Smallholder Analysis

Chapter Contents (Back)
Object-Based. Smallholder. Agricultural Parcel. Region-Based.

Morel, J.[Julien], Todoroff, P.[Pierre], Bégué, A.[Agnès], Bury, A.[Aurore], Martiné, J.F.[Jean-François], Petit, M.[Michel],
Toward a Satellite-Based System of Sugarcane Yield Estimation and Forecasting in Smallholder Farming Conditions: A Case Study on Reunion Island,
RS(6), No. 7, 2014, pp. 6620-6635.
DOI Link 1408

Sweeney, S.[Sean], Ruseva, T.[Tatyana], Estes, L.[Lyndon], Evans, T.[Tom],
Mapping Cropland in Smallholder-Dominated Savannas: Integrating Remote Sensing Techniques and Probabilistic Modeling,
RS(7), No. 11, 2015, pp. 15295.
DOI Link 1512

Jain, M.[Meha], Srivastava, A.K.[Amit K.], Balwinder-Singh, Joon, R.K.[Rajiv K.], McDonald, A.[Andrew], Royal, K.[Keitasha], Lisaius, M.C.[Madeline C.], Lobell, D.B.[David B.],
Mapping Smallholder Wheat Yields and Sowing Dates Using Micro-Satellite Data,
RS(8), No. 10, 2016, pp. 860.
DOI Link 1609

Blaes, X.[Xavier], Chomé, G.[Guillaume], Lambert, M.J.[Marie-Julie], Traoré, P.S.[Pierre Sibiry], Schut, A.G.T.[Antonius G. T.], Defourny, P.[Pierre],
Quantifying Fertilizer Application Response Variability with VHR Satellite NDVI Time Series in a Rainfed Smallholder Cropping System of Mali,
RS(8), No. 6, 2016, pp. 531.
DOI Link 1608

Lebourgeois, V.[Valentine], Dupuy, S.[Stéphane], Vintrou, É.[Élodie], Ameline, M.[Maël], Butler, S.[Suzanne], Bégué, A.[Agnès],
A Combined Random Forest and OBIA Classification Scheme for Mapping Smallholder Agriculture at Different Nomenclature Levels Using Multisource Data (Simulated Sentinel-2 Time Series, VHRS and DEM),
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704

Jain, M.[Meha], Mondal, P.[Pinki], Galford, G.L.[Gillian L.], Fiske, G.[Greg], de Fries, R.S.[Ruth S.],
An Automated Approach to Map Winter Cropped Area of Smallholder Farms across Large Scales Using MODIS Imagery,
RS(9), No. 6, 2017, pp. xx-yy.
DOI Link 1706

Jin, Z.[Zhenong], Azzari, G.[George], Burke, M.[Marshall], Aston, S.[Stephen], Lobell, D.B.[David B.],
Mapping Smallholder Yield Heterogeneity at Multiple Scales in Eastern Africa,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711

Stratoulias, D.[Dimitris], Tolpekin, V.[Valentyn], de By, R.A.[Rolf A.], Zurita-Milla, R.[Raul], Retsios, V.[Vasilios], Bijker, W.[Wietske], Hasan, M.A.[Mohammad Alfi], Vermote, E.[Eric],
A Workflow for Automated Satellite Image Processing: from Raw VHSR Data to Object-Based Spectral Information for Smallholder Agriculture,
RS(9), No. 10, 2017, pp. xx-yy.
DOI Link 1711

Aguilar, R.[Rosa], Zurita-Milla, R.[Raul], Izquierdo-Verdiguier, E.[Emma], de By, R.A.[Rolf A.],
A Cloud-Based Multi-Temporal Ensemble Classifier to Map Smallholder Farming Systems,
RS(10), No. 5, 2018, pp. xx-yy.
DOI Link 1806
The regions are small. Worldview-2 images. BibRef

