Agricultural drought identification based on Soil Moisture Index (SMI) during 2019 Indian Ocean Dipole (IOD) in Bekasi Regency

Abstract This paper examines the role of soil moisture in quantifying drought through the development of a drought index using observed and modeled soil moisture. In Nebraska, rainfall is received primarily during the crop-growing season and the supply of moisture from the Gulf of Mexico determines if the impending crop year is either normal or anomalous and any deficit of rain leads to a lack of soil moisture storage. Using observed soil moisture from the Automated Weather Data Network (AWDN), the actual available water content for plants is calculated as the difference between observed or modeled soil moisture and wilting point, which is subsequently normalized with the site-specific, soil property–based, idealistic available water for plants that is calculated as the difference between field capacity and wilting point to derive the soil moisture index (SMI). This index is categorized into five classes from no drought to extreme drought to quantitatively assess drought in both space and time. Additionally, with the aid of an in-house hydrology model, soil moisture was simulated in order to compute model-based SMI and to compare the drought duration and severity for various sites. The results suggest that the soil moisture influence, a positive feedback process reported in many earlier studies, is unquestionably a quantitative indicator of drought. Also, the severity and duration of drought across Nebraska has a clear gradient from west to east, with the Panhandle region experiencing severe to extreme drought in the deeper soil layers for longer periods (>200 days), than the central and southwestern regions (125–150 days) or the eastern regions about 100 days or less. The anomalous rainfall years can eliminate the distinction among these regions with regard to their drought extent, severity, and persistence, thus making drought a more ubiquitous phenomenon, but the recovery from drought can be subject to similar gradations. The spatial SMI maps presented in this paper can be used with the Drought Monitor maps to assess the local drought conditions more effectively.

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Climatic Influences on Agricultural Drought Risks Using Semiparametric Kernel Density Estimation

Water ◽

10.3390/w12102813 ◽

2020 ◽

Vol 12 (10) ◽

pp. 2813

Author(s):

Marangely Gonzalez Cruz ◽

E. Annette Hernandez ◽

Venkatesh Uddameri

Keyword(s):

Soil Moisture ◽

Density Estimation ◽

Kernel Density Estimation ◽

Potential Evapotranspiration ◽

Kernel Density ◽

Agricultural Drought ◽

High Plains ◽

Moisture Index ◽

The Us ◽

Soil Moisture Index

A bivariate kernel density estimation (KDE) method was utilized to develop a stochastic framework to assess how agricultural droughts are related to unfavorable meteorological conditions. KDE allows direct estimation of the bivariate cumulative density function which can be used to extract the marginal distributions with minimal subjectivity. The approach provided excellent fits to bivariate relationships between the standardized soil moisture index (SSMI) computed at three- and six-month accumulations and standardized measures of precipitation (P), potential evapotranspiration (PET), and atmospheric water deficit (AWD = P − PET) at 187 stations in the High Plains region of the US overlying the Ogallala Aquifer. The likelihood of an agricultural drought given a precipitation deficit could be as high as 40–65% within the study area during summer months and between 20–55% during winter months. The relationship between agricultural drought risks and precipitation deficits is strongest in the agriculturally intensive central portions of the study area. The conditional risks of agricultural droughts given unfavorable PET conditions are higher in the eastern humid portions than the western arid portions. Unfavorable PET had a higher impact on the six-month standardized soil moisture index (SSMI6) but was also seen to influence three-month SSMI (SSMI3). Dry states as defined by AWD produced higher risks than either P or PET, suggesting that both of these variables influence agricultural droughts. Agricultural drought risks under favorable conditions of AWD were much lower than when AWD was unfavorable. The agricultural drought risks were higher during the winter when AWD was favorable and point to the role of soil characteristics on agricultural droughts. The information provides a drought atlas for an agriculturally important region in the US and, as such, is of practical use to decision makers. The methodology developed here is also generic and can be extended to other regions with considerable ease as the global datasets required are readily available.

