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A Review of Remote Sensing for Environmental Monitoring in China

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  • Ministry of Ecology and Environment

Abstract and Figures

The natural environment is essential for human survival and development since it provides water resources, land resources, biological resources and climate resources etc. As a developing country, China has witnessed a significant change in the natural environment in recent decades; and therefore, monitoring and mastering the status of the environment is of great significance. Due to the characteristics of large-scale and dynamic observation, remote sensing technology has been an indispensable approach for environmental monitoring. This paper reviews the satellite resources, institutions and policies for environmental monitoring in China, and the advances in research and application of remote sensing from five aspects: ecological index retrieval, environmental monitoring in protected areas, rural areas, urban areas and mining areas. The remote sensing models and methods for various types of environmental monitoring, and the specific applications in China are comprehensively summarized. This paper also points out major challenges existing at the current stage: satellite sensor problems, integrated use challenges of datasets, uncertainty in the retrieval process of ecological variables, scaling effect problems, a low degree of automation, the weak ability of forecasting and comprehensive analysis, and a lack of computational power for massive datasets. Finally, the development trend and future directions are put forward to direct the research and application of environmental monitoring and protection in the new era.
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remote sensing
Review
A Review of Remote Sensing for Environmental
Monitoring in China
Jun Li 1, Yanqiu Pei 1, Shaohua Zhao 2,3,*, Rulin Xiao 2,3, Xiao Sang 1and Chengye Zhang 1
1College of Geoscience and Surveying Engineering, China University of Mining and Technology,
Beijing 100083, China; junli@cumtb.edu.cn (J.L.); zqt1800204087g@student.cumtb.edu.cn (Y.P.);
sangxiao@student.cumtb.edu.cn (X.S.); czhang@cumtb.edu.cn (C.Z.)
2Ministry of Ecology and Environment Center for Satellite Application on Ecology and Environment,
Beijing 100094, China; xiaorl@secmep.cn
3State Environmental Protection Key Laboratory of Satellite Remote Sensing, Beijing 100094, China
*Correspondence: zhaosh@secmep.cn
Received: 17 February 2020; Accepted: 30 March 2020; Published: 2 April 2020


Abstract:
The natural environment is essential for human survival and development since it provides
water resources, land resources, biological resources and climate resources etc. As a developing
country, China has witnessed a significant change in the natural environment in recent decades;
and therefore, monitoring and mastering the status of the environment is of great significance. Due to
the characteristics of large-scale and dynamic observation, remote sensing technology has been an
indispensable approach for environmental monitoring. This paper reviews the satellite resources,
institutions and policies for environmental monitoring in China, and the advances in research and
application of remote sensing from five aspects: ecological index retrieval, environmental monitoring
in protected areas, rural areas, urban areas and mining areas. The remote sensing models and
methods for various types of environmental monitoring, and the specific applications in China are
comprehensively summarized. This paper also points out major challenges existing at the current
stage: satellite sensor problems, integrated use challenges of datasets, uncertainty in the retrieval
process of ecological variables, scaling eect problems, a low degree of automation, the weak ability
of forecasting and comprehensive analysis, and a lack of computational power for massive datasets.
Finally, the development trend and future directions are put forward to direct the research and
application of environmental monitoring and protection in the new era.
Keywords:
environmental monitoring; ecological index retrieval; remote sensing technology;
research advance
1. Introduction
Natural environment is a general term for water resources, land resources, biological resources
and climate resources, which aects human beings’ survival and development. It is closely related to
the sustainable development of society and economy. Since the Industrial Revolution, the intensity of
the human use of natural resources has continuously increased with the rapid growth of populations,
and in particular, the overexploitation of resources leading to the deterioration of the environment,
has become greater and greater [
1
]. For example, the consumption of fossil fuels and the deforestation
of forest resources accelerate global climate warming, and further lead to the extinction of biological
species at an unprecedented rate. When the environmental load exceeds the limit that an ecosystem
can bear, the ecosystem would gradually weaken and be exhausted. Therefore, natural environment
monitoring is of great significance for the environmental resource protection and management, and
the survival and development of human beings [2].
Remote Sens. 2020,12, 1130; doi:10.3390/rs12071130 www.mdpi.com/journal/remotesensing
Remote Sens. 2020,12, 1130 2 of 25
The commonly used ground-based monitoring is limited by regions and only suitable for
point-based environmental monitoring in a small region. This method is time-consuming, costly and
labor intensive. Due to the characteristics of large-scale and dynamic observation, remote sensing
sensors are able to quickly acquire the wide spectral information of regional and even global targets,
such that various ecological indicators can be obtained by data modeling and retrieval. As such, remote
sensing has gradually been an indispensable approach to ecological monitoring, especially at a large or
global scale.
As a developing country, China is experiencing a significant change in natural environment, and
remote sensing is playing an important role in environmental monitoring and protection [
3
]. Many
agencies carried out a large amount of work with remote sensing to monitor ecological status and
change. As an example, Ministry of Ecology and Environment Center for Satellite Application on
Ecology and Environment, has conducted a great amount work by using international satellite data
such as Landsat, MODIS and China-launched satellite data such as HJ-1A, Gaofen-1, Gaofen-2 (more
details can be seen in Section 2). They have become operational in many services including ecological
index retrieval, human activity monitoring in protected areas, and environmental damage assessment
and so on, which provides strong support for national environment management and decision-making.
This is very useful for the ocial inspection on ecological and environmental protection, especially
for the Green Shield Action established by seven state ministries. Two typical cases are environment
degradation in the Qilian Mountain National Nature Reserve in Gansu Province and the illegal villa
construction at the northern foot of the Qinling Mountain in Shaanxi Province. In addition, many
studies and literatures documented the progress in many fields, such as ecological index retrieval [
4
6
],
urban and rural area monitoring [
7
11
], and nature reserves monitoring [
12
14
]. However, few studies
have systematically and comprehensively summarized the progress in environmental monitoring by
remote sensing in China in recent decades.
This paper aims at reviewing the satellite resources, institutions and policies for environmental
monitoring in China, and the advances in research and application of remote sensing from five aspects:
ecological index retrieval, environmental monitoring in protected areas, rural areas, urban areas and
mining areas (atmosphere and water environment monitoring will be introduced separately). This
paper also points out major challenges existing in the current stage and puts forward the development
trend and future directions, which is useful to direct the research and application of environmental
monitoring and protection in the new era.
2. Satellite Resources, Institutions and Policies for Environmental Monitoring in China
2.1. Remote Sensing Satellite and Sensor Resources for Environmental Monitoring
Global and regional environmental monitoring relies heavily on remote sensing satellite and
sensors which are capable of quickly collecting spatial and spectral information of large-extent entities
on the Earth’s surface. Table 1lists major satellite sensors used for environmental monitoring in
China in recent decades. The studies on environmental monitoring by remote sensing in China
before the year 2008 were mainly based on the datasets from international satellites, e.g., EOS-Terra,
Envisat, Landsat, Quickbird and SPOT. Since then, China has accelerated scientific and technological
research on satellites and sensors and has established a series of science and technology funding
programs. Among them, a very important program is the High-Resolution Earth Observation Project
(http://www.nmp.gov.cn/zxjs/ddgc/201012/t20101208_2125.htm), which has promoted the launch and
application of Gaofen-1 to Gaofen-6 satellites. The series of Gaofen satellites include high spatial
resolution, high spectral resolution, high temporal resolution and radar sensors. Currently, both
international and domestic satellite datasets have played an important role in environmental monitoring
research in China.
Remote Sens. 2020,12, 1130 3 of 25
Table 1. Major satellite sensors used for environmental monitoring in China.
Satellite Sensor Spectral Range
(µm)
Spatial
Resolution (m)
Revisit
Time (day)
Swath
Width (km)
Launch
Time Country
EOS-Terra/Aqua MODIS 0.62–14.38 250/500/1000 0.5 2330 1999/2002 USA
Aster 0.52–11.65 15/30/90 16 60 1999 Japan
NOAA-TIROS-N
NOAA-7–19 AVHRR 0.55–12.5 1100 6 2800 1978.10–2009.2 USA
Landsat (1–8)
MSS 0.5–1.1 80 18 185 1972.7–1984.3 USA
TM 0.45~2.35 30/60/120 16 185 1982.7–1984.3 USA
ETM+0.45~0.90 15/30/60 16d 185*170 1999.4 USA
OLI 0.433–1.39 15/30/60 16d 170*180 2013.2 USA
TIRS 10.6–11.2
11.5–12.5 100 16d 170*180 2013.2 USA
IKONOS-2 OSA 0.45–0.9 0.82/3.28 1–3 11.3 1999.9 USA
Quickbird BGIS 0.45–0.9 0.61/2.44 1–6 16.5 2001.10 USA
GeoEye GIS 0.45–0.92 0.41/1.65 3 15.2 2008.9 USA
Envisat ASAR C band 10/30/150/1000 35 5/100/400 2002.3 Europe
Sentinel-1 SAR C band
5*20/5*5/5*5/20*40
12 20/80/250/400 2014.4 Europe
Sentinel-2 MSI 0.4~2.4 10/20/60 10d 290 2016.6
2017.3 Europe
SPOT(1–3) HRV 0.50–0.89 10/20 26 60 1986.2 France
SPOT 4 HRVIR 0.50–1.75 10/20 26 60 1998.3 France
VGT 0.45–1.75 1150 26 2250 1998.3 France
SPOT 5 HRG 0.48–1.75 2.5/5/10/20 26 60 2002.5 France
VGT 0.45–1.75 1150 26 2250 2002.5 France
SPOT 6 NAOMI 0.45–0.89 1.5/6 26 60×60 2012.9 France
SPOT 7 NAOMI 0.45–0.89 1.5/6 26 60 2014.6 France
Rapid Eye MSI 0.4–0.85 5 1 77 2008.8 Germany
RADARSAT 1 SAR C band 8–100 1–3 20/50/75/100/
150/170/300/500 1995.11 Canada
RADARSAT 2 SAR C band 1–100 1–3 18/20/50/75/100/
150/170/300/500 2007.12 Canada
Remote Sens. 2020,12, 1130 4 of 25
Table 1. Cont.
