Next Article in Journal
An Improved Algorithm Robust to Illumination Variations for Reconstructing Point Cloud Models from Images
Next Article in Special Issue
Changes in HIRS Detection of Cloud over Australia from 1985 to 2001
Previous Article in Journal
A Mechanistic Approach for Modeling Soil Development Using Remotely Sensed Data Collected from Invaded Coasts
Previous Article in Special Issue
Observed HIRS and Aqua MODIS Thermal Infrared Moisture Determinations in the 2000s
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Remote Sensing
Volume 13
Issue 4
10.3390/rs13040565
remotesensing-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

NASA’s MODIS/VIIRS Global Water Reservoir Product Suite from Moderate Resolution Remote Sensing Data

by
Yao Li
1,
Gang Zhao
2,
Deep Shah
1,
Maosheng Zhao
3,4,
Sudipta Sarkar
3,4,
Sadashiva Devadiga
3,
Bingjie Zhao
1,
Shuai Zhang
5 and
Huilin Gao
1,*
1
Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX 77843, USA
2
Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305, USA
3
Terrestrial Information Systems Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
4
Science Systems and Applications Inc., Lanham, MD 20706, USA
5
College of Marine Science, University of South Florida, St. Petersburg, FL 33701, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(4), 565; https://doi.org/10.3390/rs13040565
Submission received: 30 December 2020 / Revised: 29 January 2021 / Accepted: 2 February 2021 / Published: 5 February 2021
(This article belongs to the Special Issue Analysis of Decadal-Scale Continuous Data Products from Weather Satellite Platforms)
Browse Figures
Versions Notes

Abstract

:
Global reservoir information can not only benefit local water management but can also improve our understanding of the hydrological cycle. This information includes water area, elevation, and storage; evaporation rate and volume values; and other characteristics. However, operational wall-to-wall reservoir storage and evaporation monitoring information is lacking on a global scale. Here we introduce NASA’s new MODIS/VIIRS Global Water Reservoir product suite based on moderate resolution remote sensing data—the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Visible Infrared Imaging Radiometer Suite (VIIRS). This product consists of 8-day (MxD28C2 and VNP28C2) and monthly (MxD28C3 and VNP28C3) measurements for 164 large reservoirs (MxD stands for the product from both Terra (MOD) or Aqua (MYD) satellites). The 8-day product provides area, elevation, and storage values, which were generated by first extracting water areas from surface reflectance data and then applying the area estimations to the pre-established Area–Elevation (A–E) relationships. These values were then further aggregated to monthly, with the evaporation rate and volume information added. The evaporation rate and volume values were calculated after the Lake Temperature and Evaporation Model (LTEM) using MODIS/VIIRS land surface temperature product and meteorological data from the Global Land Data Assimilation System (GLDAS). Validation results show that the 250 m area classifications from MODIS agree well with the high-resolution classifications from Landsat (R2 = 0.99). Validation of elevation and storage products for twelve Indian reservoirs show good agreement in terms of R2 values (0.71–0.96 for elevation, and 0.79–0.96 for storage) and normalized root-mean-square error (NRMSE) values (5.08–19.34% for elevation, and 6.39–18.77% for storage). The evaporation rate results for two reservoirs (Lake Nasser and Lake Mead) agree well with in situ measurements (R2 values of 0.61 and 0.66, and NRMSE values of 16.25% and 21.76%). Furthermore, preliminary results from the VIIRS reservoir product have shown good consistency with the MODIS based product, confirming the continuity of this 20-year product suite. This new global water reservoir product suite can provide valuable information with regard to water-sources-related studies, applications, management, and hydrological modeling and change analysis such as drought monitoring.

Graphical Abstract

1. Introduction

Reservoirs serve as a lifeline in water management (e.g., irrigation, hydropower generation, water supply, and flood control), especially under the ongoing fast population growth and changing climate [1,2,3,4,5]. Globally, reservoirs supply about 40% of the total irrigation water demand [1] and contribute to more than 60% of renewable energy via hydroelectricity [6]. Reservoir storage varies according to natural climate variability as well as the human water use/demand for different sectors (i.e., domestic, agricultural, and industrial). On one hand, near-real-time reservoir storage monitoring is essential for mitigating the negative effects of hydro-climatic extremes (droughts and floods) [7,8]. On the other hand, long-term records of water retained by global reservoirs can help to evaluate the human impacts on global and regional water cycles [9,10]. However, because gauge observations for reservoir storage (and/or elevation) are typically not shared, both of the aforementioned data needs are difficult to satisfy at regional and global scales.
Among the reservoir water budget terms, reservoir evaporation accounts for a substantial amount of the loss of available water—particularly for reservoirs in arid/semi-arid regions [11]. For example, the evaporation volume of Lake Tahoe (located in the western U.S.) represents 40–60% of the total reservoir output [11]. The annual evaporation rate of Lake Mead is ~1800 mm/year [12], which greatly exceeds the surrounding evapotranspiration rate (~50 mm/year) [13]. At a regional scale, the water losses due to evaporation for 200 reservoirs in Texas are equivalent to 20% of their active storage value [14]. Thus, it is crucial to incorporate information about reservoir evaporation losses into existing water management practices. Nonetheless, because reservoir evaporation information obtained through reliable in situ measurements (e.g., eddy covariance, energy balance) is hard to acquire, pan evaporation data (which is less accurate due to the lack of consideration of heat storage and fetch effects) have been commonly used as an approximation [11]. For most developing countries, even data about pan evaporation (or its equivalent) are not available.
Satellite remote sensing provides an alternative for filling in such reservoir data gaps. Since the 1990s, satellite radar altimeters have been utilized to measure the water levels of large lakes and reservoirs [15]. To date, several databases have been developed to monitor the water levels of inland water bodies at a global scale—including the Global Reservoir and Lake Monitor (G-REALM) [16], the Hydroweb database [17], and the Database for Hydrological Time Series of Inland Waters (DAHITI) [18]. Meanwhile, the global surface area variations of lakes and reservoirs have been assessed from various satellite instruments, such as the Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) [19,20,21,22,23,24]. Pekel et al. [19] developed a Global Surface Water (GSW) dataset using expert system classifiers based on Landsat observations obtained over the last three decades. The more recently published Global Reservoir Surface Area Dataset (GRSAD) provides monthly water area values for over 7000 reservoirs [20], which were generated by correcting the underestimations due to cloud contamination in the GSW dataset. Khandelwal et al. [21] generated an 8-day composite water area time series dataset for 94 reservoirs using MODIS multispectral data at 500 m resolution. In the meantime, many studies have focused on generating satellite-based reservoir storage estimations by combining elevation and area observations collected from multiple missions [17,25,26,27]. For example, Gao et al. [25] monitored storage values for 34 global reservoirs (which represented 15% of the total global reservoir capacity during that period) from 1992 to 2010 by combining water surface areas from MODIS with water elevations from satellite radar altimetry. The Hydroweb database (http://hydroweb.theia-land.fr/, accessed on 30 December 2020) estimates the storage changes for about 60 large lakes and reservoirs beginning in 1992, using multi-source satellite imagery (e.g., MODIS and Landsat) and radar altimetry data [17]. More recently, Busker et al. [27] analyzed the monthly volume variations between 1984 and 2015 for 137 lakes and reservoirs at a global scale by combining water area values from the GSW dataset [19] and elevation values from DAHITI [18].
Meanwhile, some new approaches have been recently developed to estimate evaporation rates and losses from space. For instance, Zhang et al. [14] estimated the monthly evaporation volumes based on pan-derived evaporative rates and Landsat surface areas for more than 200 reservoirs in Texas. Zhao and Gao [28] used the Penman Equation (with the heat storage and fetch effects addressed), and generated a first long-term evaporation data record for over 700 reservoirs in the contiguous United States. Zhao et al. [29] further improved the calculation of the heat storage change term by leveraging MODIS water surface temperature data. Many other approaches have been developed and tested at individual locations [30,31,32].
Despite the development of remotely sensed reservoir datasets, consistent, comprehensive, long-term, and operationally monitored reservoir products are still lacking at the global scale. Therefore, we present here a newly developed NASA global water reservoir product suite (MxD28 and VNP28) from moderate resolution remote sensing data. The suite provides 8-day and monthly measurements of area, elevation, and storage, along with monthly evaporation rates and evaporative volumetric losses for 164 global reservoirs. This paper is organized as follows. Section 2 describes the overview of the reservoir product. Section 3 explains the materials and methods that were used to generate the MODIS products. Section 4 shows the MODIS product validation results and evaluates the consistency between the MODIS and VIIRS results. Section 5 discusses the limitations and potential applications of this reservoir product.

2. Overview of the Global Water Reservoir Product Suite

2.1. Brief Description of the Selected Reservoirs

This product suite provides data for 164 reservoirs (locations are shown in Figure 1) with areas larger than 25 km2, which were selected from the Global Reservoir Bathymetry Dataset (GRBD) [33]. It includes 151 man-made reservoirs (2672 km3) and 13 regulated natural lakes (23,811 km3). The total storage capacity of the 151 man-made reservoirs represents 45.82% of the global capacity (in its category) according to the Global Reservoir and Dam Database (GRanD) [34]. Table 1 summarizes the attributes of these 151 reservoirs at the global and continental scales. More details about the individual reservoirs (both man-made reservoirs and regulated natural lakes) and their attributes are provided in Table A1. At the continental scale, it has the largest coverage in Africa (76.61%) and the lowest in Europe (20.59%) in terms of storage capacity.

2.2. Reservoir Product Components

The reservoir product suite is organized based on different temporal resolutions: 8-day (MxD28C2 and VNP28C2) and monthly (MxD28C3 and VNP28C3) (Table 2). The 8-day product includes reservoir area, elevation, and storage values. The monthly product adds reservoir evaporation rate and volume values (in addition to the above-mentioned measurements) for each reservoir.

