Comprehensive Evaluation of Sentinel-2 Red Edge and Shortwave-Infrared Bands to Estimate Soil Moisture

Liu, Ying; Qian, Jiaxin; Yue, Hui

doi:10.1109/jstars.2021.3098513

Cited by 28 publications

(14 citation statements)

References 79 publications

(90 reference statements)

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“…Repeating farmers' financial profits until 2022-23 from the 2016-16 primary year requires an annual improvement of 10.42 percent on the farmer's income. In this paper [23], they evaluated each function separately based on the performance of a built-in robot. the grid structure is used for pomegranate plants.…”

Section: Literature Surveymentioning

confidence: 99%

Smart Irrigation for Crop Management Using IoT

Fazil¹

2022

IJMRA

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Agricultural production involves cultivating plants and raising domesticated animals to produce food and feed for humans and other animals. Agriculture or farming as it is also called is a complex activity, and each aspect of it impacts the overall crop production. Farmers need to manage all segments of crop production to achieve success. Farmers make strenuous efforts to produce good quality crops but they face challenging issues of monitoring and maintaining it around the clock. The problems in the agriculture domain largely affect the food production and supply chain. This project includes the proposal of an integrated crop management system to maintain the health of crops by supplying the required amount of water and nutrients to them. One aim is to reduce the amount of water lost to unnecessary evaporation, a concern in the 21st century. As a result, other factors, such as cost, time, and effective care are also benefited. This is why, after the soil preparation and planting are completed, the growth phase of the plant also requires special attention. The paper discusses a process for detecting and solving plant health issues using an intelligent automated system.

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Section: Literature Surveymentioning

confidence: 99%

Smart Irrigation for Crop Management Using IoT

Fazil¹

2022

IJMRA

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“…In complex environments, such as VC or bare areas, the Sentinel-2 satellite can improve the estimation accuracy of SM in the 0.00-0.05 m topsoil layer (R 2 increase 0.04) by combining the SM monitoring index with the red edge and SWIR band (Liu et al, 2021b). Using different high-resolution multi-spectral images, better results were obtained by estimating SM by SWIR conversion reflectivity under high spatial resolution (Feng et al, 2008).…”

Section: Joining Of Swir Bandmentioning

confidence: 99%

“…A significant negative correlation was found between the TVDI and SM in different arid and semi-arid regions (Guo et al, 2009; Rev Bras Cienc Solo 2022;46:e0220113 Kazemzadeh et al, 2021). Due to the discrepancies in climate and soil environment in different regions, the feature space (Vis-TVDI) and LST that constitute the TVDI were modified in a suitable manner, summarized as follows: (a) replaced NDVI and used the modified soil adjusted VI (MSAVI), soil adjusted VI (SAVI), and enhanced VI (EVI) for evaluation (Zhang et al, 2014a;Ma et al, 2017;Wu et al, 2019); (b) modified the soil line and increased the combination of shortwave infrared (SWIR), near-infrared (NIR), and red light bands to reduce the sensitivity of VI to the soil background (Feng et al, 2011a;Chen et al, 2019;Liu et al, 2021b); (c) considered the influence of factors such as terrain (digital elevation model, DEM) and environmental data on the LST and made corrections to the LST (Ran et al, 2005;Sun et al, 2010;Liu et al, 2013). The correlation between the TVDI and SM obtained after the modification was clear, which improved the accuracy of SM inversion (Thi et al, 2019;Yan et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Inversion of soil moisture and its feedback on ecological restoration in arid and semi-arid areas of northwest China