Satellite Sensor Spectral Range
(µm)
Spatial
Resolution (m)
Revisit
Time (day)
Swath
Width (km)
Launch
Time Country
ALOS-1
PRISM 0.52–0.77 2.5 2 70 2006.1 Japan
AVNIR-2 0.42–0.89 10 2 70 2006.1 Japan
PALSAR L band 7–100 2 20–350 2006.1 Japan
ALOS-2 PALSAR-2 L band 1–100 14 25/50–70/350/490 2014.5 Japan
HJ-A CCD 0.43–0.90 30 4 360 2008.9 China
HSI 0.43–0.52 100 4 50 2008.9 China
HJ-B CCD 0.43–0.90 30 4 360 2008.9 China
IRS 0.43–0.52 150/300 4 720 2008.9 China
HJ-C SAR S band 5/20 31 40/100 2012.11 China
ZY-1-02C HRC/PMS 0.50–0.89 2.36/5/10 3–5 54/60 2011.11 China
ZY-3-01/02 PMS/MUX 0.45–0.89 2.1/5.8 3–5 51 2012.1
2016.5 China
Gaofen-1 PMS/WFV 0.45–0.9 2/8/16 2–4 60/800 2013.4 China
Gaofen -2 PMS/MSS 0.45–0.9 1/4 5 45 2014.8 China
Gaofen -3 SAR C band 1–500 1.5–3 10–650 2016.8 China
Gaofen -4 PMI 0.45–0.9
3.5–4.1 50/400 20 seconds 400 2015.12 China
Gaofen -5 AHSI 0.45–2.5 30 51 60 2018.5 China
VIMI 0.45–12.5 20/40 51 60
Gaofen -6 PMS/WFV 0.45–0.9 2/8/16 2–4 60/800 2018.6 China
Remote Sens. 2020,12, 1130 5 of 25
2.2. Major Research and Education Institutions Involved in Remote Sensing of the Environment
Many institutions are involved in the research and application of remote sensing in environmental
monitoring in China, which can be categorized into three types: government agencies, research
institutions and universities, as listed in Table 2. Since remote sensing technology has shown
great advantages, many ministries or commissions of China’s central government have successively
established remote sensing application agencies to specifically explore and implement applications of
remote sensing technologies in their jurisdictions. For example, the Satellite Environment Center of the
Ministry of Ecology and Environment is engaged in the application of remote sensing in ecosystem
and environment monitoring. In addition, some research institutions and universities are conducting
research into theories, methods and applications of remote sensing in various fields.
Table 2. Major institutions involved in remote sensing of environment in China.
Institutions City Fields
National Remote Sensing Center of China Beijing Remote sensing technology
management
Satellite Environment Center, Ministry of
Ecology and Environment Beijing Remote sensing of ecology and
environment
Land Satellite Remote Sensing Application
Center, Ministry of Natural Resource Beijing Remote sensing of land resource
National Satellite Ocean Application Service,
Ministry of Natural Resource Beijing Remote sensing of ocean
National Satellite Meteorological Centre,
China Meteorological Administration Beijing Remote sensing of meteorology
National Disaster Reduction Center, Ministry
of Emergency Management Beijing Application of remote sensing in
disaster reduction
China Center for Resources Satellite Date and
Application Beijing Remote sensing data acquisition
and management
Aerospace Information Research Institute,
Chinese Academy of Sciences Beijing
Comprehensive research in remote
sensing
Research Center for Eco-Environmental
Sciences, Chinese Academy of Sciences Beijing Remote sensing of environment
and ecosystem
Institute of Geographic Sciences and Natural
Resources Research, Chinese Academy of
Sciences
Beijing Application of remote sensing in
natural resource
Institute of Tibetan Plateau Research, Chinese
Academy of Sciences Beijing Remote sensing of environment
and geology
Institute of Atmospheric Physics, Chinese
Academy of Sciences Beijing Remote sensing of atmosphere
Chinese Academy of Forestry Beijing Remote sensing of forestry
Chinese Academy of Agricultural Sciences Beijing Remote sensing of agriculture
Nanjing Institute of Geography & Limnology,
Chinese Academy of Sciences Nanjing Remote sensing of environment
Institute of Soil Science, Chinese Academy of
Sciences Nanjing Application of remote sensing in
soil science
Peking University Beijing Education
Tsinghua University Beijing Education
Beijing Normal University Beijing Education
University of Chinese Academy of Sciences Beijing Education
China University of Mining and Technology
Beijing and Xuzhou
Education
China University of Geosciences Beijing and Wuhan Education
Beihang University Beijing Education
Capital Normal Univeristy Beijing Education
Wuhan University Wuhan Education
Remote Sens. 2020,12, 1130 6 of 25
Table 2. Cont.
Institutions City Fields
Central South University Changsha Education
Tongji University Shanghai Education
Sun Yat-sen University Guangzhou Education
Nanjing University Nanjing Education
Chang’an University Xi’an Education
Liaoning Technical University Fuxin Education
Xi‘an University of Science and Technology Xi’an Education
Shandong University of Science and
Technology Qingdao Education
Hohai University Nanjing Education
Lanzhou Jiaotong University Lanzhou Education
Zhengzhou University Zhengzhou Education
Southwest Jiaotong University Chengdu Education
2.3. Major Remotely-Sensed Environmental Monitoring Policies
Protecting the environment is one of China’s basic national policies. To strengthen environment
protection, Chinese central government has promulgated the new environmental protection law in
2014. The rule for Ecological Conservation Red Line (ECRL) was added to the law, and it designated
ecological protection red lines in the areas such as key ecological function zones, environmentally
sensitive zones and fragile areas where a strict protection is implemented. Besides this, the Ministry
of Science and Technology of China has released an annual report on the remote sensing monitoring
of the global environment every year since 2012, to contribute to global environmental monitoring.
China began promoting remote sensing technologies in order to implement environmental monitoring
eectively in recent years, and aimed at establishing the space–earth integration observation system
for environmental monitoring. A series of science and technology funding programs have been
implemented including the High-Resolution Earth Observation System Project, the Air Pollution
Control Action Plan, the Eco-Environment Monitoring and the Assessment of the Belt and Road etc.
3. Advances in Remote Sensing for Environmental Monitoring
According to the management model of environmental monitoring by remote sensing in China,
this paper describes the advances in remote sensing for environmental monitoring from five aspects:
ecological index retrieval, environmental monitoring in protected areas, rural areas, urban areas and
mining areas. The studies of environmental monitoring by remote sensing in China are summarized in
Table 3.
Remote Sens. 2020,12, 1130 7 of 25
Table 3. Studies of environmental monitoring by remote sensing in China.
Field of Monitoring Monitoring Element Methods and Algorithms Remote Sensing Datasets Used Accuracy
Ecological index
retrieval
Vegetation index
Band combination method, principal component
combination method, derivative band combination
method etc.
Landsat TM/ETM+/OLI, Gaofen-1,
MODIS 78%–94.55% [1521]
Soil moisture
Empirical models, semi-empirical models, physical
models MODIS, Landsat TM, Envisat-1 ASAR MRE =17.5%–32.8% [2231]
Vegetation moisture Regression model, vegetation moisture index Landsat ETM+, ASTER, Hyperion,
MODIS RMSE <0.794 kg/m2[3236]
Evapotranspiration SEBAL model, SEBS model, METRIC model,
semi-empirical model HJ-IB, FY-3, Landsat TM, MODIS MRE 12% [3748]
Land surface temperature
Single-channel algorithm,
split-window algorithm, neural network-based
algorithms
HJ-1B, ASTER, Landsat
TM/ETM+/TIRS, MODIS RMSE 2K [6,4953]
Protected area
monitoring
Land use/cover change Automatic image classification, visual
interpretation Landsat TM/ETM+/OLI,
HJ-1, SPOT, World View-2
CBERS, ALOS, Gaofen-1
80%
[54,55]
Human activity
Human activity impact index, visual interpretation
[5661]
Biodiversity level, biological
species, vegetation et al. Spectral angle classification method HJ-1A, Landsat TM/ETM+[6268]
Rural area monitoring
Solid waste Human-computer interaction interpretation Beijing-1, Gaofen-1/2 90%–95% [69,70]
Greenhouse film PGI index, support vector machine classification Gaofen-1, Landsat ETM+, Quickbird-2 90% [7173]
Soil pollution Partial least squares regression method Hyperion, HJ-1A HSI MRE <15% [7477]
Aquaculture
Human-computer interaction interpretation,
object-oriented analysis and spectral eigenvalue
method
Landsat TM/ETM+/OLI,
CBERS, HJ-1 CCD 80% [78,79]
Urban area monitoring
Urban heat island Land surface temperature retrieval algorithms HJ-1B, ASTER, Landsat
TM/ETM+/TIRS, MODIS RMSE 2K [8085]
Urban green space information Stepwise hierarchical method, pixel dichotomy
model, mono-window algorithm
Landsat ETM+/OLI,
Quickbird, ALOS, Gaofen-1
>90%
[8690]
Urban impervious surface Linear spectral unmixture analysis, dynamic
impermeability analysis Landsat TM/ETM+, urban DEM RMSE <0.02 [9196]
Expansion of urban built-up
areas Object-oriented classification Landsat 8, Quickbird, Gaofen-1 Around 90% [10,97,98]
Urban environment quality Environment indicator Landsat dataset [99,100]
Mining area monitoring
Ecological damage and impact Image interpretation, linear regression SPOT 4/5, Quickbird, Landsat
TM/ETM+, ZY-3, ASTER 85% [101106]
Ecological restoration Ecological index, pixel dichotomy model Landsat TM/ETM+, HJ-1A CCD,
MODIS [107111]
Remote Sens. 2020,12, 1130 8 of 25
3.1. Remote Sensing Retrieval of Ecological Indexes
Many ecological indexes based on remote sensing are proposed to reflect ecological status.
