Prediction of vegetation anomalies to improve food security and water management in India

Asoka, Akarsh; Mishra, Vimal

doi:10.1002/2015gl063991

Cited by 67 publications

(46 citation statements)

References 32 publications

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“…Remote sensing products have been employed to aid drought prediction either through statistical or dynamical models (Nijssen et al, 2014;Sheffield et al, 2014;Svoboda et al, 2002). For the statistical prediction, drought indices from remote sensing products (e.g., NDVI) have been commonly used as the predictand for drought prediction mostly achieved with regression models (Asoka & Mishra, 2015;De Linage et al, 2014;Funk & Brown, 2006;Liu & Juárez, 2001;Tadesse et al, 2014). For example, the NDVI is the most commonly used vegetation index for characterizing drought conditions and is available from different satellite remote sensing platforms.…”

Section: Remote Sensing and Drought Predictionmentioning

confidence: 99%

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

Hao

Singh

Xia

2018

Reviews of Geophysics

429

232

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Drought prediction is of critical importance to early warning for drought managements. This review provides a synthesis of drought prediction based on statistical, dynamical, and hybrid methods. Statistical drought prediction is achieved by modeling the relationship between drought indices of interest and a suite of potential predictors, including large‐scale climate indices, local climate variables, and land initial conditions. Dynamical meteorological drought prediction relies on seasonal climate forecast from general circulation models (GCMs), which can be employed to drive hydrological models for agricultural and hydrological drought prediction with the predictability determined by both climate forcings and initial conditions. Challenges still exist in drought prediction at long lead time and under a changing environment resulting from natural and anthropogenic factors. Future research prospects to improve drought prediction include, but are not limited to, high‐quality data assimilation, improved model development with key processes related to drought occurrence, optimal ensemble forecast to select or weight ensembles, and hybrid drought prediction to merge statistical and dynamical forecasts.

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Section: Remote Sensing and Drought Predictionmentioning

confidence: 99%

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

Hao

Singh

Xia

2018

Reviews of Geophysics

429

232

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“…In addition, the response of soil moisture to precipitation may not be strong at the monthly scale, and there is only a small proportion of deep‐rooted vegetation distributed in the study region. A lower correlation in the irrigated areas in the middle region compared with the nonirrigated areas is observed (Figure ), because soil moisture is not a primary factor that governs crop growth in this area (Asoka & Mishra, ). In nonirrigated areas, a higher correlation is observed in the arid region of the middle region, where soil moisture and precipitation are limited, when compared with areas in the upper region, where precipitation is relatively abundant.…”

Section: Resultsmentioning

confidence: 99%

Prediction of vegetation anomalies over an inland river basin in north‐western China

Niu

Sivakumar

2018

Hydrological Processes

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Vegetation cover plays an important role in linking the atmosphere, water, and land and is deemed as a key indicator in the terrestrial ecological system. Therefore, it is of great importance to monitor vegetation dynamics and understand the mechanisms of vegetation change, including that driven by climate change. This study examines (a) the evolution of vegetation dynamics over the Heihe River Basin in the typical arid zone in north‐western China using nonparametric Mann–Kendall test and Thiel Sen's slope; (b) the relationships between remotely sensed vegetation indices (normalized difference vegetation index [NDVI] and enhanced vegetation index [EVI]) and hydroclimatic variables based on correlation analysis; and (c) the prediction of vegetation anomalies using a multiple linear regression model. For the analysis, the Moderate Resolution Imaging Spectroradiometer NDVI/EVI product and the gridded daily meteorological data at a spatial resolution of 0.125° over the period 2001–2010 are considered. The results indicate that vegetation cover improved over a large proportion during 2001–2010, with a significant trend towards warm and wet, characterized by an increase in average annual temperature and precipitation by 0.042 °C/year and 5.8 mm/year, respectively. We test the feasibility of NDVI and EVI in quantifying the responses of vegetation anomaly to climate change and develop a statistical model to predict vegetation dynamics in the basin. The NDVI‐based model is found to be more reliable than the EVI‐based model, partly due to the vegetation characteristics and geomorphologic properties of the study region. The proposed model performs well when there is no lag time between meteorological factors and vegetation indices for grassland and cropland, whereas 1‐month lead time prediction is found to be best for forest. The soil water content is introduced as an extra explanatory variable, which effectively improves the prediction accuracy for different land use types. In general, the predictive ability of the proposed model is stable and satisfactory, and the model can provide useful early warning information for regional water resources management under changing climate.

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“…In most of the locations, NDVI and RZSM anomalies have positive correlations; however, some regions indicate negative correlation at higher lags due to coincidence of negative NDVI anomalies with positive soil moisture anomalies [55]. In South Africa, the semi-arid Western Cape and Eastern Cape show higher coefficients compared with other parts of the country as the vegetation growth in those regions has high reliance on root zone soil moisture [56,57]. No spatial variability in the lag correlation values was observed over Ethiopia ( Figure 10).…”

Section: Correlation Between Soil Moisture and Normalized Difference mentioning

confidence: 96%

Leveraging the Google Earth Engine for Drought Assessment Using Global Soil Moisture Data

2018

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Soil moisture is considered to be a key variable to assess crop and drought conditions. However, readily available soil moisture datasets developed for monitoring agricultural drought conditions are uncommon. The aim of this work is to examine two global soil moisture datasets and a set of soil moisture web-based processing tools developed to demonstrate the value of the soil moisture data for drought monitoring and crop forecasting using the Google Earth Engine (GEE). The two global soil moisture datasets discussed in the paper are generated by integrating the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions' satellite-derived observations into a modified two-layer Palmer model using a one-dimensional (1D) ensemble Kalman filter (EnKF) data assimilation approach. The web-based tools are designed to explore soil moisture variability as a function of land cover change and to easily estimate drought characteristics such as drought duration and intensity using soil moisture anomalies and to intercompare them against alternative drought indicators. To demonstrate the utility of these tools for agricultural drought monitoring, the soil moisture products and vegetation-and precipitation-based products were assessed over drought-prone regions in South Africa and Ethiopia. Overall, the 3-month scale Standardized Precipitation Index (SPI) and Normalized Difference Vegetation Index (NDVI) showed higher agreement with the root zone soil moisture anomalies. Soil moisture anomalies exhibited lower drought duration, but higher intensity compared with SPIs. Inclusion of the global soil moisture data into the GEE data catalog and the development of the web-based tools described in the paper enable a vast diversity of users to quickly and easily assess the impact of drought and improve planning related to drought risk assessment and early warning. The GEE also improves the accessibility and usability of the earth observation data and related tools by making them available to a wide range of researchers and the public. In particular, the cloud-based nature of the GEE is useful for providing access to the soil moisture data and scripts to users in developing countries that lack adequate observational soil moisture data or the necessary computational resources required to develop them. soil moisture monitoring offers, such as global coverage, high accuracy, and high temporal frequency essential for operational applications, the shallow penetration depth of the microwave frequencies used for soil moisture monitoring remains a limiting factor [3]. In fact, many hydrological processes and forecasting and decision-making activities linked to soil moisture require knowledge of the root zone soil moisture (RZSM) information. The latter is typically estimated using hydrologic land surface models, which are traditionally driven by some weather-related observations such as precipitation, temperature, and so forth. The credibility of the modeled RZSM estimates is strongly dependent on the quality of the for...

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Prediction of vegetation anomalies to improve food security and water management in India

Cited by 67 publications

References 32 publications

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

Prediction of vegetation anomalies over an inland river basin in north‐western China

Leveraging the Google Earth Engine for Drought Assessment Using Global Soil Moisture Data

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