Zhao, D.H.[De-Hua],
Huang, L.M.[Liang-Mei],
Li, J.L.[Jian-Long],
Qi, J.G.[Jia-Guo],
A comparative analysis of broadband and narrowband derived vegetation
indices in predicting LAI and CCD of a cotton canopy,
PandRS(62), No. 1, May 2007, pp. 25-33.
Elsevier DOI
0709
Hyperspectral remote sensing; Cotton; Broadband vegetation indices;
Narrowband VIs; Leaf area index (LAI); Canopy chlorophyll density (CCD);
Bandwidth and wavelength selection
BibRef
Yi, Q.X.[Qiu-Xiang],
Jiapaer, G.[Guli],
Chen, J.M.[Jing-Ming],
Bao, A.M.[An-Ming],
Wang, F.M.[Fu-Min],
Different units of measurement of carotenoids estimation in cotton
using hyperspectral indices and partial least square regression,
PandRS(91), No. 1, 2014, pp. 72-84.
Elsevier DOI
1404
Carotenoids
BibRef
Lex, S.[Sylvia],
Asam, S.[Sarah],
Löw, F.[Fabian],
Conrad, C.[Christopher],
Comparison of two Statistical Methods for the Derivation of the
Fraction of Absorbed Photosynthetic Active Radiation for Cotton,
PFG(2015), No. 1, 2015, pp. 55-67.
DOI Link
1503
BibRef
Muharam, F.M.[Farrah Melissa],
Maas, S.J.[Stephen J.],
Bronson, K.F.[Kevin F.],
Delahunty, T.[Tina],
Estimating Cotton Nitrogen Nutrition Status Using Leaf Greenness and
Ground Cover Information,
RS(7), No. 6, 2015, pp. 7007.
DOI Link
1507
BibRef
Suarez, L.A.,
Apan, A.,
Werth, J.,
Hyperspectral sensing to detect the impact of herbicide drift on
cotton growth and yield,
PandRS(120), No. 1, 2016, pp. 65-76.
Elsevier DOI
1610
Cotton
BibRef
Sun, S.P.[Shang-Peng],
Li, C.Y.[Chang-Ying],
Paterson, A.H.[Andrew H.],
In-Field High-Throughput Phenotyping of Cotton Plant Height Using
LiDAR,
RS(9), No. 4, 2017, pp. xx-yy.
DOI Link
1705
BibRef
Song, X.Y.[Xiao-Yu],
Yang, C.H.[Cheng-Hai],
Wu, M.Q.[Ming-Quan],
Zhao, C.J.[Chun-Jiang],
Yang, G.J.[Gui-Jun],
Hoffmann, W.C.[Wesley Clint],
Huang, W.J.[Wen-Jiang],
Evaluation of Sentinel-2A Satellite Imagery for Mapping Cotton Root
Rot,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link
1711
BibRef
Thompson, A.L.[Alison L.],
Thorp, K.R.[Kelly R.],
Conley, M.M.[Matthew M.],
Elshikha, D.M.[Diaa M.],
French, A.N.[Andrew N.],
Andrade-Sanchez, P.[Pedro],
Pauli, D.[Duke],
Comparing Nadir and Multi-Angle View Sensor Technologies for
Measuring in-Field Plant Height of Upland Cotton,
RS(11), No. 6, 2019, pp. xx-yy.
DOI Link
1903
BibRef
Ballester, C.[Carlos],
Hornbuckle, J.[John],
Brinkhoff, J.[James],
Smith, J.[John],
Quayle, W.[Wendy],
Assessment of In-Season Cotton Nitrogen Status and Lint Yield
Prediction from Unmanned Aerial System Imagery,
RS(9), No. 11, 2017, pp. xx-yy.
DOI Link
1712
BibRef
Sakamoto, T.[Toshihiro],
Refined shape model fitting methods for detecting various types of
phenological information on major U.S. crops,
PandRS(138), 2018, pp. 176-192.
