Role of cultivars and grass in the stability of soil moisture and temperature in an organic vineyard

López‐Vicente, Manuel; Pereira-Rodríguez, Laura; Silva-Dias, Rosane da; Raposo-Díaz, Xiana; Wu, Guoxiong; Paz‐González, Antonio

doi:10.1016/j.geodrs.2023.e00631

Cited by 2 publications

(1 citation statement)

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“…This suggests that although the grapevine vigor is clearly related to nearby SWC (Table 6), the effects of cover crops (such as grass at this site) are also significant. This result matches that found by other researchers [73,97,101,102], where cover crops were found to have a significant impact on the SWC in vineyards. It is probable that a better correlation between multispectral data acquired over the vines and SWC might have resulted if SWC was measured over a deeper interval than was performed in this study, since grapevines have been shown to develop deeper root systems than the cover crops (grass in this study) in vineyards where cover crops are regularly used [101].…”

Section: Impact Of Short Vs Tall Vegetation On Swc Predictionsupporting

confidence: 92%

Assessing the Potential of UAV-Based Multispectral and Thermal Data to Estimate Soil Water Content Using Geophysical Methods

Guan,

Grote

2023

Remote Sensing

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Knowledge of the soil water content (SWC) is important for many aspects of agriculture and must be monitored to maximize crop yield, efficiently use limited supplies of irrigation water, and ensure optimal nutrient management with minimal environmental impact. Single-location sensors are often used to monitor SWC, but a limited number of point measurements is insufficient to measure SWC across most fields since SWC is typically very heterogeneous. To overcome this difficulty, several researchers have used data acquired from unmanned aerial vehicles (UAVs) to predict the SWC by using machine learning on a limited number of point measurements acquired across a field. While useful, these methods are limited by the relatively small number of SWC measurements that can be acquired with conventional measurement techniques. This study uses UAV-based data and thousands of SWC measurements acquired using geophysical methods at two different depths and before and after precipitation to predict the SWC using the random forest method across a vineyard in the central United States. Both multispectral data (five reflectance bands and eleven vegetation indices calculated from these bands) and thermal UAV-based data were acquired, and the importance of different reflectance data and vegetation indices in the prediction of SWC was analyzed. Results showed that when both thermal and multispectral data were used to estimate SWC, the thermal data contributed the most to prediction accuracy, although multispectral data were also important. Reflectance data contributed as much or more to prediction accuracy than most vegetation indices. SWC measurements that had a larger sample size and greater penetration depth (~30 cm sampling depth) were more accurately predicted than smaller and shallower SWC estimates (~18 cm sampling depth). The timing of SWC estimation was also important; higher accuracy predictions were achieved in wetter soils than in drier soils, and a light precipitation event also improved prediction accuracy.

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