期刊名：Remote Sensing

期刊主页： https://www.mdpi.com/journal/remotesensing

本期文献清单聚焦在Remote Sensing期刊发表的遥感融合人工智能技术在作物产量预测方向的相关研究，涵盖多源遥感数据融合、多模态数据协同与多尺度应用等方面的相关内容，希望能为相关领域的学者提供研究灵感。

1.

Cotton Yield Prediction via UAV-Based Cotton Boll Image Segmentation Using YOLO Model and Segment Anything Model (SAM)

基于YOLO模型和Segment Anything模型（SAM）的无人机棉铃图像分割在棉花产量预测中的应用

https://www.mdpi.com/2072-4292/16/23/4346

Reddy, J.; Niu, H.; Scott, J.L.L.; Bhandari, M.; Landivar, J.A.; Bednarz, C.W.; Duffield, N. Cotton Yield Prediction via UAV-Based Cotton Boll Image Segmentation Using YOLO Model and Segment Anything Model (SAM). Remote Sens. 2024, 16, 4346. https://doi.org/10.3390/rs16234346

2.

Sugarcane Yield Estimation Using Satellite Remote Sensing Data in Empirical or Mechanistic Modeling: A Systematic Review

基于卫星遥感数据的甘蔗产量估算：经验模型与机理模型的系统综述

https://www.mdpi.com/2072-4292/16/5/863

de França e Silva, N.R.; Chaves, M.E.D.; Luciano, A.C.d.S.; Sanches, I.D.; de Almeida, C.M.; Adami, M. Sugarcane Yield Estimation Using Satellite Remote Sensing Data in Empirical or Mechanistic Modeling: A Systematic Review. Remote Sens. 2024, 16, 863. https://doi.org/10.3390/rs16050863

3.

Crop Type Mapping and Winter Wheat Yield Prediction Utilizing Sentinel-2: A Case Study from Upper Thracian Lowland, Bulgaria

利用Sentinel-2进行作物类型制图与冬小麦产量预测：保加利亚上色雷斯平原的案例研究

https://www.mdpi.com/2072-4292/16/7/1144

Kamenova, I.; Chanev, M.; Dimitrov, P.; Filchev, L.; Bonchev, B.; Zhu, L.; Dong, Q. Crop Type Mapping and Winter Wheat Yield Prediction Utilizing Sentinel-2: A Case Study from Upper Thracian Lowland, Bulgaria. Remote Sens. 2024, 16, 1144. https://doi.org/10.3390/rs16071144

4.

Advancements in Utilizing Image-Analysis Technology for Crop-Yield Estimation

图像分析技术在作物产量估算中的应用进展

https://www.mdpi.com/2072-4292/16/6/1003

Yu, F.; Wang, M.; Xiao, J.; Zhang, Q.; Zhang, J.; Liu, X.; Ping, Y.; Luan, R. Advancements in Utilizing Image-Analysis Technology for Crop-Yield Estimation. Remote Sens. 2024, 16, 1003. https://doi.org/10.3390/rs16061003

5.

Kolmogorov–Arnold Networks for Interpretable Crop Yield Prediction Across the U.S. Corn Belt

基于Kolmogorov-Arnold网络的美国玉米带可解释作物产量预测

https://www.mdpi.com/2072-4292/17/14/2500

Isik, M.S.; Ozturk, O.; Celik, M.F. Kolmogorov–Arnold Networks for Interpretable Crop Yield Prediction Across the U.S. Corn Belt. Remote Sens. 2025, 17, 2500. https://doi.org/10.3390/rs17142500

6.

Multimodal Deep Learning Integration of Image, Weather, and Phenotypic Data Under Temporal Effects for Early Prediction of Maize Yield

融合影像、气象与表型数据的多模态深度学习模型：考虑时序效应的玉米早期产量预测研究

https://www.mdpi.com/2072-4292/16/21/4043

Shamsuddin, D.; Danilevicz, M.F.; Al-Mamun, H.A.; Bennamoun, M.; Edwards, D. Multimodal Deep Learning Integration of Image, Weather, and Phenotypic Data Under Temporal Effects for Early Prediction of Maize Yield. Remote Sens. 2024, 16, 4043. https://doi.org/10.3390/rs16214043

7.

Integrating Climate and Satellite Data for Multi-Temporal Pre-Harvest Prediction of Head Rice Yield in Australia

整合气候与卫星数据用于澳大利亚稻穗产量的多时相收获前预测

https://www.mdpi.com/2072-4292/16/10/1815

Clarke, A.; Yates, D.; Blanchard, C.; Islam, M.Z.; Ford, R.; Rehman, S.-U.; Walsh, R.P. Integrating Climate and Satellite Data for Multi-Temporal Pre-Harvest Prediction of Head Rice Yield in Australia. Remote Sens. 2024, 16, 1815. https://doi.org/10.3390/rs16101815

8．

Exploring the Relationship Between Very-High-Resolution Satellite Imagery Data and Fruit Count for Predicting Mango Yield at Multiple Scales

探索超高分辨率卫星影像数据与果实数量的关系：多尺度芒果产量预测研究

https://www.mdpi.com/2072-4292/16/22/4170

Torgbor, B.A.; Sinha, P.; Rahman, M.M.; Robson, A.; Brinkhoff, J.; Suarez, L.A. Exploring the Relationship Between Very-High-Resolution Satellite Imagery Data and Fruit Count for Predicting Mango Yield at Multiple Scales. Remote Sens. 2024, 16, 4170. https://doi.org/10.3390/rs16224170

9.