This section introduces the remote sensing retrieval of a few basic ecological indexes, including
vegetation index, soil/vegetation moisture, evapotranspiration and land surface temperature.
3.1.1. Vegetation Index
Vegetation indexes are eective and empirical indicators that reflect the status of vegetation on
the ground and describe ecological conditions. The commonly used vegetation indexes include Ratio
Vegetation Index (RVI), Normalized Dierence Vegetation Index (NDVI), Environmental Vegetation
Index (EVI), Green Vegetation Index (GVI), Perpendicular Vegetation Index (PVI), Soil Adjusted
Vegetation Index (SAVI), and Dierence Environmental Vegetation Index (DVIEVI) etc.
With the fast development of space technologies, remote sensing data are becoming more and
more abundant. Some studies on vegetation indexes have been conducted in China focusing on the
improvement of retrieval accuracy. One important aspect is appropriate for dataset selection. Dierent
remote sensing datasets, e.g., Landsat TM&OLI, Gaofen-1 and MODIS, have dierent characteristics,
such as spatial resolution, spectral range and resolution. This leads to dierent levels of retrieval
accuracy [
15
]. Another important aspect is the use of band information, and some studies have
compared the original band combination method, the principal component combination method,
and the derivative band combination method to find out the best band combination for typical
indexes [
16
,
17
]. In addition, investigating the impact factors of vegetation index retrieval and making
adjustments are also an eective method of improving retrieval accuracy. For example, scholars found
that the reflectance in the near infrared and shortwave infrared bands was sensitive to topographical
variations, and carefully removed the topographic eects before using the non-band-ratio vegetation
indexes [
18
]. There are some works focusing on the application of the vegetation index. For example,
the vegetation coverage was extracted based on remote sensing images by vegetation indexes such
as NDVI, EVI and SAVI [
19
]. Some scholars used MODIS vegetation index products combined with
meteorological data to investigate the spatial and temporal changes of regional vegetation cover [
20
,
21
].
3.1.2. Soil and Vegetation Moisture
Soil moisture is a key component of the global water and energy exchange among hydrosphere,
atmosphere and biosphere. A variety of methods have been proposed to retrieve soil moisture, which
can fall into three categories: empirical models, semi-empirical models and physical models. The
most widely used are empirical models including: the soil moisture retrieval model based on MODIS
short-wave infrared spectral feature space [
22
,
23
], the active–passive remote sensing collaborative
retrieval model based on the genetic neural network algorithm [
24
], the retrieval model based on
the normalized spectral slope absorption index [
25
], and the soil moisture retrieval model based on
red-near infrared spectral feature space [
26
,
27
]. The semi-empirical models include: a coupling model
of soil moisture retrieval based on active–passive remote sensing [
28
], and a soil moisture retrieval
model based on polarized reflection information [
29
,
30
]. Due to theoretical complexity, few studies
focused on physical models. A typical model is based on the characteristics of bi-directional reflectance
of soil, which simulates the soil reflectance under natural conditions, and establishes the relationship
between soil bi-directional reflectance and soil water content [31].
Vegetation moisture is an indicator reflecting the growth of vegetation, and many vegetation
moisture indexes have been presented. The most commonly used method is to first establish the
functional relationship between the spectral index and the vegetation water content based on the
ground measured values, and to further quantitatively retrieve vegetation moisture from remote
sensing images [
32
34
]. For example, the leaf water content (LWC) of winter wheat in China’s Ningxia
Region was estimated with spectral indexes considering MODIS data and ground observation data,
and it was found that three indexes (Normalized Dierence Water Index, Simple Ratio and Shortwave
Remote Sens. 2020,12, 1130 9 of 25
Infrared Perpendicular Water Stress Index) were significantly correlated with the LWC of winter
wheat [
35
]. Another way is to make use of the optical properties of vegetation from a physical
perspective. For example, [
36
] proposed a model for quantitatively retrieving the moisture content of a
vegetation canopy based on the PROSAIL model using Hyperion hyperspectral data, which is eective
for obtaining vegetation moisture content information in a wide area.
3.1.3. Evapotranspiration
Evapotranspiration is an important parameter in the surface energy cycle. Scholars use dierent
models to calculate the amount of land surface evapotranspiration. Commonly used models include
the Surface Energy Balance Algorithm for Land (SEBAL) model, the Surface Energy Balance System
(SEBS) model, the Mapping Evapotranspiration at High Resolution with Internalized Calibration
(METRIC) model, the hybrid models and the semi-empirical models.
The SEBAL model based on the land surface energy balance model shows a clear physical process.
It first obtains the latent heat flux by calculating relevant components of surface energy balance, and
then estimates the evapotranspiration [
37
]. The SEBAL model can be used for a wide range of satellite
datasets. As an application case of international datasets, scholars estimated the evapotranspiration in
Panjin City, Liaoning Province using the SEBAL model based on Landsat 8 satellite data [
38
]. In order
to promote the application of domestic satellite data, researchers used HJ-1B and FY-3/VIRR datasets for
evapotranspiration estimation. Experiments showed that the relative deviation between the estimation
results and ground truth data was approximately 10%, which demonstrated the validity of China’s
satellite data [39,40].
The SEBS model first calculates the atmospheric turbulent flux and evaporation ratio based on the
surface energy balance equation, and then estimates the surface energy flux over a large area through a
series of surface physical parameters obtained from remote sensing combined with the simultaneously
observed meteorological data on the ground [
41
]. It can be used to estimate the daily evapotranspiration
in a large area based on the satellite data and meteorological observation data [
42
]. Furthermore,
scholars have tested its validity in estimating the evapotranspiration during dierent seasons [
43
].
Based on the estimation results of SEBS model, the relationships among the evapotranspiration and
environmental factors can be quantitatively analyzed [44].
The METRIC model [
45
] is an evapotranspiration estimation method based on high resolution
images and internal calibration. Through the analysis of METRIC model theory, the estimation
accuracy can be further promoted by improving some model parameters, such as surface roughness
parameter [
46
]. Some scholars also used the hybrid of METRIC model and other models, e.g., the
hybrid of METRIC and SEBS model [47] and the hybrid of METRIC and SEBAL model [5].
A semi-empirical model, i.e., the exponential evapotranspiration model, was proposed by [
48
] to
estimate China’s evapotranspiration for April to September of 2004 by MODIS data, and experimental
results validated that it was useful for monitoring China’s surface drought events. Figure 1reveals the
spatial patterns and temporal evolution trends of the monthly composites of daily evapotranspiration
under both clear and cloudy sky conditions.
Remote Sens. 2020,12, 1130 10 of 25
Remote Sens. 2020, 12, x FOR PEER REVIEW 11 of 26
Figure 1. Monthly composites of daily evapotranspiration for April to September of 2004 over China
[48].
3.1.4. Land Surface Temperature
Land surface temperature (LST) is an indispensable parameter to study the exchange of matter
and energy between surface and atmosphere, global ocean circulation, climate change anomalies
and other aspects. This section will introduce three types of LST retrieval algorithms: the
single-channel algorithm, the split-window algorithm and the neural network-based algorithm. The
single-channel algorithms use a single thermal infrared channel data to retrieve surface temperature
[49], and they can be categorized into two types. The empirical single-channel algorithms establish
the empirical relationship between atmospheric parameters and near-surface atmospheric
temperature [4], while the physical single-channel algorithms conduct atmospheric corrections
using the radiative transfer equation and atmospheric profiles [50]. The split-window algorithms
mainly use the linear combination of two adjacent infrared channels with different atmospheric
absorption characteristics to eliminate atmospheric effects and retrieve land surface temperature
[51]. For example, [52] proposed a split-window algorithm for surface temperature retrieval from
the Advanced Spaceborne Thermal Emission and Reflection (ASTER) data based on the exponential
simplification of the radiation transfer equation and Planck function. In order to make use of
China’s high-resolution Gaofen-5 data, [53] developed a new nonlinear, four-channel split-window
algorithm, and the experimental results are shown in Figure 2. As the neural network algorithms
were widely used in remote sensing image processing, the neural network-based algorithm was
proposed, which used appropriate band information as learning samples, and applied the deep
dynamic learning neural network and the radiation transfer model to retrieve surface temperature
[6].
Figure 1.
Monthly composites of daily evapotranspiration for April to September of 2004 over
China [48].
3.1.4. Land Surface Temperature
Land surface temperature (LST) is an indispensable parameter to study the exchange of matter
and energy between surface and atmosphere, global ocean circulation, climate change anomalies and
other aspects. This section will introduce three types of LST retrieval algorithms: the single-channel
algorithm, the split-window algorithm and the neural network-based algorithm. The single-channel
algorithms use a single thermal infrared channel data to retrieve surface temperature [
49
], and
they can be categorized into two types. The empirical single-channel algorithms establish the
empirical relationship between atmospheric parameters and near-surface atmospheric temperature [
4
],
while the physical single-channel algorithms conduct atmospheric corrections using the radiative
transfer equation and atmospheric profiles [
50
]. The split-window algorithms mainly use the linear
combination of two adjacent infrared channels with dierent atmospheric absorption characteristics to
eliminate atmospheric eects and retrieve land surface temperature [
51
]. For example, [
52
] proposed a
split-window algorithm for surface temperature retrieval from the Advanced Spaceborne Thermal
Emission and Reflection (ASTER) data based on the exponential simplification of the radiation
transfer equation and Planck function. In order to make use of China’s high-resolution Gaofen-5
data, [
53
] developed a new nonlinear, four-channel split-window algorithm, and the experimental
results are shown in Figure 2. As the neural network algorithms were widely used in remote sensing
image processing, the neural network-based algorithm was proposed, which used appropriate band
information as learning samples, and applied the deep dynamic learning neural network and the
radiation transfer model to retrieve surface temperature [6].
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Remote Sens. 2020, 12, x FOR PEER REVIEW 12 of 26
(a)
(b)
Figure 2. Land surface temperature retrieval from GF-5 simulation images in two sites. (a) YingKe
site; (b) Liuyuan site [53].
