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Airborne sensing images harness the combined advantages of hyperspectral and high spatial resolution, offering precise monitoring methods for local-scale water quality parameters in small water bodies. This study employs airborne hyperspectral remote sensing image data to explore remote sensing estimation methods for total nitrogen (TN) and total phosphorus (TP) concentrations in Lake Dianshan, Yuandang, as well as its main inflow and outflow rivers. Our findings reveal the following: (1) Spectral bands between 700 and 750 nm show the highest correlation with TN and TP concentrations during the summer and autumn seasons. Spectral reflectance bands exhibit greater sensitivity to TN and TP concentrations compared to the winter and spring seasons. (2) Seasonal models developed using the Catboost method demonstrate significantly higher accuracy than other machine learning (ML) models. On the test set, the root mean square errors (RMSEs) are 0.6 mg/L for TN and 0.05 mg/L for TP concentrations, with average absolute percentage errors (MAPEs) of 23.77% and 25.14%, respectively. (3) Spatial distribution maps of the retrieved TN and TP concentrations indicate their dependence on exogenous inputs and close association with algal blooms. Higher TN and TP concentrations are observed near the inlet (Jishui Port), with reductions near the outlet (Lanlu Port), particularly for the TP concentration. Areas with intense algal blooms near shorelines generally exhibit higher TN and TP concentrations. This study offers valuable insights for processing small water bodies using airborne hyperspectral remote sensing images and provides reliable remote sensing techniques for lake water quality monitoring and management. [ABSTRACT FROM AUTHOR]

Airborne sensing images harness the combined advantages of hyperspectral and high spatial resolution, offering precise monitoring methods for local-scale water quality parameters in small water bodies. This study employs airborne hyperspectral remote sensing image data to explore remote sensing estimation methods for total nitrogen (TN) and total phosphorus (TP) concentrations in Lake Dianshan, Yuandang, as well as its main inflow and outflow rivers. Our findings reveal the following: (1) Spectral bands between 700 and 750 nm show the highest correlation with TN and TP concentrations during the summer and autumn seasons. Spectral reflectance bands exhibit greater sensitivity to TN and TP concentrations compared to the winter and spring seasons. (2) Seasonal models developed using the Catboost method demonstrate significantly higher accuracy than other machine learning (ML) models. On the test set, the root mean square errors (RMSEs) are 0.6 mg/L for TN and 0.05 mg/L for TP concentrations, with average absolute percentage errors (MAPEs) of 23.77% and 25.14%, respectively. (3) Spatial distribution maps of the retrieved TN and TP concentrations indicate their dependence on exogenous inputs and close association with algal blooms. Higher TN and TP concentrations are observed near the inlet (Jishui Port), with reductions near the outlet (Lanlu Port), particularly for the TP concentration. Areas with intense algal blooms near shorelines generally exhibit higher TN and TP concentrations. This study offers valuable insights for processing small water bodies using airborne hyperspectral remote sensing images and provides reliable remote sensing techniques for lake water quality monitoring and management.

The use of multi-mode remote sensing data for biomass prediction is of potential value to aid planting management and yield maximization. In this study, an advanced biomass estimation approach for sugarcane fields is proposed based on multi-source remote sensing data. Since feature interpretability in agricultural data mining is significant, a feature extraction method of deep archetypal analysis (DAA) that has good model interpretability is introduced and aided by principal component analysis (PCA) for feature mining from the multi-mode multispectral and light detection and ranging (LiDAR) remote sensing data pertaining to sugarcane. In addition, an integrated regression model integrating random forest regression, support vector regression, K-nearest neighbor regression and deep network regression is developed after feature extraction by DAA to precisely predict biomass of sugarcane. In this study, the biomass prediction performance achieved using the proposed integrated learning approach is found to be predominantly better than that achieved by using conventional linear methods in all the time periods of plant growth. Of more significance, according to model interpretability of DAA, only a small set of informative features maintaining their physical meanings (four informative spectral indices and four key LiDAR metrics) can be extracted which eliminates the redundancy of multi-mode data and plays a vital role in accurate biomass prediction. Therefore, the findings in this study provide hands-on experience to planters with indications of the key or informative spectral or LiDAR metrics relevant to the biomass to adjust the corresponding planting management design. [ABSTRACT FROM AUTHOR]