3. Data and Methods

3.1. Input Datasets

The input datasets used for the development of the reservoir product are summarized in Table 3. For MxD28C2, the 8-day Terra/Aqua surface reflectance (MxD09Q1) [35] data were collected for water area extraction. For VNP28C2, we used the 8-day VIIRS/NPP surface reflectance (VNP09H1) [36]. In particular, only the near-infrared (NIR) band was used due to its high spatial resolution (i.e., 250 m for MODIS and 500 m for VIIRS). The NIR band has been commonly utilized for the extraction of water bodies because it is strongly absorbed by water but scarcely absorbed by terrestrial dry soil and vegetation [37]. The Area–Elevation (A–E) relationships were adopted from the GRBD [33], which have proven to be of high quality through validation against in situ data. Then, the 8-day water area estimations were applied to the A–E relationships to derive elevation and storage values (see Section 3.2.1 for more details). Moreover, we used the 8-day day/night land surface temperature (LST) products (MxD21A2 [38] and VNP21A2 [39]) and Global Land Data Assimilation System (GLDAS) [40] meteorological forcing data to estimate the evaporation rates and volumes (see Section 3.2.2 for a more detailed approach).

3.2. Methodology

In this section, we use MODIS products to demonstrate the algorithms, and the same algorithms were adopted to generate VIIRS-based products. Detailed descriptions of all of the algorithms involved in the product’s development (e.g., the enhancement water classification algorithm and evaporation rate estimation algorithm) are provided in the Algorithm Theoretical Basis Documentation (ATBD) [41], which is available through the product website (https://modis-land.gsfc.nasa.gov/modgwr.html, accessed on 30 December 2020). Since the product suite has yet to be formally released by NASA, the MODIS results presented in this paper were generated by executing the algorithms on the Google Earth Engine (GEE) [42], while the VIIRS results were generated on our local computer cluster (due to lack of VIIRS data on GEE).

3.2.1. Algorithm for the 8-Day Products

Figure 2 shows the algorithm for generating the MxD28C2 8-day product. First, the 8-day reservoir area values were extracted from the 250-m Near Infrared (NIR) band, along with the quality assurance (QA) band, of MODIS Terra/Aqua surface reflectance (MxD09Q1) data. During the classification process, an area enhancement algorithm (Zhang et al. [26]) was adopted to minimize the effects of various sources of contamination (e.g., cloud and snow/ice). Then, the area values were applied to the A–E relationship for the given reservoir provided by the GRBD [33] to calculate the corresponding elevation values. Lastly, the reservoir storage was estimated using Equation (1) (Gao et al. [25]):
V M O D I S =   V c −   ( A c + A M O D I S ) ( h c − h M O D I S ) / 2                                                                                      
where V c , A c , and h c represent storage, area, and water elevation values at capacity; and V M O D I S , A M O D I S , and h M O D I S are the estimated storage, area, and water elevation values from MODIS, respectively.
The water area extraction values from MODIS serve as a foundation for elevation and storage estimations. Here we use our experience with the Cahora Bassa reservoir (Mozambique, Africa) as an example to explain the classification and enhancement processes. When classifying this reservoir, we initially buffered the shapefile (provided by HydroLAKES database [43]) outward by 1 km and then further used it to extract the NIR band images. The buffered area covers the maximum water extent, and the following classification and enhancement operations were implemented inside of this area to reduce the computational cost. For each 8-day period, the MxD09Q1 NIR images that overlap with the reservoir were first selected (Figure 3a). The pixels contaminated by clouds, cloud shadow, and snow/ice (identified using the QA band of MxD09Q1) were labeled as ‘NoData’ (the blank areas in Figure 3b represent the contaminated pixels). Then, the Otsu thresholding method [44] was used to extract the water area from the NIR image (Figure 3c). It is obvious that the water area was underestimated due to contaminations (as shown in Figure 3c). Lastly, in combination with the water occurrence (percentile) image provided by the GSW (Figure 3d), we applied the enhancement algorithm developed by Zhang et al. [26] (Figure 3e) to correct the underestimations. The enhanced water areas show clear boundaries (Figure 3f). More details about the framework of MODIS water classification and enhancement can be found in Zhang et al. [26]. Note that it may happen that some extreme area values still exist even after the enhancement process. To eliminate the effects of these outliers, we used three standard deviations to remove them and used an interpolation method to fill in the data gap. This issue will be addressed in a future release of the enhancement algorithm [29].

3.2.2. Algorithm for the Monthly Products

The MxD28C3 product includes the evaporation rate and volumetric evaporation loss in addition to the area, elevation, and storage results at monthly temporal resolution. Figure 4 shows the algorithm for generating the MxD28C3 monthly product. The monthly area values were first estimated based on the composite of the 8-day area classifications and then converted into monthly elevation and storage results using the A–E relationship (Figure 4). In addition, monthly evaporation rates were estimated after the Lake Temperature and Evaporation Model (LTEM) [29] using MODIS LST product (MxD21A2) and meteorological data from GLDAS [40]. Lastly, the monthly evaporative volumetric losses were calculated as the product of evaporation rate and reservoir area values.

4. Results

This paper primarily focuses on validating the MODIS-based products (Section 4.1, Section 4.2 and Section 4.3) and evaluating the consistencies between VIIRS and MODIS products (Section 4.4). With regard to MODIS, when both 8-day MOD (Terra) and MYD (Aqua) area results are available on the same date, we selected the one with less cloud contamination. Similarly, the validations of the MODIS elevation and storage products also adopted those values associated with the less contaminated areas.

4.1. Comparing Water Surface Areas with Landsat-Based Results

At the global scale, long-term in situ reservoir area records are still lacking. Therefore, we compared the MODIS area values with Landsat-based results (at a finer spatial resolution of 30 m) for purposes of area validation. The Landsat monthly reservoir area values for all of the 164 reservoirs were collected from GRSAD between 2000 and 2018 [20]. GRSAD corrected the water area underestimation of the GSW dataset caused by both cloud/shadow/ice contamination and the Landsat-7 scan line corrector failure [20]. Note that the Landsat-based area estimation for a given month was based on the one or two images obtained during that month, while the monthly MODIS area value was derived from the composite of the 8-day classification. However, due to the deficiency of the in situ area values, we used this Landsat-based dataset to validate the overall consistency of the MODIS area products.
According to Figure 5, the MODIS-based area values agree well with the Landsat-based results (with an R2 value over 0.99). Additionally, the data points are mainly centered on the 1:1 line (slope = 0.99). The disagreements that do exist can be attributed to two sources. The first is because Landsat and MODIS collected data at different dates. If a reservoir experienced a large change within a month, it may have caused a large area discrepancy. The second is related to the low spatial resolution of MODIS, which makes it more susceptible to mixed pixels. This can explain the area underestimations for relatively small reservoirs.

4.2. Validating the MODIS Elevation and Storage Products against In Situ Observations

For the elevation and storage validations, we collected in situ daily observations for twelve Indian reservoirs (Ukai, Matatila, Ranapratap Sagar, Gandhi Sagar, Ban Sagar, Bargi, Hirakud, Jayakwadi, Sriram Sagar, Nagarjuna Sagar, Yeleru, and Tungabhadra) from the Indian Central Water Commission (http://cwc.gov.in/, accessed on 30 December 2020) between 2000 and 2019. We used the Indian reservoirs for validation purposes because they experience large dynamics that can better evaluate the efficiency of our algorithm.
The validation results of the 8-day MODIS elevation and storage products are shown in Figure 6 and Figure 7, respectively. Overall, the elevation estimations from MODIS agree well with the in situ data (Figure 6), with an average R2 value of 0.87, an average RMSE value of 2.22 m, and an average NRMSE value of 12.28%. The biases (e.g., overestimation for Tungabhadra and underestimation for Yeleru) are caused by a combination of mixed pixels of reservoir edge, parameterization of the enhancement algorithm, and the mismatch of MODIS water areas with Landsat water areas, which were used to derive A–E relationships for GRBD. With regard to the storage validations (Figure 7), they have similar patterns with those of the elevation results because they were both derived from area time series. Validations against in situ data show an average R2 value of 0.88, an average RMSE value of 0.47 km3, and an average NRMSE value of 13.20%.
Next, we validated the monthly elevation and storage products over these twelve Indian reservoirs. The daily in situ elevation and storage values were averaged at a monthly step and were then compared to the monthly MODIS products (Figure A1 and Figure A2). The validation results show similar patterns as those of the 8-day products but higher accuracies. This is because the monthly reservoir area values were generated from the composited results of three or four 8-day reservoir areas from MxD28C2, and the composition process further reduced the adverse effects of cloud contamination at the 8-day time step. As shown in Figure A1, the MODIS-based elevations show good consistency with the in situ measured data, with an average R2 value of 0.90, an average RMSE value of 1.99 m, and an average NRMSE value of 11.33%. Regarding the storage validations (Figure A2), the results are consistent with those of the elevations, with an average R2 value of 0.91, an average RMSE value of 0.43 km3, and an average NRMSE value of 11.91%.

4.3. Validating the Evaporation Rate Product against In Situ Observations

The evaporation rate results were validated over two locations—Lake Nasser in Africa and Lake Mead in North America—where high-quality in situ observations are available. The eddy covariance (EC) evaporation rate measurements for Lake Mead between 2010 and 2015 were provided by the United States Geological Survey (USGS) [12]. With regard to Lake Nasser, the evaporation rate estimations were obtained using the Bowen ratio energy budget (BREB) method [45]. Although it is not as accurate as EC observations, the BREB method has been widely utilized to estimate evaporation rates due to its operability and reliability [46]. As shown in Figure 8, the MODIS evaporation rate products have good overall agreement with values obtained via observation (with R2 values of 0.61 and 0.66, and NRMSE values of 16.25% and 21.76%). Compared to the results that were calculated using the regular Penman equation (without heat storage), our MODIS evaporation rate estimates have shown great improvements in terms of both annual peak values and seasonal variations. For Lake Nasser, the R2 value increased from 0.30 to 0.61, with the NRMSE decreasing from 26.86% to 16.25%. A greater improvement is observed for Lake Mead in terms of both R2 (from 0.26 to 0.66) and NRMSE (from 41.06% to 21.76%). These results suggest that better evaporation rate estimates are achieved when considering heat storage within the algorithm. It should be noted that this evaporation rate algorithm was validated over more locations in Zhao et al. [29]. Comparisons of evaporation rates calculated from different datasets (GLDAS + MxD21 and TerraClimate + MxD11) indicate that the performances are similar, even though the LST and meteorological data are slightly different (Figure A3).

