remote sensing based agricultural drought assessment journal.pdf

Egyptian Journal of Remote Sensing and Space Science, 2015

Owing to its severe effect on productivity of rain-fed crops and indirect effect on employment as well as per capita income, agricultural drought has become a prime concern worldwide. The occurrence of drought is mainly a climatic phenomenon which cannot be eliminated. However, its effects can be reduced if actual spatio-temporal information related to crop status is available to the decision makers. The present study attempts to assess the efficiency of remote sensing and GIS techniques for monitoring the spatio-temporal extent of agricultural drought. In the present study, NOAA-AVHRR NDVI data were used for monitoring agricultural drought through NDVI based Vegetation Condition Index. VCI was calculated for whole Rajasthan using the long term NDVI images which reveals the occurrence of drought related crop stress during the year 2002. The VCI values of normal (2003) and drought year were compared with meteorological based Standardized Precipitation Index (SPI), Rainfall Anomaly Index and Yield Anomaly Index and a good agreement was found among them. The correlation coefficient between VCI and yield of major rain-fed crops (r > 0.75) also supports the efficiency of this remote sensing derived index for assessing agricultural drought. Ó 2015 National Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/ by-nc-nd/4.0/).

Geocarto International, 2013

In the present study, prediction of agricultural drought has been addressed through prediction of agricultural yield using a model based on NDVI-SPI. It has been observed that the meteorological drought index SPI with different timescale is correlated with NDVI at different lag. Also NDVI of current fortnight is correlated with NDVI of previous lags. Based on the correlation coefficients, the Multiple Regression Model was developed to predict NDVI. The NDVI of current fortnight was found highly correlated with SPI of previous fortnight in semi-arid and transitional zones. The correlation between NDVI and crop yield was observed highest in first fortnight of August. The RMSE of predicted yield in drought year was found to be about 17.07 kg/ha which was about 6.02 per cent of average yield. In normal year, it was 24 kg/Ha denoting about 2.1 per cent of average yield.

An Agricultural drought is a type of natural disaster that seriously impacts food security. It's difficult to accurately identify a drought scenario because the interactions between short-term rainfall, soil moisture, and crop growth are so complicated. To cope with drought in the current climate change scenario, it is vital to understand the features of agricultural droughts in water-scarce regions in order to design prudent plans for the use of water resources. Drought indices based on remote sensing, as well as Geographic Information Systems (GIS), are critical tools for mapping and monitoring agricultural droughts. The primary goal of the present study is to monitor agricultural drought dynamics over the semi-arid region of Buldhana District (Western Vidarbha) during the year 2011 to 2021 by using the Normalized Difference Vegetation Index derived (NDVI) and Standard Precipitation Index (SPI) from time-series remote sensing data products. The study clearly indicates NDVI and SPI's potential as reliable indices for assessing and monitoring agricultural droughts.

High-Impact Weather Events over the SAARC Region, 2014

The Indian subcontinent has diverse vegetation with the climate varying from monsoonal in south to temperate in the North. The biological productivity of the vegetation cover therefore largely controlled by water and temperature stresses. The Normalized Deferential Vegetation Index (NDVI) was shown to be sensitive to changes in vegetation conditions. Since it is directly influenced by the chlorophylls absorption of the suns radiation. In this study rainfall data (from IMD) and MODIS-NDVI data (GLAM project) for 28 states of India was used. NDVI data from MODIS (with a resolution of 250 km) images was correlated with state wise annual precipitation for the period 2004-2008. The critical changes of NDVI and the correlation coefficients between NDVI and rainfall were examined for each pixel. The average correlation value for NDVI and rainfall was observed to be 0.64 and R value was 0.40. Spatially very strong relationship is observed in north east and 2 southern part of country. From this study we can conclude that the NDVI is majorly dependent on the rainfall. Other factors like temperature, humidity, radiation etc., also influence the vegetation growth and productivity but in lesser proportion compared to precipitation.

International journal of applied research, 2016

Drought is one of the most widespread and least understood natural phenomena. Drought can be monitored using the climatic variables like rainfall and temperature. In developing countries like India, maintenance and operation of climatic stations is laborious and costly. The application of remote sensing technology for monitoring drought make the process easy and simple. The potential contribution of easily accessible satellite data to the detection and quantification of regional droughts, in the absence of reliable meteorological data, is the objective of this study. In the present study, remote sensing technology is used for monitoring drought for Anantapur district which is second driest region in the country. For this study remote sensing images from the Landsat satellite from 2005 to 2010 were used. The Normalised Difference Vegetation Index (NDVI) and Normalised difference water index (NDWI) which are most widely used vegetation indices in recent years that measure and monitor ...