3.2. Remote Sensing Monitoring of Protected Areas
Protected areas are the land or sea where biodiversity, natural and cultural resources are
protected and preserved by law or other effective means. In China, protected areas include nature
reserves, biodiversity conservation areas, ecologically functional areas, scenic spots, national forest
parks and world natural & cultural heritage sites. This section will introduce the studies on remote
sensing monitoring of the first three types, i.e., nature reserves, biodiversity conservation areas and
ecological functional areas.
3.2.1. Nature Reserves
Nature reserves are special geographical entities designated to protect important ecosystems,
save endangered species or protect natural historical heritages. They play a vital role in the
sustainable development of ecological balance. Remote sensing for monitoring nature reserves has
been implemented mainly from two aspects.
The first is to monitor land use/cover change (LUCC) in nature reserves. The international
satellite datasets, especially the long time-series of Landsat images, were widely used for LUCC
monitoring, either by conducting image classifications based on original satellite images [12] or by
directly using available products such as EVI provided by NASA [54]. Besides that, scholars
applied the domestic HJ-1A/1B CCD data to monitor the dynamics of LUCC and proved that the
performance of HJ-1A/1B CCD data was similar to that of Landsat TM data [55].
On the other hand, the ecological and environmental changes in nature reserves were analyzed
by analyzing human activities. According to the characteristics of land use, the land types caused
by human activities are classified based on remote sensing images, and then the human activities in
nature reserves are analyzed by exploring the spatial distribution of those types of land [56,57].
These are the proxy analysis methods which take some specific types of land parcels as the proxy of
human activities. Another type of method is to construct a human activity impact index based on
the industrial, agricultural, tourism, traffic land classes and so on, and further to evaluate the
impact of human activities on nature reserves [58,59]. In addition, some scholars tried the
integrated use of remote sensing and location-aware data to analyze the impacts of human activity
on the ecological status of nature reserves, which showed the possibility of evaluating human–land
interaction at a fine-grained scale [1]. The ecological footprint model is also an effective way to
evaluate the ecological change and its spatial distribution [60]. Figure 3 plots the ecological
footprint of 319 National Nature Reserves of China in 2010.
Figure 2.
Land surface temperature retrieval from GF-5 simulation images in two sites. (
a
) YingKe site;
(b) Liuyuan site [53].
3.2. Remote Sensing Monitoring of Protected Areas
Protected areas are the land or sea where biodiversity, natural and cultural resources are protected
and preserved by law or other eective means. In China, protected areas include nature reserves,
biodiversity conservation areas, ecologically functional areas, scenic spots, national forest parks and
world natural & cultural heritage sites. This section will introduce the studies on remote sensing
monitoring of the first three types, i.e., nature reserves, biodiversity conservation areas and ecological
functional areas.
3.2.1. Nature Reserves
Nature reserves are special geographical entities designated to protect important ecosystems, save
endangered species or protect natural historical heritages. They play a vital role in the sustainable
development of ecological balance. Remote sensing for monitoring nature reserves has been
implemented mainly from two aspects.
The first is to monitor land use/cover change (LUCC) in nature reserves. The international satellite
datasets, especially the long time-series of Landsat images, were widely used for LUCC monitoring,
either by conducting image classifications based on original satellite images [
12
] or by directly using
available products such as EVI provided by NASA [
54
]. Besides that, scholars applied the domestic
HJ-1A/1B CCD data to monitor the dynamics of LUCC and proved that the performance of HJ-1A/1B
CCD data was similar to that of Landsat TM data [55].
On the other hand, the ecological and environmental changes in nature reserves were analyzed
by analyzing human activities. According to the characteristics of land use, the land types caused
by human activities are classified based on remote sensing images, and then the human activities
in nature reserves are analyzed by exploring the spatial distribution of those types of land [
56
,
57
].
These are the proxy analysis methods which take some specific types of land parcels as the proxy of
human activities. Another type of method is to construct a human activity impact index based on
the industrial, agricultural, tourism, trac land classes and so on, and further to evaluate the impact
of human activities on nature reserves [
58
,
59
]. In addition, some scholars tried the integrated use of
remote sensing and location-aware data to analyze the impacts of human activity on the ecological
status of nature reserves, which showed the possibility of evaluating human–land interaction at a
fine-grained scale [
1
]. The ecological footprint model is also an eective way to evaluate the ecological
change and its spatial distribution [
60
]. Figure 3plots the ecological footprint of 319 National Nature
Reserves of China in 2010.
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Figure 3. Spatial distribution of average ecological footprint in National Nature Reserves in 2010 [60].
3.2.2. Biodiversity Conservation Priority Areas
Biodiversity refers to the diversity of life in all forms, levels and unions of organisms on the
Earth, generally including three levels: genetic diversity, species diversity, and ecosystem diversity.
Remote sensing technologies are widely used to monitor human activities, biological species,
biodiversity levels and vegetation cover in biodiversity conservation priority areas (BCPA). The
human disturbance index can be defined on the basis of the remote sensing of land classification to
monitor and assess the human disturbance in BCPA [61]. An example of biological species
monitoring is to extract the invasive species named Canadian goldenrod using the spectral angle
classification method from the HJ-1A HIS hyperspectral data in Yixing, Jiangsu Province [62]. Yang
et al. [63] presented a monitoring and evaluation method for biodiversity at the county level, and
monitored the temporal and spatial biodiversity changes in Nanzhang County of Hubei Province,
China, as shown in Figure 4. Vegetation cover is also an important element of reflecting the
ecological status of BCPA, and remote sensing plays a key role in monitoring the spatiotemporal
change of vegetation cover [64].
Figure 3.
Spatial distribution of average ecological footprint in National Nature Reserves in 2010 [
60
].
3.2.2. Biodiversity Conservation Priority Areas
Biodiversity refers to the diversity of life in all forms, levels and unions of organisms on the Earth,
generally including three levels: genetic diversity, species diversity, and ecosystem diversity. Remote
sensing technologies are widely used to monitor human activities, biological species, biodiversity
levels and vegetation cover in biodiversity conservation priority areas (BCPA). The human disturbance
index can be defined on the basis of the remote sensing of land classification to monitor and assess
the human disturbance in BCPA [
61
]. An example of biological species monitoring is to extract the
invasive species named Canadian goldenrod using the spectral angle classification method from the
HJ-1A HIS hyperspectral data in Yixing, Jiangsu Province [
62
]. Yang et al. [
63
] presented a monitoring
and evaluation method for biodiversity at the county level, and monitored the temporal and spatial
biodiversity changes in Nanzhang County of Hubei Province, China, as shown in Figure 4. Vegetation
cover is also an important element of reflecting the ecological status of BCPA, and remote sensing plays
a key role in monitoring the spatiotemporal change of vegetation cover [64].
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Remote Sens. 2020, 12, x FOR PEER REVIEW 14 of 26
(a)
(b)
Figure 4. Results of biodiversity comprehensive assessment in Nanzhang County of Hubei Province,
China in the year of 2010 (a) and 2010 (b) [63].
3.2.3. National Key Ecological Functional Region
The National Key Ecological Functional Zones (NKEFZ) refers to the areas with special
protection and restricted exploitation. They are set up to protect, restore and improve the important
ecological functions of specific regions including regional water and soil conservation, wind and
sand control, flood regulation and storage, and to maintain and improve their function to provide
various ecological services and products. It is related to the coordinated development of economic,
social and ecological protection in the region, and the balanced development of the western region
and other regions in China [65]. Remote sensing can provide support for the monitoring and
managing of national key ecological functional zones. An important work is to quantitatively assess
the ecological services from human activities and climate. There are four types of national key
ecological services, including water conservation, water and soil conservation, wind break and sand
fixation, and biodiversity maintenance. These ecological services can be assessed by the models
based on remote sensing datasets and ground-observed meteorological data [66], e.g., the water
conservation service assessment as shown in Figure 5. Another important work is ecological
function assessment. Remote sensing can be used to evaluate ecological functions from the following
aspects: ecosystem distribution, landscape change and ecological function index [67]. Besides that,
scholars monitored the change of ecological elements in NKEFZ during a special event such as an
earthquake. For example, the vegetation coverage in China’s Qianfoshan National Forest Park was
monitored using multi-temporal remote sensing images, and the forest damage and restoration
condition was evaluated [68].
Figure 4.
Results of biodiversity comprehensive assessment in Nanzhang County of Hubei Province,
China in the year of 2010 (a) and 2010 (b) [63].
3.2.3. National Key Ecological Functional Region
The National Key Ecological Functional Zones (NKEFZ) refers to the areas with special protection
and restricted exploitation. They are set up to protect, restore and improve the important ecological
functions of specific regions including regional water and soil conservation, wind and sand control,
flood regulation and storage, and to maintain and improve their function to provide various ecological
services and products. It is related to the coordinated development of economic, social and ecological
protection in the region, and the balanced development of the western region and other regions in
China [
65
]. Remote sensing can provide support for the monitoring and managing of national key
ecological functional zones. An important work is to quantitatively assess the ecological services
from human activities and climate. There are four types of national key ecological services, including
water conservation, water and soil conservation, wind break and sand fixation, and biodiversity
maintenance. These ecological services can be assessed by the models based on remote sensing datasets
and ground-observed meteorological data [
66
], e.g., the water conservation service assessment as
shown in Figure 5. Another important work is ecological function assessment. Remote sensing can be
used to evaluate ecological functions from the following aspects: ecosystem distribution, landscape
change and ecological function index [
67
]. Besides that, scholars monitored the change of ecological
elements in NKEFZ during a special event such as an earthquake. For example, the vegetation coverage
in China’s Qianfoshan National Forest Park was monitored using multi-temporal remote sensing
images, and the forest damage and restoration condition was evaluated [68].
Remote Sens. 2020,12, 1130 14 of 25
Remote Sens. 2020, 12, x FOR PEER REVIEW 15 of 26
(a)
(b)
Figure 5. Spatial distribution and change of water conservation in national key ecological function
zones. (a) The year of 2000; (b) the year of 2014 [66].