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Suitability of assimilated data-based standardized soil moisture index for agricultural drought monitoring

Acta Ecologica Sinica ◽

10.5846/stxb201801190153 ◽

2019 ◽

Vol 39 (6) ◽

Author(s):

周洪奎 ZHOU Hongkui ◽

武建军 WU Jianjun ◽

李小涵 LI Xiaohan ◽

刘雷震 LIU Leizhen ◽

杨建华 YANG Jianhua ◽

...

Keyword(s):

Soil Moisture ◽

Agricultural Drought ◽

Drought Monitoring ◽

Moisture Index ◽

Soil Moisture Index

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Tools for Communicating Agricultural Drought over the Brazilian Semiarid Using the Soil Moisture Index

Water ◽

10.3390/w10101421 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1421 ◽

Cited By ~ 6

Author(s):

Marcelo Zeri ◽

Regina S. Alvalá ◽

Rogério Carneiro ◽

Gisleine Cunha-Zeri ◽

José Costa ◽

...

Keyword(s):

Soil Moisture ◽

Water Stress ◽

Real Time ◽

Agricultural Drought ◽

Drought Risk ◽

Continuous Exposure ◽

Short Term ◽

Moisture Index ◽

Brazilian Semiarid ◽

Soil Moisture Index

Soil moisture over the Brazilian semiarid region is presented in different visualizations that highlight spatial, temporal and short-term agricultural risk. The analysis used the Soil Moisture Index (SMI), which is based on a normalization of soil moisture by field capacity and wilting point. The index was used to characterize the actual soil moisture conditions into categories from severe drought to very wet. In addition, the temporal evolution of SMI was implemented to visualize recent trends in short-term drought and response to rainfall events at daily time steps, as new data are available. Finally, a visualization of drought risk was developed by considering a critical value of SMI (assumed as 0.4), below which water stress is expected to be triggered in plants. A novel index based on continuous exposure to critical SMI was developed to help bring awareness of real time risk of water stress over the region: the Index of Stress in Agriculture (ISA). The index was tested during a drought over the region and successfully identified locations under water stress for periods of three days or more. The monitoring tools presented here help to describe the real time conditions of drought over the region using daily observations. The information from those tools support decisions on agricultural management such as planting dates, triggering of irrigation, or harvesting.

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An empirical standardized soil moisture index for agricultural drought assessment from remotely sensed data

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2015.06.011 ◽

2016 ◽

Vol 48 ◽

pp. 74-84 ◽

Cited By ~ 62

Author(s):

Hugo Carrão ◽

Simone Russo ◽

Guadalupe Sepulcre-Canto ◽

Paulo Barbosa

Keyword(s):

Soil Moisture ◽

Remotely Sensed ◽

Agricultural Drought ◽

Remotely Sensed Data ◽

Moisture Index ◽

Drought Assessment ◽

Soil Moisture Index

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Assessment and Impact of Soil Moisture Index in Agricultural Drought Estimation Using Remote Sensing and GIS Techniques

Proceedings ◽

10.3390/ecws-3-05802 ◽

2018 ◽

Vol 7 (1) ◽

pp. 2 ◽

Cited By ~ 2

Author(s):

