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Remote Sensing of the Terrestrial Hydrologic Cycle

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: closed (31 January 2020) | Viewed by 47221

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Special Issue Editors

Prof. Dr. Qiuhong Tang

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Guest Editor

Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: water cycle; remote sensing hydrology; land surface modeling; global change; water resources
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Youcun Qi

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Guest Editor

Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: radar-based quantitative precipitation estimation; short-term quantitative precipitation forecast
Special Issues, Collections and Topics in MDPI journals

Dr. Zhihui Wang

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Guest Editor

Yellow River Institute of Hydraulic Research, Yellow River Conservancy Commission, Ministry of Water Resources, No. 45, Shunhe Road, Jinshui District, Zhengzhou 450003, China
Interests: satellite and airborne image processing; vegetation mapping; land surface parameters inversion; hydrologic modeling; water cycle response to a changing environment

Prof. Dr. Yun Pan

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Guest Editor

College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China
Interests: satellite gravimetry; groundwater hydrology; terrestrial water storage change
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Our understanding of the terrestrial hydrologic cycle has benefited from the emerging remote sensing technologies in recent decades. With the increased capacity of satellite, airborne, and ground sensors (e.g., optical, gravity, microwave, LiDAR, weather radar) from low to high spatial–temporal–spectral–radiometric resolutions, the key variables of the hydrologic cycle (e.g., precipitation, evapotranspiration, soil moisture, terrestrial water storage, groundwater, water body, wetland, snow, ice and glaciers, streamflow) can be obtained at varying spatial and temporal resolutions and accuracies via remote sensing. Applications of remote sensing provide unprecedented opportunities to advance the simulation, monitoring, and prediction of the terrestrial hydrologic cycle, and have been widely used in flood/landslide/mudslide hazard prevention and water management. Numerous remote sensing techniques and retrieval methods have been developed to improve the estimation accuracy of hydrologic variables. Data assimilation techniques have been integrating remote sensing observations and in-situ measurements to assist operational hydrologic practices. Has the hydrologic community exhausted the full potential of remote sensing?

This Special Issue aims to advance innovative remote sensing methods for improving our understanding of the terrestrial hydrologic cycle and to stimulate innovations to meet the needs of hydrologic practice. We encourage the submission of manuscripts related to all aspects of remote sensing-assisted hydrologic applications, including the estimation of water-related variables, hydrologic data assimilation, operational hydrometeorological practice, smart water management, and water hazards analysis. We welcome studies using new remote sensing sensors/platforms and combining emerging technologies such as artificial intelligence and big data analytics.

Prof. Qiuhong Tang
Prof. Youcun Qi
Dr. Zhihui Wang
Prof. Yun Pan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

satellite
airborne
weather radar
hydrologic cycle
water hazards
water management

Published Papers (13 papers)

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Editorial

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4 pages, 161 KiB

Open AccessEditorial

Editorial for the Special Issue “Remote Sensing of the Terrestrial Hydrologic Cycle”

by Qiuhong Tang, Youcun Qi, Zhihui Wang and Yun Pan

Remote Sens. 2020, 12(6), 1035; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12061035 - 23 Mar 2020

Cited by 1 | Viewed by 2253

Abstract

To address global water security issues, it is important to understand the evolving global water system and its natural and anthropogenic influencing factors [...] Full article

(This article belongs to the Special Issue Remote Sensing of the Terrestrial Hydrologic Cycle)

Research

Jump to: Editorial

16 pages, 17104 KiB

Open AccessArticle

Using the Digital Elevation Model (DEM) to Improve the Spatial Coverage of the MODIS Based Reservoir Monitoring Network in South Asia

by Shuai Zhang and Huilin Gao

Remote Sens. 2020, 12(5), 745; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12050745 - 25 Feb 2020