4.4. Consistencies between VIIRS and MODIS Products

MODIS sensors have successfully operated for more than 20 years. The MODIS successor, VIIRS, was launched on 28 October 2011, onboard the Suomi National Polar-orbiting Partnership (Suomi NPP). Therefore, VIIRS plays an important role in extending the continuity of this product suite. The reliability of the water area estimation controls the quality of the other products (i.e., the elevation, storage, and evaporation results). Because the VIIRS 8-day reflectance data (VNP09) are not available on GEE and the VIIRS input data are very large and computationally intensive, we selected six Indian reservoirs from which to evaluate the consistency of VIIRS and MODIS data by comparing the 8-day area estimations from January 2012 to August 2020.
As seen in Figure 9, the VIIRS-based area values agree well with those of MODIS, with R2 values ranging from 0.80 to 0.90. The 8-day elevation and storage values also have good consistency, as they are based on the area estimations. In addition, high-quality monthly products can be generated from 8-day composite results (which are not shown here). Note that the VIIRS results are noisier than those from MODIS because of its coarser spatial resolution (500 m vs. 250 m). We also applied the outlier removal and gap-filling procedures to the area estimations from VIIRS, which led to smoother time series values.
Additionally, we selected Lake Mead to compare the evaporation rates from January 2012 to April 2015 (Figure 10). Validation results show that the evaporation rates of VNP28C3 have a higher accuracy than those of MYD28C3 in terms of R2 (0.76 vs. 0.66) and NRMSE (15.92% vs. 21.76%). Meanwhile, the evaporation rate results suggest a good consistency between VNP28C3 and MYD28C3 (R2 = 0.92). It is noteworthy that the difference between MODIS and VIIRS evaporation rates is mainly attributed to the LST data. It has been reported that MODIS and VIIRS LST data have good agreement (>0.99) [47]. Therefore, the VIIRS-based evaporation rate values for other reservoirs should also be reliable and have good consistencies with those from MODIS.

5. Discussion

5.1. Benefits of the MODIS/VIIRS Based Water Elevations as Compared to Radar Altimetry Products

Global reservoir elevation products are mainly based on measurements collected by satellite radar altimeters. We compared the MODIS 8-day elevation product with two radar altimetry products (Hydroweb and G-REALM) in terms of reservoir coverage and temporal resolution (Figure 11). Overall, the MODIS product and radar altimetry products agree well with each other (Figure 12). Given that the MODIS and VIIRS 8-day products are very similar, Figure 11 also represents the relationship between the VIIRS product and most radar altimetry products (except that the VIIRS start date is in 2012). Despite the similarities, the MODIS/VIIRS product suite can offer new benefits over traditional radar altimetry products in two ways. First, the MODIS/VIIRS products can provide reservoir water elevations at an 8-day temporal resolution. In comparison, Hydroweb data are at the monthly temporal resolution, and G-REALM data range from 10-day, to 27-day, to 35-day (depending on the sensor). Second, this global water reservoir product suite is characterized by continuous and consistent records since 2000 for all of the 164 reservoirs (some examples are shown in Figure 12). Currently, there are 234 lakes and reservoirs included in Hydroweb, 73 of which are reservoirs (http://hydroweb.theia-land.fr/, accessed on 10 October 2020). G-REALM provides elevation values for a total of 241 reservoirs (out of 340 water bodies https://ipad.fas.usda.gov/lakes/images/LakesReservoirs.pdf, accessed on 10 October 2020). Only five reservoirs are monitored at an interval of 27 days in the period from February 2016 to the present. The 35-day products are from May 2002 to October 2010 and cover 33 reservoirs. The majority of reservoirs (203) are routinely measured at a 10-day time scale. However, 58 of them are lacking observations from August 2002 to July 2008 (examples include Qapshaghay Bogeni, Mosul, and Powell, which are shown in Figure 12). From 2000 to 2020, only 145 reservoirs from G-REALM have continuous records. By contrast, the MODIS/VIIRS products do not have this data gap issue.

5.2. Limitations and Sources of Uncertainties

There are still some limitations that need to be addressed. First, only the relatively large reservoirs are included due to the moderate resolutions of MODIS/VIIRS. For smaller reservoirs, it is more appropriate to take advantage of missions with high spatial resolutions (e.g., Landsat and Sentinel-2). Second, some reservoirs are susceptible to mountain shadows (but this has only small effects on large reservoirs). This overestimation can be eliminated using topography information. For example, Li et al. [48] overlapped water classification maps with Shuttle Radar Topography Mission (SRTM) DEM data to exclude the mountain pixels.
The sources of uncertainties with regard to the reservoir surface area are associated with both the raw image classification using the reflectance product, and the classification enhancement algorithm. The accuracy of the Ostu classification of the NIR images is affected by the mixed pixels (i.e., partially covered by water and partially covered by land) at the reservoir boundaries, as well as by ice over the reservoirs. The reliability of the enhancement algorithm depends on the data quality of both the water occurrence image and the raw water classification. In high-latitude regions, the water occurrence image generally shows small surface area dynamics (i.e., the distribution of occurrence values highly skewed to the left). Thus, the pixels with low occurrence values have relatively large uncertainties. Under such conditions, the enhancement algorithm might generate an incorrect threshold value—typically leading to an overestimation of the surface area.
The reservoir elevation and storage estimation uncertainties include reservoir surface area uncertainties (see above), A–E relationship uncertainties, and reservoir configuration uncertainties. According to Equation (1), the estimated storage will be biased if the characteristics at capacity (storage, area, and elevation) are not accurate. Even when these factors have been correctly documented, the storage capacity may have changed due to sedimentation over time. Since the reservoir elevations are inferred only from areas and A–E relationships, they are not affected by reservoir configuration uncertainties. Moreover, while a non-linear relationship may perform better for some reservoirs, linear relationships were found to perform quite well for most of the reservoirs [33]. The linear A–E relationship represents a second-order polynomial reservoir cross-section, which is more realistic than other cross-section shapes [49].
Sources of evaporation rate uncertainty mainly include forcing data uncertainty and model structure/parameter uncertainty. Specifically, the forcing data used in this study (i.e., GLADS-2.1) are a land-based meteorological record. Although the increased humidity on the reservoir surface is represented by the wind function [28,50], differences in the wind speeds between reservoir and land regions are ignored, which might introduce some uncertainties [51]. In addition, the LTEM and its parameters can also produce uncertainties. For example, the formulation of the light attenuation coefficient (λ_PAR) is simplified. However, λ_PAR is affected by suspended solids, phytoplankton concentration level, and spectral distribution of solar radiation, and thus is constantly changing [52,53].
The reservoir volumetric evaporation uncertainty can be attributed to evaporation rate and surface area uncertainties, which have been discussed above.

5.3. Future Directions and Potential Applications

Several efforts are planned in the future to improve the current MODIS/VIIRS product suite. To reduce area estimation errors, the edge detection algorithm developed in Zhao et al. [29] will be employed to overcome the limitations of the current image enhancement approach. Furthermore, the design of the Terra (MOD) and Aqua (MYD) combined product components (MCD28C2 and MCD28C3) will allow it to automatically select less contaminated images for the users (thus leading to more accurate area estimates). It is also essential to conduct a comprehensive comparison between the MODIS and VIIRS products, especially given that the MODIS-era is close to an end. This analysis will be carried out after these products are made available through NASA. Additionally, a greater number of large reservoirs can be monitored by developing A–E relationships over locations where radar altimetry and ICESat data are unavailable. For instance, both Zhang and Gao [54] and Li et al. [55] have demonstrated the potential of using DEM data for generating A–E relationships for global reservoirs. Moreover, ICESat-2 (launched in September 2018) [48] and the Surface Water and Ocean Topography (SWOT) mission (planned for launch in 2022) [56] will provide more accurate elevation observations (as compared to radar altimetry and DEM). This should lead to the generation of A–E relationships over many more reservoirs.
The reservoir product can have multiple potential applications. First, it provides critical information for water resources management—especially for reservoirs in transboundary river basins where gauge observations are not shared. Moreover, it can help to calibrate and validate the operation rules incorporated into the global hydrological models that have a reservoir module. This can improve our understanding of the reservoirs’ role in the hydrological cycle and water budgets [10,57]. For instance, Shah et al. [58] examined the role of reservoirs in water and energy budgets using the Variable Infiltration Capacity (VIC) model in the Indian sub-continent.
Additionally, time series values of reservoir storage variations at the basin scale can be used to evaluate hydrological droughts. Figure 13 shows the total reservoir storage variations in the Colorado and Murray-Darling river basins. The Colorado River basin has experienced a prolonged hydrological drought since the year 2000, even though the underlying meteorological drought only lasted for the first few years. This hydrological drought has been exacerbated by increased water use [59]. Moreover, the Murray–Darling river basin experienced the longest four-year reservoir-based hydrological drought on record from 2006 to 2010. However, the majority of the prior studies of droughts across the globe [60,61,62,63,64,65,66] considered only streamflow variations in their assessments of hydrological drought. This is because continuous long-term reservoir data with a high temporal resolution are lacking at a global scale (either for use in analysis or for the calibration of global hydrological models). Neglecting the reservoir storage component in hydrological drought assessment can lead to biases and uncertainties, as water is increasingly consumed, stored, and diverted through water management activities (i.e., reservoir operations) [66,67]. Therefore, this product can fill in this crucial research gap and can help stakeholders, water managers, and policymakers to improve existing hydrological drought management practices.

