Comparative Analysis of Landsat-8, Sentinel-2, and GF-1 Data for Retrieving Soil Moisture over Wheat Farmlands

Wang, Qi; Li, Jiancheng; Jin, Taoyong; Chang, Xiaoping; Zhu, Yongchao; Li, Yunwei; Sun, Jiaojiao; Li, Dawei

doi:10.3390/rs12172708

Cited by 40 publications

(14 citation statements)

References 51 publications

(63 reference statements)

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“…Therefore, as a starting point, certain differences can be observed between the NDVI values of the Sentinel and Landsat images, and these differences are not uniform in all the DOs. Other authors have reported similar issues, where instruments mounted on different platforms and capturing the same area provide different values in images, showing significant inconsistencies [ 26 , 27 ].…”

Section: Resultsmentioning

confidence: 97%

Can Satellite Remote Sensing Assist in the Characterization of Yeasts Related to Biogeographical Origin?

Castrillo

Blanco

Vélez

2023

Sensors

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Biogeography is a key concept associated with microbial terroir, which is responsible for the differentiation and uniqueness of wines. One of the factors influencing this microbial terroir is the vegetation, which in turn is influenced by climate, soil, and cultural practices. Remote sensing instruments can provide useful information about vegetation. This study analyses the relationship between NDVI, calculated using Sentinel-2 and Landsat-8 satellite images of different veraison dates, and microbial data obtained in 2015 from 14 commercial (organic and conventional) vineyards belonging to four Designations of Origin (DOs) from Galicia (northwest Spain). Microbial populations in grapes and musts were identified using PCR techniques and confirmed by sequencing. Statistical analyses were made using PCA, CCA, TB-PLS, and correlation analyses. This study confirms that the NDVI is positively correlated with the diversity of yeasts, both in grapes’ surface and must samples. Moreover, the results of this study show: (i) Sentinel-2 images, as well as Landsat-8 images, can establish differences in NDVI related to yeast terroir in grapes and musts, as it is the most relevant DO factor, (ii) Sentinel-2 NDVI and yeast biogeography are moderately to strongly correlated, (iii) Sentinel-2 achieved a better delimitation of the DOs than Landsat-8 and can establish more accurate differences in NDVI–yeast terroir correlations, and (iv) a higher NDVI was associated with the yeast biogeographical patterns of the DOs with higher species richness (S) consisting of weakly fermenting yeasts (Hanseniaspora uvarum, Pichia spp., Starmerella bacillaris, and Zygosaccharomyces spp). However, NDVI values did not correlate well with biogeographic patterns of yeasts previously studied at frequency level (proportion or percentage of each species) in each particular DO. This study suggests that satellite imagery has the potential to be a valuable tool for wine quality management and a decision-making instrument for DO regulators and winegrowers.

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Section: Resultsmentioning

confidence: 97%

Can Satellite Remote Sensing Assist in the Characterization of Yeasts Related to Biogeographical Origin?

Castrillo

Blanco

Vélez

2023

Sensors

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“…GF-1 is China's high-resolution remote sensing satellite, and it collects data in blue, green, red, and nearinfrared bands. It has been widely used for extracting land-use information [32,33]. Lotus is usually planted in April, grows vigorously in June, July and August, when huge lotus leaves cover the whole water surface, and gradually withers from September until all stems are withered in November.…”

Section: Processing Gf-1 Datamentioning

confidence: 99%

Extracting Lotus Fields Using the Spectral Characteristics of GF-1 Satellite Data

Dongping¹,

Cai²,

Zhang³

et al. 2022

Phyton

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The lotus (Nelumbo nucifera Gaertn.) is an aquatic plant that grows in shallow water and has long been cultivated in South China. It can improve the incomes of farmers and plays an important role in alleviating poverty in rural China. However, a modern method is required to accurately estimate the area of lotus fields. Lotus has spectral characteristics similar to those of rice, grassland, and shrubs. The features surrounding areas where it is grown are complex, small, and fragmented. Few studies have examined the remote sensing extraction of lotus fields, and automatic extraction and mapping are still challenging methods. Here, we compared the spectral characteristics of lotus fields and other ground objects and devised a remote sensing method for the rapid extraction of lotus fields. Using this method, the extraction accuracy of lotus was 96.3%. The Kappa coefficient was 0.926, which is higher than those of the unsupervised K-means classification, Mahalanobis distance, and support vector machine supervised classification, and demonstrates the potential of this method for extracting and mapping lotus fields by remote sensing.

