GORS 1(16) Increasing the Soil Moisture Estimation Accuracy of the Tringle Method using of Vegetative-Thematic Space Images Data by Temporal Models Under Syrian Conditions

Abstract

Abstract: MODIS were used to estimate surface soil moisture using a triangle method based on both spectral indices of Land Surface Temperature (LST) and Vegetation indicator (NDVI) (Normalized Difference Vegetation Index). The research was carried out on a wide area characterized by the diversity of its topography, vegetation cover, climate and amounts of precipitation in it to include all areas of rain stability located on the territory of the Syrian Arab Republic. Soil moisture was measuring at several sites that also distributed to all rainfall stable areas has been field-tested using the weighted method for estimating soil moisture 2016 season. The results of the research showed: that the relationship of soil moisture estimated spatially with that recorded in the field in the wet months is better than in the dry, as well as the relationship between them better in areas with low rainfall than in areas of high rainfall. There is a significant difference between the soil moisture estimated space and the field recorded in the coastal areas during the dry months, while in the interior regions, the differences were less between the moisture of the spatial and field soils in the dry months, because the decrease in soil moisture in the coastal areas when precipitation decreases is not associated with a decrease in the value of the vegetation index due to the poor speed of response of plants with dense vegetation to a decrease in soil moisture. There is a significant correlation between field and space estimated soil moisture during the rainy months, as it was associated with a few variations in soil moisture values ​​for the relative standard error and the space estimated error to the field recorder. There are large variations in the values ​​of soil moisture for the relative standard error and the estimated error (space) to the recorded (field) during the dry months, despite the presence of a significant correlation between the moisture of field and space soil during the same period. The possibility of adopting space images in estimating soil moisture with high reliability in the wet months for all areas of rain stability, while re-modeling the estimation of space soil moisture with field data led to an increase in the accuracy of soil moisture estimation maps. The research recommends the possibility of using this methodology to estimate soil moisture from space image data periodically for all lands of the Syrian Arab Republic, because of its importance in identifying many plant production relationships, starting from monitoring and forecasting agricultural drought to estimating the amount of plant production and biomass.