|
Carlson T
N, Ripley D A. 1997. On the relation between NDVI, fractional vegetation cover,
and leaf area index. Remote Sensing of Environment, 62, 241–252.
Chen P F,
Wang F Y. 2022. Effect of crop spectra purification on plant nitrogen
concentration estimations performed using high-spatial-resolution images
obtained with unmanned aerial vehicles. Field Crops Research, 288, 108708.
Cheng M H,
Jiao X Y, Liu Y D, Shao M C, Yu X, Bai Y, Wang Z X, Wang S Y, Tuohuti N, Liu S
B, Shi L, Yin D M, Huang X, Nie C W, Jin X L. 2022. Estimation of soil moisture
content under high maize canopy coverage from UAV multimodal data and machine
learning. Agricultural Water Management, 264,
107530.
Cui B,
Zhao Q J, Huang W J, Song X Y, Ye H C, Zhou X F. 2019. A new integrated
vegetation index for the estimation of winter wheat leaf chlorophyll content. Remote Sensing, 11, 974.
Daughtry C
S T, Walthall C L, Kim M S, De Colstoum E B, McMurtrey J E. 2000. Estimating
corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74, 229–239.
Ding J L,
Wu J C, Ding D Y, Yang Y H, Gao C M, Hu W. 2021. Effects of tillage and straw
mulching on the crop productivity and hydrothermal resource utilization in a
winter wheat-summer maize rotation system. Agricultural Water Management, 254, 106933.
Fang H,
Liu F L, Gu X B, Chen P P, Li Y P, Li Y N. 2022. The effect of source–sink on
yield and water use of winter wheat under ridge-furrow with film mulching and
nitrogen fertilization. Agricultural Water Management, 267,
107616.
Gao D H,
Qiao L, Song D, Li M Z, Sun H, An L L, Zhao R M, Tang W J, Qiao J B. 2022.
In-field chlorophyll estimation based on hyperspectral images segmentation and
pixel-wise spectra clustering of wheat canopy. Biosystems Engineering, 217, 41–55.
Gao H H,
Yan C R, Liu Q, Ding W L, Chen B Q, Li Z. 2019. Effects of plastic mulching and
plastic residue on agricultural production: A meta-analysis. Science of the Total Environment, 651, 484–492.
Gilabert M
A, González-Piqueras J, Garcı́a-Haro F J, Meliá J. 2002. A generalized
soil-adjusted vegetation index. Remote Sensing of Environment, 82, 303–310.
Gitelson A
A, Gritz Y, Merzlyak M N. 2003. Relationships between leaf chlorophyll content
and spectral reflectance and algorithms for non-destructive chlorophyll
assessment in higher plant leaves. Journal of Plant Physiology, 160, 271–282.
Goh B B,
King P, Whetton R L, Sattari S Z, Holden N M. 2022. Monitoring winter wheat
growth performance at sub-field scale using multitemporal Sentinel-2 imagery. International Journal of Applied Earth Observation and Geoinformation, 115, 103124.
Gu X B,
Cai H J, Chen P P, Li Y P, Fang H, Li Y N. 2021. Ridge-furrow film mulching
improves water and nitrogen use efficiencies under reduced irrigation and
nitrogen applications in wheat field. Field Crops Research, 270, 108214.
Guo Y H,
Fu Y H, Chen S Z, Bryant C R, Li X X, Senthilnath J, Sun H Y, Wang S X, Wu Z F,
De Beurs K. 2021. Integrating spectral and textural information for identifying
the tasseling date of summer maize using UAV based RGB images. International Journal of Applied Earth Observation and Geoinformation, 102, 102435.
Guo Y H,
Xiao Y, Li M W, Hao F H, Zhang X, Sun H Y, De Beurs K, Fu Y H, He Y H. 2022.
Identifying crop phenology using maize height constructed from multi-sources
images. International Journal of Applied Earth Observation and Geoinformation, 115, 103121.
Han L,
Yang G J, Dai H Y, Xu B, Yang H, Feng H K, Li Z H, Yang X D. 2019. Modeling
maize above-ground biomass based on machine learning approaches using UAV
remote-sensing data. Plant Methods, 15, 10.
Hasituya,
Chen Z X, Wang L M, Wu W B, Jiang Z W, Li H. 2016. Monitoring plastic-mulched
farmland by landsat-8 OLI imagery using spectral and textural features. Remote Sensing, 8, 353.
He S, Xu H
L, Zhang J X, Xue P Q. 2023. Risk assessment of oil and gas pipelines hot work
based on AHP-FCE. Petroleum, 9, 94–100.
Jay S,
Baret F, Dutartre D, Malatesta G, Héno S, Comar A, Weiss M, Maupas F. 2019.
Exploiting the centimeter resolution of UAV multispectral imagery to improve
remote-sensing estimates of canopy structure and biochemistry in sugar beet
crops. Remote Sensing of Environment, 231,
110898.
