Rice Mapping and Growth Monitoring Based on Time Series GF-6 Images and Red-Edge Bands

Jiang, Xueqin; Fang, Shenghui; Huang, Xia; Liu, Yanghua; Guo, Linlin

doi:10.3390/rs13040579

Cited by 34 publications

(15 citation statements)

References 57 publications

(89 reference statements)

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“…The main problems regarding yield prediction with VIs are that the VIs lose sensitivity in the last growth stages and they saturate at dense vegetation [44]. In this study we were not faced with the saturation error mainly because of the red-edge usage on VIs which have great potential for yield estimation [45] and rice growth monitoring [46], while the used VIs remained sensitive in all acquisition dates. The strongest correlation of VIs with yield was on the booting stage of Ronaldo and booting and heading for Gladio variety, something that is also confirmed in the literature [11,47,48].…”

Section: Discussionmentioning

confidence: 87%

Spatial Analysis of Agronomic Data and UAV Imagery for Rice Yield Estimation

2021

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In this study, a spatial analysis of agronomic and remote sensing data is carried out to derive accurate rice crop yield estimation. The variability of a series of vegetation indices (VIs) was calculated from remote sensing data obtained via a commercial UAS platform (e-Bee) at four dates (per stage of development), and the development of estimation models was conducted. The study area is located in the region of Chalastra (municipality of Thessaloniki, North Greece) and the primary data were obtained during the 2016 growing season. These data include ultra-high resolution remote sensing multispectral images of 18 plots totaling 58 hectares of Ronaldo and Gladio rice crop varieties, 97 sample point data related to yield, and many other pieces of information recorded in the producer’s field log. Ten simple and compound VIs were calculated, and the evolution of their values during the growing season as well as their comparative correlation were studied. A study of the usability of each VI was conducted for the different phenological stages of the cultivation and the variance of VIs and yield; the more correlated VIs were identified. Furthermore, three types of multitemporal VI were calculated from combinations of VIs from different dates, and their contribution to improving yield prediction was studied. As Ronaldo is a Japonica type of rice variety and Gladio is Indica type, they behave differently in terms of maturation time (Gladio is approximately 20 days earlier) and the value of every VI is affected by changes in plant physiology and phenology. These differences between the two varieties are reflected in the multitemporal study of the single-date VIs but also in the study of the values of the multitemporal VIs. In conclusion, Ronaldo’s yield is strongly dependent on multitemporal NDVI (VI6th July + VI30 Aug, R2 = 0.76), while Gladio’s yield is strongly dependent on single-date NDVI (6 July, R2 = 0.88). The compound VIs RERDVI and MCARI1 have the highest yield prediction (R2 = 0.77) for Ronaldo (VI6th July + VI30 Aug) and Gladio (R2 = 0.95) when calculated in the booting stage, respectively. For the Ronaldo variety, the examination of the multitemporal VIs increases yield prediction accuracy, while in the case of the Gladio variety the opposite is observed. The capabilities of multitemporal VIs in yield estimation by combining UAVs with more flights during the different growth stages can improve management and the cultivation practices.

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Section: Discussionmentioning

confidence: 87%

Spatial Analysis of Agronomic Data and UAV Imagery for Rice Yield Estimation

2021

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“…Although the parameter quantity of SRT decreases, the detection time does increase slightly. (2) Can it be extended to other satellites, such as GaoFen-2 and GaoFen-4?…”

Section: Discussionmentioning

confidence: 99%

“…It can not only reduce the time of remote sensing data acquisition from 4 days to 2, but also significantly improve the ability to monitor agriculture, forestry, grassland, and other resources, providing remote sensing data support for agricultural and rural development, ecological civilization construction [1], and other significant needs. GF-6 also realized the localization of the 8-band CMOS detector and added the red-edge band that can effectively reflect the unique spectral characteristics of crops [2,3].…”

Section: Introductionmentioning

confidence: 99%

SRT: A Spectral Reconstruction Network for GF-1 PMS Data Based on Transformer and ResNet

