Long-term soil moisture content estimation using satellite and climate data in agricultural area of Mongolia

Natsagdorj, Enkhjargal; Renchin, Tsolmon; Maeyer, Philippe De; Dari, Chimgee; Tseveen, Batchuluun

doi:10.1080/10106049.2018.1434686

Cited by 9 publications

(7 citation statements)

References 16 publications

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“…Otgonbayar et al [41] investigated to prepare a cropland suitability map of Mongolia using Landsat and MODIS (MOD13, MOD15, and MOD17). Furthermore, Enkhjargal et al [42] used MODIS and SPOT time-series remotely sensed data from 2000-2013 to estimate long-term soil moisture content in agricultural regions of Mongolia. Ariya [43] used Landsat images from 2000 and 2015 to assess land degradation for the agricultural area of Mongolia.…”

Section: Introductionmentioning

confidence: 99%

Determination of Appropriate Remote Sensing Indices for Spring Wheat Yield Estimation in Mongolia

Tuvdendorj

Zeng

et al. 2019

Remote Sensing

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In Mongolia, the monitoring and estimation of spring wheat yield at the regional and national levels are key issues for the agricultural policy and food management as well as for the economy and society as a whole. The remote sensing data and technique have been widely used for the estimation of crop yield and production in the world. For the current research, nine remote sensing indices were tested that include normalized difference drought index (NDDI), normalized difference water index (NDWI), vegetation condition index (VCI), temperature condition index (TCI), vegetation health index (VHI), normalized multi-band drought index (NMDI), visible and shortwave infrared drought index (VSDI), and vegetation supply water index (VSWI). These nine indices derived from MODIS/Terra satellite have so far not been used for crop yield prediction in Mongolia. The primary objective of this study was to determine the best remote sensing indices in order to develop an estimation model for spring wheat yield using correlation and regression method. The spring wheat yield data from the ground measurements of eight meteorological stations in Darkhan and Selenge provinces from 2000 to 2017 have been used. The data were collected during the period of the growing season (June–August). Based on the analysis, we constructed six models for spring wheat yield estimation. The results showed that the range of the root-mean-square error (RMSE) values of estimated spring wheat yield was between 4.1 (100 kg ha−1) to 4.8 (100 kg ha−1), respectively. The range of the mean absolute error (MAE) values was between 3.3 to 3.8 and the index of agreement (d) values was between 0.74 to 0.84, respectively. The conclusion was that the best model would be (R2 = 0.55) based on NDWI, VSDI, and NDVI out of the nine indices and could serve as the most effective predictor and reliable remote sensing indices for monitoring the spring wheat yield in the northern part of Mongolia. Our results showed that the best timing of yield prediction for spring wheat was around the end of June and the beginning of July, which is the flowering stage of spring wheat in this study area. This means an accurate yield prediction for spring wheat can be achieved two months before the harvest time using the regression model.

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Section: Introductionmentioning

confidence: 99%

Determination of Appropriate Remote Sensing Indices for Spring Wheat Yield Estimation in Mongolia

Tuvdendorj

Zeng

et al. 2019

Remote Sensing

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“…There were limited stations for soil moisture measurement in croplands over Mongolia. Soil moisture content was acquired at depths of 0-20 cm and 0-50 cm at monthly intervals (the 7th, 17th, and 27th days of each month) from April to September due to seasonal conditions [44]. Soil moisture content was averaged over the monthly intervals from May to August (2015-2020).…”

Section: Cru Data and Meteorological Datamentioning

confidence: 99%

“…If r is positive and r values are close to 1, this describes a valuable relationship between variables [56]. Linear Pearson's correlation (r) was determined at a monthly timescale [44,55] for the satellite-derived and meteorological station/NSO data.…”

Section: Model Validationmentioning

confidence: 99%

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

et al. 2021

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Soil moisture is one of the essential variables of the water cycle, and plays a vital role in agriculture, water management, and land (drought) and vegetation cover change as well as climate change studies. The spatial distribution of soil moisture with high-resolution images in Mongolia has long been one of the essential issues in the remote sensing and agricultural community. In this research, we focused on the distribution of soil moisture and compared the monthly precipitation/temperature and crop yield from 2010 to 2020. In the present study, Soil Moisture Active Passive (SMAP) and Moderate Resolution Imaging Spectroradiometer (MODIS) data were used, including the MOD13A2 Normalized Difference Vegetation Index (NDVI), MOD11A2 Land Surface Temperature (LST), and precipitation/temperature monthly data from the Climate Research Unit (CRU) from 2010 to 2020 over Mongolia. Multiple linear regression methods have previously been used for soil moisture estimation, and in this study, the Autoregressive Integrated Moving Arima (ARIMA) model was used for soil moisture forecasting. The results show that the correlation was statistically significant between SM-MOD and soil moisture content (SMC) from the meteorological stations at different depths (p < 0.0001 at 0–20 cm and p < 0.005 at 0–50 cm). The correlation between SM-MOD and temperature, as represented by the correlation coefficient (r), was 0.80 and considered statistically significant (p < 0.0001). However, when SM-MOD was compared with the crop yield for each year (2010–2019), the correlation coefficient (r) was 0.84. The ARIMA (12, 1, 12) model was selected for the soil moisture time series analysis when predicting soil moisture from 2020 to 2025. The forecasting results are shown for the 95 percent confidence interval. The soil moisture estimation approach and model in our study can serve as a valuable tool for confident and convenient observations of agricultural drought for decision-makers and farmers in Mongolia.

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“…Among Landsat 5, 7 and 8, only Landsat 8 carries two thermal bands (Parastatidis et al, 2017), therefore the SC approach is used in this study for consistency. The LST was calculated using Equation (3) by (Natsagdorj et al, 2019).…”

Section: = ⁄mentioning

confidence: 99%

Estimation of Forest Coverage in Northern Region of Mongolia Using Sentinel and Landsat Data

Norovsuren

Renchin

Tseveen

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. This paper aims to apply Forest Index (FI) and to determine forest coverage in the study area. The study area (49° 15ʹ to 49° 10ʹ N and 104° 05ʹ to 104° 15ʹ E) is located in the northern region of Mongolia and consist of mixed forest. Larch forest (86.12%) is dominating in the study area. The Sentinel-2 satellite data for the years 2015–2019 were used in the research. The land surface temperature (LST) was produced from Landsat-8 OL. FI methodology was applied for the Sentinel data in order to estimate larch forest coverage. The output map of forest coverage was compared with ground truth measurements and thematic map. The agreement between FI map and ground measurement was 85%. LST from Landsat and FI from Sentinel were sampled in to same size. The relationship between LST (Landsat-8) and FI (Sentinel-2) was reasonable (R = 0.5). FI index and LST is applicable for different forest type in the region.

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Long-term soil moisture content estimation using satellite and climate data in agricultural area of Mongolia

Cited by 9 publications

References 16 publications

Determination of Appropriate Remote Sensing Indices for Spring Wheat Yield Estimation in Mongolia

Determination of Appropriate Remote Sensing Indices for Spring Wheat Yield Estimation in Mongolia

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

Estimation of Forest Coverage in Northern Region of Mongolia Using Sentinel and Landsat Data

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