Integrating Hyperspectral, Thermal, and Ground Data with Machine Learning Algorithms Enhances the Prediction of Grapevine Yield and Berry Composition

整合高光谱、热红外与地面数据的机器学习算法增强葡萄产量与浆果成分预测

https://www.mdpi.com/2072-4292/16/23/4539

Jewan, S.Y.Y.; Gautam, D.; Sparkes, D.; Singh, A.; Billa, L.; Cogato, A.; Murchie, E.; Pagay, V. Integrating Hyperspectral, Thermal, and Ground Data with Machine Learning Algorithms Enhances the Prediction of Grapevine Yield and Berry Composition. Remote Sens. 2024, 16, 4539. https://doi.org/10.3390/rs16234539

10.

Meta-Features Extracted from Use of kNN Regressor to Improve Sugarcane Crop Yield Prediction

用kNN回归器提取元特征改进甘蔗作物产量预测

https://www.mdpi.com/2072-4292/17/11/1846

Barbosa, L.A.F.; Guilherme, I.R.; Pedronette, D.C.G.; Tisseyre, B. Meta-Features Extracted from Use of kNN Regressor to Improve Sugarcane Crop Yield Prediction. Remote Sens. 2025, 17, 1846. https://doi.org/10.3390/rs17111846

11.

Early Season Forecasting of Corn Yield at Field Level from Multi-Source Satellite Time Series Data

基于多源卫星时序数据的田间尺度玉米产量早期预测

https://www.mdpi.com/2072-4292/16/9/1573

Desloires, J.; Ienco, D.; Botrel, A. Early Season Forecasting of Corn Yield at Field Level from Multi-Source Satellite Time Series Data. Remote Sens. 2024, 16, 1573. https://doi.org/10.3390/rs16091573

12.

Local Field-Scale Winter Wheat Yield Prediction Using VENµS Satellite Imagery and Machine Learning Techniques

基于VENµS卫星影像与机器学习技术的局地田间尺度冬小麦产量预测

https://www.mdpi.com/2072-4292/16/17/3132

Chiu, M.S.; Wang, J. Local Field-Scale Winter Wheat Yield Prediction Using VENµS Satellite Imagery and Machine Learning Techniques. Remote Sens. 2024, 16, 3132. https://doi.org/10.3390/rs16173132

13.

Enhancing Crop Yield Estimation in Spinach Crops Using Synthetic Aperture Radar-Derived Normalized Difference Vegetation Index: A Sentinel-1 and Sentinel-2 Fusion Approach

基于Sentinel-1与Sentinel-2数据融合的合成孔径雷达植被指数增强菠菜产量估算研究

https://www.mdpi.com/2072-4292/17/8/1412

Mesas-Carrascosa, F.-J.; Arosemena-Jované, J.T.; Cantón-Martínez, S.; Pérez-Porras, F.; Torres-Sánchez, J. Enhancing Crop Yield Estimation in Spinach Crops Using Synthetic Aperture Radar-Derived Normalized Difference Vegetation Index: A Sentinel-1 and Sentinel-2 Fusion Approach. Remote Sens. 2025, 17, 1412. https://doi.org/10.3390/rs17081412

14.

An Interpretable Wheat Yield Estimation Model Using Time Series Remote Sensing Data and Considering Meteorological and Soil Influences

融合时序遥感数据并考虑气象与土壤影响的可解释性小麦产量估算模型

https://www.mdpi.com/2072-4292/17/18/3192

Zeng, X.; Han, D.; Tansey, K.; Wang, P.; Pei, M.; Li, Y.; Li, F.; Du, Y. An Interpretable Wheat Yield Estimation Model Using Time Series Remote Sensing Data and Considering Meteorological and Soil Influences. Remote Sens. 2025, 17, 3192. https://doi.org/10.3390/rs17183192

15.

Assessment of the Effectiveness of Spectral Indices Derived from EnMAP Hyperspectral Imageries Using Machine Learning and Deep Learning Models for Winter Wheat Yield Prediction

基于机器学习与深度学习模型的EnMAP高光谱影像光谱指数有效性评估：以冬小麦产量预测为例

https://www.mdpi.com/2072-4292/17/20/3426

Mucsi, L.; Litkey-Kovács, D.; Bonus, K.; Farmonov, N.; Elgendy, A.; Aji, L.; Sóti, M. Assessment of the Effectiveness of Spectral Indices Derived from EnMAP Hyperspectral Imageries Using Machine Learning and Deep Learning Models for Winter Wheat Yield Prediction. Remote Sens. 2025, 17, 3426. https://doi.org/10.3390/rs17203426

Remote Sensing期刊介绍

主编：Prasad S. Thenkabail, USGS Western Geographic Science Center (WGSC), USA; Dongdong Wang, Peking University, China

期刊范围涵盖遥感科学所有领域，从传感器的设计、验证和校准，到遥感在地球科学、环境生态、城市建筑等各方面的广泛应用。