Du, Z.R.[Zhen-Rong], Yang, J.Y.[Jian-Yu], Ou, C.[Cong], Zhang, T.T.[Ting-Ting],
Smallholder Crop Area Mapped with a Semantic Segmentation Deep Learning Method,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904

Vogels, M.F.A.[Marjolein F.A.], de Jong, S.M.[Steven M.], Sterk, G.[Geert], Douma, H.[Harke], Addink, E.A.[Elisabeth A.],
Spatio-Temporal Patterns of Smallholder Irrigated Agriculture in the Horn of Africa Using GEOBIA and Sentinel-2 Imagery,
RS(11), No. 2, 2019, pp. xx-yy.
DOI Link 1902

Mathenge, M.[Mwehe], Sonneveld, B.G.J.S.[Ben G. J. S.], Broerse, J.E.W.[Jacqueline E. W.],
A Spatially Explicit Approach for Targeting Resource-Poor Smallholders to Improve their Participation in Agribusiness: A Case of Nyando and Vihiga County in Western Kenya,
IJGI(9), No. 10, 2020, pp. xx-yy.
DOI Link 2010

Lv, Y.H.[Ya-Hui], Zhang, C.[Chao], Yun, W.J.[Wen-Ju], Gao, L.[Lulu], Wang, H.[Huan], Ma, J.[Jiani], Li, H.[Hongju], Zhu, D.[Dehai],
The Delineation and Grading of Actual Crop Production Units in Modern Smallholder Areas Using RS Data and Mask R-CNN,
RS(12), No. 7, 2020, pp. xx-yy.
DOI Link 2004

Kamal, M.[Mustafa], Schulthess, U.[Urs], Krupnik, T.J.[Timothy J.],
Identification of Mung Bean in a Smallholder Farming Setting of Coastal South Asia Using Manned Aircraft Photography and Sentinel-2 Images,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011

Rao, P.[Preeti], Zhou, W.Q.[Wei-Qi], Bhattarai, N.[Nishan], Srivastava, A.K.[Amit K.], Singh, B.[Balwinder], Poonia, S.[Shishpal], Lobell, D.B.[David B.], Jain, M.[Meha],
Using Sentinel-1, Sentinel-2, and Planet Imagery to Map Crop Type of Smallholder Farms,
RS(13), No. 10, 2021, pp. xx-yy.
DOI Link 2105

López-Ameodo, A.[Alberto], Álvarez, X.[Xana], Lorenzo, H.[Henrique], Rodríguez, J.L.[Juan Luis],
Multi-Temporal Sentinel-2 Data Analysis for Smallholding Forest Cut Control,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108

Zhou, W.Q.[Wei-Qi], Rao, P.[Preeti], Jat, M.L.[Mangi L.], Singh, B.[Balwinder], Poonia, S.[Shishpal], Bijarniya, D.[Deepak], Kumar, M.[Manish], Singh, L.K.[Love Kumar], Schulthess, U.[Urs], Singh, R.[Rajbir], Jain, M.[Meha],
Using Sentinel-2 to Track Field-Level Tillage Practices at Regional Scales in Smallholder Systems,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link 2112

Mashaba-Munghemezulu, Z.[Zinhle], Chirima, G.J.[George Johannes], Munghemezulu, C.[Cilence],
Mapping Smallholder Maize Farms Using Multi-Temporal Sentinel-1 Data in Support of the Sustainable Development Goals,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105

Zhang, P.[Peng], Hu, S.[Shougeng], Li, W.D.[Wei-Dong], Zhang, C.R.[Chuan-Rong], Cheng, P.[Peikun],
Improving Parcel-Level Mapping of Smallholder Crops from VHSR Imagery: An Ensemble Machine-Learning-Based Framework,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106

Ren, T.T.[Ting-Ting], Xu, H.T.[Hong-Tao], Cai, X.[Xiumin], Yu, S.N.[Sheng-Nan], Qi, J.G.[Jia-Guo],
Smallholder Crop Type Mapping and Rotation Monitoring in Mountainous Areas with Sentinel-1/2 Imagery,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202

