The 'ESA-CCI Driven Vertical Soil Moisture Profile (Root zone) Database (EDVSMPD) for Indian ... more The 'ESA-CCI Driven Vertical Soil Moisture Profile (Root zone) Database (EDVSMPD) for Indian Mainland (EDVSMP)' is soil moisture profile database at surface (~0-5 cm), 10, 20, 51 and 102 cm depths for Indian Mainland. It is developed by applying Statistical Soil Moisture Profile (SSMP) model (Pal et al., 2016; Pal and Maity, 2020) using the surface Soil Moisture (SM) product developed by Climate Change Initiative (CCI) program of the European Space Agency (ESA) (ESA-CCI) (Liu et al., 2012; Wagner et al., 2012, http://www.esa-soilmoisture-cci.org). The SSMP model is a spatially varying statistical framework that couples the memory and forcing of SM from the overlying layers and utilizes the information of Hydrologic Soil Groups (HSGs) that makes it spatially transferable. The model estimates SM at four depths i.e. 10, 20, 51 and 102 cm using the surface SM information (~ 0-5 cm) at daily scale for the time period of November 1, 1978 to December 31, 2014. The gridded, volumetric SM data (m3/m3) is available at 0.25×0.25° spatial resolution and the spatial extent of the data is 6°N to 38°N and 66°E to 98°E (latitude and longitude wise respectively).
Abstract The Climate Change Initiative (CCI) program of the European Space Agency (ESA) (ESA-CCI)... more Abstract The Climate Change Initiative (CCI) program of the European Space Agency (ESA) (ESA-CCI) provides a global surface Soil Moisture (SM) product by merging the information from various Active and Passive sensors on board for the period of 1971–2014. This study aims to assimilate this surface SM product into a recently developed model, namely Statistical Soil Moisture Profile (SSMP) model, to investigate the efficacy of this combination in developing a vertical soil moisture profile database over a climatologically vast area, such as Indian mainland. The SSMP model is a spatially varying statistical framework that couples the memory and forcing of SM from the overlying layers and utilizes the information of Hydrologic Soil Groups (HSGs) that makes it spatially transferable. The model estimates SM at four depths i.e. 10, 20, 51 and 102 cm using the surface SM information (~0–5 cm). The developed database is named as ‘ESA-CCI Driven Vertical Soil Moisture Profile (root zone) Database (EDVSMPD)’ and the simulated values are validated with observed SM data at many monitoring stations across India, maintained by India Meteorological Department (IMD). As an exemplary demonstration of the utility of the data, basin scale reconstruction of the historical agricultural droughts and its spatial distribution, using the developed EDVSMPD product, is illustrated for a medium sized drought-stricken river basin, namely Wardha, in the central part of India. The resulting SM product for a considerable length of period (more than 30 years) will be immensely useful for many hydro-climatological studies.
This study explores the potential of the Deep Learning (DL) approach to develop a model for basin... more This study explores the potential of the Deep Learning (DL) approach to develop a model for basin-scale drought assessment using information from a set of primary hydrometeorological precursors, namely air temperature, surface pressure, wind speed, relative humidity, evaporation, soil moisture and geopotential height. The novelty of the study lies in extracting the information from the hydrometeorological precursors through the efficacy of the DL algorithm, based on a one-dimensional convolutional neural network. Drought-prone regions, from where our study basins are selected, often suffer from the vagaries of rainfall that leads to drought-like situations. It is established that the proposed DL-based model is able to capture the underlying complex relationship between rainfall and the set of aforementioned hydrometeorological variables and, subsequently, shows its promise for the basin-scale meteorological drought assessment as revealed through different performance metrics and ski...