3.3. Remote Sensing Monitoring of Rural Areas
With the acceleration of China’s urbanization process, the rural economy has developed to a
certain extent, but its developing methods are extensive, which has caused some negative impacts
on its environment, such as the disorderly discharge of pollutants. The contents of rural
environment monitoring mainly include solid waste, greenhouse film, soil pollution and
aquaculture pollution.
The first is the solid waste monitoring. The interpretation signs of solid waste dumps on
high-resolution images was established based on learning samples, and then the interpretation
analysis and change detection of informal waste dumps were carried out through the
human–computer interaction and automatic change detection methods [69,70]. The informal waste
dumps can be easily identified by comparing with the authorized waste dump sites, and this method
provides technical and data support for rural environment management.
Another method is research on greenhouse film monitoring, which is composed of three main
types of methods. (1) The detection methods based on multi-source datasets. The coverage area of
greenhouse film in the village areas of Guilin City was detected based on Google Earth
high-resolution remote sensing images and DEM data [71]. (2) The index-based methods. A plastic
greenhouse film index (PGI) was proposed by [72] based on the spectral, sensitivity, and separability
analysis of plastic greenhouse films using medium-resolution images, and the PGI results in a
sample area in Weifang City, Shandong Province, China is shown in Figure 6. (3) The machine
learning methods. It was found by [73] that the support vector machine classification method works
well for detecting plastic greenhouse film information.
Remote sensor monitoring of soil pollution is mainly based on hyperspectral images, because
the hyperspectral remote sensing is able to obtain the quantitative information of soil composition
(e.g., organic matter, minerals) due to the characteristics of high spectral resolution. A commonly
used method is partial least squares regression modeling between the soil pollution concentration
and hyperspectral data using the sample data from field work. The obtained model is then used to
derive the pollution concentration in other locations [74,75]. Another method is stepwise regression
modeling. First, the feature spectral bands is obtained by first derivative, inverse-log and continuum
removing on original bands, and then the correlation between the soil pollution concentration and
feature spectral bands is established using the sample data and applied for monitoring [76,77].
Scholars also conducted work on aquaculture monitoring by remote sensing. The information
of aquaculture land can be extracted by various interpretation methods from high-resolution or
medium-resolution images for multiple time periods, and the spatial and temporal evolution
patterns of coastal aquaculture land are further studied using the models including single land use
dynamic, gravity center of migration and landscape fragmentation [78,79].
Figure 5.
Spatial distribution and change of water conservation in national key ecological function
zones. (a) The year of 2000; (b) the year of 2014 [66].
3.3. Remote Sensing Monitoring of Rural Areas
With the acceleration of China’s urbanization process, the rural economy has developed to a
certain extent, but its developing methods are extensive, which has caused some negative impacts on
its environment, such as the disorderly discharge of pollutants. The contents of rural environment
monitoring mainly include solid waste, greenhouse film, soil pollution and aquaculture pollution.
The first is the solid waste monitoring. The interpretation signs of solid waste dumps on
high-resolution images was established based on learning samples, and then the interpretation analysis
and change detection of informal waste dumps were carried out through the human–computer
interaction and automatic change detection methods [
69
,
70
]. The informal waste dumps can be easily
identified by comparing with the authorized waste dump sites, and this method provides technical
and data support for rural environment management.
Another method is research on greenhouse film monitoring, which is composed of three main
types of methods. (1) The detection methods based on multi-source datasets. The coverage area of
greenhouse film in the village areas of Guilin City was detected based on Google Earth high-resolution
remote sensing images and DEM data [
71
]. (2) The index-based methods. A plastic greenhouse film
index (PGI) was proposed by [
72
] based on the spectral, sensitivity, and separability analysis of plastic
greenhouse films using medium-resolution images, and the PGI results in a sample area in Weifang
City, Shandong Province, China is shown in Figure 6. (3) The machine learning methods. It was
found by [
73
] that the support vector machine classification method works well for detecting plastic
greenhouse film information.
Remote sensor monitoring of soil pollution is mainly based on hyperspectral images, because
the hyperspectral remote sensing is able to obtain the quantitative information of soil composition
(e.g., organic matter, minerals) due to the characteristics of high spectral resolution. A commonly
used method is partial least squares regression modeling between the soil pollution concentration
and hyperspectral data using the sample data from field work. The obtained model is then used to
derive the pollution concentration in other locations [
74
,
75
]. Another method is stepwise regression
modeling. First, the feature spectral bands is obtained by first derivative, inverse-log and continuum
removing on original bands, and then the correlation between the soil pollution concentration and
feature spectral bands is established using the sample data and applied for monitoring [76,77].
Scholars also conducted work on aquaculture monitoring by remote sensing. The information
of aquaculture land can be extracted by various interpretation methods from high-resolution or
medium-resolution images for multiple time periods, and the spatial and temporal evolution patterns
of coastal aquaculture land are further studied using the models including single land use dynamic,
gravity center of migration and landscape fragmentation [78,79].
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Remote Sens. 2020, 12, x FOR PEER REVIEW 16 of 26
Figure 6. Plastic greenhouse mapping by the plastic greenhouse film index (PGI)-based model in a
sample area in Weifang City, Shandong Province, China [72].
3.4. Remote Sensing Monitoring of Urban Areas
At present, more than half of the world’s population lives in cities. As the urbanization process
continues, this proportion will reach 66% in 2050, so the urban environment is vital to human
survival. This section will elaborate on the progress of urban environment remote sensing
monitoring in China from five aspects: urban heat island, urban green space, urban impervious
surface, urban built-up area expansion and urban environment quality.
The research on urban heat islands is documented as follows. The first type of work was to
study heat islands based on indexes. Scholars used the LST inversion algorithms to obtain urban
surface temperature from remote sensing images, and then constructed indexes such as the urban
heat island ratio index, and the thermal field variation index to evaluate the heat island intensity
[80–82]. The second type of work investigated the pattern of the thermal environment from
multi-period datasets. From the perspective of spatial dimensions, the spatial distribution
characteristics of urban islands are analyzed, and from the perspective of temporal dimension, the
evolution characteristics of urban islands with time are analyzed based on the urban LST at multiple
periods [83,84]. Another important aspect of the research explored the driving mechanism of heat
islands. On the one hand, the LST information was obtained from thermal bands and atmospheric
parameters; and on the other hand, the land use and cover (industrial land, farmland, woodland),
and landscape pattern were derived from optical bands. A correlation analysis was conducted
among them to identify the important correlation factors relevant to heat islands [81,85].
Urban green space information is also an important indicator reflecting urban environment
status. A commonly used method of extracting urban green space is the pixel-based classification
method. For example, the method of stepwise hierarchical classification methods was adopted to
extract the urban green area in Kunming City, and it was conducted in the unit of pixel [86]. In order
to solve the “salt-and-pepper” phenomenon, an object-oriented urban green land extraction method
was proposed by incorporating texture information [87]. Scholars also used the spectral mixing
model for urban green space extraction. For example, the vegetation coverage can be extracted by
the linear spectral mixing model based on Landsat ETM/ETM+ data [88,89]. In addition, the NDVI
and pixel dichotomy models were used to retrieve the vegetation coverage of Guangzhou City based
on Landsat 8 OLI data, and it was found that the dichotomy model was a more accurate method of
monitoring vegetation coverage than statistical methods [90].
The impact of urban imperviousness on urban temperature or water cycling cannot be ignored.
In the urban environment, a pixel is assumed to be the mix of multiple components (endmembers),
and the spectral value of a pixel is the linear combination of spectral values of every endmember.
Therefore, most scholars use the linear spectral unmixing model to extract imperious surface cover
from remote sensing images. Furthermore, the relationship between the impervious surface and the
Figure 6.
Plastic greenhouse mapping by the plastic greenhouse film index (PGI)-based model in a
sample area in Weifang City, Shandong Province, China [72].
3.4. Remote Sensing Monitoring of Urban Areas
At present, more than half of the world’s population lives in cities. As the urbanization process
continues, this proportion will reach 66% in 2050, so the urban environment is vital to human survival.
This section will elaborate on the progress of urban environment remote sensing monitoring in China
from five aspects: urban heat island, urban green space, urban impervious surface, urban built-up area
expansion and urban environment quality.
The research on urban heat islands is documented as follows. The first type of work was to
study heat islands based on indexes. Scholars used the LST inversion algorithms to obtain urban
surface temperature from remote sensing images, and then constructed indexes such as the urban heat
island ratio index, and the thermal field variation index to evaluate the heat island intensity [
80
82
].
The second type of work investigated the pattern of the thermal environment from multi-period
datasets. From the perspective of spatial dimensions, the spatial distribution characteristics of urban
islands are analyzed, and from the perspective of temporal dimension, the evolution characteristics of
urban islands with time are analyzed based on the urban LST at multiple periods [
83
,
84
]. Another
important aspect of the research explored the driving mechanism of heat islands. On the one hand, the
LST information was obtained from thermal bands and atmospheric parameters; and on the other hand,
the land use and cover (industrial land, farmland, woodland), and landscape pattern were derived
from optical bands. A correlation analysis was conducted among them to identify the important
correlation factors relevant to heat islands [81,85].
Urban green space information is also an important indicator reflecting urban environment status.
A commonly used method of extracting urban green space is the pixel-based classification method.
For example, the method of stepwise hierarchical classification methods was adopted to extract the
urban green area in Kunming City, and it was conducted in the unit of pixel [
86
]. In order to solve the
“salt-and-pepper” phenomenon, an object-oriented urban green land extraction method was proposed
by incorporating texture information [
87
]. Scholars also used the spectral mixing model for urban
green space extraction. For example, the vegetation coverage can be extracted by the linear spectral
mixing model based on Landsat ETM/ETM+data [
88
,
89
]. In addition, the NDVI and pixel dichotomy
models were used to retrieve the vegetation coverage of Guangzhou City based on Landsat 8 OLI data,
and it was found that the dichotomy model was a more accurate method of monitoring vegetation
coverage than statistical methods [90].