6. Conclusions

This paper presents the development and validation results of NASA’s global water reservoir product suite (MxD28 and VNP28), which includes 8-day and monthly water area, elevation, and storage values and monthly evaporation rate and volume values. This product consists of 164 reservoirs that represent nearly half of the total global capacity (Figure 1 and Table 1). The water area values show good consistency with those from high-resolution Landsat measurements (Figure 5). Validations against in situ observations over twelve reservoirs suggest a high level of accuracy for the elevation and storage products (Figure 6 and Figure 7). Validations over Lake Nasser and Lake Mead indicate that the evaporation rate product also has a high quality (Figure 8). Moreover, a comparison of 8-day reservoir area estimations between MODIS and VIIRS (Figure 9) again shows good consistency, which ensures the continuation of this long-term reservoir product.
Compared with other remotely sensed reservoir products, the advantages of our product can be summarized as follows. First, the product provides consistent measurements for area, elevation, and storage and for evaporation rate and volume values. For most of the current products, the measurements provided are generally for a specific aspect. For example, the radar altimetry products tend to combine observations from multiple satellite missions, which may have data gaps and large uncertainties (Figure 12). Second, the higher temporal resolution (8-day) can provide timely information useful for water management (e.g., water supply and flood mapping). Although Landsat can provide more accurate area estimations due to its higher spatial resolution, its longer revisit time (16 days) makes it not suitable for timely monitoring purpose. Furthermore, we have demonstrated that reservoir storage variations can be a good indicator of hydrological drought. This reservoir product can help to better understand the drought propagation process from the meteorological to hydrological perspectives.

Author Contributions

Conceptualization, Y.L., G.Z., and H.G.; methodology, Y.L., G.Z., S.Z., and H.G.; validation, Y.L., and G.Z.; resources, H.G.; data curation, G.Z., M.Z., S.S., and S.D.; writing—original draft preparation, Y.L., H.G., G.Z., and D.S.; writing—review and editing, All authors.; visualization, Y.L., and G.Z.; supervision, H.G.; project administration, H.G.; funding acquisition, H.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the NASA Science of Terra, Aqua, and Suomi NPP (TASNPP) Program (80NSSC18K0939).

Data Availability Statement

This global water reservoir product suite will be available at https://lpdaac.usgs.gov/ and https://ladsweb.modaps.eosdis.nasa.gov/, accessed on 30 December 2020.

Acknowledgments

This work has benefitted from the usage of the Google Earth Engine platform and the Texas A&M Supercomputing Facility (http://hprc.tamu.edu, accessed on 30 December 2020). We also thank the editors and three anonymous reviewers for their constructive comments.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Appendix A

