Suitability Assessment of Satellite-Derived Drought Indices for Mongolian Grassland

Chang, Sheng; Wu, Bingfang; Niu, Y.; Davdai, Bulgan; Nasanbat, Elbegjargal

doi:10.3390/rs9070650

Cited by 39 publications

(23 citation statements)

References 36 publications

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“…Nevertheless, to date, no studies of crop yield estimation using remote sensing indicators have been done yet in Mongolia. Recently, remote sensing indicators are employed to monitor the drought across the pasture lands of Mongolia [44] and provide valuable information for drought management and reduction. Compared to drought, more attention should be paid to crop yield, while these indices were not getting enough attention in the field of crop yield in 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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“…Desertification is a serious global environmental problem. Under the influence of natural environmental change and the anthropogenic causes of grassland degradation, ecological and environmental deterioration, as well as desertification have become more severe in Mongolia [1]. Plant species from 1961 to 2006 in the forest steppe, real steppe, mountain steppe, desert steppe, and desert regions in Mongolia were reduced by 50.0%, 44.7%, 30.3%, 23.8%, and 26.7%, respectively [2].…”

Section: Introductionmentioning

confidence: 99%

Desertification Information Extraction Based on Feature Space Combinations on the Mongolian Plateau

et al. 2018

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The Mongolian plateau is a hotspot of global desertification because it is heavily affected by climate change, and has a large diversity of vegetation cover across various regions and seasons. Within this arid region, it is difficult to distinguish desertified land from other land cover types using low-quality vegetation information. To address this, we analyze both the effects and the applicability of different feature space models for the extraction of desertification information with the goal of finding appropriate approaches to extract desertification data on the Mongolian plateau. First, we used Landsat 8 remote sensing images to invert NDVI (normalized difference vegetation index), MSAVI (modified soil adjusted vegetation index), TGSI (topsoil grain size index), and albedo (land surface albedo) data. Then, we constructed the feature space models of Albedo-NDVI, Albedo-MSAVI, and Albedo-TGSI, and compared their extraction accuracies. Our results show that the overall classification accuracies of the three models were 84.53%, 85.60%, and 88.27%, respectively, indicating that the three feature space models are feasible for extracting information relating to desertification on the Mongolian plateau. Further analysis indicates that the Albedo-NDVI model is suitable for areas with a high vegetation cover or a high forest ratio, whilst the Albedo-MSAVI model is suitable for areas with relatively low vegetation cover, and the Albedo-TGSI model is suitable for areas with extremely low vegetation cover, including the widely distributed Gobi Desert and other barren areas. This study provides a technical selection reference for the investigation of desertification of different zones on the Mongolian plateau.

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“…The Vegetation Health Index (VHI) is a health condition index that takes into account the effect of vegetation leaf surface and temperature on vegetation. The VHI is used to reflect the differences in the spatio-temporal patterns of drought and has a better effect of drought monitoring [18]. Sandholt et al indicated that there was a triangular or trapezoidal relationship between the vegetation index and land surface temperature, and developed the Temperature Vegetation Dryness Index (TVDI) based on the scattered point feature space of these two parameters [19].…”

Section: Introductionmentioning

confidence: 99%

Capability of Remotely Sensed Drought Indices for Representing the Spatio–Temporal Variations of the Meteorological Droughts in the Yellow River Basin

Wang

Yang

et al. 2018

Remote Sensing

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Due to the advantages of wide coverage and continuity, remotely sensed data are widely used for large-scale drought monitoring to compensate for the deficiency and discontinuity of meteorological data. However, few studies have focused on the capability of various remotely sensed drought indices (RSDIs) to represent the spatio–temporal variations of meteorological droughts. In this study, five RSDIs, namely the Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Modified Temperature Vegetation Dryness Index (MTVDI), and Normalized Vegetation Supply Water Index (NVSWI), were calculated using monthly Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) from the Moderate Resolution Imaging Spectroradiometer (MODIS). The monthly NDVI and LST data were filtered by the Savitzky–Golay (S-G) filtering method. A meteorological station-based drought index represented by the Standardized Precipitation Evapotranspiration Index (SPEI) was compared with the RSDIs. Additionally, the dimensionless Skill Score (SS) method was adopted to identify the spatiotemporally optimal RSDIs for presenting meteorological droughts in the Yellow River basin (YRB) from 2000 to 2015. The results indicated that: (1) RSDIs revealed a decreasing drought trend in the overall YRB consistent with the SPEI except for in winter, and different variations of seasonal trends spatially; (2) the optimal RSDIs in spring, summer, autumn, and winter were VHI, TCI, MTVDI, and VCI, respectively, and the average correlation coefficient between the RSDIs and the SPEI was 0.577 (α = 0.05); and (3) different RSDIs have time lags of zero–three months compared with the meteorological drought index.

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Suitability Assessment of Satellite-Derived Drought Indices for Mongolian Grassland

Cited by 39 publications

References 36 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

Desertification Information Extraction Based on Feature Space Combinations on the Mongolian Plateau

Capability of Remotely Sensed Drought Indices for Representing the Spatio–Temporal Variations of the Meteorological Droughts in the Yellow River Basin

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