The impact of urban imperviousness on urban temperature or water cycling cannot be ignored.
In the urban environment, a pixel is assumed to be the mix of multiple components (endmembers),
and the spectral value of a pixel is the linear combination of spectral values of every endmember.
Therefore, most scholars use the linear spectral unmixing model to extract imperious surface cover from
Remote Sens. 2020,12, 1130 16 of 25
remote sensing images. Furthermore, the relationship between the impervious surface and the surface
temperature in cities was studied to provide scientific reference for urban planning and environmental
evaluation [
91
95
]. Considering that most existing methods compute the impervious surface ratio
according to multi-level administrative units rather than watersheds, [
96
] proposed to carry out
urban hydrological modeling and impermeability ratio calculation on multi-level units through the
interconnection of multiple urban water systems by calculating the dynamic impermeability based on
urban DEM analysis as shown in Figure 7.
Remote Sens. 2020, 12, x FOR PEER REVIEW 17 of 26
surface temperature in cities was studied to provide scientific reference for urban planning and
environmental evaluation [91–95]. Considering that most existing methods compute the impervious
surface ratio according to multi-level administrative units rather than watersheds, [96] proposed to
carry out urban hydrological modeling and impermeability ratio calculation on multi-level units
through the interconnection of multiple urban water systems by calculating the dynamic
impermeability based on urban DEM analysis as shown in Figure 7.
Figure 7. Impervious surfaces area maps of Wuhan City from 1987 to 2017 [96].
Another type of work is to monitor the expansion of urban built-up areas. A large number of
studies were conducted on urban built-up area extraction and expansion monitoring from coarse,
medium and high-resolution remote sensing images, but most of the recent advances are focused on
the high-resolution approaches or fusion approaches based on multisource datasets [97]. For
instance, a fusion approach based on the use of multisource remotely sensed data, i.e., the
DMSP-OLS nighttime light data, the MODIS land cover product and Landsat 7 ETM+ images, was
Figure 7. Impervious surfaces area maps of Wuhan City from 1987 to 2017 [96].
Another type of work is to monitor the expansion of urban built-up areas. A large number of
studies were conducted on urban built-up area extraction and expansion monitoring from coarse,
medium and high-resolution remote sensing images, but most of the recent advances are focused on
the high-resolution approaches or fusion approaches based on multisource datasets [
97
]. For instance,
a fusion approach based on the use of multisource remotely sensed data, i.e., the DMSP-OLS nighttime
light data, the MODIS land cover product and Landsat 7 ETM+images, was proposed by [
98
] in order
to accurately extract urban built-up areas. In addition, [
10
] developed an urban built-up area extraction
Remote Sens. 2020,12, 1130 17 of 25
method to better dierentiate suburban boundaries and ensure the integrity of land cover types, with
the impervious surface of Beijing shown in Figure 8.
Remote Sens. 2020, 12, x FOR PEER REVIEW 18 of 26
proposed by [98] in order to accurately extract urban built-up areas. In addition, [10] developed an
urban built-up area extraction method to better differentiate suburban boundaries and ensure the
integrity of land cover types, with the impervious surface of Beijing shown in Figure 8.
Figure 8. Biophysical composition index distribution (A) and impervious surface distribution (B)
[10].
The remote sensing assessment of urban environment quality refers to evaluating urban
environment quality based on a variety of environment indicators that can be obtained by remote
sensing. [99] constructed the remote sensing environment index based on four indicators: greenness,
humidity, dryness and heat, and used it to evaluate the environment quality of Weinan City in
1995–2015 using Landsat data. In contrast, [100] established an environment index based on the net
primary productivity, vegetation fraction, LST and bareness fraction, and evaluated the dynamic
changes of environment quality in Yixing city, Jiangsu Province.
3.5. Remote Sensing Monitoring of MiningAreas
The exploitation of mineral resources is destructive to the mining area and the surrounding
environment. In the process of open-pit mining, the mines occupy a large area of forests, grasslands,
and farmlands, etc. In addition, soil erosion may be caused in these areas with a certain slope.
Remote sensing can effectively monitor the environment in mining areas and provide a scientific
basis for the effective restoration of the environment.
On the one hand, remote sensing is used to monitor ecological damage and its impacts on the
mining area environment during the mining process. An important monitoring object is vegetation
and landscape pattern. Scholars used the vegetation index and landscape index to reflect the
ecological status from remote sensing images and to evaluate the impact of mining processes on
different mining scales and stages, and on different types of vegetation [101–103]. Land degradation
is also a type of ecological damage caused by mining. The analytic hierarchical process method was
used to build up an evaluation index system of land degradation, which is useful for evaluating the
intensity of land degradation [104]. Moreover, the soil pollution can be monitored. For example, a
product band transformation method was proposed by [105] to study the relationship between the
soil zinc concentration and spectral value of ASTER data. Remote sensing can also help monitor the
dynamic distribution of coal and gangue. The spectral reflectance characteristics of coal and gangue
were first calculated by machine learning from a number of positive and negative samples, and then
used to extract the distribution of coal and gangue in real time [106]. A typical application case was
the Heidaigou coal mine in Northwestern China (Figure 9).
Many approaches to the land reclamation of mine sites have been conducted to relieve the
damaging effects of coal mining and restore the landscape and its immediate surroundings. Remote
sensing can be used to monitor the restoration effect on a landscape pre and post reclamation. Based
Figure 8.
Biophysical composition index distribution (
A
) and impervious surface distribution (
B
) [
10
].
The remote sensing assessment of urban environment quality refers to evaluating urban
environment quality based on a variety of environment indicators that can be obtained by remote
sensing. [
99
] constructed the remote sensing environment index based on four indicators: greenness,
humidity, dryness and heat, and used it to evaluate the environment quality of Weinan City in 1995–2015
using Landsat data. In contrast, [
100
] established an environment index based on the net primary
productivity, vegetation fraction, LST and bareness fraction, and evaluated the dynamic changes of
environment quality in Yixing city, Jiangsu Province.
3.5. Remote Sensing Monitoring of MiningAreas
The exploitation of mineral resources is destructive to the mining area and the surrounding
environment. In the process of open-pit mining, the mines occupy a large area of forests, grasslands,
and farmlands, etc. In addition, soil erosion may be caused in these areas with a certain slope. Remote
sensing can eectively monitor the environment in mining areas and provide a scientific basis for the
eective restoration of the environment.
On the one hand, remote sensing is used to monitor ecological damage and its impacts on the
mining area environment during the mining process. An important monitoring object is vegetation
and landscape pattern. Scholars used the vegetation index and landscape index to reflect the ecological
status from remote sensing images and to evaluate the impact of mining processes on dierent mining
scales and stages, and on dierent types of vegetation [
101
103
]. Land degradation is also a type of
ecological damage caused by mining. The analytic hierarchical process method was used to build
up an evaluation index system of land degradation, which is useful for evaluating the intensity of
land degradation [
104
]. Moreover, the soil pollution can be monitored. For example, a product
band transformation method was proposed by [
105
] to study the relationship between the soil zinc
concentration and spectral value of ASTER data. Remote sensing can also help monitor the dynamic
distribution of coal and gangue. The spectral reflectance characteristics of coal and gangue were
first calculated by machine learning from a number of positive and negative samples, and then used
to extract the distribution of coal and gangue in real time [
106
]. A typical application case was the
Heidaigou coal mine in Northwestern China (Figure 9).
Many approaches to the land reclamation of mine sites have been conducted to relieve the
damaging eects of coal mining and restore the landscape and its immediate surroundings. Remote
sensing can be used to monitor the restoration eect on a landscape pre and post reclamation. Based on
landscape ecological theories, remote sensing and GIS technology can be used to construct a landscape
ecological quality assessment model to quantitatively evaluate the eco-environment status in the
Remote Sens. 2020,12, 1130 18 of 25
coal mining area and its spatial–temporal change as the restoration measures continues [
107
111
].
The evaluation is conducted from various aspects including grass cover, forest cover, wetland cover,
landscape fragmentation, biological abundance, river density and so on.
Remote Sens. 2020, 12, x FOR PEER REVIEW 19 of 26
on landscape ecological theories, remote sensing and GIS technology can be used to construct a
landscape ecological quality assessment model to quantitatively evaluate the eco-environment
status in the coal mining area and its spatial–temporal change as the restoration measures continues
[107–111]. The evaluation is conducted from various aspects including grass cover, forest cover,
wetland cover, landscape fragmentation, biological abundance, river density and so on.
Figure 9. Application of the tree root based multi-layer extreme learning machine model in opencast
Heidaigou coal mine area [106].
4. Discussion
4.1. Major Challenges of Remote Sensing of Environment in China
Although a lot work has been done in remote sensing for environmental monitoring in China,
including launching a series of satellites, conducting much research work and making positive
policies, some challenges still need to be paid attention to.
(1) Satellite sensor problems. There is a lack of thermal infrared sensors with short revisit
cycles and wide imaging swath in China’s remote sensing satellite system. The performance of
thermal infrared sensors (such as signal-to-noise ratio) needs to be improved, otherwise it would
affect the accuracy of parameter inversion. In addition, more hyperspectral satellite sensors are in
need, especially the ones with larger imaging swath.
(2) Integrated use challenge of remote sensing datasets. China is a vast country with very
different natural and climatic conditions across regions. For example, the northwestern region is the
Gobi landform with few clouds, while the southwestern region could contain cloudy and rainy
mountainous regions at the same time. There is an urgent need for the integrated use of various
kinds of remote sensing datasets to satisfy such a geographically heterogeneous country. Currently,
the environmental monitoring by remote sensing is mainly based on multi-spectral data with only a
limited number of wide bands, while the hyperspectral, microwave and Laser Detection and
Ranging (LIDAR) data are not fully applied. Therefore, the performance of monitoring and
evaluating environment status is limited.