Table
Table A1. List of the 164 reservoirs and their attributes.
Table A1. List of the 164 reservoirs and their attributes.
Lake
ID		GRanD
ID		NameCtryCONTA–E Coeff. 1
a, b		Vc2 (km3)Ac2 (km2)Ec2
(m)		Lon
(°)		Lat
(°)
1 *5058BaikalRussiaAS0.0045,
312.77		23,615.3932,265.61456.88104.32252.235
23667VoltaGhanaAF0.0037,
55.59		148.008502.0086.650.0596.304
34478NasserEgyptAF0.0047,
152.82		162.006500.00183.2832.88723.969
44056Kariba ZambiaAF0.011,
424.98		180.005400.00485.4128.760−16.521
55055Bratsk RussiaAS0.0066,
367.92		169.275470.00403.85101.78456.285
64787ZaysanKazakhstanAS0.0047,
370.21		49.805490.00395.7483.34649.657
72294Guri VenezuelaSA0.014,
217.17		135.004250.00278.38−62.9967.766
81995Caniapiscau CanadaNA0.012,
489.00		53.794275.00541.08−69.78354.850
91394Robert
Bourassa 		CanadaNA0.011,
143.99		61.702905.00176.24−77.45153.785
102516Sobradinho BrazilSA0.0057,
375.27		34.103017.90392.50−40.825−9.421
11712CedarCanadaNA0.0022,
250.49		9.642668.46256.29−99.29153.162
121396La Grande−3 CanadaNA0.025,
195.26		60.002451.00257.48−75.96253.729
132365Tucurui BrazilSA0.013,
40.96		45.502606.0075.40−49.648−3.833
144375Tsimlyanskoye RussiaEU0.012,
7.64		23.862702.0039.4442.10947.612
155834Zeyskoye RussiaAS0.021,
266.44		68.402420.00316.41127.30753.770
165180Vilyuy RussiaAS0.029,
182.74		35.902170.00244.62112.48063.035
174783Khantayskoye RussiaAS0.0044,
49.76		23.502221.6159.6487.81368.162
184505Cahora Bassa MozambiqueAF0.015,
286.96		55.802739.00329.1832.700−15.584
196WillistonCanadaNA0.053,
580.99		39.471773.00674.79−122.20056.020
204472Buhayrat ath ThartharIraqAS0.040,
−19.46		85.592135.5465.0043.45933.691
215056Krasnoyarsk RussiaAS0.039,
162.77		73.302000.00240.0492.29255.935
224623Kama RussiaEU0.0074,
96.08		12.201915.00110.3256.33858.116
231957OkeechobeeUnited States NA0.0062,
−5.57		5.201536.803.90−81.10126.941
245295HungtzeChinaAS0.0075,
1.46		13.502074.6117.00118.72533.091
254474RazazahIraqAS0.015,
11.07		25.751621.0034.6943.89232.699
262023Gouin CanadaNA0.00068,
402.91		8.571570.00403.98−74.10448.358
274789Qapshaghay Bogeni KazakhstanAS0.0090,
467.11		28.101850.00483.7177.10443.923
28753Fort Berthold United States NA0.025,
528.65		29.381477.40565.10−101.43347.508
292445Aperea ParaguaySA0.022,
48.84		21.001600.0084.71−56.626−27.392
30870OaheUnited States NA0.022,
462.73		28.351429.57493.78−100.40044.458
312390Ilha Solteira BrazilSA0.032,
290.95		21.171200.00329.78−51.379−20.371
324629Saratov RussiaEU0.026,
−0.28		12.901117.7028.3647.75852.054
334350ImandraRussiaEU0.19,
−62.87		10.801062.37136.0732.55067.408
343640Kainji NigeriaAF0.040,
94.00		15.001071.23136.814.6139.866
354785NovosibirskoyeRussiaAS0.014,
98.78		8.801070.00113.9783.00054.844
364625CheboksaryRussiaEU0.024,
39.30		13.851080.3865.7347.46356.141
374359IlmenRussiaEU0.0083,
9.98		12.001120.0019.2831.28058.457
384480Jebel Aulia SudanAF0.0062,
375.01		3.50861.19380.3932.48415.240
391397Opinaca CanadaNA0.021,
194.08		8.501040.00216.10−76.58452.212
402392FurnasBrazilSA0.044,
720.07		22.591127.07769.32−46.314−20.667
412368Serra da Mesa BrazilSA0.034,
410.20		54.401784.00470.07−48.304−13.836
424624Votkinsk RussiaEU0.039,
53.14		9.40850.8286.2554.08456.795
436201Argyle AustraliaOC0.028,
66.44		10.76981.2193.97128.741−16.118
44731RainyCanadaNA0.00078,
336.09		0.69829.45336.73−93.35848.620
45307Fort PeckUnited States NA0.044,
643.32		22.77969.86685.76−106.41548.002
462375Tres Marias BrazilSA0.036,
539.11		21.001040.00576.06−45.270−18.214
472012Pipmuacan CanadaNA0.050,
360.46		13.90978.00409.16−69.77049.355
484679ChardarinskoyeKazakhstanAS0.018,
238.24		5.70800.66252.5467.96241.245
494626Nizhnekamsk RussiaEU0.014,
50.37		13.801084.0065.3452.28055.704
502456Negro UruguaySA0.019,
62.01		8.801070.0082.77−56.420−32.830
512343Chocon ArgentinaSA0.015,
365.75		22.00820.00378.20−68.758−39.270
524442Ataturk TurkeyAS0.11,
454.25		48.70817.00541.2038.32137.487
532513Itaparica BrazilSA0.033,
279.33		10.70781.21305.40−38.312−9.138
544464AssadSyriaAS0.059,
266.63		11.70610.00302.8738.55535.862
553650Lagdo CameroonAF0.037,
190.16		7.70691.12216.0013.6909.060
561269Toledo Bend United States NA0.020,
39.46		5.52636.1852.43−93.57031.179
576922Eastmain CanadaNA0.068,
245.92		6.94602.90286.82−75.88652.188
582009Outardes 4 CanadaNA0.19,
239.61		24.50640.00361.53−68.90849.708
594349KovdozeroRussiaEU0.0019,
78.18		3.70745.0079.6231.75968.604
602380Sao Simao BrazilSA0.052,
369.17		12.50703.00405.94−50.500−19.017
61610MeadUnited States NA0.14,
288.76		34.07659.30374.60−114.73436.020
625087YamdrokChinaAS0.013,
4435.36		14.60638.004443.4990.37729.095
631391Angostura MexicoNA0.081,
478.96		18.20640.00530.67−92.77916.401
644991Srisailam IndiaAS0.031,
253.30		8.29534.05269.7578.89616.088
652455Grande ArgentinaSA0.031,
16.89		5.00592.8335.08−57.944−31.271
664843Gandhi Sagar IndiaAS0.034,
379.03		6.83619.89399.9075.55524.700
672397Promissao BrazilSA0.080,
342.73		7.41513.39384.00−49.782−21.296
68282ArrowCanadaNA0.17,
351.07		10.30504.82439.30−117.77949.341
692382Agua Vermelha BrazilSA0.056,
351.62		11.03563.15383.30−50.345−19.867
704898Hirakud IndiaAS0.022,
177.26		5.38669.62192.0283.85521.520
713041Kossour Ivory CoastAF0.034,
169.78		27.681058.20206.00−5.4747.033
724784KureiskayaRussiaAS0.050,
67.89		9.96558.0095.6388.28766.950
733071StorsjonSwedenEU0.0042,
291.09		8.02484.60293.1314.47563.301
74316Flathead LakeUnited States NA0.13,
816.09		23.20510.00883.61−114.23347.677
752004KemptCanadaNA0.033,
478.60		2.22470.44494.18−70.52950.657
766700Kolyma damRussiaAS0.14,
390.91		15.08454.60453.00150.23062.055
774501Mtera TanzaniaAF0.022,
688.05		3.20478.83698.5035.984−7.136
784686KayrakkumskoyeTajikistanAS0.021,
335.24		4.20513.00346.2369.81740.279
79250KinbasketCanadaNA0.32,
622.77		24.76430.00759.15−118.57052.079
804634MingechaurskoyeAzerbaijanAS0.072,
42.02		15.73567.9783.0047.02540.795
812431Lago del Río YguazúParaguaySA0.045,
203.13		8.47620.00231.14−54.970−25.374
824858RihandIndiaAS0.062,
241.75		5.65426.36268.2283.00524.202
834422Keban BarajiTurkeyAS0.11,
772.51		30.60675.00848.7938.75938.808
842340Los BarrealesArgentinaSA0.31,
290.07		27.70413.00417.11−68.691−38.577
854859Ban SagarIndiaAS0.051,
317.64		5.17471.60341.6481.28824.191
861275Sam Rayburn United States NA0.036,
35.66		3.55455.6450.11−94.10831.066
872414Barra BonitaBrazilSA0.0023,
565.25		7.01542.00566.48−49.229−23.213
884739UkaiIndiaAS0.042,
83.60		6.62509.85105.1673.59721.258
89479Utah United States NA0.023,
1359.51		1.07380.001368.28−111.89240.359
90305Pend Oreille United States NA0.23,
541.66		54.20381.47628.80−116.99848.179
914994TungabhadraIndiaAS0.041,
483.34		3.28349.42497.7476.33015.266
924461Mosul IraqAS0.16,
273.38		11.10353.16330.0042.82536.633
934470HabbaniyahIraqAS0.071,
114.62		8.20418.40144.4342.35034.212
944946Sriram Sagar IndiaAS0.040,
319.95		2.30314.38332.5478.34218.967
952376Lago das BrisasBrazilSA0.088,
471.03		17.00559.60520.38−49.097−18.408
962356MeelpaegCanadaNA0.0041,
269.36		2.16314.90270.65−56.78048.166
974260Hendrik VerwoerdSouth AfricaAF0.069,
1236.10		5.34374.001261.9325.505−30.621
981387MalpasoMexicoNA0.30,
89.06		9.17309.45182.00−93.60017.179
991379InhernilloMexicoNA0.14,
116.66		12.00400.00173.13−101.89218.272
1004184VaaldamSouth AfricaAF0.036,
1472.82		2.61320.001484.2728.115−26.883
1015062LongyangxiaChinaAS0.18,
2518.98		24.70383.002589.15100.91736.121
1023727HoytiainenFinlandEU0.0064,
86.17		2.39293.0088.0529.47562.825
1031423BaskatongCanadaNA0.057,
207.29		2.63280.00223.14−75.98346.725
1045803Tri An LakeVietnamAS0.072,
39.48		2.76323.0062.79107.03511.108
1052007PeribonkaCanadaNA0.11,
411.54		5.18270.72440.26−71.25549.904
1064942JayakwadiIndiaAS0.032,
451.67		2.17382.39463.9175.36719.487
1073638ShiroroNigeriaAF0.086,
350.90		7.00312.00377.736.8379.972
1084379TshchikskoyeRussiaEU0.062,
16.04		3.05286.2833.6839.11644.987
109710TobinCanadaNA0.0090,
311.23		2.20263.86313.59−103.40453.660
1105796NoiThailandAS0.057,
129.50		1.97288.00145.94105.43015.206
1114483Roseires SudanAF0.025,
475.84		7.40450.00487.1234.39011.800
1124675Toktogul’skoyeKyrgyzstanAS0.55,
743.53		19.50284.30901.2472.65341.683
1136698GordonAustraliaOC0.37,
208.54		12.40278.00311.42145.979−42.728
1144964UjaniIndiaAS0.055,
482.17		1.52268.91496.8375.12018.071
1152312HondoArgentinaSA0.029,
266.72		1.74330.00276.36−64.887−27.524
1164362Ivankovo ReservoirRussiaEU0.018,
119.51		1.17220.57123.4737.12156.735
1174702TarbelaPakistanAS0.53,
351.46		13.69250.00483.5572.69134.091
1184985NagarjunaIndiaAS0.29,
100.78		6.84272.18179.8379.30916.575
1193070KallsjonSwedenEU0.028,
387.52		6.14189.74392.8013.34263.433
1204431KarakayaTurkeyAS0.22,
631.76		9.50298.00697.5439.13538.229
1214792BeasIndiaAS0.20,
371.49		6.16254.85423.6775.94931.975
1224047TshangaleleCongoAF0.031,
1119.03		1.06225.651126.0327.244−10.753
1234485FinchaaEthiopiaAF0.019,
2216.55		0.65196.132220.2637.3639.558
1244989AlmattiIndiaAS0.053,
504.12		3.11293.42519.6075.88816.333
1254707ManglaPakistanAS0.20,
320.13		9.12251.00370.6073.64333.145
1264836Ranapratap SagarIndiaAS0.14,
324.74		1.44197.66352.8175.58024.916
1273014BagreBurkina FasoAF0.057,
223.54		1.70255.00238.12−0.55411.475
1281991JuninPeruSA0.023,
4079.84		1.08206.714084.62−76.191−10.980
1294881Bargi IndiaAS0.085,
401.51		3.18236.24422.7679.92822.945
1306686Great LakeAustraliaOC0.40,
969.53		3.36176.001040.54146.730−41.980
1316800HaweaNew ZealandOC0.15,
323.54		2.18150.00345.49169.250−44.609
1323676Albufeira da QuiminhaAngolaAF0.13,
34.99		1.56129.0551.9313.790−8.963
1336629EucumbeneAustraliaOC0.46,
1097.65		4.80145.421165.24148.617−36.125
1341320Falcon United States NA0.070,
71.74		3.88311.8493.48−99.17026.562
135597PowellUnited States NA0.14,
1047.20		30.00609.381127.76−111.48636.941
1364463DukanIraqAS0.19,
462.68		6.97270.00513.6944.95535.958
1371230Cedar Creek United States NA0.094,
85.92		0.80133.0398.15−96.07532.183
1384041MagaCameroonAF0.019,
309.63		0.68148.72312.5015.05010.829
1395157Pasak ChonlasitThailandAS0.053,
33.59		0.79158.8742.00101.08414.854
1406594FairbairnAustraliaOC0.13,
186.48		2.29179.43209.81148.063−23.650
1416628HumeAustraliaOC0.15,
161.82		3.04201.90192.00147.033−36.108
1424500KikuletwaTanzaniaAF0.10,
677.01		0.60126.33689.6537.468−3.821
1434958Nizam SagarIndiaAS0.089,
419.96		0.5092.75428.2477.92918.203
1446606VictoriaAustraliaOC0.17,
7.53		0.68122.0027.73141.275−34.042
1451869GrenadaUnited States NA0.13,
49.35		1.54128.2965.53−89.77033.816
146138CanyonUnited States NA0.69,
1300.94		1.61108.391373.12−121.09140.179
1474638Aras AzerbaijanAS0.12,
762.77		1.35145.00779.9445.40039.091
1484481Khashm el-GirbaSudanAF0.093,
463.08		1.30125.00474.7635.90514.925
149370Lake CascadeUnited States NA0.16,
1455.02		0.86101.981471.57−116.05444.525
1503695SeitevareSwedenEU0.63,
419.19		1.6881.00470.1518.57166.975
1514484YardiEthiopiaAF0.33,
533.59		2.32104.87568.2540.53810.233
152119Clear Lake United States NA0.20,
1345.80		0.65100.361365.84−121.07941.926
1535196Guanting ShuikuChinaAS0.11,
465.09		4.16130.00479.09115.60040.233
1542953Barrage Al MassiraMoroccoAF0.34,
241.41		2.7680.00268.54−7.63732.475
1551319Venustiano CarranzaMexicoNA0.095,
252.29		1.31150.56266.53−100.61627.512
1564471HamrinIraqAS0.12,
80.23		4.61228.00107.5044.96734.116
1574826MatatilaIndiaAS0.10,
297.22		0.71112.07308.4678.37125.099
1581263Twin ButtesUnited States NA0.50,
576.78		0.2329.47591.37−100.52531.370
1594997SomasilaIndiaAS0.17,
74.32		1.99153.17100.5879.30514.489
1605183Hongshan ChinaAS0.23,
422.08		2.5666.90437.64119.69642.751
1616583RossAustraliaOC0.11,
32.61		0.8082.0041.77146.738−19.411
1624978Yeleru IndiaAS0.59,
57.51		0.5149.3686.5682.08417.304
1634696South Surkhan UzbekistanAS0.34,
397.80		0.8040.26411.4167.63237.829
1645287Zhaopingtai ChinaAS0.0045,
312.77		0.7146.50174.27112.77133.733
* The 164 reservoirs include 13 regulated natural lakes, whose IDs are 1, 6, 20, 23, 33, 37, 44, 62, 73, 102, 131, 150, and 151. 1 a and b are the coefficients used in the A–E relationship equation, h = a * A + b, where h and A are elevation (m) and area (km2), respectively. 2 Vc, Ac, and Ec represent storage, area, and elevation values at capacity, respectively.

