Evaluation of FORMOSAT-2 and PlanetScope Imagery for Aboveground Oil Palm Biomass Estimation in a Mature Plantation in the Congo Basin

Migolet, Pierre; Goı̈ta, Kalifa

doi:10.3390/rs12182926

Cited by 5 publications

(2 citation statements)

References 54 publications

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“…PlanetScope is a constellation of more than 130 CubeSats that cover the Earth's land surface daily. Planet images contain four spectral bands (blue, green, red, and NIR) with a spatial resolution of 3 m [26]. They were downloaded from the official website (https://www.planet.com/, accessed on 12 November 2021) and atmospherically and radiometrically corrected to obtain surface reflectance products.…”

Section: Satellite Image Acquisition and Processingmentioning

confidence: 99%

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Evaluation of Hybrid Models for Maize Chlorophyll Retrieval Using Medium- and High-Spatial-Resolution Satellite Images

Guo

et al. 2023

Remote Sensing

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Accurate estimation of the leaf or canopy chlorophyll content is crucial for monitoring crop growth conditions. Remote sensing monitoring of crop chlorophyll is a non-destructive, large-area, and real-time method that requires reliable retrieval models and satellite data. High-resolution satellite imagery generally has better object recognition capabilities. However, the influence of the spectral and spatial resolution of medium- and high-spatial-resolution satellite imagery on chlorophyll retrieval is currently unexplored, especially in conjunction with radiative transfer models (RTMs). This has important implications for the accurate quantification of crop chlorophyll over large areas. Therefore, the objectives of this study were to establish an RTM for the retrieval of maize chlorophyll and to compare the chlorophyll retrieval capability of the model using medium- and high-spatial-resolution satellite images. We constructed a hybrid model consisting of the PROSAIL model and the Gaussian process regression (GPR) algorithm to retrieve maize leaf and canopy chlorophyll contents (LCC and CCC). In addition, an active learning (AL) strategy was incorporated into the hybrid model to enhance the model’s accuracy and efficiency. Sentinel-2 imagery with a spatial resolution of 10 m and 3 m-resolution Planet imagery were utilized for the LCC and CCC retrieval, respectively, using the hybrid model. The accuracy of the model was verified using field-measured maize chlorophyll data obtained in Dajianchang Town, Wuqing District, Tianjin City, in 2018. The results showed that the AL strategy increased the accuracy of the chlorophyll retrieval. The hybrid model for LCC retrieval with 10-band Sentinel-2 without AL had an R2 of 0.567 and an RMSE of 5.598, and the model with AL had an R2 of 0.743 and an RMSE of 3.964. Incorporating the AL strategy improved the model performance (R2 = 0.743 and RMSE = 3.964). The Planet imagery provided better results for chlorophyll retrieval than 4-band Sentinel-2 imagery but worse performance than 10-band Sentinel-2 imagery. Additionally, we tested the model using maize chlorophyll data obtained from Youyi Farm in Heilongjiang Province in 2021 to evaluate the model’s robustness and scalability. The test results showed that the hybrid model used with 10-band Sentinel-2 images achieved good accuracy in the Youyi Farm area (LCC: R2 = 0.792, RMSE = 2.8; CCC: R2 = 0.726, RMSE = 0.152). The optimal hybrid model was applied to images from distinct periods to map the spatiotemporal distribution of the chlorophyll content. The uncertainties in the chlorophyll content retrieval results from different periods were relatively low, demonstrating that the model had good temporal scalability. Our research results can provide support for the precise management of maize growth.

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Section: Satellite Image Acquisition and Processingmentioning

confidence: 99%

“…Some researchers used Planet imagery for the retrieval of vegetation parameters. For example, Migolet and Goïta [26] estimated the total aboveground dry biomass of oil palm in the Congo Basin using Planet imagery. Some medium-spatial-resolution satellite images are open-source data and have good spectral and spatial resolutions.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Hybrid Models for Maize Chlorophyll Retrieval Using Medium- and High-Spatial-Resolution Satellite Images

Guo

et al. 2023

Remote Sensing

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Estimation of the total dry aboveground biomass in the tropical forests of Congo Basin using optical, LiDAR, and radar data

Migolet

Goı̈ta

Pambo

et al. 2022

GIScience & Remote Sensing

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A Review: Progression of Remote Sensing (RS) and Geographical Information System (GIS) Applications in Oil Palm Management and Sustainability

Baharim

Adnan²,

Mohd³

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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In agriculture management and cultivation, many researchers tend to introduce and implement new methods or techniques to improve the sectors in order to sustain a good production from the sectors. The oil palm plantation is one of the sectors that have received an improvement in development in many aspects. Thus, this paper reviews in detail the recent expansion of oil palm management and sustainability through the latest application technologies specifically in Remote Sensing (RS) and Geographical Information System (GIS) knowledge which covered land classification and crop changes, disease detection and pest control, age estimation for oil palm, above-ground biomass (AGB) and carbon estimation, tree counting for oil palm assessment and land suitability with soil nutrients. In the end, it concluded the most significant GIS and RS tools for oil palm management come from the implementation of Machine Learning (ML) and Deep Learning (DL) knowledge in it which can be improved over time through recent technologies and variation analysis to enhance the results.

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Evaluation of FORMOSAT-2 and PlanetScope Imagery for Aboveground Oil Palm Biomass Estimation in a Mature Plantation in the Congo Basin

Cited by 5 publications

References 54 publications

Evaluation of Hybrid Models for Maize Chlorophyll Retrieval Using Medium- and High-Spatial-Resolution Satellite Images

Evaluation of Hybrid Models for Maize Chlorophyll Retrieval Using Medium- and High-Spatial-Resolution Satellite Images

Estimation of the total dry aboveground biomass in the tropical forests of Congo Basin using optical, LiDAR, and radar data

A Review: Progression of Remote Sensing (RS) and Geographical Information System (GIS) Applications in Oil Palm Management and Sustainability

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