Vegetation Effects on Soil Moisture Retrieval from Water Cloud Model Using PALSAR-2 for Oil Palm Trees

Shashikant, Veena; Shariff, Abdul Rashid Mohamed; Wayayok, Aimrun; Kamal, Rowshon; Lee, Yang Ping; Takeuchi, Wataru

doi:10.3390/rs13204023

Cited by 7 publications

(2 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Given that our soil moisture has a range of 0-0.274 cm 3 /cm 3 with a mean value of 0.133 cm 3 /cm 3 and a standard error of 0.002 cm 3 /cm 3 , the relationship between backscattering intensity and soil moisture can be modeled using a linear function. This simplification has also been used by previous studies [40,45,46]. The relationship between backscattering intensity and soil moisture is written as follows:…”

Section: Soil Moisture Model and Parameter Inversionmentioning

confidence: 99%

Soil Moisture Inversion in Grassland Ecosystem Using Remote Sensing Considering Different Grazing Intensities and Growing Seasons

Cui

Wang

et al. 2023

Sustainability

View full text Add to dashboard Cite

Although vegetation community information such as grazing gradient, biomass, and density have been well characterized in typical grassland communities with Stipa grandis and Leymus chinensis as dominant species, their impact on the soil moisture (SM) inversion is still unclear. This study investigated the characteristics of a grassland vegetation community at different grazing gradients and growing seasons and its impact on SM inversion using remote sensing data. The water cloud model (WCM) was used for SM inversion, and both field and remote sensing data collected from 2019 to 2021 were used for calibration and prediction. The study found that the calibrated WCM achieved prediction results of SM inversion with average R2 values of 0.41 and 0.38 at different grazing gradients and growing seasons, respectively. Vegetation biomass and height were significantly correlated with vegetation indexes, and the highest model prediction accuracy was achieved for biomass and height around 121.1 g/m2 [102.3–139.9] and 18.6 cm [17.3–19.8], respectively. Generally, NDWI1 produced higher SM estimation accuracy than NDWI2. The growing season of vegetation also affects the accuracy of the WCM to retrieve SM, with the highest accuracy achieved in mid-growing season I. Therefore, the developed WCM with optimal height and biomass of vegetation communities can enhance the SM prediction capacity; it thus can be potentially used for SM prediction in typical grasslands.

show abstract

Section: Soil Moisture Model and Parameter Inversionmentioning

confidence: 99%

Soil Moisture Inversion in Grassland Ecosystem Using Remote Sensing Considering Different Grazing Intensities and Growing Seasons

Cui

Wang

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…Water management: Remote sensing provides valuable information about soil moisture levels (Shashikant et al, 2021), irrigation needs and water stress (Galvez-Valencia, Garces-Gomez, Lemus Rodriguez, & ArangoArgoti, 2021) in oil palm plantations. By using satellite data or ground-based sensors, managers can optimize irrigation schedules, improve water use efficiency and minimize the risk of water-related issues such as drought stress or flooding.…”

Section: Theoretical Review 21 Remote Sensing Technology In the Oil P...mentioning

confidence: 99%

Applying bag of words approach to determine remote sensing technology acceptance among smallholder plantations

Diana

Farida

2023

AGJSR

View full text Add to dashboard Cite

PurposeTechnology acceptance is a measure of that technology’s usefulness. Oil palm is one of the biggest contributors to Indonesia’s revenues, thus fueling its economy. Using remote sensing would allow a plantation to monitor and forecast its production and the amount of fertilizer used. This review aims to provide a policy recommendation in the form of a strategy to improve the added value of Indonesia’s oil palm and support the government in increasing oil palm production. This recommendation needs to be formulated by determining the users’ acceptance of remote sensing technology (state-owned plantations, private plantation companies and smallholder plantations).Design/methodology/approachThis review’s methodology used sentiment analysis through text mining (bag of words model). The study’s primary data were from focus group discussions (FGDs), questionnaires, observations on participants, audio-visual documentation and focused discussions based on group category. The results of interviews and FGDs were transcribed into text and analyzed to 1) find words that can represent the content of the document; 2) classify and determine the frequency (word cloud); and finally 3) analyze the sentiment.FindingsThe result showed that private plantation companies and state-owned plantations had extremely high positive sentiments toward using remote sensing in their oil palm plantations, whereas smallholders had a 60% resistance. However, there is still a possibility for this technology’s adoption by smallholders, provided it is free and easily applied.Research limitations/implicationsBasically, technology is applied to make work easier. However, not everyone is tech-savvy, especially the older generations. One dimension of technology acceptance is user/customer retention. New technology would not be immediately accepted, but there would be user perceptions about its uses and ease. At first, people might be reluctant to accept a new technology due to the perception that it is useless and difficult. Technology acceptance is the gauge of how useful technology is in making work easier compared to conventional ways.Practical implicationsTherefore, technology acceptance needs to be improved among smallholders by intensively socializing the policies, and through dissemination and dedication by academics and the government.Social implicationsThe social implications of using technology are reducing the workforce, but the company will be more profitable and efficient.Originality/valueRemote sensing is one of the topics that people have not taken up in a large way, especially sentiment analysis. Acceptance of technology that utilizes remote sensing for plantations is very useful and efficient. In the end, company profits can be allocated more toward empowering the community and the environment.

show abstract

Strategic Short Note: Comparing Soil Moisture Retrieval from Water Cloud Model and Neural Network Using PALSAR-2 for Oil Palm Estates

Shashikant¹,

Shariff²,

Wayayok³

et al. 2023

IoT and AI in Agriculture

View full text Add to dashboard Cite

Vegetation Effects on Soil Moisture Retrieval from Water Cloud Model Using PALSAR-2 for Oil Palm Trees

Cited by 7 publications

References 59 publications

Soil Moisture Inversion in Grassland Ecosystem Using Remote Sensing Considering Different Grazing Intensities and Growing Seasons

Soil Moisture Inversion in Grassland Ecosystem Using Remote Sensing Considering Different Grazing Intensities and Growing Seasons

Applying bag of words approach to determine remote sensing technology acceptance among smallholder plantations

Strategic Short Note: Comparing Soil Moisture Retrieval from Water Cloud Model and Neural Network Using PALSAR-2 for Oil Palm Estates

Contact Info

Product

Resources

About