Liu¹,

Yuanyuan²,

Zhou³

et al. 2022

Revista Brasileira De Ciência Do Solo

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Soil moisture (SM) plays an important role in regulating the global water cycle, especially in arid areas, and is one of the main indicators of ecological environmental health. Although traditional methods can accurately measure SM at a single sample site, they are limited in large-scale and dynamic SM monitoring. Therefore, we used the Landsat images as the data source and the soil adjusted vegetation index (SAVI) to build the adjusted SAVI (aSAVI) index by modifying the soil adjustment parameter L and introducing the short-wave infrared band. According to the theory of temperature vegetation dryness index (TVDI) and feature space, we introduced a model, combined the measured SM data (Minqin Basin, China) through a comparative analysis of four vegetation indices (NDVI, SAVI, MSAVI, aSAVI) determine the optimal model. Taking the Minqin Basin as the study area, the spatiotemporal variation characteristics of SM in three sub-regions (the entire study area, irrigated region, and periphery of the irrigated regions) were quantitatively analyzed and compared in four different periods: pre-Comprehensive Treatment Program of the Shiyang River Basin (pre-CTSRB) (2000)(2001)(2002)(2003)(2004)(2005), CTSRB I (2006-2010), CTSRB II (2011-2016), and CTSRB-end (2017-2021) to evaluate the ecological restoration effects of treatment programs from the SM perspective. The results showed that: 1) SM values derived from TVDI inversion and the aSAVI were more accurate, and the model sensitivity decreased with soil depth; 2) the mean value of SM fluctuated across the four periods but decreased slightly over the entire time series. The spatial variations of the SM were characterized by a "descending then ascending" trend. Soil moisture increased in 21.35 % of areas at 0.00-0.10 m in the past 22 years, and 59.66 % at 0.10-0.20 m. There was a negative correlation between the mean variation trend of SM and the percentage of area where SM fell in different periods; 3) the treatment program positively affected the ecological restoration of the Minqin Basin from the SM perspective. The area where SM increased was larger than that of decreasing SM, especially in 0.10-0.20 m soil layer. The increase can promote growth and confer resistance to desertification.

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“…Advances in satellite technology, data processing, and petrophysics ensure that these techniques will be increasingly important in the future. Many studies have used satellite data, such as the Sentinel and Landsat series, to observe and predict soil properties such as pH [21,22], cation exchange capacity [22], soil organic carbon [22,23], soil organic matter [21,24], clay content [21,22,24], salinity [25], and soil water content estimation [26][27][28][29][30][31][32]. Reference [24] showed that using Sentinel-2 satellite data to identify differences in soil properties can guide on-ground soil sampling and significantly reduce the time and cost of conventional sampling efforts.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Soil Water Content and Electrical Conductivity Using Random Forest Methods with UAV Multispectral and Ground-Coupled Geophysical Data

et al. 2022

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The volumetric water content (VWC) of soil is a critical parameter in agriculture, as VWC strongly influences crop yield, provides nutrients to plants, and maintains the microbes that are needed for the biological health of the soil. Measuring VWC is difficult, as it is spatially and temporally heterogeneous, and most agricultural producers use point measurements that cannot fully capture this parameter. Electrical conductivity (EC) is another soil parameter that is useful in agriculture, since it can be used to indicate soil salinity, soil texture, and plant nutrient availability. Soil EC is also very heterogeneous; measuring EC using conventional soil sampling techniques is very time consuming and often fails to capture the variability in EC at a site. In contrast to the point-based methods used to measure VWC and EC, multispectral data acquired with unmanned aerial vehicles (UAV) can cover large areas with high resolution. In agriculture, multispectral data are often used to calculate vegetation indices (VIs). In this research UAV-acquired VIs and raw multispectral data were used to predict soil VWC and EC. High-resolution geophysical methods were used to acquire more than 41,000 measurements of VWC and 8,000 measurements of EC in 18 traverses across a field that contained 56 experimental plots. The plots varied by crop type (corn, soybeans, and alfalfa) and drainage (no drainage, moderate drainage, high drainage). Machine learning was performed using the random forest method to predict VWC and EC using VIs and multispectral data. Prediction accuracy was determined for several scenarios that assumed different levels of knowledge about crop type or drainage. Results showed that multispectral data improved prediction of VWC and EC, and the best predictions occurred when both the crop type and degree of drainage were known, but drainage was a more important input than crop type. Predictions were most accurate in drier soil, which may be due to the lower overall variability of VWC and EC under these conditions. An analysis of which multispectral data were most important showed that NDRE, VARI, and blue band data improved predictions the most. The final conclusions of this study are that inexpensive UAV-based multispectral data can be used to improve estimation of heterogenous soil properties, such as VWC and EC in active agricultural fields. In this study, the best estimates of these properties were obtained when the agriculture parameters in a field were fairly homogeneous (one crop type and the same type of drainage throughout the field), although improvements were observed even when these conditions were not met. The multispectral data that were most useful for prediction were those that penetrated deeper into the soil canopy or were sensitive to bare soil.

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Comprehensive Evaluation of Sentinel-2 Red Edge and Shortwave-Infrared Bands to Estimate Soil Moisture

Cited by 28 publications

References 79 publications

Smart Irrigation for Crop Management Using IoT

Smart Irrigation for Crop Management Using IoT

Inversion of soil moisture and its feedback on ecological restoration in arid and semi-arid areas of northwest China

Prediction of Soil Water Content and Electrical Conductivity Using Random Forest Methods with UAV Multispectral and Ground-Coupled Geophysical Data

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