Appendix B

Remotesensing 13 00565 g0a1 550
Figure A1. Validation of monthly elevation products for twelve Indian reservoirs from 2000 to 2019.
Figure A1. Validation of monthly elevation products for twelve Indian reservoirs from 2000 to 2019.
Remotesensing 13 00565 g0a1

Appendix C

Remotesensing 13 00565 g0a2 550
Figure A2. Validation of monthly storage products for twelve Indian reservoirs from 2000 to 2019.
Figure A2. Validation of monthly storage products for twelve Indian reservoirs from 2000 to 2019.
Remotesensing 13 00565 g0a2

Appendix D

Remotesensing 13 00565 g0a3 550
Figure A3. Comparisons of evaporation rates calculated from different datasets (GLDAS + MxD21 and TerraClimate + MxD11) for (a) Lake Nasser and (b) Lake Mead.
Figure A3. Comparisons of evaporation rates calculated from different datasets (GLDAS + MxD21 and TerraClimate + MxD11) for (a) Lake Nasser and (b) Lake Mead.
Remotesensing 13 00565 g0a3

References

  1. Biemans, H.; Haddeland, I.; Kabat, P.; Ludwig, F.; Hutjes, R.; Heinke, J.; Von Bloh, W.; Gerten, D. Impact of reservoirs on river discharge and irrigation water supply during the 20th century. Water Resour. Res. 2011, 47. [Google Scholar] [CrossRef] [Green Version]
  2. Plate, E.J. Flood risk and flood management. J. Hydrol. 2002, 267, 2–11. [Google Scholar] [CrossRef]
  3. Cooke, G.D.; Welch, E.B.; Peterson, S.; Nichols, S.A. Restoration and Management of Lakes and Reservoirs; CRC Press: Boca Raton, FL, USA, 2016. [Google Scholar]
  4. Veldkamp, T.; Wada, Y.; Aerts, J.; Döll, P.; Gosling, S.N.; Liu, J.; Masaki, Y.; Oki, T.; Ostberg, S.; Pokhrel, Y. Water scarcity hotspots travel downstream due to human interventions in the 20th and 21st century. Nat. Commun. 2017, 8, 15697. [Google Scholar] [CrossRef] [PubMed]
  5. Schewe, J.; Heinke, J.; Gerten, D.; Haddeland, I.; Arnell, N.W.; Clark, D.B.; Dankers, R.; Eisner, S.; Fekete, B.M.; Colón-González, F.J. Multimodel assessment of water scarcity under climate change. Proc. Natl. Acad. Sci. USA 2014, 111, 3245–3250. [Google Scholar] [CrossRef] [Green Version]
  6. Murdock, H.E.; Gibb, D.; André, T.; Appavou, F.; Brown, A.; Epp, B.; Kondev, B.; McCrone, A.; Musolino, E.; Ranalder, L. Renewables 2019 Global Status Report; REN21 Secretariat: Paris, France, 2019; Available online: https://www.ren21.net/wp-content/uploads/2019/05/gsr_2019_full_report_en.pdf (accessed on 11 November 2020).
  7. Mehran, A.; Mazdiyasni, O.; AghaKouchak, A. A hybrid framework for assessing socioeconomic drought: Linking climate variability, local resilience, and demand. J. Geophys. Res. Atmos. 2015, 120, 7520–7533. [Google Scholar] [CrossRef]
  8. Zhou, Y. Exploring multidecadal changes in climate and reservoir storage for assessing nonstationarity in flood peaks and risks worldwide by an integrated frequency analysis approach. Water Res. 2020, 185, 116265. [Google Scholar] [CrossRef] [PubMed]
  9. Zhou, T.; Nijssen, B.; Gao, H.; Lettenmaier, D.P. The contribution of reservoirs to global land surface water storage variations. J. Hydrometeorol. 2016, 17, 309–325. [Google Scholar] [CrossRef]
  10. Yigzaw, W.; Li, H.Y.; Demissie, Y.; Hejazi, M.I.; Leung, L.R.; Voisin, N.; Payn, R. A New Global Storage-Area-Depth Dataset for Modeling Reservoirs in Land Surface and Earth System Models. Water Resour. Res. 2018, 54, 10–372. [Google Scholar] [CrossRef]
  11. Friedrich, K.; Grossman, R.L.; Huntington, J.; Blanken, P.D.; Lenters, J.; Holman, K.D.; Gochis, D.; Livneh, B.; Prairie, J.; Skeie, E. Reservoir evaporation in the Western United States: Current science, challenges, and future needs. Bull. Am. Meteorol. Soc. 2018, 99, 167–187. [Google Scholar] [CrossRef]
  12. Moreo, M. Evaporation data from Lake Mead and Lake Mohave, Nevada and Arizona, March 2010 through April 2015. US Geol. Surv. 2015. [Google Scholar] [CrossRef]
  13. Mu, Q.; Zhao, M.; Running, S.W. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ. 2011, 115, 1781–1800. [Google Scholar] [CrossRef]
  14. Zhang, H.; Gorelick, S.M.; Zimba, P.V.; Zhang, X. A remote sensing method for estimating regional reservoir area and evaporative loss. J. Hydrol. 2017, 555, 213–227. [Google Scholar] [CrossRef]
  15. Birkett, C. The contribution of TOPEX/POSEIDON to the global monitoring of climatically sensitive lakes. J. Geophys. Res. Ocean. 1995, 100, 25179–25204. [Google Scholar] [CrossRef]
  16. Birkett, C.; Reynolds, C.; Beckley, B.; Doorn, B. From research to operations: The USDA global reservoir and lake monitor. In Coastal Altimetry; Springer: Berlin/Heidelberg, Germany, 2011; pp. 19–50. [Google Scholar]
  17. Crétaux, J.-F.; Jelinski, W.; Calmant, S.; Kouraev, A.; Vuglinski, V.; Bergé-Nguyen, M.; Gennero, M.-C.; Nino, F.; Del Rio, R.A.; Cazenave, A. SOLS: A lake database to monitor in the Neac r Real Time water level and storage variations from remote sensing data. Adv. Space Res. 2011, 47, 1497–1507. [Google Scholar] [CrossRef]
  18. Schwatke, C.; Dettmering, D.; Bosch, W.; Seitz, F. DAHITI–an innovative approach for estimating water level time series over inland waters using multi-mission satellite altimetry. Hydrol. Earth Syst. Sci. 2015, 19, 4345–4364. [Google Scholar] [CrossRef] [Green Version]
  19. Pekel, J.-F.; Cottam, A.; Gorelick, N.; Belward, A.S. High-resolution mapping of global surface water and its long-term changes. Nature 2016, 540, 418–422. [Google Scholar] [CrossRef] [PubMed]
  20. Zhao, G.; Gao, H. Automatic correction of contaminated images for assessment of reservoir surface area dynamics. Geophys. Res. Lett. 2018, 45, 6092–6099. [Google Scholar] [CrossRef]
  21. Khandelwal, A.; Karpatne, A.; Marlier, M.E.; Kim, J.; Lettenmaier, D.P.; Kumar, V. An approach for global monitoring of surface water extent variations in reservoirs using MODIS data. Remote Sens. Environ. 2017, 202, 113–128. [Google Scholar] [CrossRef]
  22. Donchyts, G.; Baart, F.; Winsemius, H.; Gorelick, N.; Kwadijk, J.; Van De Giesen, N. Earth’s surface water change over the past 30 years. Nat. Clim. Chang. 2016, 6, 810. [Google Scholar] [CrossRef]
  23. Yao, F.; Wang, J.; Wang, C.; Crétaux, J.-F. Constructing long-term high-frequency time series of global lake and reservoir areas using Landsat imagery. Remote. Sens. Environ. 2019, 232, 111210. [Google Scholar] [CrossRef]
  24. Ling, F.; Li, X.; Foody, G.M.; Boyd, D.; Ge, Y.; Li, X.; Du, Y. Monitoring surface water area variations of reservoirs using daily MODIS images by exploring sub-pixel information. ISPRS-J. Photogramm. Remote Sens. 2020, 168, 141–152. [Google Scholar] [CrossRef]
  25. Gao, H.; Birkett, C.; Lettenmaier, D.P. Global monitoring of large reservoir storage from satellite remote sensing. Water Resour. Res. 2012, 48. [Google Scholar] [CrossRef] [Green Version]
  26. Zhang, S.; Gao, H.; Naz, B.S. Monitoring reservoir storage in South Asia from multisatellite remote sensing. Water Resour. Res. 2014, 50, 8927–8943. [Google Scholar] [CrossRef]
  27. Busker, T.; de Roo, A.; Gelati, E.; Schwatke, C.; Adamovic, M.; Bisselink, B.; Pekel, J.F.; Cottam, A. A global lake and reservoir volume analysis using a surface water dataset and satellite altimetry. Hydrol. Earth Syst. Sci. 2019, 23, 669–690. [Google Scholar] [CrossRef] [Green Version]
  28. Zhao, G.; Gao, H. Estimating reservoir evaporation losses for the United States: Fusing remote sensing and modeling approaches. Remote Sens. Environ. 2019, 226, 109–124. [Google Scholar] [CrossRef] [Green Version]
  29. Zhao, G.; Gao, H.; Cai, X. Estimating lake temperature profile and evaporation losses by leveraging MODIS LST data. Remote Sens. Environ. 2020, 251, 112104. [Google Scholar] [CrossRef]
  30. Althoff, D.; Rodrigues, L.N.; da Silva, D.D. Evaluating evaporation methods for estimating small reservoir water surface evaporation in the Brazilian savannah. Water 2019, 11, 1942. [Google Scholar] [CrossRef] [Green Version]
  31. Mhawej, M.; Fadel, A.; Faour, G. Evaporation rates in a vital lake: A 34-year assessment for the Karaoun Lake. Int. J. Remote Sens. 2020, 41, 5321–5337. [Google Scholar] [CrossRef]
  32. Meng, X.; Liu, H.; Du, Q.; Xu, L.; Liu, Y. Evaluation of the Performance of Different Methods for Estimating Evaporation over a Highland Open Freshwater Lake in Mountainous Area. Water 2020, 12, 3491. [Google Scholar] [CrossRef]
  33. Li, Y.; Gao, H.; Zhao, G.; Tseng, K.-H. A high-resolution bathymetry dataset for global reservoirs using multi-source satellite imagery and altimetry. Remote Sens. Environ. 2020, 244, 111831. [Google Scholar] [CrossRef]
