Development of angle indexes for soil moisture estimation, dry matter detection and land-cover discrimination

Khanna, Shruti; Palacios-Orueta, Alicia; Whiting, Michael L.; Ustin, Susan L.; Riaño, David; Litago, Javier

doi:10.1016/j.rse.2006.12.018

Cited by 107 publications

(97 citation statements)

References 31 publications

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“…Since some studies demonstrated that the inclusion of SWIR improved the retrieval accuracy of LAI [61,62], data delivery from Sentinel-2 in this spectral region will certainly be valuable. Moreover, the SWIR bands may play an important role for discriminating the spectral signal for different soil and vegetation variables, such as dry matter or water content [63]. However, further research is still required in this regard.…”

Section: Optimal Spectral Samplingmentioning

confidence: 99%

Optimal Exploitation of the Sentinel-2 Spectral Capabilities for Crop Leaf Area Index Mapping

et al. 2012

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Abstract:The continuously increasing demand of accurate quantitative high quality information on land surface properties will be faced by a new generation of environmental Earth observation (EO) missions. One current example, associated with a high potential to contribute to those demands, is the multi-spectral ESA Sentinel-2 (S2) system. The present study focuses on the evaluation of spectral information content needed for crop leaf area index (LAI) mapping in view of the future sensors. Data from a field campaign were used to determine the optimal spectral sampling from available S2 bands applying inversion of a radiative transfer model (PROSAIL) with look-up table (LUT) and artificial neural network (ANN) approaches. Overall LAI estimation performance of the proposed LUT approach (LUT N50 ) was comparable in terms of retrieval performances with a tested and approved ANN method. Employing seven-and eight-band combinations, the LUT N50 approach obtained LAI RMSE of 0.53 and normalized LAI RMSE of 0.12, which was comparable to the results of the ANN. However, the LUT N50 method showed a higher robustness and insensitivity to different band settings. Most frequently selected wavebands were located in near infrared and red edge spectral regions. In conclusion, our results emphasize the potential benefits of the Sentinel-2 mission for agricultural applications.

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Section: Optimal Spectral Samplingmentioning

confidence: 99%

Optimal Exploitation of the Sentinel-2 Spectral Capabilities for Crop Leaf Area Index Mapping

et al. 2012

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“…where a, b and c are Euclidian distances between the vertices NIR and SWIR1 and SWIR2, and NIR and SWIR2, respectively (Source: Khanna et al 2007). …”

Section: Methodsmentioning

confidence: 99%

“…During this stage, estimating soil moisture provides an alternative way to detect canopy water stress. However, the NDWI and SIWSI are not efficient in estimating soil moisture, and methods for accurately detecting soil moisture have been relatively unsuccessful (Khanna et al 2007). There is a need to develop new indices with a set of multispectral bands to provide a more realistic indication on moisture in vegetation and soil.…”

Section: Introductionmentioning

confidence: 99%

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Monitoring of seasonal dryness/wetness conditions using shortwave angle slope index for early season agricultural drought assessment

Das

Mvr

2013

Geomatics, Natural Hazards and Risk

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In the present study, shortwave angle slope index (SASI) was derived from nearinfrared (NIR), shortwave infrared 1 (SWIR1) and shortwave infrared 2 (SWIR2) bands of Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m 8-day composite images in three meteorological sub-divisions of India. The SASI, sensitive to surface wetness, was found to be strongly correlated with rainfall and its seasonal profiles were able to distinguish the early season drought incidents. Comparison between normalized difference vegetation index (NDVI) and SASI showed that during the beginning of the crop season, SASI was relatively better in discriminating the surface dryness conditions prevailing in different years. The SASI, integrated over the period of June to August, better captured the changing dryness/wetness patterns in different years. A normal SASI profile was developed by averaging the SASI values of two normal years. Integrated SASI (InSASI) was computed for the normal SASI profile. Week-wise area under a SASI curve was computed. Based on deviations from the area under the normal SASI curve, dryness/wetness patterns in terms of normal, moderate dry and severe dry were mapped for June, July and August, separately. These patterns are in good agreement with India Meteorological Department rainfall deviations. The study concludes that the early season/sowing-period agricultural drought assessment based on SASI and InSASI could complement the existing vegetation index-based agricultural drought monitoring mechanism.

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“…These indices are fitted with the samples SMC in order to provide an empirical law which links both parameters. In the literature this regression is established as linear or non-linear according to the index and the performance of the method is measured by the coefficient of determination [24,35,37,57]. Four indices were evaluated in this paper: WISOIL, NSMI, NINSOL and NINSON.…”

Section: Local Criteria To Retrieve Smc From Reflectance Spectrummentioning

confidence: 99%

Improvement of Soil Moisture Retrieval from Hyperspectral VNIR-SWIR Data Using Clay Content Information: From Laboratory to Field Experiments

et al. 2015

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Abstract:The aim of this work is to study the constraints and performance of SMC retrieval methodologies in the VNIR (Visible-Near InfraRed) and SWIR (ShortWave InfraRed) regions (from 0.4 to 2.5 µm) when passing from controlled laboratory conditions to field conditions. Five different approaches of signal processing found in literature were considered. Four local criteria are spectral indices (WISOIL, NSMI, NINSOL and NINSON). These indices are the ratios between the spectral reflectances acquired at two specific wavelengths to characterize moisture content in soil. The last criterion is based in the convex hull concept and it is a global method, which is based on the analysis of the full spectral signature of the soil. The database was composed of 464 and 9 spectra, respectively, measured over bare soils in laboratory and in-situ. For each measurement, SMC and texture were well-known and the database was divided in two parts dedicated to calibration and validation steps. The calibration part was used to define the empirical relation between SMC and SMC retrieval approaches, with coefficients of determination (R 2 ) between 0.72 and 0.92. A clay content (CC) dependence was detected for the NINSOL and NINSON indices. Consequently, two new criteria were proposed taking into account the CC contribution (NINSOLCC and NINSONCC). The well-marked regression between SMC and global/local indices, and the interest of using the CC, were confirmed during the validation step using laboratory data (R² in all cases) and using in-situ data, where WISOIL, NINSOLCC and NINSONCC criteria stand out among the NSMI and CH.

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Development of angle indexes for soil moisture estimation, dry matter detection and land-cover discrimination

Cited by 107 publications

References 31 publications

Optimal Exploitation of the Sentinel-2 Spectral Capabilities for Crop Leaf Area Index Mapping

Optimal Exploitation of the Sentinel-2 Spectral Capabilities for Crop Leaf Area Index Mapping

Monitoring of seasonal dryness/wetness conditions using shortwave angle slope index for early season agricultural drought assessment

Improvement of Soil Moisture Retrieval from Hyperspectral VNIR-SWIR Data Using Clay Content Information: From Laboratory to Field Experiments

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