基于微波与光学卫星数据集图像融合的作物产量估算与变化检测框架

IF 2.3 Q2 GEOSCIENCES, MULTIDISCIPLINARY Quaternary Pub Date : 2023-04-19 DOI:10.3390/quat6020028
Ravneet Kaur, R. K. Tiwari, R. Maini, Sartajvir Singh
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引用次数: 2

摘要

作物产量预测是农业的重要组成部分之一,在农业可持续发展的决策过程中起着重要作用。遥感为土壤湿度等重要农业参数的测量提供了最有效和最经济的解决方案,但从粗分辨率数据集,特别是微波数据集中检索土壤水分含量仍然是一项具有挑战性的任务。本文提出了一种基于机器学习的框架,利用基于光学的中分辨率成像光谱辐射计(MODIS)和基于微波的散射计卫星(SCATSAT-1)数据集生成高分辨率土壤水分产品,即分类图和变化图。在该框架中,结合了基于最近邻的图像融合(NNIF)、人工神经网络(ANN)和基于后分类的变化检测(PCCD)来生成主题和变化图。为了验证该框架的有效性,本文还实施了基于随机森林后分类的变化检测(RFPCD),结果表明,在σ°-HH的变化图计算中,该框架的结果(88.67 ~ 91.80%)优于随机森林后分类的变化图计算结果(86.80 ~ 87.80%)。该研究对于通过在所有天气条件下提供高分辨率土壤水分产品进行作物产量预测分析具有重要意义。
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A Framework for Crop Yield Estimation and Change Detection Using Image Fusion of Microwave and Optical Satellite Dataset
Crop yield prediction is one of the crucial components of agriculture that plays an important role in the decision-making process for sustainable agriculture. Remote sensing provides the most efficient and cost-effective solution for the measurement of important agricultural parameters such as soil moisture level, but retrieval of the soil moisture contents from coarse resolution datasets, especially microwave datasets, remains a challenging task. In the present work, a machine learning-based framework is proposed to generate the enhanced resolution soil moisture products, i.e., classified maps and change maps, using an optical-based moderate resolution imaging spectroradiometer (MODIS) and microwave-based scatterometer satellite (SCATSAT-1) datasets. In the proposed framework, nearest-neighbor-based image fusion (NNIF), artificial neural networks (ANN), and post-classification-based change detection (PCCD) have been integrated to generate thematic and change maps. To confirm the effectiveness of the proposed framework, random forest post-classification-based change detection (RFPCD) has also been implemented, and it is concluded that the proposed framework achieved better results (88.67–91.80%) as compared to the RFPCD (86.80–87.80%) in the computation of change maps with σ°-HH. This study is important in terms of crop yield prediction analysis via the delivery of enhanced-resolution soil moisture products under all weather conditions.
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来源期刊
Quaternary
Quaternary GEOSCIENCES, MULTIDISCIPLINARY-
CiteScore
3.30
自引率
4.30%
发文量
44
审稿时长
11 weeks
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