Jay S,
Gorretta N, Morel J, Maupas F, Bendoula R, Rabatel G, Dutartre D, Comar A,
Baret F. 2017. Estimating leaf chlorophyll content in sugar beet canopies using
millimeter- to centimeter-scale reflectance imagery. Remote Sensing of Environment, 198, 173–186.
Jin X L,
Liu S Y, Baret F, Hemerlé M, Comar A. 2017. Estimates of plant density of wheat
crops at emergence from very low altitude UAV imagery. Remote Sensing of Environment, 198, 105–114.
Lee H, Wang
J F, Leblon B. 2020. Using linear regression, random forests, and support
vector machine with unmanned aerial vehicle multispectral images to predict
canopy nitrogen weight in corn. Remote Sensing, 12, 2071.
Li D,
Shang Y F, He W, Chen C J. 2015. EXR: Greening data center network with
software defined exclusive routing. IEEE Transactions on Computers, 64, 2534–2544.
Li R Y, Xu
M Q, Chen Z Y, Gao B B, Cai J, Shen F X, He X L, Zhuang Y, Chen D L. 2021.
Phenology-based classification of crop species and rotation types using fused
MODIS and Landsat data: The comparison of a random-forest-based model and a
decision-rule-based model. Soil and Tillage Research, 206, 104838.
Li Z H,
Zhao Y, Taylor J, Gaulton R, Jin X L, Song X Y, Li Z H, Meng Y, Chen P F, Feng
H K, Wang C, Guo W, Xu X G, Chen L P, Yang G J. 2022. Comparison and
transferability of thermal, temporal and phenological-based in-season
predictions of above-ground biomass in wheat crops from proximal crop
reflectance data. Remote Sensing of Environment, 273,
112967.
Liao Z Q,
Dai Y L, Wang H, Ketterings Q M, Lu J S, Zhang F C, Li Z J, Fan J L. 2023. A
double-layer model for improving the estimation of wheat canopy nitrogen
content from unmanned aerial vehicle multispectral imagery. Journal of
Integrative Agriculture, 22, 2248–2270.
Liu K,
Zhou Q B, Wu W B, Xia T, Tang H J. 2016. Estimating the crop leaf area index
using hyperspectral remote sensing. Journal of Integrative Agriculture, 15, 475–491.
Luo L C,
Wang Z H, Huang M, Hui X L, Wang S, Zhao Y, He H X, Zhang X, Diao C P, Cao H B,
Ma Q X, Liu J S. 2018. Plastic film mulch increased winter wheat grain yield
but reduced its protein content in dryland of Northwest China. Field Crops Research, 218, 69–77.
Mao Z H,
Deng L, Duan F Z, Li X J, Qiao D Y. 2020. Angle effects of vegetation indices
and the influence on prediction of SPAD values in soybean and maize. International Journal of Applied Earth Observation and Geoinformation, 93, 102198.
Narmilan
A, Gonzalez F, Salgadoe S, Kumarasiri U W L, Weerasinghe H A S, Kulasekara B.
2022. Predicting canopy chlorophyll content in sugarcane crops using machine
learning algorithms and spectral vegetation indices derived from UAV
multispectral imagery. Remote Sensing, 14, 1–22.
Pei S Z,
Liao Z Q, Dai Y L, Bai W Q, Fan J L. 2023. Nitrogen nutrition diagnosis for
cotton under mulched drip irrigation using unmanned aerial vehicle
multispectral images. Journal of Integrative Agriculture, 22,
2536–2552.
Qi H X, Wu
Z Y, Zhang L, Li J W, Zhou J K, Jun Z, Zhu B Y. 2021. Monitoring of peanut
leaves chlorophyll content based on drone-based multispectral image feature
extraction. Computers and Electronics in Agriculture, 187, 106292.
Qiao L,
Gao D H, Zhao R M, Tang W J, An L L, Li M Z, Sun H. 2022a. Improving estimation
of LAI dynamic by fusion of morphological and vegetation indices based on UAV
imagery. Computers and Electronics in Agriculture, 192, 106603.
Qiao L,
Zhao R M, Tang W J, An L L, Sun H, Li M Z, Wang N, Liu Y, Liu G H. 2022b.
Estimating maize LAI by exploring deep features of vegetation index map from
UAV multispectral images. Field Crops Research, 289,
108739.
Reyniers
M, Walvoort D J J, De Baardemaaker J. 2006. A linear model to predict with a
multi-spectral radiometer the amount of nitrogen in winter wheat. International Journal of Remote Sensing, 27, 4159–4179.
Rodriguez-Galiano
V F, Chica-Olmo M, Abarca-Hernandez F, Atkinson P M, Jeganathan C. 2012. Random
forest classification of mediterranean land cover using multi-seasonal imagery
and multi-seasonal texture. Remote Sensing of Environment, 121, 93–107.
