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17 pages, 1736 KiB

Open AccessArticle

Satellite Estimation of Chlorophyll-a Using Moderate Resolution Imaging Spectroradiometer (MODIS) Sensor in Shallow Coastal Water Bodies: Validation and Improvement

by Mohd Manzar Abbas, Assefa M. Melesse, Leonard J. Scinto and Jennifer S. Rehage

Water 2019, 11(8), 1621; https://doi.org/10.3390/w11081621 - 6 Aug 2019

Cited by 40 | Viewed by 7323

Abstract

The size and distribution of Phytoplankton populations are indicators of the ecological status of a water body. The chlorophyll-a (Chl-a) concentration is estimated as a proxy for the distribution of phytoplankton biomass. Remote sensing is the only practical method for the synoptic assessment [...] Read more.

The size and distribution of Phytoplankton populations are indicators of the ecological status of a water body. The chlorophyll-a (Chl-a) concentration is estimated as a proxy for the distribution of phytoplankton biomass. Remote sensing is the only practical method for the synoptic assessment of Chl-a at large spatial and temporal scales. Long-term records of ocean color data from the MODIS Aqua Sensor have proven inadequate to assess Chl-a due to the lack of a robust ocean color algorithm. Chl-a estimation in shallow and coastal water bodies has been a challenge and existing operational algorithms are only suitable for deeper water bodies. In this study, the Ocean Color 3M (OC3M) derived Chl-a concentrations were compared with observed data to assess the performance of the OC3M algorithm. Subsequently, a regression analysis between in situ Chl-a and remote sensing reflectance was performed to obtain a green-red band algorithm for coastal (case 2) water. The OC3M algorithm yielded an accurate estimate of Chl-a for deep ocean (case 1) water (RMSE = 0.007, r² = 0.518, p < 0.001), but failed to perform well in the coastal (case 2) water of Chesapeake Bay (RMSE = 23.217, r² = 0.009, p = 0.356). The algorithm developed in this study predicted Chl-a more accurately in Chesapeake Bay (RMSE = 4.924, r² = 0.444, p < 0.001) than the OC3M algorithm. The study indicates a maximum band ratio formulation using green and red bands could improve the satellite estimation of Chl-a in coastal waters. Full article

(This article belongs to the Special Issue Satellite Application on Support to Water Monitoring and Management)

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16 pages, 4097 KiB

Open AccessArticle

Modelling Reservoir Turbidity Using Landsat 8 Satellite Imagery by Gene Expression Programming

by Li-Wei Liu and Yu-Min Wang

Water 2019, 11(7), 1479; https://doi.org/10.3390/w11071479 - 16 Jul 2019

Cited by 32 | Viewed by 5443

Abstract

This study aimed to develop a reliable turbidity model to assess reservoir turbidity based on Landsat-8 satellite imagery. Models were established by multiple linear regression (MLR) and gene-expression programming (GEP) algorithms. Totally 55 and 18 measured turbidity data from Tseng-Wen and Nan-Hwa reservoir [...] Read more.

This study aimed to develop a reliable turbidity model to assess reservoir turbidity based on Landsat-8 satellite imagery. Models were established by multiple linear regression (MLR) and gene-expression programming (GEP) algorithms. Totally 55 and 18 measured turbidity data from Tseng-Wen and Nan-Hwa reservoir paired and screened with satellite imagery. Finally, MLR and GEP were applied to simulated 13 turbid water data for critical turbidity assessment. The coefficient of determination (R²), root mean squared error (RMSE), and relative RMSE (R-RMSE) calculated for model performance evaluation. The result show that, in model development, MLR and GEP shows a similar consequent. However, in model testing, the R², RMSE, and R-RMSE of MLR and GEP are 0.7277 and 0.8278, 0.7248 NTU and 0.5815 NTU, 22.26% and 17.86%, respectively. Accuracy assessment result shows that GEP is more reasonable than MLR, even in critical turbidity situation, GEP is more convincible. In the model performance evaluation, MLR and GEP are normal and good level, in critical turbidity condition, GEP even belongs to outstanding level. These results exhibit GEP denotes rationality and with relatively good applicability for turbidity simulation. From this study, one can conclude that GEP is suitable for turbidity modeling and is accurate enough for reservoir turbidity estimation. Full article

(This article belongs to the Special Issue Satellite Application on Support to Water Monitoring and Management)

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17 pages, 3906 KiB

Open AccessArticle

Validation of GPM Precipitation Products by Comparison with Ground-Based Parsivel Disdrometers over Jianghuai Region

by Zuhang Wu, Yun Zhang, Lifeng Zhang, Xiaolong Hao, Hengchi Lei and Hepeng Zheng

Water 2019, 11(6), 1260; https://doi.org/10.3390/w11061260 - 16 Jun 2019

Cited by 19 | Viewed by 3988

Abstract

In this study, we evaluated the performance of rain-retrieval algorithms for the Version 6 Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM DPR) products, against disdrometer observations and improved their retrieval algorithms by using a revised shape parameter µ derived from long-term Particle Size [...] Read more.