Cited by 10 | Viewed by 3709

Abstract

Satellite remote sensing of near real-time reservoir storage variations has important implications for flood monitoring and water resources management. However, satellite altimetry data, which are essential for estimating storage variations, are only available for a limited number of reservoirs. This lack of high-density spatial coverage directly hinders the potential use of remotely sensed reservoir information for improving the skills of hydrological modeling over highly regulated river basins. To solve this problem, a reservoir storage dataset with high-density spatial coverage was developed by combining the water surface area estimated from Moderate Resolution Imaging Spectroradiometer (MODIS) imageries with the Digital Elevation Model (DEM) data collected by the Shuttle Radar Topography Mission (SRTM). By including more reservoirs, this reservoir dataset represents 46.6% of the overall storage capacity in South Asia. The results were validated over five reservoirs where gauge observations are accessible. The storage estimates agree well with observations, with coefficients of determination ranging from 0.47 to 0.91 and normalized root mean square errors (NRMSE) ranging from 15.46% to 37.69%. Given the general availability of MODIS and SRTM data, this algorithm can be potentially applied for monitoring global reservoirs at a high density. Full article

(This article belongs to the Special Issue Remote Sensing of the Terrestrial Hydrologic Cycle)

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23 pages, 6964 KiB

Open AccessArticle

Evaluation of Evapotranspiration for Exorheic Catchments of China during the GRACE Era: From a Water Balance Perspective

by Yulong Zhong, Min Zhong, Yuna Mao and Bing Ji

Remote Sens. 2020, 12(3), 511; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12030511 - 05 Feb 2020

Cited by 55 | Viewed by 3958

Abstract

Evapotranspiration (ET) is usually difficult to estimate at the regional scale due to scarce direct measurements. This study uses the water balance equation to calculate the regional ET with observations of precipitation, runoff, and terrestrial water storage changes (TWSC) in nine exorheic catchments of China. We compared the regional ET estimates from a water balance perspective with and without considering TWSC (ET_WB: ET estimates with considering TWSC, and ET_PQ: ET estimates from precipitation minus runoff without considering TWSC). Results show that the regional annual ET ranges from 417.7 mm/yr to 831.5 mm/yr in the nine exorheic catchments based on the water balance equation. The impact of ignoring TWSC on calculating ET is notable, as the root mean square errors (RMSEs) of annual ET between ET_WB and ET_PQ range from 12.0–105.8 mm/yr (2.6–12.7% in corresponding annual ET) among the exorheic catchments. We also compared the estimated regional ET with other ET products. Different precipitation products are assessed to explain the inconsistency between different ET products and regional ET from a water balance perspective. The RMSEs between ET estimates from Gravity Recovery and Climate Experiment (GRACE) and ET from land surface models can be reduced if the deviation of precipitation forcing data is considered. ET estimates from Global Land Evaporation Amsterdam Model (GLEAM) can be improved by reducing the uncertainty of precipitation forcing data in three semiarid catchments. This study emphasizes the importance of considering TWSC when calculating the regional ET using a water balance equation and provides more accurate ET estimates to help improve modeled ET results. Full article

(This article belongs to the Special Issue Remote Sensing of the Terrestrial Hydrologic Cycle)

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25 pages, 2955 KiB

Open AccessArticle

Evaluation of the Radar QPE and Rain Gauge Data Merging Methods in Northern China

by Qingtai Qiu, Jia Liu, Jiyang Tian, Yufei Jiao, Chuanzhe Li, Wei Wang and Fuliang Yu

Remote Sens. 2020, 12(3), 363; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12030363 - 22 Jan 2020