Cai, Z.W.[Zhi-Wen], Hu, Q.[Qiong], Zhang, X.Y.[Xin-Yu], Yang, J.Y.[Jing-Ya], Wei, H.D.[Hao-Dong], He, Z.[Zhen], Song, Q.[Qian], Wang, C.[Cong], Yin, G.F.[Gao-Fei], Xu, B.D.[Bao-Dong],
An Adaptive Image Segmentation Method with Automatic Selection of Optimal Scale for Extracting Cropland Parcels in Smallholder Farming Systems,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208

Xing, H.Q.[Hua-Qiao], Chen, B.Y.[Bing-Yao], Lu, M.[Miao],
A Sub-Seasonal Crop Information Identification Framework for Crop Rotation Mapping in Smallholder Farming Areas with Time Series Sentinel-2 Imagery,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link 2212

Mei, W.Y.[Wei-Ye], Wang, H.Y.[Hao-Yu], Fouhey, D.[David], Zhou, W.Q.[Wei-Qi], Hinks, I.[Isabella], Gray, J.M.[Josh M.], van Berkel, D.[Derek], Jain, M.[Meha],
Using Deep Learning and Very-High-Resolution Imagery to Map Smallholder Field Boundaries,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208

Wang, S.[Sherrie], Waldner, F.[François], Lobell, D.B.[David B.],
Unlocking Large-Scale Crop Field Delineation in Smallholder Farming Systems with Transfer Learning and Weak Supervision,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link 2212

Brewer, K.[Kiara], Clulow, A.[Alistair], Sibanda, M.[Mbulisi], Gokool, S.[Shaeden], Naiken, V.[Vivek], Mabhaudhi, T.[Tafadzwanashe],
Predicting the Chlorophyll Content of Maize over Phenotyping as a Proxy for Crop Health in Smallholder Farming Systems,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202

Masiza, W.[Wonga], Chirima, J.G.[Johannes George], Hamandawana, H.[Hamisai], Kalumba, A.M.[Ahmed Mukalazi], Magagula, H.B.[Hezekiel Bheki],
A Proposed Satellite-Based Crop Insurance System for Smallholder Maize Farming,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204

Li, C.X.[Cheng-Xiu], Chimimba, E.G.[Ellasy Gulule], Kambombe, O.[Oscar], Brown, L.A.[Luke A.], Chibarabada, T.P.[Tendai Polite], Lu, Y.[Yang], Anghileri, D.[Daniela], Ngongondo, C.[Cosmo], Sheffield, J.[Justin], Dash, J.[Jadunandan],
Maize Yield Estimation in Intercropped Smallholder Fields Using Satellite Data in Southern Malawi,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link 2206

Trivedi, M.B.[Manushi B.], Marshall, M.[Michael], Estes, L.[Lyndon], de Bie, C.A.J.M., Chang, L.[Ling], Nelson, A.[Andrew],
Cropland Mapping in Tropical Smallholder Systems with Seasonally Stratified Sentinel-1 and Sentinel-2 Spectral and Textural Features,
RS(15), No. 12, 2023, pp. xx-yy.
DOI Link 2307

Pan, Y.[Yang], Wang, X.Y.[Xin-Yu], Zhang, L.P.[Liang-Pei], Zhong, Y.F.[Yan-Fei],
E2EVAP: End-to-end vectorization of smallholder agricultural parcel boundaries from high-resolution remote sensing imagery,
PandRS(203), 2023, pp. 246-264.
Elsevier DOI 2310
Agricultural parcel, End-to-end vectorization, Semantic-contour interaction, Topological loss, Deep attention corner snake BibRef

Qiu, B.W.[Bing-Wen], Hu, X.[Xiang], Yang, P.[Peng], Tang, Z.H.[Zheng-Hong], Wu, W.B.[Wen-Bin], Li, Z.R.[Zheng-Rong],
A robust approach for large-scale cropping intensity mapping in smallholder farms from vegetation, brownness indices and SAR time series,
PandRS(203), 2023, pp. 328-344.
Elsevier DOI 2310
Cropping intensity, Crop phenology, Conterminous China, Senetinel-1/2, Time series, Google Earth Engine BibRef