In this study, we evaluated the performance of rain-retrieval algorithms for the Version 6 Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM DPR) products, against disdrometer observations and improved their retrieval algorithms by using a revised shape parameter µ derived from long-term Particle Size Velocity (Parsivel) disdrometer observations in Jianghuai region from 2014 to 2018. To obtain the optimized shape parameter, raindrop size distribution (DSD) characteristics of summer and winter seasons over Jianghuai region are analyzed, in terms of six rain rate classes and two rain categories (convective and stratiform). The results suggest that the GPM DPR may have better performance for winter rain than summer rain over Jianghuai region with biases of 40% (80%) in winter (summer). The retrieval errors of rain category-based µ (3–5%) were proved to be the smallest in comparison with rain rate-based µ (11–13%) or a constant µ (20–22%) in rain-retrieval algorithms, with a possible application to rainfall estimations over Jianghuai region. Empirical D_m–Z_e and N_w–D_m relationships were also derived preliminarily to improve the GPM rainfall estimates over Jianghuai region. Full article

(This article belongs to the Special Issue Satellite Application on Support to Water Monitoring and Management)

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15 pages, 7288 KiB

Open AccessArticle

Hydrologic Evaluation of Integrated Multi-Satellite Retrievals for GPM over Nanliu River Basin in Tropical Humid Southern China

by Zhenqing Liang, Sheng Chen, Junjun Hu, Chaoying Huang, Asi Zhang, Liusi Xiao, Zengxin Zhang and Xinhua Tong

Water 2019, 11(5), 932; https://doi.org/10.3390/w11050932 - 2 May 2019

Cited by 5 | Viewed by 2793

Abstract

Version 5B Integrated Multi-satellite Retrievals for GPM (IMERG) products were evaluated with rain gauge observations as reference over the Nanliu River basin in Southern China since March 2014 to December 2016 through the Xinanjiang hydrologic model. The IMERG products include the early run [...] Read more.

Version 5B Integrated Multi-satellite Retrievals for GPM (IMERG) products were evaluated with rain gauge observations as reference over the Nanliu River basin in Southern China since March 2014 to December 2016 through the Xinanjiang hydrologic model. The IMERG products include the early run satellite-only IMERG product (IMERGERUncal), final run satellite-only and gauge-corrected IMERG products (IMERGFRUncal and IMERGFRCal, respectively). Direct comparison with the gauge observations indicates that both early run and final run IMERG products have good performances in capturing the precipitation at spatial and temporal characteristics. IMERGFRUncal and IMERGERUncal show compatible capabilities to detect rainfall in a daily scale with highly correlative coefficient (CC) about 0.67, relative bias (RB) about −20.79%, and root mean square error (RMSE) about 10.68 mm. IMERGFRCal performed a little better than IMERGFRUncal and IMERGERUncal with higher CC (0.7) and lower magnitude of RB (4.90%). Simulated stream flows with daily IMERG products as forcing data show a large deviation from the observed stream flows with low Nash-Sutcliffe index (NSCE) < 0.3, indicating that all of these IMERG products have limited potentials of hydrological utilization in this basin. Particularly, IMERGFRCal shows relatively poor NSCE (0.28) and underestimates the stream flow by 7.83%. IMERGFRUncal and IMERGERUncal exhibit better performance than IMERGFRCal in the hydrological simulation with higher NSCE (0.30 and 0.29, respectively) and larger deviations with RBs about −56.73% and −59.49%, respectively. This result manifests that the IMERG products users need to be cautious when using IMERG products for hydrological applications in this basin. Additionally, this study is expected to offer insights into IMERG’ potentials in the hydrological utility and thus provide useful feedbacks to the IMERG algorithm developers and the users. Full article

(This article belongs to the Special Issue Satellite Application on Support to Water Monitoring and Management)

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15 pages, 4209 KiB

Open AccessArticle

Formation and Evolution of Soil Salinization in Shouguang City Based on PMS and OLI/TM Sensors

by Fang Dong, Yongjie Tang, Xuerui Xing, Zhanhong Liu and Liting Xing

Water 2019, 11(2), 345; https://doi.org/10.3390/w11020345 - 18 Feb 2019

Cited by 11 | Viewed by 3440

Abstract

To explore the evolution process of soil salinization in Shouguang, the current study applied the Pan and Multi-spectra Sensor (PMS), Operational Land Imager (OLI) and Thematic Mapper (TM) data to establish a remote sensing monitoring model of soil salinization. Based on the vegetation [...] Read more.