Elsevier DOI
1804
MODIS, MOD12Q2, Phenology, Barley, Wheat, Cotton
BibRef
Yeom, J.[Junho],
Jung, J.H.[Jin-Ha],
Chang, A.[Anjin],
Maeda, M.[Murilo],
Landivar, J.[Juan],
Automated Open Cotton Boll Detection for Yield Estimation Using
Unmanned Aircraft Vehicle (UAV) Data,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link
1901
BibRef
Bian, J.[Jiang],
Zhang, Z.T.[Zhi-Tao],
Chen, J.Y.[Jun-Ying],
Chen, H.Y.[Hai-Ying],
Cui, C.F.[Chen-Feng],
Li, X.[Xianwen],
Chen, S.B.[Shuo-Bo],
Fu, Q.P.[Qiu-Ping],
Simplified Evaluation of Cotton Water Stress Using High Resolution
Unmanned Aerial Vehicle Thermal Imagery,
RS(11), No. 3, 2019, pp. xx-yy.
DOI Link
1902
BibRef
Ballester, C.[Carlos],
Brinkhoff, J.[James],
Quayle, W.C.[Wendy C.],
Hornbuckle, J.[John],
Monitoring the Effects of Water Stress in Cotton Using the Green Red
Vegetation Index and Red Edge Ratio,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link
1904
BibRef
Ashapure, A.[Akash],
Jung, J.H.[Jin-Ha],
Yeom, J.[Junho],
Chang, A.[Anjin],
Maeda, M.[Murilo],
Maeda, A.[Andrea],
Landivar, J.[Juan],
A novel framework to detect conventional tillage and no-tillage
cropping system effect on cotton growth and development using
multi-temporal UAS data,
PandRS(152), 2019, pp. 49-64.
Elsevier DOI
1905
Unmanned aerial system, Conventional tillage, No-tillage, Precision agriculture
BibRef
He, L.M.[Li-Ming],
Mostovoy, G.[Georgy],
Cotton Yield Estimate Using Sentinel-2 Data and an Ecosystem Model
over the Southern US,
RS(11), No. 17, 2019, pp. xx-yy.
DOI Link
1909
BibRef
Polinova, M.[Maria],
Salinas, K.[Keren],
Bonfante, A.[Antonello],
Brook, A.[Anna],
Irrigation Optimization Under a Limited Water Supply by the
Integration of Modern Approaches into Traditional Water Management on
the Cotton Fields,
RS(11), No. 18, 2019, pp. xx-yy.
DOI Link
1909
BibRef
Ashapure, A.[Akash],
Jung, J.H.[Jin-Ha],
Chang, A.[Anjin],
Oh, S.C.[Sung-Chan],
Maeda, M.[Murilo],
Landivar, J.[Juan],
A Comparative Study of RGB and Multispectral Sensor-Based Cotton
Canopy Cover Modelling Using Multi-Temporal UAS Data,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link
1912
BibRef
Sun, S.P.[Shang-Peng],
Li, C.Y.[Chang-Ying],
Chee, P.W.[Peng W.],
Paterson, A.H.[Andrew H.],
Jiang, Y.[Yu],
Xu, R.[Rui],
Robertson, J.S.[Jon S.],
Adhikari, J.[Jeevan],
Shehzad, T.[Tariq],
Three-dimensional photogrammetric mapping of cotton bolls in situ
based on point cloud segmentation and clustering,
PandRS(160), 2020, pp. 195-207.
Elsevier DOI
2001
Clustering, Field-based high throughput phenotyping, LiDAR,
Point cloud, Segmentation, Spatial distribution
BibRef
Lin, Y.K.[Yu-Kun],
Zhu, Z.[Zhe],
Guo, W.X.[Wen-Xuan],
Sun, Y.Z.[Ya-Zhou],
Yang, X.Y.[Xiao-Yuan],
Kovalskyy, V.[Valeriy],
Continuous Monitoring of Cotton Stem Water Potential using Sentinel-2
Imagery,
RS(12), No. 7, 2020, pp. xx-yy.