The focus of this study is to evaluate the efficacy of Machine Learning (ML) algorithms in the lo... more The focus of this study is to evaluate the efficacy of Machine Learning (ML) algorithms in the long-lead prediction of El Niño (La Niña) Modoki (ENSO Modoki) index (EMI). We evaluated two widely used non-linear ML algorithms namely Support Vector Regression (SVR) and Random Forest (RF) to forecast the EMI at various lead times, viz. 6, 12, 18 and 24 months. The predictors for the EMI are identified using Kendall’s tau correlation coefficient between the monthly EMI index and the monthly anomalies of the slowly varying climate variables such as sea surface temperature (SST), sea surface height (SSH) and soil moisture content (SMC). The importance of each of the predictors is evaluated using the Supervised Principal Component Analysis (SPCA). The results indicate both SVR and RF to be capable of forecasting the phase of the EMI realistically at both 6-months and 12-months lead times though the amplitude of the EMI is underestimated for the strong events. The analysis also indicates th...
Journal of the Indian Society of Remote Sensing, 2019
The present study aims to explore and compare the potential of different Artificial Intelligence-... more The present study aims to explore and compare the potential of different Artificial Intelligence-based Soft Computing (AISC) techniques to prepare surface Soil Moisture Content (SMC) map using fine-resolution (* 5 m), quad-polarized Synthetic Aperture Radar (SAR) data obtained from Radar Imaging Satellite 1 (RISAT1). Potential of three different AISC techniques, i.e. Support Vector Machine (SVM), Random Forest (RF) and Genetic Programming (GP), is explored. The estimated surface SMC is validated with the field soil moisture values in both bare and vegetated lands (\ 30 cm height). Different techniques have their own merits and demerits; however, we recommend GP to be most useful due to its other features. For example, GP provides the mathematical relationship, importance and sensitivity of each individual input to the surface SMC. This helps us to quantify the contribution of quad-polarized backscattering coefficients and soil texture information. It is noticed that the use of only SAR data without soil texture information may be acceptable with reasonable accuracy with an enormous benefit of its applicability to the locations without soil texture information. Using this, an exemplary fine-resolution (* 5 m) SMC map is developed. Such high-resolution maps for large spatial extent are expected to be highly useful in many applications. Keywords Soil moisture Á Remote sensing Á Synthetic Aperture Radar (SAR) Á Quad-polarized data Á Radar Imaging Satellite 1 Á Artificial Intelligence-based Soft Computing (AISC) techniques Electronic supplementary material The online version of this article (
IEEE Transactions on Geoscience and Remote Sensing, 2017
This paper attempts to probabilistically estimate the surface soil moisture content (SMC) by usin... more This paper attempts to probabilistically estimate the surface soil moisture content (SMC) by using the synthetic aperture radar data provided by radar imaging satellite1. The novelty of this paper lies in: 1) developing a combined index to understand the role of all the backscattering coefficients with different polarization and soil texture information in influencing the SMC; 2) using normalized incidence angles, which enables the model to be applicable for different incidence angles; and 3) determination of uncertainty range of the estimated SMC. The dimensionality problem, which is frequently observed in the multivariate analysis, is reduced in the development of the combined index by the use of supervised principal component analysis (SPCA). The SPCA also ensures the maximum attainable association between the developed combined index and surface SMC above wilting point (WP). The association between the combined index and the surface SMC above WP is modeled through joint probability distribution by using the Frank copula. The model is developed and validated with the field soil moisture values over 334 monitoring points within the study area. The outcomes obtained by applying the proposed model indicate an encouraging potential of the model to be applied for bareland and vegetated land (<30 cm height).