Cited by 19 | Viewed by 4010

Abstract

Radar-rain gauge merging methods have been widely used to produce high-quality precipitation with fine spatial resolution by combing the advantages of the rain gauge observation and the radar quantitative precipitation estimation (QPE). Different merging methods imply a specific choice on the treatment of radar and rain gauge data. In order to improve their applicability, significant studies have focused on evaluating the performances of the merging methods. In this study, a categorization of the radar-rain gauge merging methods was proposed as: (1) Radar bias adjustment category, (2) radar-rain gauge integration category, and (3) rain gauge interpolation category for a total of six commonly used merging methods, i.e., mean field bias (MFB), regression inverse distance weighting (RIDW), collocated co-kriging (CCok), fast Bayesian regression kriging (FBRK), regression kriging (RK), and kriging with external drift (KED). Eight different storm events were chosen from semi-humid and semi-arid areas of Northern China to test the performance of the six methods. Based on the leave-one-out cross validation (LOOCV), conclusions were obtained that the integration category always performs the best, the bias adjustment category performs the worst, and the interpolation category ranks between them. The quality of the merging products can be a function of the merging method that is affected by both the quality of radar QPE and the ability of the rain gauge to capture small-scale rainfall features. In order to further evaluate the applicability of the merging products, they were then used as the input to a rainfall-runoff model, the Hybrid-Hebei model, for flood forecasting. It is revealed that a higher quality of the merging products indicates a better agreement between the observed and the simulated runoff. Full article

(This article belongs to the Special Issue Remote Sensing of the Terrestrial Hydrologic Cycle)

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21 pages, 8515 KiB

Open AccessArticle

External Groundwater Alleviates the Degradation of Closed Lakes in Semi-Arid Regions of China

by Jiaqi Chen, Jiming Lv, Ning Li, Qingwei Wang and Jian Wang

Remote Sens. 2020, 12(1), 45; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12010045 - 20 Dec 2019

Cited by 22 | Viewed by 2697

Abstract

There are a large number of lakes with beaded distribution in the semi-arid areas of the Inner Mongolian Plateau, and some of them have degraded or even disappeared during the past three decades. We studied the reasons of the disappearance of these lakes by determining the way of replenishment of these lakes and the impact of the natural-social environment of the basin, with the aim of saving these gradually disappearing lakes. Based on remote sensing image and hydrological analysis, this paper studied the recharge of Daihai Lake and Huangqihai Lake. The deep learning method was used to establish the time-series of lake evolution. The same method was combined with the innovative woodland and farmland extraction method to set up the time-series of ground classification composition in the basins. Using relevant survey data, combined with soil water infiltration test, water chemical, and isotopic signature analysis of various water bodies, we found that the Daihai Lake area is the largest in dry season and the smallest in rainy season and the other lake is not satisfied with this phenomenon. In addition, we calculated the specific recharge and consumption of the study basin. These experiments indicated that the exogenous groundwater is recharged directly through the faults at the bottom of Daihai Lake, while the exogenous groundwater is recharged in Huangqihai Lake through rivers indirectly. Large-scale exploitation of groundwater for agricultural irrigation and industrial production is the main cause of lake degradation. Reducing the extraction of groundwater for agricultural irrigation is an important measure to restore lake ecology. Full article

(This article belongs to the Special Issue Remote Sensing of the Terrestrial Hydrologic Cycle)

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21 pages, 6434 KiB

Open AccessArticle

Quantitative Evaluations and Error Source Analysis of Fengyun-2-Based and GPM-Based Precipitation Products over Mainland China in Summer, 2018

by Jintao Xu, Ziqiang Ma, Guoqiang Tang, Qingwen Ji, Xiaoxiao Min, Wei Wan and Zhou Shi

Remote Sens. 2019, 11(24), 2992; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11242992 - 12 Dec 2019