(3) Uncertainty in the remote sensing of ecological variables. For instance, since a mixed pixel
contains several materials, the retrieved land surface temperature is the average of materials within
the mixed pixel. What’s more, the retrieval models are usually simplified and empirical, which
further enlarges the uncertainty.
Figure 9.
Application of the tree root based multi-layer extreme learning machine model in opencast
Heidaigou coal mine area [106].
4. Discussion
4.1. Major Challenges of Remote Sensing of Environment in China
Although a lot work has been done in remote sensing for environmental monitoring in China,
including launching a series of satellites, conducting much research work and making positive policies,
some challenges still need to be paid attention to.
(1) Satellite sensor problems. There is a lack of thermal infrared sensors with short revisit cycles
and wide imaging swath in China’s remote sensing satellite system. The performance of thermal
infrared sensors (such as signal-to-noise ratio) needs to be improved, otherwise it would aect the
accuracy of parameter inversion. In addition, more hyperspectral satellite sensors are in need, especially
the ones with larger imaging swath.
(2) Integrated use challenge of remote sensing datasets. China is a vast country with very dierent
natural and climatic conditions across regions. For example, the northwestern region is the Gobi
landform with few clouds, while the southwestern region could contain cloudy and rainy mountainous
regions at the same time. There is an urgent need for the integrated use of various kinds of remote
sensing datasets to satisfy such a geographically heterogeneous country. Currently, the environmental
monitoring by remote sensing is mainly based on multi-spectral data with only a limited number
of wide bands, while the hyperspectral, microwave and Laser Detection and Ranging (LIDAR) data
are not fully applied. Therefore, the performance of monitoring and evaluating environment status
is limited.
(3) Uncertainty in the remote sensing of ecological variables. For instance, since a mixed pixel
contains several materials, the retrieved land surface temperature is the average of materials within
the mixed pixel. What’s more, the retrieval models are usually simplified and empirical, which further
enlarges the uncertainty.
Remote Sens. 2020,12, 1130 19 of 25
(4) Scaling eect problems. As for temporal scale, remote sensing observation is instantaneous,
but the application analysis is usually conducted at a daily rate. Currently, the linear relationship
is commonly used to transfer the instantaneous scale to the daily scale. However, the changes in
temperature, wind speed, humidity and surface roughness make the actual situation more complicated.
As for the spatial scale, most of the retrieving models assume a uniform surface, but the surface is
highly heterogeneous. It is dicult to accurately represent the physical status of a region using only a
few site observation data as the inputs of models. In addition, the authenticity test is important to
verify the applicability of remote sensing retrieving models and model optimization. At present, the
ground truth data are point-observation data, thus it is dicult to verify the pixel-unit remote sensing
estimation results using the point-observation ground truth data.
(5) Low degree of automation. Few products can be used for operational application in environment
monitoring. The applications in the fields of monitoring natural reserve, biodiversity reserve,
environmental damage and environmental status assessment, are relatively automated. Most of others
depend on semi-automatic processes which require many manual interpretations.
(6) Weak ability of forecasting and comprehensive analysis. Remote sensing of the environment
focuses more on monitoring, but the ability to predict environmental trends is insucient, and the
comprehensive ability to analyze the quality and status of environment based on remote sensing is
still lacking.
(7) Lack of computational power for massive remote sensing data. In many application cases of
environmental monitoring, the number of remote sensing images is significantly large, especially for
large-extent and high-frequency monitoring needs. Although the Google Earth Engine is a possible
solution to this problem, we still have a long way to go.
4.2. Outlook of Remote Sensing of Environment in China
Although China has made great achievements in improving the environment, it still now faces
serious environmental pressure. The environmental degradation occurs frequently due to economically
motivated activities. To maintain environmental safety, lucid waters and lush mountains, it is very
necessary for China to strengthen the protection and supervision of the environment with remote
sensing technology, especially for the China Ecological Conservation Red Line (ECRL), implying the
areas which have especially important ecological functions and must be carefully protected. Currently,
the Ministry of Ecology and Environment of China, is carrying out remote sensing supervision for
ECRL and national parks [112].
Nevertheless, in view of the existing problems, the following directions are issued so as to promote
the technological development and meet the requirements of national environment management.
(1) Promoting multi-source data fusion. Multi-source remote sensing data can make up for
the shortcomings of the single type of data on temporal resolution, spatial resolution or spectral
resolution and provide complementary information. Therefore, how to estimate ecological index
using multi-source data fusion including remote sensing data, ecological data, environmental data,
meteorological data, social and economic data, is an important direction.
(2) Improving the retrieval accuracy of remote sensing parameters. Considering the uncertainty
of the remote sensing retrieval process, the accuracy of ecological parameter retrieval is improved by
optimizing models and developing land surface data assimilation systems. Presently, the ecological
index retrieval is mainly based on the relationship between remote sensing data and ground-measured
data. Verification relies on measured data, but there are also errors in the process of obtaining in-situ
data. Thus, the accurate evaluation of model retrieval and how to reduce the dependence degree of
retrieval models on ground-measured data are research directions in the future.
(3) Modelling of scale eect. The scale eect is the main problem that remote sensing of the
environment needs to face. The accuracy of remote sensing retrieval is related to the spatial and temporal
resolution of the dataset used. Therefore, it is necessary to select an appropriate spatiotemporal scale
according to the requirements, and further select an available remote sensing dataset.
Remote Sens. 2020,12, 1130 20 of 25
(4) Improving the level of production automation. Besides the accuracy of products, the state and
the public also have high requirements in the timeliness of products in many application scenarios,
which directly impacts the quality of applications. A focus of future work will be to increase the
automation level of remote sensing products through process optimization, customized function
development and aggregating computational resources.
(5) Enhancing the ability of forecasting and comprehensive analysis, shifting remote sensing of the
environment from monitoring to a synthetic ability with monitoring and forecasting, and strengthening
the comprehensive analysis of results are important directions for future work.
(6) Promoting open access to satellite datasets. At present, China’s satellite data resources
are mainly concentrated in the China Center for Resources Satellite Data and Application, and a
few ministries and agencies. The colleges, research institutions and the public have a relatively
poor accessibility to domestic satellite data. It is necessary to formulate relevant policies and
protocols to increase the openness of satellite data, to promote applications, and to enhance
information dissemination.
(7) Implementing ECRL supervision by remote sensing. There is an urgent need to supervise the
ECRL in constructing the national ecological safety and ecological civilization, which can eectively
restrain ecological damage in China and provide a reference for other countries.
Author Contributions:
J.L. and S.Z. conceived and designed the review, and wrote most of the manuscript. Y.P.,
R.X. and X.S. collected the materials. C.Z. wrote Sections 2.2,3.1.1 and 3.5. All authors provided critical feedback
on the manuscript. All authors have read and agreed to the published version of the manuscript.
Funding:
This research was funded by the National Key R&D Program of China (No. 2017YFB0503905), National
Natural Science Foundation of China (No. 41901291), the Major Projects of High Resolution Earth Observation
Systems of National Science and Technology (No. 05-Y30B01-9001-19/20-1) and Yueqi Young Scholar Project of
China University of Mining and Technology at Beijing.
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
Li, J.; Zhang, Y.; Qin, Q.; Yan, Y. Investigating the Impact of Human Activity on Land Use/Cover Change in
China’s Lijiang River Basin from the Perspective of Flow and Type of Population. Sustainability
2017
,9, 383.
[CrossRef]
2.
Muraoka, H.; Ishii, R.; Nagai, S.; Suzuki, R.; Motohka, T.; Noda, H.M.; Hirota, M.; Nasahara, K.N.; Oguma, H.;
Muramatsu, K. Linking Remote Sensing and In Situ Ecosystem/Biodiversity Observations by “Satellite Ecology”;
Springer: Tokyo, Japan, 2012; pp. 277–308.
3.
Zhao, S.; Wang, Q.; Li, Y.; Liu, S.; Wang, Z.; Zhu, L.; Wang, Z. An overview of satellite remote sensing
technology used in China’s environmental protection. Earth Sci. Inform. 2017,10, 137–148. [CrossRef]
4.
Zhao, S.; Qin, Q.; Zhang, F.; Wang, Q.; Yao, Y.; You, L.; Jiang, H.; Cui, R. Research on Using a Mono-Window
Algorithm for Land Surface Temperature Retrieval from Chinese Satellite for Environment and Natural
Disaster Monitoring(HJ-1B) Data. Spectrosc. Spectr. Anal. 2011,31, 1552–1556. [CrossRef]
5.
Lian, J.; Huang, M.; Li, X.; Liu, W. Evapotranspiration estimation for oasis transect in middle reach of Heihe
river basin based on remote sensing. Trans. Chin. Soc. Agric. Eng. 2014,30, 120–129. [CrossRef]
6.
Mao, K.; Yang, J.; Han, X.; Tang, S.; Yuan, Z.; Gao, C. Retrieving land surface temperature based on deep
dynamic learning NN algorithm and radiation transmission model. China Agric. Inform.
2018
,30, 47–57.
[CrossRef]
7.
Yue, W.; Xu, J.; Xu, L. An analysis on eco-environmental eect of urban land use based on remote sensing
images: A case study of urban thermal environment and NDVI. Acta Ecol. Sin.
2006
,26, 1450–1460. [CrossRef]
8.
Liu, G.; Zhang, L.; Zhang, Q. Trajectory and Driving Forces of Change in Farmland in Nanjing During the
Period From 1985 to 2010. J. Ecol. Rural Environ. 2013,29, 688–694. [CrossRef]
9.
Xiao, J.; Liao, X.; Wang, T. Cultivated Land Use Changes in Hunan Based on Remote Sensing. Hunan Agric.
Sci. 2014, 52–54. [CrossRef]
Remote Sens. 2020,12, 1130 21 of 25
10.
Meng, Q.; Zhang, L.; Sun, Z.; Meng, F.; Wang, L.; Sun, Y. Characterizing spatial and temporal trends of
surface urban heat island eect in an urban main built-up area: A 12-year case study in Beijing, China.
Remote Sens. Environ. 2018,204, 826–837. [CrossRef]
11.
