Using UAV Visible Images to Estimate the Soil Moisture of Steppe

Lu, Fengshuai; Sun, Yi; Hou, Fujiang

doi:10.3390/w12092334

Cited by 39 publications

(22 citation statements)

References 48 publications

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“…More recent and relevant studies on the application of UAS remote sensing for soil moisture monitoring [ 113 , 114 , 115 , 116 ], water consumption and use efficiency [ 117 , 118 , 119 , 120 , 121 ], and surface energy budget [ 122 , 123 ] should be investigated to enrich future research directions in PV. These works demonstrate how the application of high-resolution remote sensing technology would improve knowledge of soil characteristics as a key element in site-specific vineyard management.…”

Section: Perspective and Future Challengesmentioning

confidence: 99%

Advances in Unmanned Aerial System Remote Sensing for Precision Viticulture

Sassu

Gambella

Ghiani

et al. 2021

Sensors

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New technologies for management, monitoring, and control of spatio-temporal crop variability in precision viticulture scenarios are numerous. Remote sensing relies on sensors able to provide useful data for the improvement of management efficiency and the optimization of inputs. unmanned aerial systems (UASs) are the newest and most versatile tools, characterized by high precision and accuracy, flexibility, and low operating costs. The work aims at providing a complete overview of the application of UASs in precision viticulture, focusing on the different application purposes, the applied equipment, the potential of technologies combined with UASs for identifying vineyards’ variability. The review discusses the potential of UASs in viticulture by distinguishing five areas of application: rows segmentation and crop features detection techniques; vineyard variability monitoring; estimation of row area and volume; disease detection; vigor and prescription maps creation. Technological innovation and low purchase costs make UASs the core tools for decision support in the customary use by winegrowers. The ability of the systems to respond to the current demands for the acquisition of digital technologies in agricultural fields makes UASs a candidate to play an increasingly important role in future scenarios of viticulture application.

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Section: Perspective and Future Challengesmentioning

confidence: 99%

Advances in Unmanned Aerial System Remote Sensing for Precision Viticulture

Sassu

Gambella

Ghiani

et al. 2021

Sensors

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“…Pest and disease infestations are often associated with the unfavourable moisture conditions of soil and vegetation, i.e., excess water or drought. In contrast with weed, pest, or disease detection, soil moisture regime-related UAV applications are commonly based on the thermal region of the spectra [104,105], although visible reflectance [106] or multispectral data [107] are also widely used. Thermal imaging was not considered in the reviewed articles.…”

Section: Discussionmentioning

confidence: 99%

Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases

et al. 2021

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The development of UAV (unmanned aerial vehicle) imaging technologies for precision farming applications is rapid, and new studies are published frequently. In cases where measurements are based on aerial imaging, there is the need to have ground truth or reference data in order to develop reliable applications. However, in several precision farming use cases such as pests, weeds, and diseases detection, the reference data can be subjective or relatively difficult to capture. Furthermore, the collection of reference data is usually laborious and time consuming. It also appears that it is difficult to develop generalisable solutions for these areas. This review studies previous research related to pests, weeds, and diseases detection and mapping using UAV imaging in the precision farming context, underpinning the applied reference measurement techniques. The majority of the reviewed studies utilised subjective visual observations of UAV images, and only a few applied in situ measurements. The conclusion of the review is that there is a lack of quantitative and repeatable reference data measurement solutions in the areas of mapping pests, weeds, and diseases. In addition, the results that the studies present should be reflected in the applied references. An option in the future approach could be the use of synthetic data as reference.

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“…Aerial imagery is often used for extrapolation and correlation of fieldwork datasets [26]- [29]. Soil samples or soil parameter measurements can correlate well with such data [30]. Roughness [31], texture [32], moisture [30] or nutrient levels [33] can be calculated from both RGb and multispectral imagery acquired from UAVs.…”

mentioning

confidence: 99%

“…Soil samples or soil parameter measurements can correlate well with such data [30]. Roughness [31], texture [32], moisture [30] or nutrient levels [33] can be calculated from both RGb and multispectral imagery acquired from UAVs. Data processing results in Digital Elevation Model (DEMs), Digital Surface Models (DSMs), point clouds and orthophotomaps.…”

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confidence: 99%

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Estimation of Bare Soil Moisture from Remote Sensing Indices in the 0.4–2.5 mm Spectral Range

Katarzyna

Stypułkowska

Jakub

et al. 2021

Transactions on Aerospace Research

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Soil moisture content (SMC) is an important element of the environment, influencing water availability for plants and atmospheric parameters, and its monitoring is important for predicting floods or droughts and for weather and climate modeling. Optical methods for measuring soil moisture use spectral reflection analysis in the 350–2500 nm range. Remote sensing is considered to be an effective tool for monitoring soil parameters over large areas and to be more cost effective than in situ measurements. The aim of this study was to assess the SMC of bare soil on the basis of hyperspectral data from the ASD FieldSpec 4 Hi-Res field spectrometer by determining remote sensing indices and visualization based on multispectral data obtained from UAVs. Remote sensing measurements were validated on the basis of field humidity measurements with the HH2 Moisture Meter and ML3 ThetaProbe Soil Moisture Sensor. A strong correlation between terrestrial and remote sensing data was observed for 7 out of 11 selected indexes and the determination coefficient R2 values ranged from 67%– 87%. The best results were obtained for the NINSON index, with determination coefficient values of 87%, NSMI index (83.5%) and NINSOL (81.7%). We conclude that both hyperspectral and multispectral remote sensing data of bare soil moisture are valuable, providing good temporal and spatial resolution of soil moisture distribution in local areas, which is important for monitoring and forecasting local changes in climate.

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Using UAV Visible Images to Estimate the Soil Moisture of Steppe

Cited by 39 publications

References 48 publications

Advances in Unmanned Aerial System Remote Sensing for Precision Viticulture

Advances in Unmanned Aerial System Remote Sensing for Precision Viticulture

Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases

Estimation of Bare Soil Moisture from Remote Sensing Indices in the 0.4–2.5 mm Spectral Range

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