Cited by 39 | Viewed by 2924

Abstract

Satellite-based quantitative precipitation estimates (QPE) with a fine quality are of great importance to global water cycle and matter and energy exchange research. In this study, we firstly apply various statistical indicators to evaluate and compare the main current satellite-based precipitation products from Chinese Fengyun (FY)-2 and the Global Precipitation Measurement (GPM), respectively, over mainland China in summer, 2018. We find that (1) FY-2G QPE and Integrated Multi-satellitE Retrievals for GPM (IMERG) perform significantly better than FY-2E QPE, using rain gauge data, with correlation coefficients (CC) varying from 0.65 to 0.90, 0.80 to 0.90, and 0.40 to 0.53, respectively; (2) IMERG agrees well with rain gauge data at monthly scale, while it performs worse than FY-2G QPE at hourly and daily scales, which may be caused by its algorithms; (3) FY-2G QPE underestimates the precipitation in summer, while FY-2E QPE and IMERG generally overestimate the precipitation; (4) there is an interesting error phenomenon in that both FY-based and GPM-based precipitation products perform more poorly during the period from 06:00 to 10:00 UTC than other periods at diurnal scale; and (5) FY-2G QPE agrees well with IMERG in terms of spatial patterns and consistency (CC of ~0.81). These findings can provide valuable preliminary references for improving next generation satellite-based QPE retrieval algorithms and instructions for applying these data in various practical fields. Full article

(This article belongs to the Special Issue Remote Sensing of the Terrestrial Hydrologic Cycle)

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20 pages, 8707 KiB

Open AccessArticle

Detecting Winter Wheat Irrigation Signals Using SMAP Gridded Soil Moisture Data

by Zhen Hao, Hongli Zhao, Chi Zhang, Hao Wang and Yunzhong Jiang

Remote Sens. 2019, 11(20), 2390; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11202390 - 15 Oct 2019

Cited by 7 | Viewed by 2735

Abstract

The southern part of the Hebei Province is one of China’s major crop-producing regions. Due to the continuous decline in groundwater level, agricultural water use is facing significant challenges. Precision agricultural irrigation management is undoubtedly an effective way to solve this problem. Based on multisource data (time series soil moisture active passive (SMAP) data, Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and evapotranspiration (ET), and meteorological station precipitation), the irrigation signal (frequency, timing and area) is detected in the southern part of the Hebei Province. The SMAP data was processed by the 5-point moving average method to reduce the error caused by the uncertainty of the microwave data derived SM. Irrigation signals can be detected by removing the precipitation effect and setting the SM change threshold. Based on the validation results, the overall accuracy of the irrigation signal detection is 77.08%. Simultaneously, considering the spatial resolution limitation of SMAP pixels, the SMAP irrigation area was downscaled using the winter wheat area extracted from MODIS NDVI. The analytical results of 55 winter wheat samples (5 samples in a group) showed that winter wheat covered by one SMAP pixel had an 82.72% growth consistency in surface water irrigation period, which can indicate a downscaling effectiveness. According to the above statistical analysis, this paper considers that although the spatial resolution of SMAP data is insufficient, it can reflect the change of SM more sensitively. In areas where the crop pattern is relatively uniform, the introduction of high-resolution crop pattern distribution can be used not only to detect irrigation signals but also to validate the effectiveness of irrigation signal detection by analyzing crop growth consistency. Therefore, the downscaling results can indicate the true winter wheat irrigation timing, area and frequency in the study area. Full article

(This article belongs to the Special Issue Remote Sensing of the Terrestrial Hydrologic Cycle)

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18 pages, 3743 KiB

Open AccessArticle

Convective/Stratiform Precipitation Classification Using Ground-Based Doppler Radar Data Based on the K-Nearest Neighbor Algorithm

by Zhida Yang, Peng Liu and Yi Yang

Remote Sens. 2019, 11(19), 2277; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11192277 - 29 Sep 2019