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“…Baghdadi [23] Sentinel-1/2; Landsat-8 RMSE = 6% NNs Kong [25] Radarsat-2/GF-1 R 2 = [0.82-0.87] AIEM and WCM Attarzadeh [28] Sentinel-1/2 RMSE = [4.94-6.41%] SVR Mattia [57] Sentinel-1/2 R 2 = 0.5 Change detection technique Han [58] GF-3/GF-1 RMSE = [0.0271-0.0321 cm 3 /cm 3 ] Optimal solution method Adab [59] SMAP/Landsat 8 R 2 = 0.73 Machine learning Wang [60] Sentinel-1/2; Landsat-8; GF-1 R…”

Section: Smc Retrieval Over Vegetated Areasmentioning

confidence: 99%

Soil Moisture Retrieval in Farmland Areas with Sentinel Multi-Source Data Based on Regression Convolutional Neural Networks

Liu

et al. 2021

Sensors

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As an important component of the earth ecosystem, soil moisture monitoring is of great significance in the fields of crop growth monitoring, crop yield estimation, variable irrigation, and other related applications. In order to mitigate or eliminate the impacts of sparse vegetation covers in farmland areas, this study combines multi-source remote sensing data from Sentinel-1 radar and Sentinel-2 optical satellites to quantitatively retrieve soil moisture content. Firstly, a traditional Oh model was applied to estimate soil moisture content after removing vegetation influence by a water cloud model. Secondly, support vector regression (SVR) and generalized regression neural network (GRNN) models were used to establish the relationships between various remote sensing features and real soil moisture. Finally, a regression convolutional neural network (CNNR) model is constructed to extract deep-level features of remote sensing data to increase soil moisture retrieval accuracy. In addition, polarimetric decomposition features for real Sentinel-1 PolSAR data are also included in the construction of inversion models. Based on the established soil moisture retrieval models, this study analyzes the influence of each input feature on the inversion accuracy in detail. The experimental results show that the optimal combination of R2 and root mean square error (RMSE) for SVR is 0.7619 and 0.0257 cm3/cm3, respectively. The optimal combination of R2 and RMSE for GRNN is 0.7098 and 0.0264 cm3/cm3, respectively. Especially, the CNNR model with optimal feature combination can generate inversion results with the highest accuracy, whose R2 and RMSE reach up to 0.8947 and 0.0208 cm3/cm3, respectively. Compared to other methods, the proposed algorithm improves the accuracy of soil moisture retrieval from synthetic aperture radar (SAR) and optical data. Furthermore, after adding polarization decomposition features, the R2 of CNNR is raised by 0.1524 and the RMSE of CNNR decreased by 0.0019 cm3/cm3 on average, which means that the addition of polarimetric decomposition features effectively improves the accuracy of soil moisture retrieval results.

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Comparative Analysis of Landsat-8, Sentinel-2, and GF-1 Data for Retrieving Soil Moisture over Wheat Farmlands

Cited by 40 publications

References 51 publications

Can Satellite Remote Sensing Assist in the Characterization of Yeasts Related to Biogeographical Origin?

Can Satellite Remote Sensing Assist in the Characterization of Yeasts Related to Biogeographical Origin?

Extracting Lotus Fields Using the Spectral Characteristics of GF-1 Satellite Data

Soil Moisture Retrieval in Farmland Areas with Sentinel Multi-Source Data Based on Regression Convolutional Neural Networks

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