Land Surface Water Index (LSWI) response to rainfall and NDVI using the MODIS Vegetation Index product

Chandrasekar, K.; Sai, M. V. R. Sesha; Roy, P. S.; Dwevedi, R. S.

doi:10.1080/01431160802575653

Cited by 209 publications

(96 citation statements)

References 25 publications

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“…Normalized Differential Moisture Index (NDMI) (also known as land surface water index (LSWI)or normalized difference water index (NDWI)) is sensitive to surface soil moisture [20,21,23]. Liquid water has strong light absorption in the shortwave infrared (SWIR) band, which makes NDMI highly sensitive to the total vegetation water content [24]. It is widely used for irrigated cropland classification [25,26].…”

Section: Introductionmentioning

confidence: 99%

Irrigation History Estimation Using Multitemporal Landsat Satellite Images: Application to an Intensive Groundwater Irrigated Agricultural Watershed in India

et al. 2018

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Groundwater has rapidly evolved as a primary source for irrigation in Indian agriculture. Over-exploitation of the groundwater substantially depletes the natural water table and has negative impacts on the water resource availability. The overarching goal of the proposed research is to identify the historical evolution of irrigated cropland for the post-monsoon (rabi) and summer cropping seasons in the Berambadi watershed (Area = 89 km 2 ) of Kabini River basin, southern India. Approximately five-year interval irrigated area maps were generated using 30 m spatial resolution Landsat satellite images for the period from 1990 to 2016. The potential of Support Vector Machine (SVM) was assessed to discriminate irrigated and non-irrigated croplands. Three indices, Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI) and Enhanced Vegetation Index (EVI), were derived from multi-temporal Landsat satellite images. Spatially distributed intensive ground observations were collected for training and validation of the SVM models. The irrigated and non-irrigated croplands were estimated with high classification accuracy (kappa coefficient greater than 0.9). At the watershed scale, this approach allowed highlighting the contrasted evolution of multiple-cropping (two successive crops in rabi and summer seasons that often imply dual irrigation) with a steady increase in the upstream and a recent decrease in the downstream of the watershed. Moreover, the multiple-cropping was found to be much more frequent in the valleys. These intensive practices were found to have significant impacts on the water resources, with a drastic decline in the water table level (more than 50 m). It also impacted the ecosystem: Groundwater level decline was more pronounced in the valleys and the rivers are no more fed by the base flow.

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Section: Introductionmentioning

confidence: 99%

Irrigation History Estimation Using Multitemporal Landsat Satellite Images: Application to an Intensive Groundwater Irrigated Agricultural Watershed in India

et al. 2018

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“…With the rapid development of remote sensing technology in recent years, water mapping and change detection based on satellite remote sensing images has become a main approach [13][14][15][16][17][18]. Synthetic Aperture Radar (SAR) data has been used to identify surface water area [19]; however, relatively limited availability of SAR data has blocked large scale and long term applications in water body mapping.…”

Section: Introductionmentioning

confidence: 99%

Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors

Zhou

Dong

Xiao

et al. 2017

Water

169

105

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Open surface water bodies play an important role in agricultural and industrial production, and are susceptible to climate change and human activities. Remote sensing data has been increasingly used to map open surface water bodies at local, regional, and global scales. In addition to image statistics-based supervised and unsupervised classifiers, spectral index-and threshold-based approaches have also been widely used. Many water indices have been proposed to identify surface water bodies; however, the differences in performances of these water indices as well as different sensors on water body mapping are not well documented. In this study, we reviewed and compared existing open surface water body mapping approaches based on six widely-used water indices, including the tasseled cap wetness index (TCW), normalized difference water index (NDWI), modified normalized difference water index (mNDWI), sum of near infrared and two shortwave infrared bands (Sum457), automated water extraction index (AWEI), land surface water index (LSWI), as well as three medium resolution sensors (Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-2 MSI). A case region in the Poyang Lake Basin, China, was selected to examine the accuracies of the open surface water body maps from the 27 combinations of different algorithms and sensors. The results showed that generally all the algorithms had reasonably high accuracies with Kappa Coefficients ranging from 0.77 to 0.92. The NDWI-based algorithms performed slightly better than the algorithms based on other water indices in the study area, which could be related to the pure water body dominance in the region, while the sensitivities of water indices could differ for various water body conditions. The resultant maps from Landsat 8 and Sentinel-2 data had higher overall accuracies than those from Landsat 7. Specifically, all three sensors had similar producer accuracies while Landsat 7 based results had a lower user accuracy. This study demonstrates the improved performance in Landsat 8 and Sentinel-2 for open surface water body mapping efforts.

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“…However, Kim et al (2004) reported that the water index LSWI using the SWIR band centered at 2 130 nm appeared to be more useful for detecting vegetation water status. The SWIR band centered at 2 130 nm is less affected by ozone and Rayleigh scattering, water vapor, and aerosols than that at 1 640 nm and has a good response to cumulative rainfall (Vermote et al, 1997;Chandrasekar et al, 2010). Therefore, the combined index LSWI using NIR and SWIR centered at 2 130 nm (viz.…”

Section: Introductionmentioning

confidence: 99%

Multi-year monitoring of paddy rice planting area in Northeast China using MODIS time series data

Shi

Huang

Zhang

2013

J. Zhejiang Univ. Sci. B

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Abstract:The objective of this study was to investigate the tempo-spatial distribution of paddy rice in Northeast China using moderate resolution imaging spectroradiometer (MODIS) data. We developed an algorithm for detection and estimation of the transplanting and flooding periods of paddy rice with a combination of enhanced vegetation index (EVI) and land surface water index with a central wavelength at 2 130 nm (LSWI 2130 ). In two intensive sites in Northeast China, fine resolution satellite imagery was used to validate the performance of the algorithm at pixel and 3×3 pixel window levels, respectively. The commission and omission errors in both of the intensive sites were approximately less than 20%. Based on the algorithm, annual distribution of paddy rice in Northeast China from 2001 to 2009 was mapped and analyzed. The results demonstrated that the MODIS-derived area was highly correlated with published agricultural statistical data with a coefficient of determination (R 2 ) value of 0.847. It also revealed a sharp decline in 2003, especially in the Sanjiang Plain located in the northeast of Heilongjiang Province, due to the oversupply and price decline of rice in 2002. These results suggest that the approaches are available for accurate and reliable monitoring of rice cultivated areas and variation on a large scale.

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Land Surface Water Index (LSWI) response to rainfall and NDVI using the MODIS Vegetation Index product

Cited by 209 publications

References 25 publications

Irrigation History Estimation Using Multitemporal Landsat Satellite Images: Application to an Intensive Groundwater Irrigated Agricultural Watershed in India

Irrigation History Estimation Using Multitemporal Landsat Satellite Images: Application to an Intensive Groundwater Irrigated Agricultural Watershed in India

Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors

Multi-year monitoring of paddy rice planting area in Northeast China using MODIS time series data

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