Information of Soil Moisture Content (SMC) at different depths i.e. vertical Soil Moisture (SM) p... more Information of Soil Moisture Content (SMC) at different depths i.e. vertical Soil Moisture (SM) profile is important as it influences several hydrological processes. In the era of microwave remote sensing, spatial distribution of soil moisture information can be retrieved from satellite data for large basins. However, satellite data can provide only the surface (~0–10 cm) soil moisture information. In this study, a methodological framework is proposed to estimate the vertical SM profile knowing the information of SMC at surface layer. The approach is developed by coupling the memory component of SMC within a layer and the forcing component from soil layer lying above by an Auto-Regressive model with an exogenous input (ARX) where forcing component is the exogenous input. The study highlights the mutual reliance between SMC at different depths at a given location assuming the ground water table is much below the study domain. The methodology is demonstrated for three depths: 25, 50 and 80 cm using SMC values of 10 cm depth. Model performance is promising for all three depths. It is further observed that forcing is predominant than memory for near surface layers than deeper layers. With increase in depth, contribution of SM memory increases and forcing dissipates. Potential of the proposed methodology shows some promise to integrate satellite estimated surface soil moisture maps to prepare a fine resolution, 3-dimensional soil moisture profile for large areas, which is kept as future scope of this study.
Information on vertical Soil Moisture Content (SMC) profile is important for several hydrometeoro... more Information on vertical Soil Moisture Content (SMC) profile is important for several hydrometeorological processes. This study borrows the idea of coupling the memory and forcing from a previous study and develops a spatially-varying Statistical Soil Moisture Profile (SSMP) model to estimate the vertical SMC profile. It uses the only surface soil moisture (0-5 cm) values and Hydrological Soil Groups (HSGs) information of the location. The focus of the study is incorporation of the HSG information to ensure the spatial transferability of the proposed model by capturing the spatial variations of soil moisture profile with the change in soil hydraulic properties. The wide range of soil moisture data for model development as well as for spatial validation are obtained from 171 stations from different networks of International Soil Moisture Network (ISMN) at five different depths, i.e., 5, 10, 20, 51 and 102 cm. The HSG information at the locations are extracted from the Web Soil Survey (WSS) database. The potential of spatial transferability of the SSMP model is assessed by applying it to the new stations within the corresponding HSG. Model performances are promising for all four depth pairs (5-10, 10-20, 20-51 and 51-102 cm) of all four HSGs during both model development and spatial validation given
The 'ESA-CCI Driven Vertical Soil Moisture Profile (Root zone) Database (EDVSMPD) for Indian ... more The 'ESA-CCI Driven Vertical Soil Moisture Profile (Root zone) Database (EDVSMPD) for Indian Mainland (EDVSMP)' is soil moisture profile database at surface (~0-5 cm), 10, 20, 51 and 102 cm depths for Indian Mainland. It is developed by applying Statistical Soil Moisture Profile (SSMP) model (Pal et al., 2016; Pal and Maity, 2020) using the surface Soil Moisture (SM) product developed by Climate Change Initiative (CCI) program of the European Space Agency (ESA) (ESA-CCI) (Liu et al., 2012; Wagner et al., 2012, http://www.esa-soilmoisture-cci.org). The SSMP model is a spatially varying statistical framework that couples the memory and forcing of SM from the overlying layers and utilizes the information of Hydrologic Soil Groups (HSGs) that makes it spatially transferable. The model estimates SM at four depths i.e. 10, 20, 51 and 102 cm using the surface SM information (~ 0-5 cm) at daily scale for the time period of November 1, 1978 to December 31, 2014. The gridded, volumetric SM data (m3/m3) is available at 0.25×0.25° spatial resolution and the spatial extent of the data is 6°N to 38°N and 66°E to 98°E (latitude and longitude wise respectively).