Rondeaux
G, Steven M, Baret F. 1996. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55, 95–107.
Schneider
P, Roberts D A, Kyriakidis P C. 2008. A VARI-based relative greenness from
MODIS data for computing the Fire Potential Index. Remote Sensing of Environment, 112, 1151–1167.
Shao G M,
Han W T, Zhang H H, Liu S Y, Wang Y, Zhang L Y, Cui X. 2021. Mapping maize crop
coefficient Kc using random forest algorithm based on leaf
area index and UAV-based multispectral vegetation indices. Agricultural Water Management, 252, 106906.
Sun Q,
Chen L P, Xu X B, Gu X H, Hu X Q, Yang F T, Pan Y C. 2022. A new comprehensive
index for monitoring maize lodging severity using UAV-based multi-spectral
imagery. Computers and Electronics in Agriculture, 202, 107362.
Tao H L,
Feng H K, Xu L J, Miao M K, Yang G J, Yang X D, Fan L L. 2020. Estimation of
the yield and plant height of winter wheat using UAV-based hyperspectral
images. Sensors, 20, 1231.
Wang F L,
Yang M, Ma L F, Zhang T, Qin W L, Li W, Zhang Y H, Sun Z C, Wang Z M, Li F, Yu
K. 2022. Estimation of above-ground biomass of winter wheat based on
consumer-grade multi-spectral UAV. Remote Sensing, 14,
1251.
Wang W H,
Wu Y P, Zhang Q F, Zheng H B, Yao X, Zhu Y, Cao W X, Cheng T. 2021. AAVI: A
novel approach to estimating leaf nitrogen concentration in rice from unmanned
aerial vehicle multispectral imagery at early and middle growth stages. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14,
6716–6728.
Wang W H,
Zheng H B, Wu Y P, Yao X, Zhu Y, Cao W X, Cheng T. 2022. An assessment of
background removal approaches for improved estimation of rice leaf nitrogen
concentration with unmanned aerial vehicle multispectral imagery at various
observation times. Field Crops Research, 283, 108543.
Wittstruck
L, Jarmer T, Trautz D, Waske B. 2022. Estimating LAI from winter wheat using
UAV data and CNNs. IEEE Geoscience and Remote Sensing Letters, 19,
2503405.
Xu X B,
Nie C W, Jin X L, Li Z H, Zhu H C, Xu H G, Wang J W, Zhao Y, Feng H K. 2021. A
comprehensive yield evaluation indicator based on an improved fuzzy
comprehensive evaluation method and hyperspectral data. Field Crops Research, 270, 108204.
Xue J R,
Su B F. 2017. Significant remote sensing vegetation indices: A review of
developments and applications. Journal of Sensors, 2017,
1353691.
Yan S C,
Wu Y, Fan J L, Zhang F C, Zheng J, Guo J J, Lu J S, Wu L F, Qiang S C, Xiang Y
Z. 2022. Source–sink relationship and yield stability of two maize cultivars in
response to water and fertilizer inputs in Northwest China. Agricultural Water Management, 262, 107332.
Zhang P,
Du P J, Guo S C, Zhang W, Tang P F, Chen J K, Zheng H R. 2022. A novel index
for robust and large-scale mapping of plastic greenhouse from Sentinel-2
images. Remote Sensing of Environment, 276,
113042.
Zhang P P,
Ye Q Q, Yu Y. 2021a. Research on farmers’ satisfaction with ecological
restoration performance in coal mining areas based on fuzzy comprehensive
evaluation. Global Ecology and Conservation, 32,
e01934.
Zhang Y,
Hui J, Qin Q M, Sun Y H, Zhang T Y, Sun H, Li M Z. 2021b.
Transfer-learning-based approach for leaf chlorophyll content estimation of
winter wheat from hyperspectral data. Remote Sensing of Environment, 267, 112724.
Zhao X, Gu
X B, Yang Z C, Li Y N, Zhang L, Zhou J M. 2022. Effects of soil preparation and
mulching practices together with different urea applications on the water and
nitrogen use of winter wheat in semi-humid and drought-prone areas. Agricultural Water Management, 263, 107484.
Zheng H B,
Ma J F, Zhou M, Li D, Yao X, Cao W X, Zhu Y, Cheng T. 2020. Enhancing the
nitrogen signals of rice canopies across critical growth stages through the
integration of textural and spectral information from unmanned aerial vehicle
(UAV) multispectral imagery. Remote Sensing, 12, 957.
Zhu W X,
Rezaei E E, Nouri H, Sun Z G, Li J, Yu D Y, Siebert S. 2022. UAV-based
indicators of crop growth are robust for distinct water and nutrient management
but vary between crop development phases. Field Crops Research, 284, 108582.
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