Arnab Saha ◽

Manti Patil ◽

Vikas Chandra Goyal ◽

Devendra Singh Rathore

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Surface Temperature ◽

Land Surface ◽

Vegetation Index ◽

Near Infrared ◽

Agricultural Drought ◽

Statistical Regression ◽

Moisture Index ◽

Soil Moisture Index

Soil moisture takes an important part involving climate, vegetation and drought. This paper explains how to calculate the soil moisture index and the role of soil moisture. The objective of this study is to assess the moisture content in soil and soil moisture mapping by using remote sensing data in the selected study area. We applied the remote sensing technique which relies on the use of the soil moisture index (SMI) which uses the data obtained from satellite sensors in its algorithm. The relationship between land surface temperature (LST) and the normalized difference vegetation index (NDVI) are based on experimental parameterization for the soil moisture index. Multispectral satellite data (visible, red and near-infrared (NIR) and thermal infrared sensor (TIRS) bands) were utilized for assessment of LST and to make vegetation indices map. Geographic Information System (GIS) and image processing software were utilized to determine the LST and NDVI. NDVI and LST are considered as essential data to obtain SMI calculation. The statistical regression analysis of NDVI and LST were shown in standardized regression coefficient. NDVI values are within range −1 to 1 where negative values present loss of vegetation or contaminated vegetation, whereas positive values explain healthy and dense vegetation. LST values are the surface temperature in °C. SMI is categorized into classes from no drought to extreme drought to quantitatively assess drought. The final result is obtainable with the values range from 0 to 1, where values near 1 are the regions with a low amount of vegetation and surface temperature and present a higher level of soil moisture. The values near 0 are the areas with a high amount of vegetation and surface temperature and present the low level of soil moisture. The results indicate that this method can be efficiently applied to estimate soil moisture from multi-temporal Landsat images, which is valuable for monitoring agricultural drought and flood disaster assessment.

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Drought Propagation in Semi-Arid River Basins in Latin America: Lessons from Mexico to the Southern Cone

Water ◽

10.3390/w10111564 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1564 ◽

Cited By ~ 7

Author(s):

Melanie Oertel ◽

Francisco Meza ◽

Jorge Gironás ◽

Christopher A. Scott ◽

Facundo Rojas ◽

...

Keyword(s):

Soil Moisture ◽

Drought Monitoring ◽

Moisture Index ◽

Data Set ◽

Basin Scale ◽

Soil Moisture Index ◽

Streamflow Data ◽

Governmental Institutions ◽

Negative Impacts ◽

Combined Data

Detecting droughts as early as possible is important in avoiding negative impacts on economy, society, and environment. To improve drought monitoring, we studied drought propagation (i.e., the temporal manifestation of a precipitation deficit on soil moisture and streamflow). We used the Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Streamflow Index (SSI), and Standardized Soil Moisture Index (SSMI) in three drought-prone regions: Sonora (Mexico), Maipo (Chile), and Mendoza-Tunuyán (Argentina) to study their temporal interdependence. For this evaluation we use precipitation, temperature, and streamflow data from gauges that are managed by governmental institutions, and satellite-based soil moisture from the ESA CCI SM v03.3 combined data set. Results confirm that effective drought monitoring should be carried out (1) at river-basin scale, (2) including several variables, and (3) considering hydro-meteorological processes from outside its boundaries.

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Soil moisture obtained through remote sensing to assess drought events

Revista Brasileira de Engenharia Agrícola e Ambiental ◽

10.1590/1807-1929/agriambi.v24n9p575-580 ◽

2020 ◽

Vol 24 (9) ◽

pp. 575-580 ◽

Cited By ~ 1

Author(s):

Tiago de M. Inocêncio ◽

Alfredo Ribeiro Neto ◽

Alzira G. S. S. Souza

Keyword(s):

Climate Change ◽

Soil Moisture ◽

Condition Index ◽

Semiarid Region ◽

Moisture Index ◽

Moisture Condition ◽

Soil Moisture Condition ◽

Northeast Region ◽

Promising Tool ◽

Soil Moisture Index

ABSTRACT The sequence of drought events in the Northeast of Brazil in recent decades raises attention to the importance of studying this phenomenon. The objective of this study was to evaluate the duration and severity of drought events from 1988 to 2018 in hydrographic basins of the state of Pernambuco, Brazil, using two drought indexes: Standardized Soil Moisture Index and Soil Moisture Condition Index, calculated based on data of the Soil Moisture Project of the European Space Agency’s Climate Change Initiative. The duration of the droughts was determined considering the months between their beginning and end, and their severity was based on the area formed in the graph between the curve of the index and the x-axis. The soil moisture database showed to be a promising tool for the analysis and monitoring of drought events in the Northeast region of Brazil, mainly for analysis and monitoring of drought events. The indexes allowed the evaluation of the drought phenomenon over the 30-year period, showing increases from 2012, which were more pronounced in the Semiarid region. The hydrographic basins responded differently to a same event, depending on the climate characteristics of the region in which they are located. Consecutive years with rainfall below the historical mean increased the magnitude of the droughts, as found for the 2012-2017 period, in which the indexes presented delays to return to more favorable values, showing the effect that one drought year has on the following year.

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