Abstract The Climate Change Initiative (CCI) program of the European Space Agency (ESA) (ESA-CCI)... more Abstract The Climate Change Initiative (CCI) program of the European Space Agency (ESA) (ESA-CCI) provides a global surface Soil Moisture (SM) product by merging the information from various Active and Passive sensors on board for the period of 1971–2014. This study aims to assimilate this surface SM product into a recently developed model, namely Statistical Soil Moisture Profile (SSMP) model, to investigate the efficacy of this combination in developing a vertical soil moisture profile database over a climatologically vast area, such as Indian mainland. The SSMP model is a spatially varying statistical framework that couples the memory and forcing of SM from the overlying layers and utilizes the information of Hydrologic Soil Groups (HSGs) that makes it spatially transferable. The model estimates SM at four depths i.e. 10, 20, 51 and 102 cm using the surface SM information (~0–5 cm). The developed database is named as ‘ESA-CCI Driven Vertical Soil Moisture Profile (root zone) Database (EDVSMPD)’ and the simulated values are validated with observed SM data at many monitoring stations across India, maintained by India Meteorological Department (IMD). As an exemplary demonstration of the utility of the data, basin scale reconstruction of the historical agricultural droughts and its spatial distribution, using the developed EDVSMPD product, is illustrated for a medium sized drought-stricken river basin, namely Wardha, in the central part of India. The resulting SM product for a considerable length of period (more than 30 years) will be immensely useful for many hydro-climatological studies.
This study explores the potential of the Deep Learning (DL) approach to develop a model for basin... more This study explores the potential of the Deep Learning (DL) approach to develop a model for basin-scale drought assessment using information from a set of primary hydrometeorological precursors, namely air temperature, surface pressure, wind speed, relative humidity, evaporation, soil moisture and geopotential height. The novelty of the study lies in extracting the information from the hydrometeorological precursors through the efficacy of the DL algorithm, based on a one-dimensional convolutional neural network. Drought-prone regions, from where our study basins are selected, often suffer from the vagaries of rainfall that leads to drought-like situations. It is established that the proposed DL-based model is able to capture the underlying complex relationship between rainfall and the set of aforementioned hydrometeorological variables and, subsequently, shows its promise for the basin-scale meteorological drought assessment as revealed through different performance metrics and ski...
The focus of this study is to evaluate the efficacy of Machine Learning (ML) algorithms in the lo... more The focus of this study is to evaluate the efficacy of Machine Learning (ML) algorithms in the long-lead prediction of El Niño (La Niña) Modoki (ENSO Modoki) index (EMI). We evaluated two widely used non-linear ML algorithms namely Support Vector Regression (SVR) and Random Forest (RF) to forecast the EMI at various lead times, viz. 6, 12, 18 and 24 months. The predictors for the EMI are identified using Kendall’s tau correlation coefficient between the monthly EMI index and the monthly anomalies of the slowly varying climate variables such as sea surface temperature (SST), sea surface height (SSH) and soil moisture content (SMC). The importance of each of the predictors is evaluated using the Supervised Principal Component Analysis (SPCA). The results indicate both SVR and RF to be capable of forecasting the phase of the EMI realistically at both 6-months and 12-months lead times though the amplitude of the EMI is underestimated for the strong events. The analysis also indicates th...
Journal of the Indian Society of Remote Sensing, 2019
The present study aims to explore and compare the potential of different Artificial Intelligence-... more The present study aims to explore and compare the potential of different Artificial Intelligence-based Soft Computing (AISC) techniques to prepare surface Soil Moisture Content (SMC) map using fine-resolution (* 5 m), quad-polarized Synthetic Aperture Radar (SAR) data obtained from Radar Imaging Satellite 1 (RISAT1). Potential of three different AISC techniques, i.e. Support Vector Machine (SVM), Random Forest (RF) and Genetic Programming (GP), is explored. The estimated surface SMC is validated with the field soil moisture values in both bare and vegetated lands (\ 30 cm height). Different techniques have their own merits and demerits; however, we recommend GP to be most useful due to its other features. For example, GP provides the mathematical relationship, importance and sensitivity of each individual input to the surface SMC. This helps us to quantify the contribution of quad-polarized backscattering coefficients and soil texture information. It is noticed that the use of only SAR data without soil texture information may be acceptable with reasonable accuracy with an enormous benefit of its applicability to the locations without soil texture information. Using this, an exemplary fine-resolution (* 5 m) SMC map is developed. Such high-resolution maps for large spatial extent are expected to be highly useful in many applications. Keywords Soil moisture Á Remote sensing Á Synthetic Aperture Radar (SAR) Á Quad-polarized data Á Radar Imaging Satellite 1 Á Artificial Intelligence-based Soft Computing (AISC) techniques Electronic supplementary material The online version of this article (