Cited by 9 | Viewed by 3306

Abstract

Stratiform and convective rain types are associated with different cloud physical processes, vertical structures, thermodynamic influences and precipitation types. Distinguishing convective and stratiform systems is beneficial to meteorology research and weather forecasting. However, there is no clear boundary between stratiform and convective precipitation. In this study, a machine learning algorithm, K-nearest neighbor (KNN), is used to classify precipitation types. Six Doppler radar (WSR-98D/SA) data sets from Jiangsu, Guangzhou and Anhui Provinces in China were used as training and classification samples, and the 2A23 product of the Tropical Precipitation Measurement Mission (TRMM) was used to obtain the training labels and evaluate the classification performance. Classifying precipitation types using KNN requires three steps. First, features are selected from the radar data by comparing the range of each variable for different precipitation types. Second, the same unclassified samples are classified with different k values to choose the best-performing k. Finally, the unclassified samples are put into the KNN algorithm with the best k to classify precipitation types, and the classification performance is evaluated. Three types of cases, squall line, embedded convective and stratiform cases, are classified by KNN. The KNN method can accurately classify the location and area of stratiform and convective systems. For stratiform classifications, KNN has a 95% probability of detection, 8% false alarm rate, and 87% cumulative success index; for convective classifications, KNN yields a 78% probability of detection, a 13% false alarm rate, and a 69% cumulative success index. These results imply that KNN can correctly classify almost all stratiform precipitation and most convective precipitation types. This result suggests that KNN has great potential in classifying precipitation types. Full article

(This article belongs to the Special Issue Remote Sensing of the Terrestrial Hydrologic Cycle)

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22 pages, 6417 KiB

Open AccessArticle

Estimation of Vegetation Latent Heat Flux over Three Forest Sites in ChinaFLUX using Satellite Microwave Vegetation Water Content Index

by Yipu Wang, Rui Li, Qilong Min, Leiming Zhang, Guirui Yu and Yves Bergeron

Remote Sens. 2019, 11(11), 1359; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11111359 - 06 Jun 2019

Cited by 10 | Viewed by 3314

Abstract

Latent heat flux (LE) and the corresponding water vapor lost from the Earth’s surface to the atmosphere, which is called Evapotranspiration (ET), is one of the key processes in the water cycle and energy balance of the global climate system. Satellite remote sensing is the only feasible technique to estimate LE over a large-scale region. While most of the previous satellite LE methods are based on the optical vegetation index (VI), here we propose a microwave-VI (EDVI) based LE algorithm which can work for both day and night time, and under clear or non-raining conditions. This algorithm is totally driven by multiple-sensor satellite products of vegetation water content index, solar radiation, and cloud properties, with some aid from a reanalysis dataset. The satellite inputs and the performance of this algorithm are validated with in situ measurements at three ChinaFLUX forest sites. Our results show that the selected satellite observations can indeed serve as the inputs for the purpose of estimating ET. The instantaneous estimations of LE (LE_cal) from this algorithm show strong positive temporal correlations with the in situ measured LE (LE_obs) with the correlation coefficients (R) of 0.56–0.88 in the study years. The mean bias is kept within 16.0% (23.0 W/m²) across the three sites. At the monthly scale, the correlations between the retrieval and the in situ measurements are further improved to an R of 0.84–0.95 and the bias is less than 14.3%. The validation results also indicate that EDVI-based LE method can produce stable LE_cal under different cloudy skies with good accuracy. Being independent of any in situ measurements as inputs, this algorithm shows great potential for estimating ET under both clear and cloudy skies on a global scale for climate study. Full article

(This article belongs to the Special Issue Remote Sensing of the Terrestrial Hydrologic Cycle)

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26 pages, 13368 KiB

Open AccessArticle

Transition Characteristics of the Dry-Wet Regime and Vegetation Dynamic Responses over the Yarlung Zangbo River Basin, Southeast Qinghai-Tibet Plateau

by Liu Liu, Qiankun Niu, Jingxia Heng, Hao Li and Zongxue Xu

Remote Sens. 2019, 11(10), 1254; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11101254 - 27 May 2019