DOI Link
2004
BibRef
Wang, T.Y.[Tian-Yi],
Thomasson, J.A.[J. Alex],
Yang, C.H.[Cheng-Hai],
Isakeit, T.[Thomas],
Nichols, R.L.[Robert L.],
Automatic Classification of Cotton Root Rot Disease Based on UAV
Remote Sensing,
RS(12), No. 8, 2020, pp. xx-yy.
DOI Link
2004
BibRef
Feng, A.[Aijing],
Zhou, J.F.[Jian-Feng],
Vories, E.[Earl],
Sudduth, K.A.[Kenneth A.],
Evaluation of Cotton Emergence Using UAV-Based Narrow-Band Spectral
Imagery with Customized Image Alignment and Stitching Algorithms,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link
2006
BibRef
Ren, Y.[Yu],
Meng, Y.H.[Yan-Hua],
Huang, W.J.[Wen-Jiang],
Ye, H.C.[Hui-Chun],
Han, Y.X.[Yu-Xing],
Kong, W.P.[Wei-Ping],
Zhou, X.F.[Xian-Feng],
Cui, B.[Bei],
Xing, N.C.[Nai-Chen],
Guo, A.[Anting],
Geng, Y.[Yun],
Novel Vegetation Indices for Cotton Boll Opening Status Estimation
Using Sentinel-2 Data,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link
2006
BibRef
Wang, T.Y.[Tian-Yi],
Thomasson, J.A.[J. Alex],
Isakeit, T.[Thomas],
Yang, C.H.[Cheng-Hai],
Nichols, R.L.[Robert L.],
A Plant-by-Plant Method to Identify and Treat Cotton Root Rot Based
on UAV Remote Sensing,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link
2008
BibRef
Al-Shammari, D.[Dhahi],
Fuentes, I.[Ignacio],
Whelan, B.M.[Brett M.],
Filippi, P.[Patrick],
Bishop, T.F.A.[Thomas F. A.],
Mapping of Cotton Fields Within-Season Using Phenology-Based Metrics
Derived from a Time Series of Landsat Imagery,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link
2009
BibRef
Oh, S.C.[Sung-Chan],
Chang, A.[Anjin],
Ashapure, A.[Akash],
Jung, J.H.[Jin-Ha],
Dube, N.[Nothabo],
Maeda, M.[Murilo],
Gonzalez, D.[Daniel],
Landivar, J.[Juan],
Plant Counting of Cotton from UAS Imagery Using Deep Learning-Based
Object Detection Framework,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link
2009
BibRef
Ashapure, A.[Akash],
Jung, J.H.[Jin-Ha],
Chang, A.[Anjin],
Oh, S.C.[Sung-Chan],
Yeom, J.[Junho],
Maeda, M.[Murilo],
Maeda, A.[Andrea],
Dube, N.[Nothabo],
Landivar, J.[Juan],
Hague, S.[Steve],
Smith, W.[Wayne],
Developing a machine learning based cotton yield estimation framework
using multi-temporal UAS data,
PandRS(169), 2020, pp. 180-194.
Elsevier DOI
2011
Precision agriculture, Cotton genotype selection, UAS, ANN
BibRef
Li, X.R.[Xing-Rong],
Yang, C.H.[Cheng-Hai],
Huang, W.J.[Wen-Jiang],
Tang, J.[Jia],
Tian, Y.Q.[Yan-Qin],
Zhang, Q.[Qing],
Identification of Cotton Root Rot by Multifeature Selection from
Sentinel-2 Images Using Random Forest,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link
2011
BibRef
Chen, P.F.[Peng-Fei],
Wang, F.Y.[Fang-Yong],
New Textural Indicators for Assessing Above-Ground Cotton Biomass
Extracted from Optical Imagery Obtained via Unmanned Aerial Vehicle,
RS(12), No. 24, 2020, pp. xx-yy.
