An integrated methodology for soil moisture analysis using multispectral data in Mongolia

Abstract

Soil moisture (SM) content is one of the most important environmental variables in relation to land surface climatology, hydrology, and ecology. Long-term SM data-sets on a regional scale provide reasonable information about climate change and global warming specific regions. The aim of this research work is to develop an integrated methodology for SM of kastanozems soils using multispectral satellite data. The study area is Tuv (48°40′30″N and 106°15′55″E) province in the forest steppe zones in Mongolia. In addition to this, land surface temperature (LST) and normalized difference vegetation index (NDVI) from Landsat satellite images were integrated for the assessment. Furthermore, we used a digital elevation model (DEM) from ASTER satellite image with 30-m resolution. Aspect and slope maps were derived from this DEM. The soil moisture index (SMI) was obtained using spectral information from Landsat satellite data. We used regression analysis to develop the model. The model shows how SMI from satellite depends on LST, NDVI, DEM, Slope, and Aspect in the agricultural area. The results of the model were correlated with the ground SM data in Tuv province. The results indicate that there is a good agreement between output SM and SM of ground truth for agricultural area. Further research is focused on moisture mapping for different natural zones in Mongolia. The innovative part of this research is to estimate SM using drivers which are vegetation, land surface temperature, elevation, aspect, and slope in the forested steppe area. This integrative methodology can be applied for different regions with forest and desert steppe zones.

Los drones una herramienta para una agricultura eficiente: un futuro de alta tecnología

“…El contenido de humedad del suelo (SM) es una de las variables ambientales más importantes en relación con la climatología de la superficie terrestre, la hidrología y la ecología. Los conjuntos de datos de SM a largo plazo a escala regional proporcionan información razonable sobre el cambio climático y las regiones específicas del calentamiento global utilizando datos satelitales multiespectrales (Natsagdorj et al, 2017). La agricultura de precisión requiere una alta gestión espacial de los insumos para la producción agrícola.…”

Spatial Distribution of Soil Moisture in Mongolia Using SMAP and MODIS Satellite Data: A Time Series Model (2010–2025)

“…There are many ways to estimate soil moisture, including direct and indirect methods. The most accurate method is direct measurement in the field (gravimetric method) to estimate soil moisture by point measurement [5], but this is costly [6]. Therefore, remote sensing techniques have become popular for estimating soil moisture at a regional scale due to the sensing ability of the regional SM with low-resolution images.…”

“…Chelsea et al [21] used LST/NDVI data to obtain an enhanced spatial resolution of soil moisture from SMAP at 9 km down to 1 km. Natsagdorj et al [5] developed a model for soil moisture using multiple regression analysis, and the model showed that the type of soil moisture index from the satellite measurements depends on the LST, NDVI, elevation, slope, and aspect. The results indicate a good correlation between the developed model and ground truth measurements in the subprovinces of Mongolia.…”

Uso de drones y sensores remotos para el monitoreo de laderas: una revisión

“…Los Drones o VANT (Vehículos Aéreos No Tripulados) se utilizan cada vez más en actividades de investigación y en diferentes campos de aplicación, como la producción de cartografía [12], [13], supervisión de obras civiles [14], seguimiento de la agricultura [15]- [17], identificación de posibles zonas de riesgo [18], [19], manejo de desastres [20], [21], se han demostrado otros ejemplos de aplicaciones para el monitoreo de incendios forestales, como en el estudio de Zarco-Tejada y Berni [22] quienes utilizaron un UAV de ala fija con sensores térmicos e hiperespectrales. También se han reportado experimentos para clasificación de árboles [23], cálculo del Índice de Vegetación de Diferencia Normalizada [24] y monitoreo de temperaturas de arroyos [25].…”