Cited by 18 | Viewed by 4037

Abstract

The dry-wet transition is of great importance for vegetation dynamics, however the response mechanism of vegetation variations is still unclear due to the complicated effects of climate change. As a critical ecologically fragile area located in the southeast Qinghai-Tibet Plateau, the Yarlung Zangbo River (YZR) basin, which was selected as the typical area in this study, is significantly sensitive and vulnerable to climate change. The standardized precipitation evapotranspiration index (SPEI) and the normalized difference vegetation index (NDVI) based on the GLDAS-NOAH products and the GIMMS-NDVI remote sensing data from 1982 to 2015 were employed to investigate the spatio-temporal characteristics of the dry-wet regime and the vegetation dynamic responses. The results showed that: (1) The spatio-temporal patterns of the precipitation and temperature simulated by the GLDAS-NOAH fitted well with those of the in-situ data. (2) During the period of 1982–2015, the whole YZR basin exhibited an overall wetting tendency. However, the spatio-temporal characteristics of the dry-wet regime exhibited a reversal phenomenon before and after 2000, which was jointly identified by the SPEI and runoff. That is, the YZR basin showed a wetting trend before 2000 and a drying trend after 2000; the arid areas in the basin showed a tendency of wetting whereas the humid areas exhibited a trend of drying. (3) The region where NDVI was positively correlated with SPEI accounted for approximately 70% of the basin area, demonstrating a similar spatio-temporal reversal phenomenon of the vegetation around 2000, indicating that the dry-wet condition is of great importance for the evolution of vegetation. (4) The SPEI showed a much more significant positive correlation with the soil water content which accounted for more than 95% of the basin area, implying that the soil water content was an important indicator to identify the dry-wet transition in the YZR basin. Full article

(This article belongs to the Special Issue Remote Sensing of the Terrestrial Hydrologic Cycle)

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16 pages, 3294 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Addressing Challenges for Mapping Irrigated Fields in Subhumid Temperate Regions by Integrating Remote Sensing and Hydroclimatic Data

by Tianfang Xu, Jillian M. Deines, Anthony D. Kendall, Bruno Basso and David W. Hyndman

Remote Sens. 2019, 11(3), 370; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11030370 - 12 Feb 2019

Cited by 26 | Viewed by 4832

Abstract

High-resolution mapping of irrigated fields is needed to better estimate water and nutrient fluxes in the landscape, food production, and local to regional climate. However, this remains a challenge in humid to subhumid regions, where irrigation has been expanding into what was largely rainfed agriculture due to trends in climate, crop prices, technologies and practices. One such region is southwestern Michigan, USA, where groundwater is the main source of irrigation water for row crops (primarily corn and soybeans). Remote sensing of irrigated areas can be difficult in these regions as rainfed areas have similar characteristics. We present methods to address this challenge and enhance the contrast between neighboring rainfed and irrigated areas, including weather-sensitive scene selection, applying recently developed composite indices and calculating spatial anomalies. We create annual, 30m-resolution maps of irrigated corn and soybeans for southwestern Michigan from 2001 to 2016 using a machine learning method (random forest). The irrigation maps reasonably capture the spatial and temporal pattern of irrigation, with accuracies that exceed available products. Analysis of the irrigation maps showed that the irrigated area in southwestern Michigan tripled in the last 16 years. We also discuss the remaining challenges for irrigation mapping in humid to subhumid areas. Full article

(This article belongs to the Special Issue Remote Sensing of the Terrestrial Hydrologic Cycle)

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18 pages, 5843 KiB

Open AccessArticle

Quantifying the Evapotranspiration Rate and Its Cooling Effects of Urban Hedges Based on Three-Temperature Model and Infrared Remote Sensing

by Zhendong Zou, Yajun Yang and Guo Yu Qiu

Remote Sens. 2019, 11(2), 202; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11020202 - 21 Jan 2019

