A new approach for predicting drought-related vegetation stress: Integrating satellite, climate, and biophysical data over the U.S. central plains

Tadesse, Tsegaye; Brown, Jesslyn F.; Hayes, Michael J.

doi:10.1016/j.isprsjprs.2005.02.003

Cited by 91 publications

(47 citation statements)

References 19 publications

(20 reference statements)

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“…The basic idea of this approach is that temporal variations in soil-moisture cause changes in vegetation, which can be captured by NDVI. However, the correlations between soil-moisture estimated by this approach to field measurements are low (Tadesse et al, 2005;Wang et al, 2007), probably because soil-moisture is not the only factor controlling vegetation and factors like temperature and management can also come into play. Using NDWI (Normalized Difference Water Index) instead of NDVI did not produce improved correlations (Gu et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Technical Note: Comparing and ranking soil drought indices performance over Europe, through remote-sensing of vegetation

Peled

Dutra

Viterbo

et al. 2010

Hydrol. Earth Syst. Sci.

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Abstract. In the past years there have been many attempts to produce and improve global soil-moisture datasets and drought indices. However, comparing and validating these various datasets is not straightforward. Here, interannual variations in drought indices are compared to interannual changes in vegetation, as captured by NDVI. By comparing the correlations of the different indices with NDVI we evaluated which drought index describes most realistically the actual changes in vegetation. Strong correlation between NDVI and the drought indices were found in areas that are classified as warm temperate climate with hot or warm dry summers. In these areas we ranked the PDSI, PSDI-SC, SPI3, and NSM indices, based on the interannual correlation with NDVI, and found that NSM outperformed the rest. Using this best performing index, and the ICA (Independent Component Analysis) technique, we analyzed the response of vegetation to temperature and soil-moisture stresses over Europe.

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Section: Introductionmentioning

confidence: 99%

Technical Note: Comparing and ranking soil drought indices performance over Europe, through remote-sensing of vegetation

Peled

Dutra

Viterbo

et al. 2010

Hydrol. Earth Syst. Sci.

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“…Tadesse et al (2005) integrated AVHRR NDVI 14 day dataset along with Meteorological drought indices from climate data and some biophysical parameters like land cover, eco-regions etc. to predict drought related vegetation stress over U.S. Central Plains.…”

Section: Satellite Sensors For Agricultural Drought Studiesmentioning

confidence: 99%

Integrating effective drought index (EDI) and remote sensing derived parameters for agricultural drought assessment and prediction in Bundelkhand region of India

Padhee

Nikam

Aggarwal

et al. 2014

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

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ABSTRACT:Drought is an extreme condition due to moisture deficiency and has adverse effect on society. Agricultural drought occurs when restraining soil moisture produces serious crop stress and affects the crop productivity. The soil moisture regime of rain-fed agriculture and irrigated agriculture behaves differently on both temporal and spatial scale, which means the impact of meteorologically and/or hydrological induced agriculture drought will be different in rain-fed and irrigated areas. However, there is a lack of agricultural drought assessment system in Indian conditions, which considers irrigated and rain-fed agriculture spheres as separate entities. On the other hand recent advancements in the field of earth observation through different satellite based remote sensing have provided researchers a continuous monitoring of soil moisture, land surface temperature and vegetation indices at global scale, which can aid in agricultural drought assessment/monitoring. Keeping this in mind, the present study has been envisaged with the objective to develop agricultural drought assessment and prediction technique by spatially and temporally assimilating effective drought index (EDI) with remote sensing derived parameters. The proposed technique takes in to account the difference in response of rain-fed and irrigated agricultural system towards agricultural drought in the Bundelkhand region (The study area).The key idea was to achieve the goal by utilizing the integrated scenarios from meteorological observations and soil moisture distribution. EDI condition maps were prepared from daily precipitation data recorded by Indian Meteorological Department (IMD), distributed within the study area. With the aid of frequent MODIS products viz. vegetation indices (VIs), and land surface temperature (LST), the coarse resolution soil moisture product from European Space Agency (ESA) were downscaled using linking model based on Triangle method to a finer resolution soil moisture product. EDI and spatially downscaled soil moisture products were later used with MODIS 16 days NDVI product as key elements to assess and predict agricultural drought in irrigated and rain-fed agricultural systems in Bundelkhand region of India. Meteorological drought, soil moisture deficiency and NDVI degradation were inhabited for each and every pixel of the image in GIS environment, for agricultural impact assessment at a 16 day temporal scale for Rabi seasons (OctoberApril) between years 2000 to 2009. Based on the statistical analysis, good correlations were found among the parameters EDI and soil moisture anomaly; NDVI anomaly and soil moisture anomaly lagged to 16 days and these results were exploited for the development of a linear prediction model. The predictive capability of the developed model was validated on the basis of spatial distribution of predicted NDVI which was compared with MODIS NDVI product in the beginning of preceding Rabi season (Oct -Dec of 2010).The predictions of the model were based on future meteorological data (year 2...

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“…Remote sensing is an effective tool for monitoring and estimating the spatially and temporally explicit information in a large region (Archer, 2004;Chen et al, 2012;Tadesse et al, 2005;Zhang et al, 2012). Satellite images frequently acquired over long periods provide unique opportunities to study the long-term land cover changes.…”

Section: Introductionmentioning

confidence: 99%

Remote sensing monitoring recent rapid increase of coal mining activity of an important energy base in northern China, a case study of Mu Us Sandy Land

Xie

2015

Resources, Conservation and Recycling

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A new approach for predicting drought-related vegetation stress: Integrating satellite, climate, and biophysical data over the U.S. central plains

Cited by 91 publications

References 19 publications

Technical Note: Comparing and ranking soil drought indices performance over Europe, through remote-sensing of vegetation

Technical Note: Comparing and ranking soil drought indices performance over Europe, through remote-sensing of vegetation

Integrating effective drought index (EDI) and remote sensing derived parameters for agricultural drought assessment and prediction in Bundelkhand region of India

Remote sensing monitoring recent rapid increase of coal mining activity of an important energy base in northern China, a case study of Mu Us Sandy Land

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