DOI Link
2012
BibRef
Wang, N.[Nan],
Zhai, Y.G.[Yong-Guang],
Zhang, L.F.[Li-Fu],
Automatic Cotton Mapping Using Time Series of Sentinel-2 Images,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link
2104
BibRef
Marang, I.J.[Ian J.],
Filippi, P.[Patrick],
Weaver, T.B.[Tim B.],
Evans, B.J.[Bradley J.],
Whelan, B.M.[Brett M.],
Bishop, T.F.A.[Thomas F. A.],
Murad, M.O.F.[Mohammed O. F.],
Al-Shammari, D.[Dhahi],
Roth, G.[Guy],
Machine Learning Optimised Hyperspectral Remote Sensing Retrieves
Cotton Nitrogen Status,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link
2104
BibRef
Lin, Z.[Zhe],
Guo, W.X.[Wen-Xuan],
Cotton Stand Counting from Unmanned Aerial System Imagery Using
MobileNet and CenterNet Deep Learning Models,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link
2107
BibRef
And:
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link
2206
BibRef
Xun, L.[Lan],
Zhang, J.H.[Jia-Hua],
Cao, D.[Dan],
Yang, S.S.[Shan-Shan],
Yao, F.M.[Feng-Mei],
A novel cotton mapping index combining Sentinel-1 SAR and Sentinel-2
multispectral imagery,
PandRS(181), 2021, pp. 148-166.
Elsevier DOI
2110
Cotton, Automatic mapping, Cotton Mapping Index, Sentinel-1, Sentinel-2
BibRef
Hu, T.[Tao],
Hu, Y.[Yina],
Dong, J.Q.[Jian-Quan],
Qiu, S.J.[Si-Jing],
Peng, J.[Jian],
Integrating Sentinel-1/2 Data and Machine Learning to Map Cotton
Fields in Northern Xinjiang, China,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link
2112
BibRef
Li, Q.Q.[Qi-Qi],
Liu, G.L.[Gui-Lin],
Chen, W.J.[Wei-Jia],
Toward a Simple and Generic Approach for Identifying Multi-Year
Cotton Cropping Patterns Using Landsat and Sentinel-2 Time Series,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link
2112
BibRef
Ma, Y.[Yiru],
Zhang, Q.[Qiang],
Yi, X.[Xiang],
Ma, L.[Lulu],
Zhang, L.[Lifu],
Huang, C.P.[Chang-Ping],
Zhang, Z.[Ze],
Lv, X.[Xin],
Estimation of Cotton Leaf Area Index (LAI) Based on Spectral
Transformation and Vegetation Index,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link
2201
BibRef
Han, L.J.[Li-Jing],
Ding, J.L.[Jian-Li],
Wang, J.J.[Jin-Jie],
Zhang, J.Y.[Jun-Yong],
Xie, B.Q.[Bo-Qiang],
Hao, J.P.[Jian-Ping],
Monitoring Oasis Cotton Fields Expansion in Arid Zones Using the
Google Earth Engine: A Case Study in the Ogan-Kucha River Oasis,
Xinjiang, China,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link
2201
BibRef
Fei, H.[Hao],
Fan, Z.[Zehua],
Wang, C.[Chengkun],
Zhang, N.N.[Nan-Nan],
Wang, T.[Tao],
Chen, R.[Rengu],
Bai, T.C.[Tie-Cheng],
Cotton Classification Method at the County Scale Based on
Multi-Features and Random Forest Feature Selection Algorithm and
Classifier,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link
2202
BibRef
Hong, Y.[Yong],
Li, D.R.[De-Ren],
Wang, M.[Mi],
Jiang, H.N.[Hao-Nan],
Luo, L.K.[Leng-Kun],
Wu, Y.P.[Yan-Ping],
Liu, C.[Chen],
Xie, T.[Tianjin],
Zhang, Q.[Qing],
Jahangir, Z.[Zahid],
Cotton Cultivated Area Extraction Based on Multi-Feature Combination
and CSSDI under Spatial Constraint,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link
