Rapidly assessing the 1997 drought in Papua New Guinea using composite AVHRR imagery

McVicar, Tim R.; Bierwirth, P. N.

doi:10.1080/01431160120728

Cited by 77 publications

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“…Even for polar orbiting satellites with daily repeat times (e.g., MODIS and AVHRR), the specific time-of-daytime thermal measurement will often not be observed due to cloud cover, resulting in a gap for the entire day. Persistent cloudy conditions in places with a distinct wet season (e.g., the tropics) can result in few or no kE estimates over a month (e.g., McVicar and Bierwirth, 2001). Evaluation of upscaling error at the monthly time step allows for the influence of data gaps to be intrinsically assessed, and thus is particularly relevant to remote sensing studies.…”

Section: Theorymentioning

confidence: 99%

“…While self-preservation is assumed for upscaling remote sensing estimates of kE, in practical application the restriction that the entire daytime must be clear-sky is not enforced (e.g., Anderson et al, 1997Anderson et al, , 2007aAnderson et al, , 2007bMcVicar and Bierwirth, 2001;McVicar and Jupp, 2002). Rather, the specific time-of-day observation must be clear-sky, but the rest of the day can be clear or cloudy.…”

Section: Introductionmentioning

confidence: 98%

See 1 more Smart Citation

Upscaling latent heat flux for thermal remote sensing studies: Comparison of alternative approaches and correction of bias

Niel

McVicar

Roderick

et al. 2012

Journal of Hydrology

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Section: Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Upscaling latent heat flux for thermal remote sensing studies: Comparison of alternative approaches and correction of bias

Niel

McVicar

Roderick

et al. 2012

Journal of Hydrology

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“…McVicar and Bierwirth (2001) investigated the utility of satellite data as a drought assessment tool for the 1997 drought in Papua New Guinea. They found a strong correlation (r 2 =0.809) between accumulated rainfall and an integrated measurement of surface temperature (T s ) and NDVI over meteorological stations.…”

Section: Introductionmentioning

confidence: 99%

A new approach for predicting drought-related vegetation stress: Integrating satellite, climate, and biophysical data over the U.S. central plains

Tadesse

Brown

Hayes

2005

ISPRS Journal of Photogrammetry and Remote Sensing

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“…Previously, Carlson [15] constructed the vegetation supply water index (VSWI) using the NDVI and LST to assess summer drought, because the ratio of LST to NDVI is shown to increase during drought. VSWI describes the soil moisture changes in agricultural land well and is a rapid and cost-effective method for monitoring drought conditions [16]. Sandholt et al [17] found that the scatter of LST and NDVI data occupies a triangular space, the NDVI-LST spectral space, and that an index based on this relationship (the temperature vegetation dryness index, TVDI) can be used to better monitor regional drought.…”

Section: Introductionmentioning

confidence: 99%

Suitability Assessment of Satellite-Derived Drought Indices for Mongolian Grassland

Chang

Niu

et al. 2017

Remote Sensing

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Abstract:In Mongolia, drought is a major natural disaster that can influence and devastate large regions, reduce livestock production, cause economic damage, and accelerate desertification in association with destructive human activities. The objective of this article is to determine the optimal satellite-derived drought indices for accurate and real-time expression of grassland drought in Mongolia. Firstly, an adaptability analysis was performed by comparing nine remote sensing-derived drought indices with reference indicators obtained from field observations using several methods (correlation, consistency percentage (CP), and time-space analysis). The reference information included environmental data, vegetation growth status, and region drought-affected (RDA) information at diverse scales (pixel, county, and region) for three types of land cover (forest steppe, steppe, and desert steppe). Second, a meteorological index (PED), a normalized biomass (NorBio) reference indicator, and the RDA-based drought CP method were adopted for describing Mongolian drought. Our results show that in forest steppe regions the normalized difference water index (NDWI) is most sensitive to NorBio (maximum correlation coefficient (MAX_R): up to 0.92) and RDA (maximum CP is 87%), and is most consistent with RDA spatial distribution. The vegetation health index (VHI) and temperature condition index (TCI) are most correlated with the PED index (MAX_R: 0.75) and soil moisture (MAX_R: 0.58), respectively. In steppe regions, the NDWI is most closely related to soil moisture (MAX_R: 0.69) and the VHI is most related to the PED (MAX_R: 0.76), NorBio (MCC: 0.95), and RDA data (maximum CP is 89%), exhibiting the most consistency with RDA spatial distribution. In desert steppe areas, the vegetation condition index (VCI) correlates best with NorBio (MAX_R: 0.92), soil moisture (MAX_R: 0.61), and RDA spatial distribution, while TCI correlates best with the PED (MAX_R: 0.75) and the RDA data (maximum CP is 79%). The VHI is a combination of constructed VCI and TCI, and can be used instead of them. Finally, the mode method was adopted to identify appropriate drought indices. The best two indices (VHI and NDWI) can be utilized to develop a combination drought model for accurately monitoring and quantifying drought in the future. Additionally, the new framework can be adopted to investigate and analyze the suitability of satellite-derived drought indices and determine the most appropriate index/indices for other countries or areas.

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Rapidly assessing the 1997 drought in Papua New Guinea using composite AVHRR imagery

Cited by 77 publications

References 0 publications

Upscaling latent heat flux for thermal remote sensing studies: Comparison of alternative approaches and correction of bias

Upscaling latent heat flux for thermal remote sensing studies: Comparison of alternative approaches and correction of bias

A new approach for predicting drought-related vegetation stress: Integrating satellite, climate, and biophysical data over the U.S. central plains

Suitability Assessment of Satellite-Derived Drought Indices for Mongolian Grassland

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