Buthelezi, S.[Siphiwokuhle], Mutanga, O.[Onisimo], Sibanda, M.[Mbulisi], Odindi, J.[John], Clulow, A.D.[Alistair D.], Chimonyo, V.G.P.[Vimbayi G. P.], Mabhaudhi, T.[Tafadzwanashe],
Assessing the Prospects of Remote Sensing Maize Leaf Area Index Using UAV-Derived Multi-Spectral Data in Smallholder Farms across the Growing Season,
RS(15), No. 6, 2023, pp. 1597.
DOI Link 2304

Campos-Taberner, M.[Manuel], Javier-García-Haro, F.[Francisco], Martínez, B.[Beatriz], Sánchez-Ruiz, S.[Sergio], Moreno-Martínez, Á.[Álvaro], Camps-Valls, G.[Gustau], Amparo-Gilabert, M.[María],
Land use classification over smallholding areas in the European Common Agricultural Policy framework,
PandRS(197), 2023, pp. 320-334.
Elsevier DOI 2303
Land use (LU) monitoring, Common Agricultural Policy (CAP), Classification, Sentinel-2, Deep Learning, kNDVI BibRef

Weitkamp, T.[Timon], Karimi, P.[Poolad],
Evaluating the Effect of Training Data Size and Composition on the Accuracy of Smallholder Irrigated Agriculture Mapping in Mozambique Using Remote Sensing and Machine Learning Algorithms,
RS(15), No. 12, 2023, pp. xx-yy.
DOI Link 2307

Onojeghuo, A.O.[Alex Okiemute], Miao, Y.X.[Yu-Xin], Blackburn, G.A.[George Alan],
Deep ResU-Net Convolutional Neural Networks Segmentation for Smallholder Paddy Rice Mapping Using Sentinel 1 SAR and Sentinel 2 Optical Imagery,
RS(15), No. 6, 2023, pp. 1517.
DOI Link 2304

Cai, Z.W.[Zhi-Wen], Wei, H.D.[Hao-Dong], Hu, Q.[Qiong], Zhou, W.[Wei], Zhang, X.Y.[Xin-Yu], Jin, W.J.[Wen-Jie], Wang, L.[Ling], Yu, S.[Shuxia], Wang, Z.[Zhen], Xu, B.D.[Bao-Dong], Shi, Z.H.[Zhi-Hua],
Learning spectral-spatial representations from VHR images for fine-scale crop type mapping: A case study of rice-crayfish field extraction in South China,
PandRS(199), 2023, pp. 28-39.
Elsevier DOI 2305
Convolutional neural networks, Very high-resolution images, Smallholder agriculture, Spatiotemporal transferability, Rice-crayfish fields BibRef

Lambert, M.J., Blaes, X., Traoré, P.S., Defourny, P.,
Estimate yield at parcel level from S2 time serie in sub-Saharan smallholder farming systems,
crops, regression analysis, soil, time series, vegetation mapping, Leaf Area Index, S2 time serie, Sentinel-2 satellite, Yield estimation BibRef

Izquierdo-Verdiguier, E., Zurita-Milla, R.[Raul], de By, R.A.[Rolf A.],
On the use of guided regularized random forests to identify crops in smallholder farm fields,
crops, feature selection, geophysical image processing, image classification, image resolution, soil, time series, Time series analysis BibRef

Zurita-Milla, R.[Raul], Izquierdo-Verdiguier, E., de By, R.A.[Rolf A.],
Identifying crops in smallholder farms using time series of WorldView-2 images,
crops, farming, feature extraction, feature selection, WorldView-2 image time series, automatic crop classification, Vegetation mapping BibRef

Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Subpixel Target, Subpixel Land Use, Tiny Objects .

Last update:Nov 30, 2023 at 15:51:27