The use of multi-mode remote sensing data for biomass prediction is of potential value to aid planting management and yield maximization. In this study, an advanced biomass estimation approach for sugarcane fields is proposed based on multi-source remote sensing data. Since feature interpretability in agricultural data mining is significant, a feature extraction method of deep archetypal analysis (DAA) that has good model interpretability is introduced and aided by principal component analysis (PCA) for feature mining from the multi-mode multispectral and light detection and ranging (LiDAR) remote sensing data pertaining to sugarcane. In addition, an integrated regression model integrating random forest regression, support vector regression, K-nearest neighbor regression and deep network regression is developed after feature extraction by DAA to precisely predict biomass of sugarcane. In this study, the biomass prediction performance achieved using the proposed integrated learning approach is found to be predominantly better than that achieved by using conventional linear methods in all the time periods of plant growth. Of more significance, according to model interpretability of DAA, only a small set of informative features maintaining their physical meanings (four informative spectral indices and four key LiDAR metrics) can be extracted which eliminates the redundancy of multi-mode data and plays a vital role in accurate biomass prediction. Therefore, the findings in this study provide hands-on experience to planters with indications of the key or informative spectral or LiDAR metrics relevant to the biomass to adjust the corresponding planting management design.

A greenhouse is an important land-use type, which can effectively improve agricultural production conditions and increase crop yields. It is of great significance to obtain the spatial distribution data of greenhouses quickly and accurately for regional agricultural production and food security. Based on the Google Earth Engine cloud platform and Landsat 8 images, this study selected a total of 18 indicators from three aspects of spectral features, texture features and terrain features to construct greenhouse identification features. From a variety of classification algorithms for remote-sensing recognition of greenhouses, this study selected three classifiers with higher accuracy (classification and regression trees (CART), random forest model (randomForest) and maximum entropy model (gmoMaxEnt)) to construct an integrated classification algorithm, and then extracted the spatial distribution data of greenhouses in Jiangsu Province. The results show that: (1) Google Earth Engine with its own massive data and cloud computing capabilities, combined with integrated classification algorithms, can achieve rapid remote-sensing mapping of large-scale greenhouses under complex terrain, and the classification accuracy is higher than that of a single classification algorithm. (2) The combination of different spectral, texture and terrain features has a greater impact on the extraction of regional greenhouses, the combination of all three aspects of features has the highest accuracy. Spectral features are the key factors for greenhouse remote-sensing mapping, but terrain and texture features can also enhance classification accuracy. (3) The greenhouse in Jiangsu Province has significant spatial differentiation and spatial agglomeration characteristics. The most widely distributed greenhouses are mainly concentrated in the agriculturally developed areas such as Dongtai City, Hai’an County, Rudong County and Pizhou City.

A greenhouse is an important land-use type, which can effectively improve agricultural production conditions and increase crop yields. It is of great significance to obtain the spatial distribution data of greenhouses quickly and accurately for regional agricultural production and food security. Based on the Google Earth Engine cloud platform and Landsat 8 images, this study selected a total of 18 indicators from three aspects of spectral features, texture features and terrain features to construct greenhouse identification features. From a variety of classification algorithms for remote-sensing recognition of greenhouses, this study selected three classifiers with higher accuracy (classification and regression trees (CART), random forest model (randomForest) and maximum entropy model (gmoMaxEnt)) to construct an integrated classification algorithm, and then extracted the spatial distribution data of greenhouses in Jiangsu Province. The results show that: (1) Google Earth Engine with its own massive data and cloud computing capabilities, combined with integrated classification algorithms, can achieve rapid remote-sensing mapping of large-scale greenhouses under complex terrain, and the classification accuracy is higher than that of a single classification algorithm. (2) The combination of different spectral, texture and terrain features has a greater impact on the extraction of regional greenhouses, the combination of all three aspects of features has the highest accuracy. Spectral features are the key factors for greenhouse remote-sensing mapping, but terrain and texture features can also enhance classification accuracy. (3) The greenhouse in Jiangsu Province has significant spatial differentiation and spatial agglomeration characteristics. The most widely distributed greenhouses are mainly concentrated in the agriculturally developed areas such as Dongtai City, Hai'an County, Rudong County and Pizhou City. [ABSTRACT FROM AUTHOR]