Sensitivity studies of the moisture effects on MODIS SWIR reflectance and vegetation water indices

Wang, Lingli; Qu, John J.; Hao, Xianjun; Zhu, Qin

doi:10.1080/01431160802226034

Cited by 79 publications

(47 citation statements)

References 17 publications

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“…2a), during the period of flooding and rice transplanting, especially for single and late rice (in May and July) when temperatures are optimal for their growth. The presence of non-paddy rice vegetation, during the flooding and rice transplanting period, will increase the greenness signal through increases in NIR reflectance, and a decrease red reflectance (Huete et al, 1985;Wang et al, 2008). In accordance with Eqs.…”

Section: Detection Of Flooding and Rice Transplanting Areasupporting

confidence: 54%

Detection and estimation of mixed paddy rice cropping patterns with MODIS data

Peng

Huete

Huang

et al. 2011

International Journal of Applied Earth Observation and Geoinfor

143

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Section: Detection Of Flooding and Rice Transplanting Areasupporting

confidence: 54%

Detection and estimation of mixed paddy rice cropping patterns with MODIS data

Peng

Huete

Huang

et al. 2011

International Journal of Applied Earth Observation and Geoinfor

143

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“…In the dry season, green vegetation cover of rubber plantations is low and the spectral signal is dominated by open soils, which leads to an increase in SWIR reflectance. The decrease in soil water content in the dry season may further increase SWIR reflectance [50]. The SWIR maximum of rubber plantations therefore represents the coldest and driest time of the year.…”

Section: Discussionmentioning

confidence: 99%

Mapping Rubber Plantations and Natural Forests in Xishuangbanna (Southwest China) Using Multi-Spectral Phenological Metrics from MODIS Time Series

et al. 2013

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Abstract:We developed and evaluated a new approach for mapping rubber plantations and natural forests in one of Southeast Asia's biodiversity hot spots, Xishuangbanna in China. We used a one-year annual time series of Moderate Resolution Imaging Spectroradiometer (MODIS), Enhanced Vegetation Index (EVI) and short-wave infrared (SWIR) reflectance data to develop phenological metrics. These phenological metrics were used to classify rubber plantations and forests with the Random Forest classification algorithm. We evaluated which key phenological characteristics were important to discriminate rubber plantations and natural forests by estimating the influence of each metric on the classification accuracy. As a benchmark, we compared the best classification with a classification based on the full, fitted time series data. Overall classification accuracies derived from EVI and SWIR time series alone were 64.4% and 67.9%, respectively. Combining the phenological metrics from EVI and SWIR time series improved the accuracy to 73.5%. Using the full, smoothed time series data instead of metrics derived from the time series improved the overall accuracy only slightly (1.3%), indicating that the phenological metrics were sufficient to explain the seasonal changes captured by the MODIS time series. The results demonstrate a promising utility of phenological metrics for mapping and monitoring rubber expansion with MODIS.

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“…Firstly, the NDVI uses only the red and near-infrared bands (bands 1 and 2 from MODIS data), other bands of surface reflectance, which are also sensitive to vegetation water status [37][38][39][40][41] can be taken into consideration to improve the sensitivity of remote sensing-based drought monitoring. In this study, we took the blue reflectance band as an example to show that the bands other than NIR and red reflectance can also have high correlations with in situ drought indices (see Figure 11).…”

Section: Facters Which Affect the Performance Of Viupd-derived Vcimentioning

confidence: 99%

Evaluating an Enhanced Vegetation Condition Index (VCI) Based on VIUPD for Drought Monitoring in the Continental United States

et al. 2016

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Drought is a complex hazard, and it has an impact on agricultural, ecological, and socio-economic systems. The vegetation condition index (VCI), which is derived from remote-sensing data, has been widely used for drought monitoring. However, VCI based on the normalized difference vegetation index (NDVI) does not perform well in certain circumstances. In this study, we examined the utility of the vegetation index based on the universal pattern decomposition method (VIUPD) based VCI for drought monitoring in various climate divisions across the continental United States (CONUS). We compared the VIUPD-derived VCI with the NDVI-derived VCI in various climate divisions and during different sub-periods of the growing season. It was also compared with other remote-sensing-based drought indices, such as the temperature condition index (TCI), precipitation condition index (PCI) and the soil moisture condition index (SMCI). The VIUPD-derived VCI had stronger correlations with long-term in situ drought indices, such as the Palmer Drought Severity Index (PDSI) and the standardized precipitation index (SPI-3, SPI-6, SPI-9, and SPI-12) than did the NDVI-derived VCI, and other indices, such as TCI, PCI and SMCI. The VIUPD has considerable potential for drought monitoring. As VIUPD can make use of the information from all the observation bands, the VIUPD-derived VCI can be regarded as an enhanced VCI.

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Sensitivity studies of the moisture effects on MODIS SWIR reflectance and vegetation water indices

Cited by 79 publications

References 17 publications

Detection and estimation of mixed paddy rice cropping patterns with MODIS data

Detection and estimation of mixed paddy rice cropping patterns with MODIS data

Mapping Rubber Plantations and Natural Forests in Xishuangbanna (Southwest China) Using Multi-Spectral Phenological Metrics from MODIS Time Series

Evaluating an Enhanced Vegetation Condition Index (VCI) Based on VIUPD for Drought Monitoring in the Continental United States

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