  34. Lehner, B.; Reidy Liermann, C.; Revenga, C.; Vorosmarty, C.; Fekete, B.; Crouzet, P.; Doll, P.; Endejan, M.; Frenken, K.; Magome, J.; et al. Global Reservoir and Dam Database, Version 1 (GRanDv1): Dams, Revision 01; NASA Socioeconomic Data and Applications Center (SEDAC): Palisades, NY, USA, 2011. [Google Scholar]
  35. Vermote, E. MOD09Q1 MODIS/Terra Surface Reflectance 8-Day L3 Global 250m SIN Grid V006. NASA EOSDIS Land Process. DAAC 2015. Available online: https://lpdaac.usgs.gov/products/mod09q1v006/ (accessed on 1 February 2021).
  36. Vermote, E.; Franch, B.; Claverie, M. VIIRS/NPP Surface Reflectance 8-Day L3 Global 500m SIN Grid V001. NASA EOSDIS Land Process. DAAC 2016. Available online: https://lpdaac.usgs.gov/products/vnp09h1v001/ (accessed on 1 February 2021).
  37. McFeeters, S.K. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. Int. J. Remote Sens. 1996, 17, 1425–1432. [Google Scholar] [CrossRef]
  38. Hulley, G.; Hook, S. MOD21 MODIS/Terra Land Surface Temperature/3-Band Emissivity 5-Min L2 1km V006. NASA EOSDIS Land Process. DAAC 2017. Available online: https://lpdaac.usgs.gov/products/mod21v006/ (accessed on 1 February 2021).
  39. Hulley, G.; Hook, S. VIIRS/NPP Land Surface Temperature and Emissivity 6-Min L2 Swath 750m V001. NASA EOSDIS Land Process. DAAC 2018. Available online: https://lpdaac.usgs.gov/products/vnp21v001/ (accessed on 1 February 2021).
  40. Rodell, M.; Houser, P.; Jambor, U.; Gottschalck, J.; Mitchell, K.; Meng, C.-J.; Arsenault, K.; Cosgrove, B.; Radakovich, J.; Bosilovich, M. The global land data assimilation system. Bull. Am. Meteorol. Soc. 2004, 85, 381–394. [Google Scholar] [CrossRef] [Green Version]
  41. Zhao, G.; Li, Y.; Zhang, S.; Shah, D.; Gao, H. Collection 6.1 MODIS Reservoir Product Algorithm Theoretical Basis Document (ATBD) Version 1.0. NASA GSFC 2021. Available online: https://modis-land.gsfc.nasa.gov/modgwr.html (accessed on 1 February 2021).
  42. Gorelick, N.; Hancher, M.; Dixon, M.; Ilyushchenko, S.; Thau, D.; Moore, R. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 2017, 202, 18–27. [Google Scholar] [CrossRef]
  43. Messager, M.L.; Lehner, B.; Grill, G.; Nedeva, I.; Schmitt, O. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nat. Commun. 2016, 7, 13603. [Google Scholar] [CrossRef] [PubMed]
  44. Otsu, N. A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 1979, 9, 62–66. [Google Scholar] [CrossRef] [Green Version]
  45. Elsawwaf, M.; Willems, P.; Pagano, A.; Berlamont, J. Evaporation estimates from Nasser Lake, Egypt, based on three floating station data and Bowen ratio energy budget. Theor. Appl. Climatol. 2010, 100, 439–465. [Google Scholar] [CrossRef] [Green Version]
  46. Lenters, J.D.; Kratz, T.K.; Bowser, C.J. Effects of climate variability on lake evaporation: Results from a long-term energy budget study of Sparkling Lake, northern Wisconsin (USA). J. Hydrol. 2005, 308, 168–195. [Google Scholar] [CrossRef]
  47. Liu, Y.; Yu, Y.; Yu, P.; Göttsche, F.M.; Trigo, I.F. Quality assessment of S-NPP VIIRS land surface temperature product. Remote Sens. 2015, 7, 12215–12241. [Google Scholar] [CrossRef] [Green Version]
  48. Li, Y.; Gao, H.; Jasinski, M.F.; Zhang, S.; Stoll, J.D. Deriving High-Resolution Reservoir Bathymetry from ICESat-2 Prototype Photon-Counting Lidar and Landsat Imagery. IEEE Trans. Geosci. Remote Sens. 2019, 57, 7883–7893. [Google Scholar] [CrossRef]
  49. Gao, H.; Zhang, S.; Durand, M.; Lee, H. Satellite remote sensing of lakes and wetlands. In Hydrologic Remote Sensing; CRC Press: Boca Raton, FL, USA, 2016; pp. 57–72. [Google Scholar]
  50. McJannet, D.L.; Webster, I.T.; Cook, F.J. An area-dependent wind function for estimating open water evaporation using land-based meteorological data. Environ. Model. Softw. 2012, 31, 76–83. [Google Scholar] [CrossRef]
  51. Schwab, D.J.; Morton, J.A. Estimation of overlake wind speed from overland wind speed: A comparison of three methods. J. Great Lakes Res. 1984, 10, 68–72. [Google Scholar] [CrossRef]
  52. Lee, Z.P.; Darecki, M.; Carder, K.L.; Davis, C.O.; Stramski, D.; Rhea, W.J. Diffuse attenuation coefficient of downwelling irradiance: An evaluation of remote sensing methods. J. Geophys. Res. Oceans 2005, 110. [Google Scholar] [CrossRef]
  53. Pinhassi, J.; DeLong, E.F.; Béjà, O.; González, J.M.; Pedrós-Alió, C. Marine bacterial and archaeal ion-pumping rhodopsins: Genetic diversity, physiology, and ecology. Microbiol. Mol. Biol. Rev. 2016, 80, 929–954. [Google Scholar] [CrossRef] [Green Version]
  54. Zhang, S.; Gao, H. Using the Digital Elevation Model (DEM) to improve the spatial coverage of the MODIS based reservoir monitoring network in South Asia. Remote Sens. 2020, 12, 745. [Google Scholar] [CrossRef] [Green Version]
  55. Li, Y.; Gao, H.; Allen, G.H.; Zhang, Z. Constructing Reservoir Area-Volume-Elevation Curve from TanDEM-X DEM Data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2021. [Google Scholar] [CrossRef]
  56. Biancamaria, S.; Lettenmaier, D.P.; Pavelsky, T.M. The SWOT mission and its capabilities for land hydrology. Surv. Geophys. 2016, 37, 117–147. [Google Scholar] [CrossRef] [Green Version]
  57. Getirana, A.; Jung, H.C.; Van Den Hoek, J.; Ndehedehe, C.E. Hydropower dam operation strongly controls Lake Victoria’s freshwater storage variability. Sci. Total Environ. 2020, 726, 138343. [Google Scholar] [CrossRef] [PubMed]
  58. Shah, H.L.; Zhou, T.; Sun, N.; Huang, M.; Mishra, V. Roles of Irrigation and Reservoir Operations in Modulating Terrestrial Water and Energy Budgets in the Indian Subcontinental River Basins. J. Geophys. Res. Atmos. 2019, 124, 12915–12936. [Google Scholar] [CrossRef]
  59. Udall, B.; Overpeck, J. The twenty-first century Colorado River hot drought and implications for the future. Water Resour. Res. 2017, 53, 2404–2418. [Google Scholar] [CrossRef] [Green Version]
  60. Van Loon, A.F. Hydrological drought explained. Wiley Interdiscip. Rev. Water 2015, 2, 359–392. [Google Scholar] [CrossRef]
  61. Shah, D.; Mishra, V. Drought onset and termination in India. J. Geophys. Res. Atmos. 2020, 125, e2020JD032871. [Google Scholar] [CrossRef]
  62. Shah, D.; Mishra, V. Integrated Drought Index (IDI) for drought monitoring and assessment in India. Water Resour. Res. 2020, 56, e2019WR026284. [Google Scholar] [CrossRef]
  63. Konapala, G.; Mishra, A. Quantifying climate and catchment control on hydrological drought in the continental United States. Water Resour. Res. 2020, 56, e2018WR024620. [Google Scholar] [CrossRef]
  64. Wanders, N.; Wada, Y. Human and climate impacts on the 21st century hydrological drought. J. Hydrol. 2015, 526, 208–220. [Google Scholar] [CrossRef]
  65. Tallaksen, L.M.; Van Lanen, H.A. Hydrological Drought: Processes and Estimation Methods for Streamflow and Groundwater; Elsevier: Amsterdam, The Netherlands, 2004; Volume 48. [Google Scholar]
  66. Yang, X.; Zhang, M.; He, X.; Ren, L.; Pan, M.; Yu, X.; Wei, Z.; Sheffield, J. Contrasting Influences of Human Activities on Hydrological Drought Regimes Over ChinaBased on High-Resolution Simulations. Water Resour. Res. 2020, 56, e2019WR025843. [Google Scholar] [CrossRef]
  67. Wada, Y.; Bierkens, M.F.; De Roo, A.; Dirmeyer, P.A.; Famiglietti, J.S.; Hanasaki, N.; Konar, M.; Liu, J.; Schmied, H.M.; Oki, T. Human-water interface in hydrological modelling: Current status and future directions. Hydrol. Earth Syst. Sci. 2017, 21, 4169–4193. [Google Scholar] [CrossRef] [Green Version]
Remotesensing 13 00565 g001 550
Figure 1. Locations of the 151 man-made reservoirs (in red and blue) and 13 regulated natural lakes (in yellow) are contained in this product. The reservoirs labeled in blue are used for validation purposes.
Figure 1. Locations of the 151 man-made reservoirs (in red and blue) and 13 regulated natural lakes (in yellow) are contained in this product. The reservoirs labeled in blue are used for validation purposes.
Remotesensing 13 00565 g001
Remotesensing 13 00565 g002 550
Figure 2. Flow chart of the algorithm for deriving the MxD28C2 product, which contains 8-day area, elevation, and storage results for the 164 reservoirs. The green boxes represent the product components.
Figure 2. Flow chart of the algorithm for deriving the MxD28C2 product, which contains 8-day area, elevation, and storage results for the 164 reservoirs. The green boxes represent the product components.
Remotesensing 13 00565 g002
Remotesensing 13 00565 g003 550
Figure 3. A demonstration of the water enhancement algorithm using the Cahora Bassa reservoir (Mozambique, Africa) on 1 January 2007, as an example: (a) original Near Infrared (NIR) image, (b) clear areas in NIR extruding the cloud contaminations, (c) water areas extracted from image (b), (d) water occurrence (percentile) image, (e) percentile threshold used for enhancement, and (f) water areas after enhancement.
