In terms of traditional threshold methods, uniform thresholds are used for cloud detection based on remote sensing images; however, due to complex surface structures and cloud conditions, such an approach is typically difficult to effectively implement for high-precision cloud detection. To solve this problem, a new cloud detection algorithm is proposed based on global land cover data. Specifically, a high spatial-resolution at 30-m Global Land Cover Data set with global coverage was employed as background data for image inversions, which further supported cloud detection in remote sensing images. Notably, threshold settings can be varied for different land cover types. Such an algorithm can effectively improve the accuracy of cloud pixel identification for thin and broken clouds, even over bright areas. Moreover, Landsat 5 data are used to perform cloud detection experiments based on this algorithm. The thresholds are considering land cover variations. The thresholds of land cover types spatiotemporally vary, such as vegetation, differed by latitude and over time. In addition, six common land cover types are selected for cloud detection experiments. Then, validations analyses are conducted through visual interpretation and the results indicated that the algorithm is capable of achieving a high cloud detection accuracy. Specifically, the overall RMSE of cloud cover is 4.44%, and the accuracies of cloud and clear-sky pixel identifications is 86.5% and 98.7%, respectively.

Water is an important factor in human survival and development. With the acceleration of urbanization, the problem of black and odorous water bodies has become increasingly prominent. It not only affects the living environment of residents in the city, but also threatens their diet and water quality. Therefore, the accurate monitoring and management of urban black and odorous water bodies is particularly important. At present, when researching water quality issues, the methods of fixed-point sampling and laboratory analysis are relatively mature, but the time and labor costs are relatively high. However, empirical models using spectral characteristics and different water quality parameters often lack universal applicability. In addition, a large number of studies on black and odorous water bodies are qualitative studies of water body types, and there are few spatially continuous quantitative analyses. Quantitative research on black and odorous waters is needed to identify the risk of health and environmental problems, as well as providing more accurate guidance on mitigation and treatment methods. In order to achieve this, a universal continuous black and odorous water index (CBOWI) is proposed that can classify waters based on evaluated parameters as well as quantitatively determine the degree of pollution and trends. The model of CBOWI is obtained by partial least squares machine learning through the parameters of the national black and odorous water classification standard. The fitting accuracy and monitoring accuracy of the model are 0.971 and 0.738, respectively. This method provides a new means to monitor black and odorous waters that can also help to improve decision-making and management.

Prior knowledge of the background land surface reflectance (LSR) constitutes one of the most important factors affecting the precision of cloud shadow detection. To resolve this problem, a surface reflectance-based cloud shadow detection (SRCSD) algorithm is proposed for multitemporal Landsat images. Monthly surface reflectance data sets constructed from MODIS surface reflectance products (MOD09A1) were used to provide the background LSR for cloud shadow detection. Based on the background LSR, the possible variation in the top of atmosphere (TOA) reflectance for each clear pixel can be estimated using the radiative transfer equation under different atmospheric conditions. If a pixel has a smaller TOA reflectance than the minimum value of the possible range under clear conditions, it is identified as being shadow covered. One hundred and twenty-five Landsat 8 Operational Land Imager scenes covered by various surface types were selected to evaluate the feasibility of the algorithm. A validation using manual cloud shadow masks showed that the average producer’s accuracy and user’s accuracy were approximately 0.805 and 0.893, respectively. A comparison of the results of the SRCSD algorithm with those of an object-based cloud shadow detection algorithm (Fmask) recently developed for Landsat images revealed that SRCSD generally detects cloud shadows better than Fmask. The most significant improvement of the SRCSD algorithm is the better detection capability for thin and broken cloud shadows, and this algorithm can be extended to multiple types of satellite data after proper modification.

Cloud and cloud shadow detection is a crucial procedure in remote sensing image processing. While the threshold method is a common method for its easy implementation, it compromises on accuracy for complex surface conditions. To address such limitations, a cloud detection algorithm based on land type has been proposed. As an extension of this, a novel cloud shadow detection algorithm based on an a Priori Land type and Reflectance Database Support (denoted as PLRDS) is proposed. This algorithm uses GlobeLand30 data and Landsat-8 images as support information for land cover and reflectance data, respectively. By utilizing this support information, optimal bands and thresholds are determined for cloud shadow detection in each land type. The threshold determination is discussed in terms of absolute and grey areas. To evaluate the PLRDS algorithm, experiments were conducted on Landsat-8 data for each land type and on complex surfaces. The results show good consistency compared to false-color images. In addition, a quantitative validation was conducted with the producer’s accuracy of cloud shadow and clear land (PAS and PAC), user’s accuracy of cloud shadow and clear land (UAS and UAC), and overall accuracy (OA). The OA of the proposed algorithm is 0.9719. Among all the land types, bareland and artificial surfaces achieved the lowest and highest OA, which reached 0.9867 and 0.9483, respectively. These results indicate the accuracy and good performance of the PLRDS cloud shadow detection algorithm.

Cloud shadow detection is one of the basic step for remote sensing image processing. The threshold method is commonly used currently because of its easy implementation and good accuracy. Aiming at the problems that common threshold settings are difficult to meet the complex surface conditions and the results is two-value, this paper proposes a detection method of cloud shadow pixels based on land cover data support by calculating shadow probabilities using Landsat 8 data as an example. Then, a validation with visual interpretation is used to verify the accuracy. The results show that the method can achieve high cloud shadow detection results.

UDTCDA (universal dynamic threshold cloud detection algorithm) is a new cloud detection method which was proposed recently. This cloud detection method is supported by priori surface reflectance obtained from MODIS surface reflectance product (MOD09). Reflectance of four bands in the wavelength of visible to near infrared are used to detect the cloudy pixels. Because there is a priori reference data on the ground surface, pixels of thin cloud and the fractional cloud can be well detected from the clear pixels. MOD35 is the MODIS cloud mask product, combined use of reflectance and brightness temperature to determine the cloud pixels at 250m, and 1km resolutions. 22 out of 36 bands in the visible, near-infrared, and thermal infrared bands are used to create a high quality cloud mask. The methods of visual interpretation and comparison with the CALIPSO data are used to evaluate the two cloud detection algorithms.