To explore the evolution process of soil salinization in Shouguang, the current study applied the Pan and Multi-spectra Sensor (PMS), Operational Land Imager (OLI) and Thematic Mapper (TM) data to establish a remote sensing monitoring model of soil salinization. Based on the vegetation and salinity indexes, we extracted the information of soil salinization in the flourishing period of plant growth in Shouguang in 2017. At the same time, we monitored the spatial and temporal patterns of soil salinization in Shouguang from 1998 to 2017. We compared the range of soil salinization reflected by remote sensing data and the regional groundwater level and revealed the formation and evolution mechanism of soil salinization in Shouguang. The results reflected that the distribution of soil salinization in Shouguang demonstrated obvious banding characteristics in distribution, and soil salinization gradually increased from the south to the north. Based on the imagery interpretation of Landsat images of three periods from 1998 to 2017, we found that the area of saline land in Shouguang severely decreased as a whole, but the coastal salinization intensified. Moreover, the inversion of surface soil salinity using the GF-1 satellite PMS image has a high precision, and the goodness of fit (R²) is up to 0.871. Compared with the GF-1 image, the Landsat image is more suitable for grading and monitoring soil salinization in a wide range. We also confirmed that the change in ground water level is the main reason for the evolution of salinization. Excessive exploitation of groundwater by vegetable production caused the intensification of seawater intrusion and secondary salinization in coastal areas, while the water level dropped in areas far from the coastline. To prevent the deterioration from soil salinization in Shouguang, it is necessary for us to extract the local groundwater resources reasonably and optimally. Full article

(This article belongs to the Special Issue Satellite Application on Support to Water Monitoring and Management)

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19 pages, 16293 KiB

Open AccessArticle

The Potential Utility of Satellite Soil Moisture Retrievals for Detecting Irrigation Patterns in China

by Xiaohu Zhang, Jianxiu Qiu, Guoyong Leng, Yongmin Yang, Quanzhou Gao, Yue Fan and Jiashun Luo

Water 2018, 10(11), 1505; https://doi.org/10.3390/w10111505 - 24 Oct 2018

Cited by 27 | Viewed by 4239

Abstract

Climate change and anthropogenic activities, including agricultural irrigation have significantly altered the global and regional hydrological cycle. However, human-induced modification to the natural environment is not well represented in land surface models (LSMs). In this study, we utilize microwave-based soil moisture products to [...] Read more.

Climate change and anthropogenic activities, including agricultural irrigation have significantly altered the global and regional hydrological cycle. However, human-induced modification to the natural environment is not well represented in land surface models (LSMs). In this study, we utilize microwave-based soil moisture products to aid the detection of under-represented irrigation processes throughout China. The satellite retrievals used in this study include passive microwave observations from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and its successor AMSR2, active microwave observations from the Advanced Scatterometer (ASCAT), and the blended multi-sensor soil moisture product from the European Space Agency (i.e., ESA CCI product). We first conducted validations of the three soil moisture retrievals against in-situ observations (collected from the nationwide agro-meteorological network) in irrigated areas in China. It is found that compared to the conventional Spearman’s rank correlation and Pearson correlation coefficients, entropy-based mutual information is more suitable for evaluating soil moisture anomalies induced by irrigation. In general, around 60% of uncertainties in the anomaly of “ground truth” time series can be resolved by soil moisture retrievals, with ASCAT outperforming the others. Following this, the potential utility of soil moisture retrievals in mapping irrigation patterns in China is investigated by examining the difference in probability distribution functions (detected by two-sample Kolmogorov-Smirnov test) between soil moisture retrievals and benchmarks of the numerical model ERA-Interim without considering the irrigation process. Results show that microwave remote sensing provides a promising alternative to detect the under-represented irrigation process against the reference LSM ERA-Interim. Specifically, the highest performance in detecting irrigation intensity is found when using ASCAT in Huang-Huai-Hai Plain, followed by advanced microwave scanning radiometer (AMSR) and ESA CCI. Compared to ASCAT, the irrigation detection capabilities of AMSR exhibit higher discrepancies between descending and ascending orbits, since the soil moisture retrieval algorithm of AMSR is based on surface temperature and, thus, more affected by irrigation practices. This study provides insights into detecting the irrigation extent using microwave-based soil moisture with aid of LSM simulations, which has great implications for numerical model development and agricultural managements across the country. Full article