Figure 3. A demonstration of the water enhancement algorithm using the Cahora Bassa reservoir (Mozambique, Africa) on 1 January 2007, as an example: (a) original Near Infrared (NIR) image, (b) clear areas in NIR extruding the cloud contaminations, (c) water areas extracted from image (b), (d) water occurrence (percentile) image, (e) percentile threshold used for enhancement, and (f) water areas after enhancement.
Remotesensing 13 00565 g003
Remotesensing 13 00565 g004 550
Figure 4. Flow chart of the algorithm for deriving the MxD28C3 product, which contains monthly area, elevation, storage, evaporation rate, and volumetric evaporation loss results for the 164 reservoirs. The green boxes represent the product components.
Figure 4. Flow chart of the algorithm for deriving the MxD28C3 product, which contains monthly area, elevation, storage, evaporation rate, and volumetric evaporation loss results for the 164 reservoirs. The green boxes represent the product components.
Remotesensing 13 00565 g004
Remotesensing 13 00565 g005 550
Figure 5. The density plot of monthly area estimations between Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) from February 2000 to December 2018 for the 164 reservoirs. Note that the x-axis and y-axis use a logarithmic scale, and there are a total of 37,228 (227 × 164) pairs.
Figure 5. The density plot of monthly area estimations between Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) from February 2000 to December 2018 for the 164 reservoirs. Note that the x-axis and y-axis use a logarithmic scale, and there are a total of 37,228 (227 × 164) pairs.
Remotesensing 13 00565 g005
Remotesensing 13 00565 g006 550
Figure 6. Validation of MODIS 8-day elevation products for twelve Indian reservoirs from 2000 to 2019.
Figure 6. Validation of MODIS 8-day elevation products for twelve Indian reservoirs from 2000 to 2019.
Remotesensing 13 00565 g006
Remotesensing 13 00565 g007 550
Figure 7. Validation of MODIS 8-day storage products for twelve Indian reservoirs from 2000 to 2019.
Figure 7. Validation of MODIS 8-day storage products for twelve Indian reservoirs from 2000 to 2019.
Remotesensing 13 00565 g007
Remotesensing 13 00565 g008 550
Figure 8. Validation of the evaporation rates for (a) Lake Nasser and (b) Lake Mead using Bowen ratio energy budget (BREB) estimations and eddy covariance (EC) measurements. The evaporation rate for Lake Mead (from 2010 to 2015) was calculated using MYD21A2 land surface temperature (LST) data because the current version of MOD21A2 only covers the years from 2000 to 2005 (when acquired in November 2020). The evaporation rate for Lake Nasser (from 2000 to 2004) was calculated using MOD21A2 because the MYD21A2 product started from July 2002.
Figure 8. Validation of the evaporation rates for (a) Lake Nasser and (b) Lake Mead using Bowen ratio energy budget (BREB) estimations and eddy covariance (EC) measurements. The evaporation rate for Lake Mead (from 2010 to 2015) was calculated using MYD21A2 land surface temperature (LST) data because the current version of MOD21A2 only covers the years from 2000 to 2005 (when acquired in November 2020). The evaporation rate for Lake Nasser (from 2000 to 2004) was calculated using MOD21A2 because the MYD21A2 product started from July 2002.
Remotesensing 13 00565 g008
Remotesensing 13 00565 g009 550
Figure 9. Comparison of 8-day area values between MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) from January 2012 to August 2020.
Figure 9. Comparison of 8-day area values between MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) from January 2012 to August 2020.
Remotesensing 13 00565 g009
Remotesensing 13 00565 g010 550
Figure 10. Comparison of monthly evaporation rates for Lake Mead between MODIS and VIIRS.
Figure 10. Comparison of monthly evaporation rates for Lake Mead between MODIS and VIIRS.
Remotesensing 13 00565 g010
Remotesensing 13 00565 g011 550
Figure 11. Comparison of reservoir coverage and temporal resolution of elevation measurements between satellite radar altimetry products (i.e., Hydroweb and G-REALM) and MODIS/VIIRS based products.
Figure 11. Comparison of reservoir coverage and temporal resolution of elevation measurements between satellite radar altimetry products (i.e., Hydroweb and G-REALM) and MODIS/VIIRS based products.
Remotesensing 13 00565 g011
Remotesensing 13 00565 g012 550
Figure 12. Comparison of elevation time series values between radar altimetry products (Hydroweb and G-REALM) and the MODIS-based product. Note that the elevation measurements provided by G-REALM are not available between 2002 and 2008 for the Qapshaghay Bogeni, Mosul, and Powell reservoirs due to a data gap. These are filled in by linear interpolation.
Figure 12. Comparison of elevation time series values between radar altimetry products (Hydroweb and G-REALM) and the MODIS-based product. Note that the elevation measurements provided by G-REALM are not available between 2002 and 2008 for the Qapshaghay Bogeni, Mosul, and Powell reservoirs due to a data gap. These are filled in by linear interpolation.
Remotesensing 13 00565 g012
Remotesensing 13 00565 g013 550
Figure 13. Total reservoir storage variations in the (a) Colorado and (b) Murray-Darling river basins.
Figure 13. Total reservoir storage variations in the (a) Colorado and (b) Murray-Darling river basins.
Remotesensing 13 00565 g013
Table
Table 1. Distribution and attributes of the 151 man-made reservoirs in this product.
Table 1. Distribution and attributes of the 151 man-made reservoirs in this product.
Number of ReservoirsArea 1 (km2)Storage 1 (km3)Storage pct. (%)
Global151134,137267245.82
Africa (AF)2130,04363676.61
Asia (AS)5336,03678235.87
Europe (EU)1010,19110220.59
North America (NA)3831,36061947.37
Oceania (OC)821663845.77
South America (SA)2124,34049552.92
1 The area and storage columns correspond to the values at capacity. “Storage pct.” represents the percentage of storage included in our product with respect to the total storage capacity (referring to Global Reservoir and Dam Database (GRanD)) of the corresponding geographical domain.
Table
Table 2. Overview of the reservoir product suite.
Table 2. Overview of the reservoir product suite.
SensorProductMeasured Parameters Temporal ResolutionTime Span
MODISMxD28C2Area, elevation, storage8-day2000 to present
MODISMxD28C3Area, elevation, storage, evaporation rate and volumeMonthly2000 to present
VIIRSVNP28C2Area, elevation, storage8-day2012 to present
VIIRSVNP28C3Area, elevation, storage, evaporation rate and volumeMonthly 2012 to present
Table
Table 3. Summary of the input data names, sources, and purposes used in this product.
Table 3. Summary of the input data names, sources, and purposes used in this product.
Input Data NameSourcePurpose
8-day MODIS (Terra/Aqua) surface reflectanceMxD09Q1Area
8-day VIIRS (NPP) surface reflectanceVNP09H1Area
Area–Elevation (A–E) relationshipGRBDElevation and storage
8-day MODIS (Terra/Aqua) land surface temperature 1MxD21A2Evaporation rate
8-day VIIRS (NPP) land surface temperature 1VNP21A2Evaporation rate
Meteorological dataGLDASEvaporation rate
1 The land surface temperature contains day/night surface temperature for inland water areas.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Li, Y.; Zhao, G.; Shah, D.; Zhao, M.; Sarkar, S.; Devadiga, S.; Zhao, B.; Zhang, S.; Gao, H. NASA’s MODIS/VIIRS Global Water Reservoir Product Suite from Moderate Resolution Remote Sensing Data. Remote Sens. 2021, 13, 565. https://doi.org/10.3390/rs13040565

AMA Style

Li Y, Zhao G, Shah D, Zhao M, Sarkar S, Devadiga S, Zhao B, Zhang S, Gao H. NASA’s MODIS/VIIRS Global Water Reservoir Product Suite from Moderate Resolution Remote Sensing Data. Remote Sensing. 2021; 13(4):565. https://doi.org/10.3390/rs13040565

Chicago/Turabian Style

Li, Yao, Gang Zhao, Deep Shah, Maosheng Zhao, Sudipta Sarkar, Sadashiva Devadiga, Bingjie Zhao, Shuai Zhang, and Huilin Gao. 2021. "NASA’s MODIS/VIIRS Global Water Reservoir Product Suite from Moderate Resolution Remote Sensing Data" Remote Sensing 13, no. 4: 565. https://doi.org/10.3390/rs13040565

APA Style

Li, Y., Zhao, G., Shah, D., Zhao, M., Sarkar, S., Devadiga, S., Zhao, B., Zhang, S., & Gao, H. (2021). NASA’s MODIS/VIIRS Global Water Reservoir Product Suite from Moderate Resolution Remote Sensing Data. Remote Sensing, 13(4), 565. https://doi.org/10.3390/rs13040565

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop