Long-term effects of harvest on boreal forest soils in relation to a remote sensing-based soil moisture index

2020 ◽  
Vol 462 ◽  
pp. 117986 ◽  
Author(s):  
Paul D. Sewell ◽  
Sylvie A. Quideau ◽  
Miles Dyck ◽  
Ellen Macdonald
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Boreal Forest ◽  
Forest Soils ◽  
Long Term Effects ◽  
Moisture Index ◽  
Soil Moisture Index

scholarly journals Detection of Groundwater in Iraq-Wasit Governorate Using Remote Sensing and GIS

2021 ◽  
Vol 2114 (1) ◽  
pp. 012091
Author(s):  
Ali Sadeq Bahet ◽  
Mutasim Ibrahim Malik
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Water ◽  
Vegetation Cover ◽  
Ground Cover ◽  
Underground Water ◽  
Remote Sensing And Gis ◽  
Moisture Index ◽  
Water Index ◽  
Soil Moisture Index

Abstract The groundwater in Iraq has been studied for the need for it due to the shortage of surface water levels.The vegetation cover index, the soil moisture index, and the surface water index were used to detect the presence of groundwater in Wasit Governorate, Iraq. Those indicators that appear on the ground cover and indicate the presence of groundwater in the study area were compared with the coordinates of wells underground water. The results were identical with information obtained from the Ministry of Water Resources.

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

2009 ◽  
Vol 6 (5) ◽  
pp. 6247-6264 ◽  
Author(s):  
E. Peled ◽  
E. Dutra ◽  
P. Viterbo ◽  
A. Angert
Keyword(s):  
Soil Moisture ◽  
External Validation ◽  
Technical Note ◽  
Temperate Climate ◽  
Validation Dataset ◽  
Moisture Index ◽  
Analysis Technique ◽  
Global Soil ◽  
Soil Moisture Index

Abstract. Climate change induces long-term changes in soil-moisture. These changes can have important effects on the terrestrial biosphere, which can feedback into the climate system. In the past years there have been many attempts to produce and improve global soil-moisture datasets, however, comparing and validating these various datasets is not an easy task. Here, interannual variations in indices of soil moisture are compared to interannual changes in vegetation, as captured by NDVI. By comparing the correlations of the different indices with NDVI we evaluated which soil moisture index provides the most reliable soil moisture representation. We showed that NDVI can be used as an external validation dataset to soil moisture indices, in areas that are classified as warm temperate climate with hot or warm dry summers. Using the best performing index, NSM (Normalizes Soil Moisture), and the ICA (Independent Component Analysis) technique, we analyzed the response of vegetation to temperature and soil-moisture stresses over Europe.

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

Proceedings ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 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.

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

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1564 ◽  
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.

scholarly journals Soil moisture obtained through remote sensing to assess drought events

2020 ◽  
Vol 24 (9) ◽  
pp. 575-580 ◽  
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.

scholarly journals Long-term effects of timber harvesting on hemicellulolytic microbial populations in coniferous forest soils

2015 ◽  
Vol 10 (2) ◽  
pp. 363-375 ◽  
Author(s):  
Hilary T C Leung ◽  
Kendra R Maas ◽  
Roland C Wilhelm ◽  
William W Mohn
Keyword(s):  
Forest Soils ◽  
Coniferous Forest ◽  
Timber Harvesting ◽  
Microbial Populations ◽  
Long Term Effects

scholarly journals Development of the Soil Moisture Index to Quantify Agricultural Drought and Its “User Friendliness” in Severity-Area-Duration Assessment

2008 ◽  
Vol 9 (4) ◽  
pp. 660-676 ◽  
Author(s):  
Venkataramana Sridhar ◽  
Kenneth G. Hubbard ◽  
Jinsheng You ◽  
Eric D. Hunt
Keyword(s):  
Soil Moisture ◽  
Agricultural Drought ◽  
Extreme Drought ◽  
Weather Data ◽  
Moisture Index ◽  
Drought Duration ◽  
Available Water ◽  
Soil Moisture Index ◽  
The Difference ◽  
Wilting Point

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.

Soil Moisture Index

1917 ◽  
Vol 63 (2) ◽  
pp. 151-152
Author(s):  
Francis Ramaley
Keyword(s):  
Soil Moisture ◽  
Moisture Index ◽  
Soil Moisture Index
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