2204
BibRef
Jeong, S.[Seungtaek],
Shin, T.[Taehwan],
Ban, J.O.[Jong-Oh],
Ko, J.[Jonghan],
Simulation of Spatiotemporal Variations in Cotton Lint Yield in the
Texas High Plains,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link
2204
BibRef
Yin, C.X.[Cai-Xia],
Lv, X.[Xin],
Zhang, L.[Lifu],
Ma, L.[Lulu],
Wang, H.H.[Hui-Han],
Zhang, L.S.[Lin-Shan],
Zhang, Z.[Ze],
Hyperspectral UAV Images at Different Altitudes for Monitoring the
Leaf Nitrogen Content in Cotton Crops,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link
2206
BibRef
Ramamoorthy, P.[Purushothaman],
Samiappan, S.[Sathishkumar],
Wubben, M.J.[Martin J.],
Brooks, J.P.[John P.],
Shrestha, A.[Amrit],
Panda, R.M.[Rajendra Mohan],
Reddy, K.R.[K. Raja],
Bheemanahalli, R.[Raju],
Hyperspectral Reflectance and Machine Learning Approaches for the
Detection of Drought and Root-Knot Nematode Infestation in Cotton,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link
2208
BibRef
Yan, P.[Puchen],
Han, Q.[Qisheng],
Feng, Y.M.[Yang-Ming],
Kang, S.Z.[Shao-Zhong],
Estimating LAI for Cotton Using Multisource UAV Data and a Modified
Universal Model,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link
2209
BibRef
Chen, P.C.[Peng-Chao],
Xu, W.C.[Wei-Cheng],
Zhan, Y.L.[Yi-Long],
Yang, W.G.[Wei-Guang],
Wang, J.[Juan],
Lan, Y.[Yubin],
Evaluation of Cotton Defoliation Rate and Establishment of Spray
Prescription Map Using Remote Sensing Imagery,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link
2209
BibRef
Yang, M.[Mi],
Huang, C.P.[Chang-Ping],
Kang, X.Y.[Xiao-Yan],
Qin, S.Z.[Shi-Zhe],
Ma, L.[Lulu],
Wang, J.[Jin],
Zhou, X.T.[Xiao-Ting],
Lv, X.[Xin],
Zhang, Z.[Ze],
Early Monitoring of Cotton Verticillium Wilt by Leaf Multiple
'Symptom' Characteristics,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link
2211
BibRef
Chen, X.Y.[Xiang-Yu],
Lv, X.[Xin],
Ma, L.[Lulu],
Chen, A.[Aiqun],
Zhang, Q.[Qiang],
Zhang, Z.[Ze],
Optimization and Validation of Hyperspectral Estimation Capability of
Cotton Leaf Nitrogen Based on SPA and RF,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link
2211
BibRef
Zhou, X.T.[Xiao-Ting],
Yang, M.[Mi],
Chen, X.Y.[Xiang-Yu],
Ma, L.[Lulu],
Yin, C.X.[Cai-Xia],
Qin, S.Z.[Shi-Zhe],
Wang, L.[Lu],
Lv, X.[Xin],
Zhang, Z.[Ze],
Estimation of Cotton Nitrogen Content Based on Multi-Angle
Hyperspectral Data and Machine Learning Models,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link
2303
BibRef
Han, J.W.[Jian-Wen],
Wang, M.Y.[Ming-Yue],
Wang, N.[Nan],
Wang, J.[Jiawen],
Peng, J.[Jie],
Feng, C.H.[Chun-Hui],
Research on Cotton Field Irrigation Amount Calculation Based on
Electromagnetic Induction Technology,
RS(15), No. 8, 2023, pp. 1975.
DOI Link
2305
BibRef
Tian, Y.H.[Yu-Hang],
Shuai, Y.M.[Yan-Min],
Shao, C.Y.[Cong-Ying],
Wu, H.[Hao],
Fan, L.L.[Lian-Lian],
Li, Y.M.[Yao-Ming],
Chen, X.[Xi],
Narimanov, A.[Abdujalil],
Usmanov, R.[Rustam],
Baboeva, S.[Sevara],
Extraction of Cotton Information with Optimized Phenology-Based
Features from Sentinel-2 Images,
RS(15), No. 8, 2023, pp. 1988.
DOI Link
2305
BibRef
Zhang, N.N.[Nan-Nan],
Zhang, X.[Xiao],
Shang, P.[Peng],
Ma, R.[Rui],
Yuan, X.[Xintao],
Li, L.[Li],
Bai, T.C.[Tie-Cheng],
Detection of Cotton Verticillium Wilt Disease Severity Based on
Hyperspectrum and GWO-SVM,
RS(15), No. 13, 2023, pp. 3373.
DOI Link
2307
BibRef
Zou, C.[Chen],
Chen, D.H.[Dong-Hua],
Chang, Z.[Zhu],
Fan, J.W.[Jing-Wei],
Zheng, J.[Jian],
Zhao, H.P.[Hai-Ping],
Wang, Z.[Zuo],
Li, H.[Hu],
Early Identification of Cotton Fields Based on Gf-6 Images in Arid
and Semiarid Regions (China),
RS(15), No. 22, 2023, pp. 5326.
DOI Link
2311
BibRef
Wang, Y.K.[Yu-Kun],
Xiao, C.Y.[Chen-Yu],
Wang, Y.[Yao],
Li, K.[Kexin],
Yu, K.[Keke],
Geng, J.[Jijia],
Li, Q.Z.[Qiang-Zi],
Yang, J.T.[Jiu-Tao],
Zhang, J.[Jie],
Zhang, M.C.[Ming-Cai],
Lu, H.Y.[Huai-Yu],
Du, X.[Xin],
Du, M.W.[Ming-Wei],
Tian, X.L.[Xiao-Li],
Li, Z.[Zhaohu],
Monitoring of Cotton Boll Opening Rate Based on UAV Multispectral
Data,
RS(16), No. 1, 2024, pp. xx-yy.
DOI Link
2401
BibRef
Dhal, S.B.[Sambandh Bhusan],
Kalafatis, S.[Stavros],
Braga-Neto, U.[Ulisses],
Gadepally, K.C.[Krishna Chaitanya],
Landivar-Scott, J.L.[Jose Luis],
Zhao, L.[Lei],
Nowka, K.[Kevin],
Landivar, J.[Juan],
Pal, P.[Pankaj],
Bhandari, M.[Mahendra],
Testing the Performance of LSTM and ARIMA Models for In-Season
Forecasting of Canopy Cover (CC) in Cotton Crops,
RS(16), No. 11, 2024, pp. 1906.
DOI Link
2406
BibRef
Yadav, P.K.[Pappu Kumar],
Thomasson, J.A.[J. Alex],
Hardin, R.[Robert],
Searcy, S.W.[Stephen W.],
Braga-Neto, U.[Ulisses],
Popescu, S.C.[Sorin C.],
Rodriguez, R.[Roberto],
Martin, D.E.[Daniel E.],
Enciso, J.[Juan],
AI-Driven Computer Vision Detection of Cotton in Corn Fields Using
UAS Remote Sensing Data and Spot-Spray Application,
RS(16), No. 15, 2024, pp. 2754.
DOI Link
2408
BibRef
Aghayev, A.[Amil],
Rezník, T.[Tomáš],
Konecný, M.[Milan],
Enhancing Agricultural Productivity: Integrating Remote Sensing
Techniques for Cotton Yield Monitoring and Assessment,
IJGI(13), No. 10, 2024, pp. 340.
DOI Link
2411
BibRef
Palacharla, P.K.[Pavan K.],
Durbha, S.S.[Surya S.],
King, R.L.[Roger L.],
Gokaraju, B.[Balakrishna],
Lawrence, G.W.[Gary W.],
A hyperspectral reflectance data based model inversion methodology to
detect reniform nematodes in cotton,
MultiTemp11(249-252).
IEEE DOI
1109
BibRef
Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Peatland, Analysis and Change .