IEEE Transactions on Geoscience and Remote Sensing, 2017
This paper attempts to probabilistically estimate the surface soil moisture content (SMC) by usin... more This paper attempts to probabilistically estimate the surface soil moisture content (SMC) by using the synthetic aperture radar data provided by radar imaging satellite1. The novelty of this paper lies in: 1) developing a combined index to understand the role of all the backscattering coefficients with different polarization and soil texture information in influencing the SMC; 2) using normalized incidence angles, which enables the model to be applicable for different incidence angles; and 3) determination of uncertainty range of the estimated SMC. The dimensionality problem, which is frequently observed in the multivariate analysis, is reduced in the development of the combined index by the use of supervised principal component analysis (SPCA). The SPCA also ensures the maximum attainable association between the developed combined index and surface SMC above wilting point (WP). The association between the combined index and the surface SMC above WP is modeled through joint probability distribution by using the Frank copula. The model is developed and validated with the field soil moisture values over 334 monitoring points within the study area. The outcomes obtained by applying the proposed model indicate an encouraging potential of the model to be applied for bareland and vegetated land (<30 cm height).
Information of Soil Moisture Content (SMC) at different depths i.e. vertical Soil Moisture (SM) p... more Information of Soil Moisture Content (SMC) at different depths i.e. vertical Soil Moisture (SM) profile is important as it influences several hydrological processes. In the era of microwave remote sensing, spatial distribution of soil moisture information can be retrieved from satellite data for large basins. However, satellite data can provide only the surface (~0–10 cm) soil moisture information. In this study, a methodological framework is proposed to estimate the vertical SM profile knowing the information of SMC at surface layer. The approach is developed by coupling the memory component of SMC within a layer and the forcing component from soil layer lying above by an Auto-Regressive model with an exogenous input (ARX) where forcing component is the exogenous input. The study highlights the mutual reliance between SMC at different depths at a given location assuming the ground water table is much below the study domain. The methodology is demonstrated for three depths: 25, 50 and 80 cm using SMC values of 10 cm depth. Model performance is promising for all three depths. It is further observed that forcing is predominant than memory for near surface layers than deeper layers. With increase in depth, contribution of SM memory increases and forcing dissipates. Potential of the proposed methodology shows some promise to integrate satellite estimated surface soil moisture maps to prepare a fine resolution, 3-dimensional soil moisture profile for large areas, which is kept as future scope of this study.
Information on vertical Soil Moisture Content (SMC) profile is important for several hydrometeoro... more Information on vertical Soil Moisture Content (SMC) profile is important for several hydrometeorological processes. This study borrows the idea of coupling the memory and forcing from a previous study and develops a spatially-varying Statistical Soil Moisture Profile (SSMP) model to estimate the vertical SMC profile. It uses the only surface soil moisture (0-5 cm) values and Hydrological Soil Groups (HSGs) information of the location. The focus of the study is incorporation of the HSG information to ensure the spatial transferability of the proposed model by capturing the spatial variations of soil moisture profile with the change in soil hydraulic properties. The wide range of soil moisture data for model development as well as for spatial validation are obtained from 171 stations from different networks of International Soil Moisture Network (ISMN) at five different depths, i.e., 5, 10, 20, 51 and 102 cm. The HSG information at the locations are extracted from the Web Soil Survey (WSS) database. The potential of spatial transferability of the SSMP model is assessed by applying it to the new stations within the corresponding HSG. Model performances are promising for all four depth pairs (5-10, 10-20, 20-51 and 51-102 cm) of all four HSGs during both model development and spatial validation given
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