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Mapping soil moisture in the central Ebro river valley (northeast Spain) with Landsat and NOAA satellite imagery: a comparison with meteorological data

Taylor & Francis
International Journal of Remote Sensing
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This paper analyses and maps the spatial distribution of soil moisture using remote sensing: National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) and Landsat-Enhanced Thematic Mapper (ETM+) images. The study was carried out in the central Ebro river valley (northeast Spain), and examines the spatial relationships between the distribution of soil moisture and several meteorological and geographical variables following a long, intense dry period (winter 2000). Soil moisture estimates were obtained using thermal, visible and near-infrared data and by applying the ‘triangle method’, which describes relationships between surface temperature (Ts ) and fractional vegetation cover (Fr ). Low differences were found between the soil moisture estimates obtained using AVHRR and ETM+ sensors. Soil moisture estimated using remote sensing is close to estimations obtained from climate indices. This fact, and the high similarity between estimations of both sensors, suggests the reasonable reliability of soil moisture remote sensing estimations. Moreover, in estimations from both sensors the spatial distribution of soil moisture was largely accounted for by meteorological variables, mainly precipitation in the dry period. The results indicate the high reliability of remote sensing for determining areas affected by water deficits and for quantifying drought intensity.
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... Soil moisture information has many benefits such as being used to predict weather patterns, manage water storage areas/reservoirs, information on irrigation schedules during the growing period and information on crop yields, and can be used as an early warning against various disasters, one of which is drought [2]. Research by Serrano et al., [3] regarding the estimation of soil moisture by utilising remote sensing technology using satellite image data based on spectral information seen from infrared is proven to be used at different spatial scales and has the advantage of monitoring soil moisture temporally so that it can presenting the moisture value of the soil. In predicting the value of soil moisture, this study will use the method Soil Moisture Index (SMI) obtained from image processing using the relationship between Land Surface Temperature (LST) with the Normalized Difference Vegetation Index (NDVI) [3]. ...
... Research by Serrano et al., [3] regarding the estimation of soil moisture by utilising remote sensing technology using satellite image data based on spectral information seen from infrared is proven to be used at different spatial scales and has the advantage of monitoring soil moisture temporally so that it can presenting the moisture value of the soil. In predicting the value of soil moisture, this study will use the method Soil Moisture Index (SMI) obtained from image processing using the relationship between Land Surface Temperature (LST) with the Normalized Difference Vegetation Index (NDVI) [3]. ...
... LSTmin is the minimum temperature in a particular class of vegetation fraction, and LSTmax is the maximum temperature for a specific type of vegetation fraction [3]. Calculation of the maximum and minimum temperature is processed using statistical data to determine the upper or dry moisture limit calculated using equation 2 and equation 3: ...
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Drought is a natural phenomenon that has adverse effects on agriculture, the economy, and human well-being. The primary objective of this research was to comprehensively understand the drought conditions in Sistan and Balouchestan Province from 2002 to 2017 from two perspectives: vegetation cover and hydrology. To achieve this goal, the study utilized MODIS satellite data in the first part to monitor vegetation cover as an indicator of agricultural drought. In the second part, GRACE satellite data were employed to analyze changes in groundwater resources as an indicator of hydrological drought. To assess vegetation drought, four indices were used: Vegetation Health Index (VHI), Vegetation Drought Index (VDI), Visible Infrared Drought Index (VSDI), and Temperature Vegetation Drought Index (TVDI). To validate vegetation drought indices, they were compared with Global Land Data Assimilation System (GLDAS) precipitation data. The vegetation indices showed a strong, statistically significant correlation with GLDAS precipitation data in most regions of the province. Among all indices, the VHI showed the highest correlation with precipitation (moderate (0.3–0.7) in 51.7% and strong (≥0.7) in 45.82% of lands). The output of vegetation indices revealed that the study province has experienced widespread drought in recent years. The results showed that the southern and central regions of the province have faced more severe drought classes. In the second part of this research, hydrological drought monitoring was conducted in fifty third-order sub-basins located within the study province using the Total Water Storage (TWS) deficit, Drought Severity, and Total Storage Deficit Index )TSDI Index). Annual average calculations of the TWS deficit over the period from April 2012 to 2016 indicated a substantial depletion of groundwater reserves in the province, amounting to a cumulative loss of 12.2 km3 Analysis results indicate that drought severity continuously increased in all study basins until the end of the study period. Studies have shown that all the studied basins are facing severe and prolonged water scarcity. Among the 50 studied basins, the Rahmatabad basin, located in the semi-arid northern regions of the province, has experienced the most severe drought. This basin has experienced five drought events, particularly one lasting 89 consecutive months and causing a reduction of more than 665.99 km3. of water in month 1, placing it in a critical condition. On the other hand, the Niskoofan Chabahar basin, located in the tropical southern part of the province near the Sea of Oman, has experienced the lowest reduction in water volume with 10 drought events and a decrease of approximately 111.214 km3. in month 1. However, even this basin has not been spared from prolonged droughts. Analysis of drought index graphs across different severity classes confirmed that all watersheds experienced drought conditions, particularly in the later years of this period. Data analysis revealed a severe water crisis in the province. Urgent and coordinated actions are needed to address this challenge. Transitioning to drought-resistant crops, enhancing irrigation efficiency, and securing water rights are essential steps towards a sustainable future.
... & Tucker, 2005;Kogan, 1990;Liu et al., 1994;Malo & Nicholson, 1990;Tucker et al., 1981;Vincente-Serrano et al, 2004 . Bartholic et al., 1972;Heilman et al., 1976;Hashimoto et al., 1984;Omasa et al., 1981 . ...
... The four dominant SM remote sensing methods based on spectrum characteristics are described below (Walker 1999;Vicente et al. 2004) • Visible: calculation of SM by determining soil albedo index of refraction. • Thermal infrared: calculation of SM by measuring surface soil temperature. ...
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Soil moisture (SM) has an important role in the earth's water cycle and is a key variable in water resources management. Considering the critical state of water resources in the Urmia Lake basin, northwest Iran, this study examined the potential for utilizing a variety of remote sensing data and products, in conjunction with a promising downscaling method, to monitor soil moisture with a reasonable spatial and temporal resolution, as a novel and effective tool for agricultural and water resource management. Accordingly, remote sensing products of surface soil moisture were scaled to MODIS's image scale (∼1 km) using the UCLA downscaling method and Temperature, Vegetation, Drought Index (TVDI) values obtained from the scattering space method. Results showed that the LPRM, ESA-CCI, and GLDAS downscaled images had the highest inverse correlation with the TVDI (best results) accordingly equal to −0.600, −0.787, and −0.630. Also, based on the evaluation of the obtained results with the ground stations data, the LPRM and the ESA-CCI downscaled images had the best statistical indices values accordingly in 2010 and 2014 that confirm the promising application of remote sensing soil moisture data (rLPRM (2010) = 0.92, MAELPRM (2010) = 4.14%, RMSELPRM (2010) = 6.39% and rESA-CCI (2014) = 0.7, MAEESA-CCI (2014) = 2.23%, RMSEESA-CCI (2014) = 2.59%). HIGHLIGHTS Soil moisture spatio-temporal monitoring was carried out as an important step in the path of sustainable development.; The research conducted on the downscaling of soil moisture radar products using MODIS images alongside scattering space and UCLA methods proved their ability in various land uses.; LPRM and ESA-CCI products were found to have the highest accuracy in monitoring soil moisture in the Urmia Lake basin.;
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... In general, the problem has been studied through the use of indexes such as VDI or Vegetation Dryness Index (or VDI), the TVDI or Temperature Vegetation Dryness Index (TVDI), and the improved TVDI or iTVDI (among many others, see [4][5][6]). These indices are based on the NDVI (normalized differential vegetation index), the surface temperature, Ts, and the air temperature close to the surface, or Ta. ...
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... Indeks kelembaban tanah didefinisikan sebagai proporsi perbedaan antara kelembaban tanah saat ini dan titik layu permanen terhadap kapasitas lapang dan kelembaban tanah sisa. Nilai indeks berkisar antara 0-1, nilai 0 menunjukkan kondisi kering ekstrim dan nilai 1 menunjukkan kondisi basah ekstrim (Vicente-Serrano et al., 2004;Zeng et al., 2004;Zhan et al., 2004). ...
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