Measurement and zonation of soil surface moisture in arid and semi-arid regions using Landsat 8 images

Bidgoli, Reza Dehghani; Koohbanani, Hamidreza; Keshavarzi, Ali; Kumar, Vinod

doi:10.1007/s12517-020-05837-2

Cited by 10 publications

(5 citation statements)

References 37 publications

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“…through the changes in the reflected radiation characteristics of the ground material. Currently, research methods are mainly of three types: visible-near-infrared remote sensing [10,11], thermal infrared remote sensing [12,13], and microwave remote sensing [14,15]. Remote sensing sensors in the visible-near-infrared band (0.4-0.2.5 µm) receive reflection information primarily from the surface, in response to solar short-wave radiation.…”

Section: Introductionmentioning

confidence: 99%

Surface Soil Moisture Inversion and Distribution Based on Spatio-Temporal Fusion of MODIS and Landsat

Wang

et al. 2022

Sustainability

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Soil moisture plays an important role in hydrology, climate, agriculture, and ecology, and remote sensing is one of the most important tools for estimating the soil moisture over large areas. Soil moisture, which is calculated by remote sensing inversion, is affected by the uneven distribution of vegetation and therefore the results cannot accurately reflect the spatial distribution of the soil moisture in the study area. This study analyzes the soil moisture of different vegetation covers in the Wushen Banner of Inner Mongolia, recorded in 2016, and using Landsat and MODIS images fused with multispectral bands. Firstly, we compared and analyzed the ability of the visible optical and short-wave infrared drought index (VSDI), the normalized differential infrared index (NDII), and the short-wave infrared water stress index (SIWSI) in monitoring the soil moisture in different vegetation cover soils. Secondly, we used the stepwise multiple regression analysis method in order to correlate the multispectral fusion bands with the field-measured soil water content and established a soil moisture inversion model based on the multispectral fusion bands. As the results show, there was a strong correlation between the established model and the measured soil water content of the different vegetation cover soils: in the bare soil, R2 was 0.86; in the partially vegetated cover soil, R2 was 0.84; and in the highly vegetated cover soil, R2 was 0.87. This shows that the established model could better reflect the actual condition of the surface soil moisture in the different vegetation covers.

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Section: Introductionmentioning

confidence: 99%

Surface Soil Moisture Inversion and Distribution Based on Spatio-Temporal Fusion of MODIS and Landsat

Wang

et al. 2022

Sustainability

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“…Interestingly, the direction of the relationship between these two variables was the same as in our study, reverse. Bidgoli et al (2020) reported a significant advantage of NDMI over other vegetation indices for soil moisture assessment (correlation coefficients were 0.57-0.61), although none of the studied aerospace indices was suitable for accurate measurement of soil moisture content in arid climate. However, the issue of remote sensing screening of soil moisture content in desert and arid regions remains debatable, as Koohbanani & Yazdani (2018) provided the evidence that NDMI is an appropriate indicator to screen soil moisture content, especially in the zones with poor vegetation cover.…”

Section: Tablementioning

confidence: 98%

Normalised difference moisture index in water stress assessment of maize crops

Lykhovyd,

Sharii

2024

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Remote sensing is a promising technique for better management of water resources in agriculture through improvement of dynamic control and operational scheduling on irrigated croplands. The main goal of this study was to identify the possibilities of application of the normalised difference moisture index (NDMI) to water stress monitoring in maize crops, and to determine the relationship between the index and soil moisture content. The study was carried out in 2019–2021 in the experimental fields of disturbed maize, cultivated on dark-chestnut soils in the Southern Ukraine at the NAAS Institute of Climate-Smart Agriculture. The crop cultivation technology was common for the conditions of the steppe zone of Ukraine. Actual soil moisture content was determined by gravimetric method in the pre-sowing and post-harvest period. The NDMI values were calculated using cloudless aerospace images from the satellites Landsat 8, Sentinel-2, and MODIS with 250 m resolution. It was revealed that the seasonal NDMI dynamics perfectly reflected the water-supply conditions of the disturbed maize, and could be used for operational monitoring and scheduling of irrigation. The parameters of the water-supply conditions were determined in 2021, which was the wettest year of the study: the cumulative seasonal NDMI reached 1.71, while the highest water stress was recorded in the driest year, 2020, – the cumulative NDMI was 0.15. Additionally, there was a moderately strong negative correlation between NDMI and soil moisture content, and the coefficient of determination was 0.62. The linear regression models, developed to predict soil moisture content in the 0–100 cm layer depending on the NDMI values, had good fitting quality and reasonable accuracy, but they required further calibration and extension of the initial dataset to provide more robust and reliable results for practical implementation. Based on the results of the study, spatial NDMI could be considered a good and reliable tool for improving irrigation water management. Further studies should focus on the practical implementation of the NDMI-based model of moisture-content estimation, as well as on the possibilities of the index usage for mapping irrigated lands.

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“…Hence, statistically significant differences were observed between the mean of post-event NIR-reflectance between "unaffected" and "moderately affected", "unaffected" and "severely affected", "moderately affected" and 13 "severely affected" crop. The SWIR-1 (B11) and SWIR-2 (B12) bands were sensitive to the surface wetness (Bidgoli et al 2020). The surface wetness is attributed both by the leaf and soil moisture.…”

Section: Spectral Response To Hailstorm Damagementioning

confidence: 99%

Satellite-based assessment of hailstorm-affected potato crop for insurance purpose

et al. 2022

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Assessing the extent of hailstorm affected crop is one of the thrust areas for quantifying mid-season adversaries under crop insurance values chain. This study evaluated the preand post-hailstorm responses on spectral bands and vegetation indices derived from Sentinel-2 data for assessing the severity class of the affected potato crop. The potato crop was mapped using pre-event satellite data with overall accuracy of 88% (k=0.82).Pair-wise Games-Howell t-test showed significant differences among the post-hailstorm potato severity classes in Red, Near Infrared & Short-wave Infra-red (SWIR) bands and Normalized vegetation indices. Percentage change (from pre-to post-event) in band reflectance and vegetation indices showed a better sensitivity in differentiating damage severity. Differential behaviour of SWIR-1 (Band-11) and SWIR-2 (Band-12) were observed within severely affected potato crop under dry and wet soil conditions. Decision matrix based on percentage change in Normalized difference Vegetation Index (DNDVI)and Normalized difference Tillage Index (DNDVI) could able to capture the damage severity classes with an overall accuracy of 86.7%. Higher proportion of affected area were found to be associated with larger percentage of Potato yield reduction based on measured yield data at Insurance unit level. The proposed methodology could be adopted for operational assessment of the impact of hailstorm events on crops.

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Measurement and zonation of soil surface moisture in arid and semi-arid regions using Landsat 8 images

Cited by 10 publications

References 37 publications

Surface Soil Moisture Inversion and Distribution Based on Spatio-Temporal Fusion of MODIS and Landsat

Surface Soil Moisture Inversion and Distribution Based on Spatio-Temporal Fusion of MODIS and Landsat

Normalised difference moisture index in water stress assessment of maize crops

Satellite-based assessment of hailstorm-affected potato crop for insurance purpose

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