A New Remote Sensing Dryness Index Based on the Near-Infrared and Red Spectral Space

Zhang, Jieyun; Zhang, Qingling; Bao, Anming; Wang, Yujuan

doi:10.3390/rs11040456

Cited by 28 publications

(21 citation statements)

References 67 publications

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“…Bare soils are not affected by vegetation cover, so their reflectivity in red and NIR bands is only affected by the soil moisture content. If there is a decrease in soil moisture, the reflective+ity of the red and NIR bands increases [38]. When vegetation cover increases, reflectance in the NIR band is higher than in the red band.…”

Section: Discussionmentioning

confidence: 99%

Soil Moisture Analysis by Means of Multispectral Images According to Land Use and Spatial Resolution on Andosols in the Colombian Andes

Casamitjana

Torres-Madronero

et al. 2020

Applied Sciences

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Surface soil moisture is an important hydrological parameter in agricultural areas. Periodic measurements in tropical mountain environments are poorly representative of larger areas, while satellite resolution is too coarse to be effective in these topographically varied landscapes, making spatial resolution an important parameter to consider. The Las Palmas catchment area near Medellin in Colombia is a vital water reservoir that stores considerable amounts of water in its andosol. In this tropical Andean setting, we use an unmanned aerial vehicle (UAV) with multispectral (visible, near infrared) sensors to determine the correlation of three agricultural land uses (potatoes, bare soil, and pasture) with surface soil moisture. Four vegetation indices (the perpendicular drought index, PDI; the normalized difference vegetation index, NDVI; the normalized difference water index, NDWI, and the soil-adjusted vegetation index, SAVI) were applied to UAV imagery and a 3 m resolution to estimate surface soil moisture through calibration with in situ field measurements. The results showed that on bare soil, the indices that best fit the soil moisture results are NDVI, NDWI and PDI on a detailed scale, whereas on potatoes crops, the NDWI is the index that correlates significantly with soil moisture, irrespective of the scale. Multispectral images and vegetation indices provide good soil moisture understanding in tropical mountain environments, with 3 m remote sensing images which are shown to be a good alternative to soil moisture analysis on pastures using the NDVI and UAV images for bare soil and potatoes.

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Section: Discussionmentioning

confidence: 99%

Soil Moisture Analysis by Means of Multispectral Images According to Land Use and Spatial Resolution on Andosols in the Colombian Andes

Casamitjana

Torres-Madronero

et al. 2020

Applied Sciences

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“…Zhang et al [7] proposed a new dryness monitoring indicator, the ratio dryness monitoring index (RDMI). Surface dryness monitoring is important to assess water deficiency as a disaster to harm human lives and ecosystems.…”

Section: Overview Of Contributionsmentioning

confidence: 99%

Advances in Remote Sensing-Based Disaster Monitoring and Assessment

Park

Takeuchi

2019

Remote Sensing

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“…Amani et al (2017) transferred the NIR-Red spectral space to the soil moisture (SM)-PVI space, combined these data with the Land Surface Temperature (LST), and defined a new dryness index named the Temperature-vegetation-soil Moisture Dryness Index (TVMDI). Zhang et al (2019) established a new dryness monitoring indicator called the Ratio Dryness Monitoring Index (RDMI) based on the wet edge, dry edge, and the soil line in NIR-Red space.…”

Section: Introductionmentioning

confidence: 99%

Comparative assessment of drought monitoring indices susceptibility using geospatial techniques

Hui

Liu

Qian

2021

Preprint

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There are two main categories of dryness monitoring indices based on spectral feature space. One category uses the vertical distance from any point to a line passing through the coordinate origin, which is perpendicular to a soil line, to monitor the dryness conditions. The most popular indices are the Perpendicular Dryness Index (PDI) and the modified perpendicular dryness index (MPDI). The other category uses the distance from any point in feature space to the coordinate origin to represent the dryness status, for instance, the soil moisture (SM) monitoring index (SMMI) and the modified soil moisture monitoring index (MSMMI). In this study, the performances and differences of these four indicators were evaluated using field-measured SM (FSM) data based on Gaofen-1 (GF-1) wide field of view (WFV), Landsat-8 Operational Land Imager (OLI), and Sentinel-2 Multi-Spectral Instrument (MSI) sensors. Performance evaluations were conducted in two study areas, namely an arid and semi-arid region of northwest China and a humid agricultural region of southwest Canada. We employed gradient-based structural similarity (GSSIM) to quantitatively assess the similarity of the structural information and structural characteristics among these four indicators. Monitoring SM in bare soil or low vegetation-covered areas in the semi-arid region, the SMMI, PDI, MSMMI, and MPDI from Near-infrared (NIR)-Red had significantly negative linear correlations with the FSM at 0-5 cm depth (P < 0.01). However, SMMI was better than PDI in estimating SM in bare soil, which was better than MSMMI and MPDI for GF-1. Moreover, the PDI and SMMI had similar SM evaluation abilities, which were better than those of MPDI and MSMMI for Landsat-8. The GSSIM map of the SMMI/PDI and the MSMMI/MPDI showed that the low change areas accounted for 99.89% and 98.89% for GF-1, respectively, and 95.78% and 94.45% for Landsat-8, respectively. This result indicated that the SMMI, PDI, MSMMI, and MPDI values from NIR-Red in low vegetation cover were similar. In monitoring SM in agricultural vegetation areas, the accuracy of the four indices from Short-wave Infrared (SWIR) feature space was higher than that from NIR-Red feature space for Sentinel-2. The SM monitoring effect of MSMMI and MPDI was better than that of SMMI and PDI. Due to the lack of SWIR band, GF-1 was limited in monitoring SM in vegetation-covered areas. The SMMI and MSMMI, which do not rely on the soil line, were more suitable than PDI and MPDI for retrieving SM in the complex surface environment depending on the soil line and the number of parameters. GF-1 with 16 m resolution had higher accuracy in SM assessment than Landsat-8 with 30 m resolution and had almost the same accuracy as Sentinel-2 with 20 m.

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A New Remote Sensing Dryness Index Based on the Near-Infrared and Red Spectral Space

Cited by 28 publications

References 67 publications

Soil Moisture Analysis by Means of Multispectral Images According to Land Use and Spatial Resolution on Andosols in the Colombian Andes

Soil Moisture Analysis by Means of Multispectral Images According to Land Use and Spatial Resolution on Andosols in the Colombian Andes

Advances in Remote Sensing-Based Disaster Monitoring and Assessment

Comparative assessment of drought monitoring indices susceptibility using geospatial techniques

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