabad, Kanpur and Lucknow. MODIS NDVI data from 2000-2015 was used for monitoring of agricultural drought through NDVI based vegetation condition index (VCI) for all the three districts. The monthly SPI and VCI values from July to October were correlated with productivity index (PI) of kharif rice.Both the indices (SPI and VCI) were positively correlated with PI for all the districts. In Allahabad SPI and VCI during September month showed a significant correlation (0.70**& 0.61*) while in Kanpur VCI during October and SPI of July and August were significantly correlated with PI of kharif Rice. The multiple regression equation developed for predicting kharif rice PI in Allahabad, Kanpur and Lucknow districts explained 69 to 76 per cent variabilityin PI. 


2008 ◽  
Vol 9 (6) ◽  
pp. 1212-1230 ◽  
Author(s):  
Kingtse C. Mo

Abstract Drought indices derived from the North American Land Data Assimilation System (NLDAS) Variable Infiltration Capacity (VIC) and Noah models from 1950 to 2000 are intercompared and evaluated for their ability to classify drought across the United States. For meteorological drought, the standardized precipitation index (SPI) is used to measure precipitation deficits. The standardized runoff index (SRI), which is similar to the SPI, is used to classify hydrological drought. Agricultural drought is measured by monthly-mean soil moisture (SM) anomaly percentiles based on probability distributions (PDs). The PDs for total SM are regionally dependent and influenced by the seasonal cycle, but the PDs for SM monthly-mean anomalies are unimodal and Gaussian. Across the eastern United States (east of 95°W), the indices derived from VIC and Noah are similar, and they are able to detect the same drought events. Indices are also well correlated. For river forecast centers (RFCs) across the eastern United States, different drought indices are likely to detect the same drought events. The monthly-mean soil moisture (SM) percentiles and runoff indices between VIC and Noah have large differences across the western interior of the United States. For small areas with a horizontal resolution of 0.5° on the time scales of one to three months, the differences of SM percentiles and SRI between VIC and Noah are larger than the thresholds used to classify drought. For the western RFCs, drought events selected according to SM percentiles or SRI derived from different NLDAS systems do not always overlap.


2019 ◽  
Vol 11 (15) ◽  
pp. 1773 ◽  
Author(s):  
Jae-Hyun Ryu ◽  
Kyung-Soo Han ◽  
Yang-Won Lee ◽  
No-Wook Park ◽  
Sungwook Hong ◽  
...  

Satellite-based remote sensing techniques have been widely used to monitor droughts spanning large areas. Various agricultural drought indices have been developed to assess the intensity of agricultural drought and to detect damaged crop areas. However, to better understand the responses of agricultural drought to meteorological drought, agricultural management practices should be taken into consideration. This study aims to evaluate the responses to drought under different forms of agricultural management for the extreme drought that occurred on the Korean Peninsula in 2014 and 2015. The 3-month standardized precipitation index (SPI3) and the 3-month vegetation health index (VHI3) were selected as a meteorological drought index and an agricultural drought index, respectively. VHI3, which comprises the 3-month temperature condition index (TCI3) and the 3-month vegetation condition index (VCI3), differed significantly in the study area during the extreme drought. VCI3 had a different response to the lack of precipitation in South and North Korea because it was affected by irrigation. However, the time series of TCI3 were similar in South and North Korea. These results meant that each drought index has different characteristics and should be utilized with caution. Our results are expected to help comprehend the responses of the agricultural drought index on meteorological drought depending on agricultural management.


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