Retrieving Surface Soil Water Content Using a Soil Texture Adjusted Vegetation Index and Unmanned Aerial System Images

Gu, Hanming; Lin, Zhe; Guo, Wenxuan; Deb, Sanjit K.

doi:10.3390/rs13010145

Cited by 16 publications

(9 citation statements)

References 91 publications

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“…Shu et al (Meiyan et al, 2022) constructed a prediction model for the aboveground biomass of maize in multiple growth periods by combining multispectral and UAV digital images with maize LAI and plant height. To realize the prediction of soil water content, Gu et al (Gu et al, 2021) used multispectral and thermal infrared images to determine soil water content and then constructed a temperature vegetation dryness index by combining the obtained canopy temperature and VI. Liu et al (Liu et al, 2019) combined VI and texture features to determine the aboveground biomass of winter rape, allowing the analysis of which input features were the most important and successful predictions of the aboveground biomass for the next year.…”

Section: Introductionmentioning

confidence: 99%

Estimating stomatal conductance of citrus under water stress based on multispectral imagery and machine learning methods

Xie

Chen²,

Yu³

et al. 2023

Front. Plant Sci.

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IntroductionCanopy stomatal conductance (Sc) indicates the strength of photosynthesis and transpiration of plants. In addition, Sc is a physiological indicator that is widely employed to detect crop water stress. Unfortunately, existing methods for measuring canopy Sc are time-consuming, laborious, and poorly representative.MethodsTo solve these problems, in this study, we combined multispectral vegetation index (VI) and texture features to predict the Sc values and used citrus trees in the fruit growth period as the research object. To achieve this, VI and texture feature data of the experimental area were obtained using a multispectral camera. The H (Hue), S (Saturation) and V (Value) segmentation algorithm and the determined threshold of VI were used to obtain the canopy area images, and the accuracy of the extraction results was evaluated. Subsequently, the gray level co-occurrence matrix (GLCM) was used to calculate the eight texture features of the image, and then the full subset filter was used to obtain the sensitive image texture features and VI. Support vector regression, random forest regression, and k-nearest neighbor regression (KNR) Sc prediction models were constructed, which were based on single and combined variables.ResultsThe analysis revealed the following: 1) the accuracy of the HSV segmentation algorithm was the highest, achieving more than 80%. The accuracy of the VI threshold algorithm using excess green was approximately 80%, which achieved accurate segmentation. 2) The citrus tree photosynthetic parameters were all affected by different water supply treatments. The greater the degree of water stress, the lower the net photosynthetic rate (Pn), transpiration rate (Tr), and Sc of the leaves. 3) In the three Sc prediction models, The KNR model, which was constructed by combining image texture features and VI had the optimum prediction effect (training set: R2 = 0.91076, RMSE = 0.00070; validation set; R2 = 0.77937, RMSE = 0.00165). Compared with the KNR model, which was only based on VI or image texture features, the R2 of the validation set of the KNR model based on combined variables was improved respectively by 6.97% and 28.42%.DiscussionThis study provides a reference for large-scale remote sensing monitoring of citrus Sc by multispectral technology. Moreover, it can be used to monitor the dynamic changes of Sc and provide a new technique for gaining a better understanding of the growth status and water stress of citrus crops.

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Section: Introductionmentioning

confidence: 99%

Estimating stomatal conductance of citrus under water stress based on multispectral imagery and machine learning methods

Xie

Chen²,

Yu³

et al. 2023

Front. Plant Sci.

View full text Add to dashboard Cite

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“…UAV data are much easier to collect than geophysical data, and they cover the entire survey area instead of individual traverses. The equipment needed to collect UAV-based data is also much less expensive than most geophysical instruments [36][37][38][39][40][41][42][43][44][45][46]. Unlike satellite data, the data acquisition time can be chosen by the user.…”

Section: Introductionmentioning

confidence: 99%

“…Vegetation indices calculated using these data have been used to predict yields in corn [55,56], rice [54,57], and sugar beets [56]. Some researchers also use UAV data for soil salinity monitoring of cropland [58] and surface soil moisture estimation [36,[40][41][42].…”

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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“…Up until now, only a few studies have been conducted to estimate ET components using SM observations, mainly since the accurate values of SM observation are difficult to obtain. Instead, previous studies usually use the land surface temperature and vegetation index, which can serve as proxy indicators of the surface moisture status over a range in spatial scales [27], to estimate plant T and E s [28,29]. Along with the constant development of remote sensing techniques on SM (e.g., the global Soil Moisture Climate Change Initiative project, SMCCI, 1978; Soil Moisture and Ocean Salinity, SMOS, 2009; Soil Moisture Active Passive, SMAP, 2015), SM data have become increasingly available in recent years [30,31].…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, the increasing accuracy and a variety of spatial scales of SM datasets will provide much assistance in ET estimates. However, many of these remote sensing techniques can only provide SM estimates for the surface [27,[33][34][35][36][37], which cannot satisfy the transpiration water requirements for the vegetation with deeper roots, such as trees and shrubs. Therefore, in order to investigate the SM effect on vegetation T estimation, effective SM data from the deeper sources should first be obtained.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Evapotranspiration and Its Components across China Based on a Modified Priestley–Taylor Algorithm Using Monthly Multi-Layer Soil Moisture Data

et al. 2021

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Although soil moisture (SM) is an important constraint factor of evapotranspiration (ET), the majority of the satellite-driven ET models do not include SM observations, especially the SM at different depths, since its spatial and temporal distribution is difficult to obtain. Based on monthly three-layer SM data at a 0.25° spatial resolution determined from multi-sources, we updated the original Priestley Taylor–Jet Propulsion Laboratory (PT-JPL) algorithm to the Priestley Taylor–Soil Moisture Evapotranspiration (PT-SM ET) algorithm by incorporating SM control into soil evaporation (Es) and canopy transpiration (T). Both algorithms were evaluated using 17 eddy covariance towers across different biomes of China. The PT-SM ET model shows increased R2, NSE and reduced RMSE, Bias, with more improvements occurring in water-limited regions. SM incorporation into T enhanced ET estimates by increasing R2 and NSE by 4% and 18%, respectively, and RMSE and Bias were respectively reduced by 34% and 7 mm. Moreover, we applied the two ET algorithms to the whole of China and found larger increases in T and Es in the central, northeastern, and southern regions of China when using the PT-SM algorithm compared with the original algorithm. Additionally, the estimated mean annual ET increased from the northwest to the southeast. The SM constraint resulted in higher transpiration estimate and lower evaporation estimate. Es was greatest in the northwest arid region, interception was a large fraction in some rainforests, and T was dominant in most other regions. Further improvements in the estimation of ET components at high spatial and temporal resolution are likely to lead to a better understanding of the water movement through the soil–plant–atmosphere continuum.

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Retrieving Surface Soil Water Content Using a Soil Texture Adjusted Vegetation Index and Unmanned Aerial System Images

Cited by 16 publications

References 91 publications

Estimating stomatal conductance of citrus under water stress based on multispectral imagery and machine learning methods

Estimating stomatal conductance of citrus under water stress based on multispectral imagery and machine learning methods

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

Estimation of Evapotranspiration and Its Components across China Based on a Modified Priestley–Taylor Algorithm Using Monthly Multi-Layer Soil Moisture Data

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