Cited by 33 | Viewed by 4742

Abstract

The evapotranspiration (ET) of urban hedges has been assumed to be an important component of the urban water budget and energy balance for years. However, because it is difficult to quantify the ET rate of urban hedges through conventional evapotranspiration methods, the ET rate, characteristics, and the cooling effects of urban hedges remain unclear. This study aims to measure the ET rate and quantify the cooling effects of urban hedges using the ‘three-temperature model + infrared remote sensing (3T + IR)’, a fetch-free and high-spatiotemporal-resolution method. An herb hedge and a shrub hedge were used as field experimental sites in Shenzhen, a subtropical megacity. After verification, the ‘3T + IR’ technique was proven to be a reasonable method for measuring the ET of urban hedges. The results are as follows. (1) The ET rate of urban hedges was very high. The daily average rates of the herb and shrub hedges were 0.38 mm·h⁻¹ and 0.33 mm·h⁻¹, respectively, on the hot summer day. (2) Urban hedges had a strong ability to reduce the air temperature. The two hedges could consume 68.44% and 60.81% of the net radiation through latent heat of ET on the summer day, while their cooling rates on air temperature were 1.29 °C min⁻¹ m⁻² and 1.13 °C min⁻¹ m⁻², respectively. (3) Hedges could also significantly cool the urban underlying surface. On the summer day, the surface temperatures of the two hedges were 19 °C lower than that of the asphalt pavement. (4) Urban hedges had markedly higher ET rates (0.19 mm·h⁻¹ in the summer day) and cooling abilities (0.66 °C min⁻¹ m⁻² for air and 9.14 °C for underlying surface, respectively) than the lawn used for comparison. To the best of our knowledge, this is the first research to quantitatively measure the ET rate of urban hedges, and our findings provide new insight in understanding the process of ET in urban hedges. This work may also aid in understanding the ET of urban vegetation. Full article

(This article belongs to the Special Issue Remote Sensing of the Terrestrial Hydrologic Cycle)

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16 pages, 4185 KiB

Open AccessArticle

Extended Dependence of the Hydrological Regime on the Land Cover Change in the Three-North Region of China: An Evaluation under Future Climate Conditions

by Yi Yao, Xianhong Xie, Shanshan Meng, Bowen Zhu, Kang Zhang and Yibing Wang

Remote Sens. 2019, 11(1), 81; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11010081 - 04 Jan 2019

Cited by 17 | Viewed by 3674

Abstract

The hydrological regime in arid and semi-arid regions is quite sensitive to climate and land cover changes (LCC). The Three-North region (TNR) in China experiences diverse climate conditions, from arid to humid zones. In this region, substantial LCC has occurred over the past decades due to ecological restoration programs and urban expansion. At a regional scale, the hydrological effects of LCC have been demonstrated to be less observable than the effects of climate change, but it is unclear whether or not the effects of LCC may be intensified by future climate conditions. In this study, we employed remote sensing datasets and a macro-scale hydrological modeling to identify the dependence of the future hydrological regime of the TNR on past LCC. The hydrological effects over the period from 2020–2099 were evaluated based on a Representative Concentration Pathway climate scenario. The results indicated that the forest area increased in the northwest (11,691 km²) and the north (69 km²) of China but declined in the northeast (30,042 km²) over the past three decades. Moreover, the urban area has expanded by 1.3% in the TNR. Under the future climate condition, the hydrological regime will be influenced significantly by LCC. Those changes from 1986 to 2015 may alter the future hydrological cycle mainly by promoting runoff (3.24 mm/year) and decreasing evapotranspiration (3.23 mm/year) over the whole region. The spatial distribution of the effects may be extremely uneven: the effects in humid areas would be stronger than those in other areas. Besides, with rising temperatures and precipitation from 2020 to 2099, the LCC may heighten the risk of dryland expansion and flooding more than climate change alone. Despite uncertainties in the datasets and methods, the regional-scale hydrological model provides new insights into the extended impacts of ecological restoration and urbanization on the hydrological regime of the TNR. Full article

(This article belongs to the Special Issue Remote Sensing of the Terrestrial Hydrologic Cycle)

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