Spatial and temporal dynamics of vegetation in the San Pedro River basin area

Qi, Jiaguo; Marsett, R.; Moran, M. Susan; Goodrich, David C.; Heilman, P.; Kerr, Yann H.; Dedieu, Gérard; Chehbouni, Abdelghani; Zhang, X.X

doi:10.1016/s0168-1923(00)00195-7

Cited by 156 publications

(91 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The addition of the NDVI and soil characteristics in addition to microwave observations to these models reduced the RMSE for soil moisture retrieval by 30% approximately. This also opens the discussion for use of more sophisticated vegetation indices such as Fractional Green Vegetation Cover, Green Leaf Area Index, and Soil Adjusted Total Vegetation Index [63,64] to differentiate vegetation and soil response in soil moisture retrieval using microwave remote sensing data. Validation results showed that fuzzy logic and neural network models performed better compared to multiple regression.…”

Section: Discussionmentioning

confidence: 99%

Non-parametric Methods for Soil Moisture Retrieval from Satellite Remote Sensing Data

et al. 2009

View full text Add to dashboard Cite

Satellite remote sensing observations have the potential for efficient and reliable mapping of spatial soil moisture distributions. However, soil moisture retrievals from active microwave remote sensing data are typically complex due to inherent difficulty in characterizing interactions among land surface parameters that contribute to the retrieval process. Therefore, adequate physical mathematical descriptions of microwave backscatter interaction with parameters such as land cover, vegetation density, and soil characteristics are not readily available. In such condition, non-parametric models could be used as possible alternative for better understanding the impact of variables in the retrieval process and relating it in the absence of exact formulation. In this study, non-parametric methods such as neural networks, fuzzy logic are used to retrieve soil moisture from active microwave remote sensing data. The inclusion of soil characteristics and Normalized Difference Vegetation Index (NDVI) derived from infrared and visible measurement, have significantly improved soil moisture retrievals and reduced root mean square error (RMSE) by around 30% in the retrievals. Soil moisture derived from these methods was compared with ESTAR soil moisture (RMSE ~4.0%) and field soil moisture measurements (RMSE OPEN ACCESSRemote Sensing 2009, 1 4 6.5%). Additionally, the study showed that soil moisture retrievals from highly vegetated areas are less accurate than bare soil areas.

show abstract

Section: Discussionmentioning

confidence: 99%

Non-parametric Methods for Soil Moisture Retrieval from Satellite Remote Sensing Data

et al. 2009

View full text Add to dashboard Cite

show abstract

“…SMA with fixed or variable endmembers has been used for the estimation of FVC in various environments including arid/semi-arid regions [21][22][23][24] and at scales from regional to global [25][26][27][28]. For grassland, linear SMA models with two endmembers (vegetation and non-vegetation) or three endmembers (live grass, senesced grass and soil) are effective in estimating endmember fractions due to their simplicity and interpretability [2][3][4]13,29].…”

Section: Introductionmentioning

confidence: 99%

“…Numerous studies use a vegetation index (VI) as a signal or signature to maximize the differences among the endmembers. For example, NDVI based SMA has been used to estimate FVC in a large number of landscapes with various remote sensing data sources [26,[33][34][35]. However, NDVI has been criticized because FVC tends to be overestimated as it approaches certain proportions [36], especially in moderately vegetated areas [1,37].…”

Section: Introductionmentioning

confidence: 99%

Improving Estimates of Grassland Fractional Vegetation Cover Based on a Pixel Dichotomy Model: A Case Study in Inner Mongolia, China

Wei

Zeng

et al. 2014

Remote Sensing

View full text Add to dashboard Cite

Abstract:Linear spectral mixture analysis (SMA) is commonly used to infer fractional vegetation cover (FVC), especially for pixel dichotomy models. However, several sources of uncertainty including normalized difference vegetation index (NDVI) saturation and selection of endmembers inhibit the effectiveness of SMA for the estimation of FVC. In this study, Moderate-resolution Imaging Spectroradiometer (MODIS) and Landsat 8/Operational Land Imager (OLI) remote sensing data for the early growing season and in situ measurement of spectral reflectance are used to determine the value of endmembers including VI soil and VI veg , with equally weighted RVI and NDVI measures used in combination to minimize the inherent biases in pure NDVI-based FVC. Their ability to improve estimates of grassland FVC is analyzed at different resolutions. These are shown to improve FVC estimates over NDVI-based SMA models using fixed values for the endmembers. Grassland FVC changes for Inner Mongolia, China from 2000 to 2013 are then monitored using the MODIS data. The results show that changes in most grassland areas are not significant, but in parts of Hulunbeier, south Tongliao, middle Xilin Gol and Erdos, grassland FVC has increased significantly.

show abstract

“…Climate variability has a large impact on terrestrial ecosystems and aboveground biomass production (Qi et al 2000;Nemani et al 2003;Fabricante et al 2009;García-Romero et al 2010). Vegetation, as an important component of terrestrial ecosystems, links pedosphere, atmosphere, and hydrosphere of the Earth's system (Salim et al 2008;Zhong et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Interannual variability and correlation of vegetation cover and precipitation in Eastern China

Jiang

Zhang

et al. 2013

Theor Appl Climatol

View full text Add to dashboard Cite

Based on the SPOT/VEGETATION Normalized Difference Vegetation Index (NDVI) data and daily precipitation data of 357 meteorological stations, the spatial and temporal variability of vegetation cover, measured by NDVI, and precipitation as well as their relationships are investigated in Eastern China, which is portioned into three subregions (regions I, II, and III), for the period 1998-2010. The results show that high NDVI values appear mainly in Northeastern China and in August while high precipitation (PRETOT) occurs in Southeastern China and in July (June for Southern China). Extreme precipitation days (RD95p) and amount (EPRETOT) coincide well with PRETOT. Extreme precipitation intensity (RINTEN) has a similar spatial variability to PRETOT but with a smaller seasonal variation than PRETOT. Growing season NDVI is positively correlated with PRETOT in 11.7 % of the study area (mostly in arid to subhumid regions of Northern China), where precipitation is a limiting factor for vegetation growth. In contrast, a negative correlation between growing season NDVI and PRETOT is found in 4.8 % of the study area, mostly in areas around the Yangtze River and deep Northeastern China. No significant correlations between these two variables are found for the other regions because vegetation response to precipitation is affected by other factors such as temperature, radiation, and human disturbance. On a monthly scale, there is a positive correlation between NDVI and PRETOT in May (for region II) and September (all subregions except region I). NDVI variations lag 1 month behind PRETOT in June (for region I) and October. Correlations between NDVI and RD95p, EPRETOT are similar to that with PRETOT, but the relationships between NDVI and RINTEN are relatively weaker than with PRETOT. This study provides the technical basis for agriculture development and ecological construction in Eastern China.

show abstract

Spatial and temporal dynamics of vegetation in the San Pedro River basin area

Cited by 156 publications

References 11 publications

Non-parametric Methods for Soil Moisture Retrieval from Satellite Remote Sensing Data

Non-parametric Methods for Soil Moisture Retrieval from Satellite Remote Sensing Data

Improving Estimates of Grassland Fractional Vegetation Cover Based on a Pixel Dichotomy Model: A Case Study in Inner Mongolia, China

Interannual variability and correlation of vegetation cover and precipitation in Eastern China

Contact Info

Product

Resources

About