Zhang

Qian

et al. 2022

Remote Sensing

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The time of acquiring remote sensing data was halved after the joint operation of Gao Fen-6 (GF-6) and Gao Fen-1 (GF-1) satellites. Meanwhile, GF-6 added four bands, including the “red-edge” band that can effectively reflect the unique spectral characteristics of crops. However, GF-1 data do not contain these bands, which greatly limits their application to crop-related joint monitoring. In this paper, we propose a spectral reconstruction network (SRT) based on Transformer and ResNet to reconstruct the missing bands of GF-1. SRT is composed of three modules: (1) The transformer feature extraction module (TFEM) fully extracts the correlation features between spectra. (2) The residual dense module (RDM) reconstructs local features and avoids the vanishing gradient problem. (3) The residual global construction module (RGM) reconstructs global features and preserves texture details. Compared with competing methods, such as AWAN, HRNet, HSCNN-D, and M2HNet, the proposed method proved to have higher accuracy by a margin of the mean relative absolute error (MRAE) and root mean squared error (RMSE) of 0.022 and 0.009, respectively. It also achieved the best accuracy in supervised classification based on support vector machine (SVM) and spectral angle mapper (SAM).

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“…Of these, Sentinel-2 is the only open-access satellite, and Worldview-3 (The spatial resolution of multispectral and SWIR bands was 1.24 m and 3.70 m, respectively) is the main commercial satellite [93]. China's GaoFen-6 (GF-6) has two red edge bands with 16 m spatial resolution, but it lacks SWIR bands, which limits its application value to a certain extent [94]. Hyperspectral satellite sensors, such as China's ZiYuan-1 (ZY-1) 02D, GaoFen-5 (GF-5), and OuBiTe hyperspectral satellite-2/3 (OHS-2/3), have more detailed bands and higher spatial resolution, which are also effective data sources for monitoring SM in agricultural areas [95][96].…”

Section: Outlook and Limitationsmentioning

confidence: 99%

Comprehensive Evaluation of Sentinel-2 Red Edge and Shortwave-Infrared Bands to Estimate Soil Moisture

Liu

Qian

Yue

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This article aims to explore the applicability of SMMI (Soil moisture monitoring index), MSMMI (Modified soil moisture monitoring index), PDI (Perpendicular drought index), and MPDI (Modified perpendicular drought index) in estimating soil moisture (SM) in farmland. The random forest classifier (RFC) was used to obtain two-stage land cover types maps. The sensitivity of Sentinel-2 spectral bands to the measured SM at a depth of 0-5 cm was optimized by random forest regression (RFR). According to the sensitive bands, SMMI and PDI from different feature spaces were constructed to explore their feasibility for monitoring SM under different land cover types. Secondly, fractional vegetation cover (FVC) in the study area was estimated by nine kinds of FVC estimation models and compared with the measured FVC. The effects of different FVC methods on estimating SM by MSMMI and MPDI were evaluated. The results show that red edge and short-wave infrared (SWIR) bands of Sentinel-2 had irreplaceable effects on the land cover classification. In terms of monitoring SM in bare soil areas, the soil moisture indices with SWIR bands had high correlations with measured SM. For vegetation-covered areas, MSMMI from the FVCgr model (Dimidiate pixel model with red edge bands) and the SWIR1-SWIR2 feature space had the highest correlation with the measured 0-5 cm depth SM. Whether vegetation-covered areas or bare soil areas, the combination of red edge and SWIR bands can effectively improve the estimation accuracy of SM. MSMMI can be used as the best soil moisture monitoring index in the study area. Sentinel-2 images, with great potential, can effectively estimate SM at a depth of 0-5 cm in farmland with complex environments.

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Rice Mapping and Growth Monitoring Based on Time Series GF-6 Images and Red-Edge Bands

Cited by 34 publications

References 57 publications

Spatial Analysis of Agronomic Data and UAV Imagery for Rice Yield Estimation

Spatial Analysis of Agronomic Data and UAV Imagery for Rice Yield Estimation

SRT: A Spectral Reconstruction Network for GF-1 PMS Data Based on Transformer and ResNet

Comprehensive Evaluation of Sentinel-2 Red Edge and Shortwave-Infrared Bands to Estimate Soil Moisture

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