2010

Remote sensing index NDVI or its derivatives are used for agricultural drought monitoring and early warning at regional scale worldwide. Studies have shown that NDVI has lagged response to rainfall deficit. Moreover the red band used in NDVI is highly absorbed by crop canopy in comparison to short infrared which has high penetration so thus there remains a discrepancy between the levels of penetration in crop canopy. In contrast, Normalized Difference Water Index (NDWI) uses both the bands in near infrared region and is very sensitive to liquid water content of vegetation canopy and so rainfall. So this study was conducted to evaluate the sensitivity of NDWI in detecting and monitoring the agricultural drought in comparison with NDVI. In the study three indices of NDVI, NDWI5 and NDWI6 were computed using MODIS 09A1 surface reflectance product from June to October of 2002 (drought year) and 2003 (normal year) for the state of Rajasthan. NOAA Climate Prediction Centre (CPC) rainfall product was used and averaged at district level. The NDWI5 showed very strong relation with current rainfall than NDWI6 and weakest was shown by NDVI. The relation of NDVI with lagged rainfall was much better than with current rainfall. The spatial comparison of changes in NDVI and NDWI5 between the drought year (2002) and normal year (2003) for each 8 days composite showed that NDWI5 very well picks up the intensity and extent of drought. Study also showed that NDWI5 is more sensitive to agricultural drought than NDWI6. The study recommends use of NDWI5 for better early detection and monitoring of agricultural drought in operational drought management programmes.

Natural Hazards, 2010

Drought is a serious climatic condition that affects nearly all climatic zones worldwide, with semi-arid regions being especially susceptible to drought conditions because of their low annual precipitation and sensitivity to climate changes. Drought indices such as the standardized precipitation index (SPI) using meteorological data and vegetation indices from satellite data were developed for quantifying drought conditions. Remote sensing of semi-arid vegetation can provide vegetation indices which can be used to link drought conditions when correlated with various meteorological data based drought indices. The present study was carried out for drought monitoring for three districts namely Bhilwara, Kota and Udaipur of Rajasthan state in India using SPI, normalized difference vegetation index (NDVI), water supply vegetation index (WSVI) and vegetation condition index (VCI) derived from the Advanced Very High resolution Radiometer (AVHRR). The SPI was computed at different time scales of 1, 2, 3, 6, 9 and 12 months using monthly rainfall data. The NDVI and WSVI were correlated to the SPI and it was observed that for the three stations, the correlation coefficient was high for different time scales. Bhilwara district having the best correlation for the 9-month time scale shows late response while Kota district having the best correlation for 1-month shows fast response. On the basis of the SPI analysis, it was found that the area was worst affected by drought in the year 2002. This was validated on the basis of NDVI, WSVI and VCI. The study clearly shows that integrated analysis of ground measured data and satellite data has a great potential in drought monitoring.

Indian Geographical Journal, 2015

Drought is a multi-dimensional disaster that plays a deterministic approach in many climatic zones throughout the world. The drought restricts the growth of a region in many ways viz. physically, culturally and economically. The paper discusses the use of geoinformatics in disaster risk management planning, which exists as a very powerful tool in identifying the drought occurrences and in processing the spatial information for better predictions for drought. The drought condition of Karur District, Tamil Nadu, has been considered in this study based on the decreasing trend of rainfall. The data has been compiled from various data repositories such as Open Series Map (OSM) sheets, NRSC Bhuvan portal, and Indian Meteorological Department (IMD). The drought assessment is drawn from two indices, first using the Normalized Difference Vegetation Index (NDVI) for the years 2011 to 2015 and second, with rainfall to generate Standard Precipitation Index (SPI) for the span of 30 years from 1984 to 2013. The drought condition shows a continuous increase from 2012 to 2015 in January months. The drought vulnerability in the study area shows the highest record of very high vulnerability in the villages Paramathi and Tennilai of Aravakurichi Taluk. The map has been classed into four categories viz. very high, high, moderate, and low vulnerable levels. The SPI values and extreme dryness occurred in the years 2000 and 2013. The result also shows that the drought condition prevails over Aravakurichi taluk with dryness with no rainfall for a pronged period. This type of versatile study provides detailed knowledge about climatic based drought assessment, which is helpful to the administrators in making proper plans against disasters like drought.

The Indian subcontinent has diverse vegetation with the climate varying from monsoonal in south to temperate in the North. The biological productivity of the vegetation cover therefore largely controlled by water and temperature stresses. The Normalized Deferential Vegetation Index (NDVI) was shown to be sensitive to changes in vegetation conditions. Since it is directly influenced by the chlorophylls absorption of the suns radiation. In this study rainfall data (from IMD) and MODIS-NDVI data (GLAM project) for 28 states of India was used. NDVI data from MODIS (with a resolution of 250 km) images was correlated with state wise annual precipitation for the year 2004-2008. The correlation value for NDVI and rainfall was observed to be 0.64 and R value was 0.40. From this study we can conclude that the NDVI is majorly dependent on the rainfall. Other 2 factors like temperature, humidity, radiation etc., also influence the vegetation growth and productivity but in lesser proportion compared to precipitation.