(This article belongs to the Special Issue Satellite Application on Support to Water Monitoring and Management)

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17 pages, 2946 KiB

Open AccessArticle

Monitoring Coastal Chlorophyll-a Concentrations in Coastal Areas Using Machine Learning Models

by Yong Sung Kwon, Seung Ho Baek, Young Kyun Lim, JongCheol Pyo, Mayzonee Ligaray, Yongeun Park and Kyung Hwa Cho

Water 2018, 10(8), 1020; https://doi.org/10.3390/w10081020 - 2 Aug 2018

Cited by 34 | Viewed by 5696

Abstract

Harmful algal blooms have negatively affected the aquaculture industry and aquatic ecosystems globally. Remote sensing using satellite sensor systems has been applied on large spatial scales with high temporal resolutions for effective monitoring of harmful algal blooms in coastal waters. However, oceanic color [...] Read more.

Harmful algal blooms have negatively affected the aquaculture industry and aquatic ecosystems globally. Remote sensing using satellite sensor systems has been applied on large spatial scales with high temporal resolutions for effective monitoring of harmful algal blooms in coastal waters. However, oceanic color satellites have limitations, such as low spatial resolution of sensor systems and the optical complexity of coastal waters. In this study, bands 1 to 4, obtained from Landsat-8 Operational Land Imager satellite images, were used to evaluate the performance of empirical ocean chlorophyll algorithms using machine learning techniques. Artificial neural network and support vector machine techniques were used to develop an optimal chlorophyll-a model. Four-band, four-band-ratio, and mixed reflectance datasets were tested to select the appropriate input dataset for estimating chlorophyll-a concentration using the two machine learning models. While the ocean chlorophyll algorithm application on Landsat-8 Operational Land Imager showed relatively low performance, the machine learning methods showed improved performance during both the training and validation steps. The artificial neural network and support vector machine demonstrated a similar level of prediction accuracy. Overall, the support vector machine showed slightly superior performance to that of the artificial neural network during the validation step. This study provides practical information about effective monitoring systems for coastal algal blooms. Full article

(This article belongs to the Special Issue Satellite Application on Support to Water Monitoring and Management)

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Review

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29 pages, 339 KiB

Open AccessReview

The Role of Satellite-Based Remote Sensing in Improving Simulated Streamflow: A Review

by Dejuan Jiang and Kun Wang

Water 2019, 11(8), 1615; https://doi.org/10.3390/w11081615 - 4 Aug 2019

Cited by 86 | Viewed by 7779

Abstract

A hydrological model is a useful tool to study the effects of human activities and climate change on hydrology. Accordingly, the performance of hydrological modeling is vitally significant for hydrologic predictions. In watersheds with intense human activities, there are difficulties and uncertainties in [...] Read more.

A hydrological model is a useful tool to study the effects of human activities and climate change on hydrology. Accordingly, the performance of hydrological modeling is vitally significant for hydrologic predictions. In watersheds with intense human activities, there are difficulties and uncertainties in model calibration and simulation. Alternative approaches, such as machine learning techniques and coupled models, can be used for streamflow predictions. However, these models also suffer from their respective limitations, especially when data are unavailable. Satellite-based remote sensing may provide a valuable contribution for hydrological predictions due to its wide coverage and increasing tempo-spatial resolutions. In this review, we provide an overview of the role of satellite-based remote sensing in streamflow simulation. First, difficulties in hydrological modeling over highly regulated basins are further discussed. Next, the performance of satellite-based remote sensing (e.g., remotely sensed data for precipitation, evapotranspiration, soil moisture, snow properties, terrestrial water storage change, land surface temperature, river width, etc.) in improving simulated streamflow is summarized. Then, the application of data assimilation for merging satellite-based remote sensing with a hydrological model is explored. Finally, a framework, using remotely sensed observations to improve streamflow predictions in highly regulated basins, is proposed for future studies. This review can be helpful to understand the effect of applying satellite-based remote sensing on hydrological modeling. Full article

(This article belongs to the Special Issue Satellite Application on Support to Water Monitoring and Management)

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