Zhao, S.; Liu, S.; Liu, Q.; Wu, Y.; Wu, D. Progress of Urban Ecological Environment Monitoring by Remote
Sensing in China. Ecol. Environ. Sci. 2019,28, 1261–1271. [CrossRef]
12.
Nie, Y.; Zhang, Y.; Liu, L.; Zhang, J. Monitoring Glacier Change Based on Remote Sensing in the Mt.
Qomolangma National Nature Preserve,1976-2006. Acta Geogr. Sin. 2010,65, 13–28.
13.
Li, R.; Zhu, B.; Tong, X.; Yue, Y.; Gan, H.; Wan, S. Change analysis in Hainan Dongzhai Wetland Reserve based
on remote sensing data obtained during 2002-2013. Remote Sens. Land Resour.
2017
,29, 149–155. [CrossRef]
14.
Zhao, Y.; De, S.; Bao, Y.; Yang, W.; Zhao, C.; Bai, Y.; Zhao, Y. Dynamic Monitoring of Fractional Vegetation
Cover of Eco-Function Area of Grassland on Northern Foot of Yinshan Mountains through Remote Sensing
Technology. Res. Environ. Sci. 2017,30, 240–248. [CrossRef]
15.
Wang, C.; Zhang, D.; Ren, W. Comparison of Vegetation Coverage Extracting Based on MODIS Data. J. Atmos.
Environ. Opt. 2010,05, 457–462. [CrossRef]
16.
Zhai, T.; Jin, G.; Deng, X.; Li, Z.; Wang, Y. Study of vegetation information extraction method based on
Landsat8 satellite images. Sci. Surv. Mapp. 2016,41, 126–131. [CrossRef]
17.
Zhao, B.; Zhang, Y. Study on Vegetation Information Extraction Method Based on Landsat-8 OLI Images.
Geomat. Spat. Inf. Technol. 2018,41, 79–82.
18.
Zhu, G.; Liu, Y.; Ju, W.; Chen, J. Evaluation of topographic eects on four commonly used vegetation indices.
J. Remote Sens. 2013,17, 210–234.
19.
Gao, G.; Yang, B.; Wang, L.; Cheng, L.; Li, D. Comparison of methods for extracting vegetation information
from GF-1. Agric. Technol. 2017,37, 46–47. [CrossRef]
20.
Qi, J.; Niu, S.; Ma, L.; He, H. Spatio-Temporal Variation of Vegetation Cover in Shule River Valley During
2000-2014. J. Ecol. Rural Environ. 2016,32, 757–766. [CrossRef]
21.
Li, L.; Tian, M.; Liang, H.; Chen, Y.; Feng, C.; Qu, K.; Qian, J. Spatial and Temporal Changes of Vegetation
Coverage and Influencing Factors in Hulun Buir Grassland During 2000-2016. J. Ecol. Rural Environ.
2018
,
34, 584–591. [CrossRef]
22.
Zhao, S.; Yang, Y.; Qiu, G.; Qin, Q.; Yao, Y.; Xiong, Y.; Li, C. Remote detection of bare soil moisture using
a surface-temperature-based soil evaporation transfer coecient. Int. J. Appl. Earth Obs. Geoinf.
2010
,12,
351–358. [CrossRef]
23.
Yao, Y.; Qin, Q.; Zhao, S.; Shen, X.; Sui, X. New Index for Soil Moisture Monitoring Based on
T
s
-Albedo
Spectral Information. Spectrosc. Spectr. Anal. 2011,31, 1557–1561. [CrossRef]
24.
Yu, F.; Zhao, Y.; Li, H. Soil moisture retrieval based on GA-BP neural networks algorithm. J. Infrared Millim.
Waves 2012,31, 283–288. [CrossRef]
25.
Zheng, X.; Sun, Y.; Qin, Q.; Ren, H.; Gao, Z.; Wu, L.; Meng, Q.; Wang, J.; Wang, J. Bare Soil Moisture Inversion
Model Based on Visible-Shortwave Infrared Reflectance. Spectrosc. Spectr. Anal.
2015
,35, 2113–2118.
[CrossRef]
26.
Gao, Z.; Wang, J.; Zheng, X.; Sun, Y.; Qin, Q. Soil Moisture Monitoring Based on Angle Dryness Index.
Spectrosc. Spectr. Anal. 2016,36, 1378–1381. [CrossRef]
27.
Li, J.; Dong, H.; Wang, X.; You, L. Reconstructing missing data in soil moisture content derived from remote
sensing based on optimum interpolation. Remote Sens. Land Resour. 2018,30, 45–52. [CrossRef]
28.
Yu, F.; Zhao, Y. A new semi-empirical model for soil moisture content retrieval by ASAR and TM data in
vegetation-covered areas. Sci. China-Earth Sci. 2011,54, 1955–1964. [CrossRef]
29.
Zhao, S.; Zhang, C.; Xiong, Y.; Xiong, W.; You, D.; Li, Y. Soil Moisture Mapping Using two Scenes SAR
Imagery Without Knowing Information on Surface Parameters. J. Indian Soc. Remote Sens.
2016
,44, 651–656.
[CrossRef]
30.
Zhang, Y.; Yu, Y.; Zhao, H.; Xuan, J. Retrieval method of soil moisture based on polarized information.
Infrared Laser Eng. 2018,47, 206–211. [CrossRef]
31.
Cheng, J.; Ji, W.; Zhou, Y.; Shi, Z. Soil bidirectional reflectance characteristics as aected by soil moisture.
Acta Pedol. Sin. 2011,48, 255–262.
32.
Li, Y.; Yang, W.; Tong, L.; Jian, J.; Gu, X. Remote Sensing Quantitative Monitoring and Analysis Fuel Moisture
Content Based on Spectral Index. Acta Opt. Sin. 2009,29, 1403–1407. [CrossRef]
Remote Sens. 2020,12, 1130 22 of 25
33.
Pan, P.; Yang, W.; Jian, J.; Dai, X. Remote Sensing Retrieval Model of Vegetation Moisture Content Based on
Spectral Index: A Case Study in Maoergai of Mibjiang River’ Upstream. Remote Sens. Inf. 2013,28, 69–73.
34.
Wen, Y.; Huang, C.; Lu, L.; Gu, J. Theretrieval of Vegetation Water Content based on ASTER Images in Middle
of Heihe River Basin. Remote Sens. Technol. Appl. 2015,30, 876–883. [CrossRef]
35.
Zhao, S.; Wang, Q.; Yao, Y.; Du, S.; Zhang, C.; Li, J.; Zhao, J. Estimating and Validating Wheat Leaf Water
Content with Three MODIS Spectral Indexes: A Case Study in Ningxia Plain, China. J. Agric. Sci. Technol.
2016,18, 387–398.
36.
Song, X.; Ma, J.; Li, X.; Leng, P.; Zhou, F.; Li, S. Estimation of vegetation canopy water content using Hyperion
hyperspectral data. Spectrosc. Spectr. Anal. 2013,33, 2833–2837. [CrossRef]
37.
Bastiaanssen, W.G.; Menenti, M.; Feddes, R.A.; Holtslag, A.A.M. A remote sensing surface energy balance
algorithm for land (SEBAL): 1. Formulation. J. Hydrol. 1998,212, 198–212. [CrossRef]
38.
Yu, W.; Ji, R.; Xu, D.; Jia, Q.; Feng, R.; Sun, L.; Wu, J.; Zhang, Y. Daily evapotranspiration estimation of Panjin
wetland based on SEBAL model and its distribution characteristics. Sci. Soil Water Conserv.
2017
,15, 8–15.
[CrossRef]
39.
Zhang, N.; Wang, W.; Wang, Y. Remote Sensing Estimation of Land Surface Evapotranspiration Based on
HJ-1B Data and SEBAL Model. Geospat. Inf. 2013,11, 69–73. [CrossRef]
40.
Zhao, H.; Zhao, Y.; Li, F.; Zhang, H. Modelling evapotranspiration in provincial regions based on FY-3/VIRR
remote sensing data. Trans. Chin. Soc. Agric. Eng. 2014,30, 111–118. [CrossRef]
41.
Su, B. The Surface Energy Balance System (SEBS) for Estimation of Turbulent Heat Fluxes. Hydrol. Earth Syst.
Sci. 1988,6, 85–99. [CrossRef]
42. Yang, Y.; Feng, Z.; Zhou, J. Evapotranspiration in Heihe River Basin based on SEBS model. J. Lanzhou Univ.
2008,44, 1–6. [CrossRef]
43.
Ma, Q.; Liu, X.; Li, Y.; Wang, Y.; Wu, J. Estimation and Spatio-Temporal Distribution of Evapotranspiration in
Small-Scaled Catchments in Subtropics of China Based on Landsat 8 Data. J. Ecol. Rural Environ.
2016
,32,
901–907. [CrossRef]
44.
Wen, Y.; Guo, Q.; Wang, Y. Evapotranspiration and the Factors Aecting It in Chakou Basin Studied with the
SEBS Model. J. Irrig. Drain. 2018,37, 80–87. [CrossRef]
45.
Allen, R.G.; Tasumi, M.; Trezza, R. Satellite-Based Energy Balance for Mapping Evapotranspiration with
Internalized Calibration (METRIC)—Model. Hydrol. Earth Syst. Sci. 2007,4, 380–394. [CrossRef]
46.
Yu, J.; Yao, Y.; Zhao, S.; Jia, K.; Zhang, X.; Zhao, X.; Sun, L. Estimating latent heat flux over farmland from
Landsat images using the improved METRIC model. Remote Sens. Land Resour. 2018,30, 83–88. [CrossRef]
47.
He, L.; Wang, Y.; Bie, Q.; Fang, J.; Zhao, C. Estimation of field evapotranspiration in the middle reaches of
Heihe River basin based on SEBS-METRIC Method. J. Lanzhou Univ. 2013,49, 504–510. [CrossRef]
48.
Yao, Y.; Qin, Q.; Fadhil, A.M.; Li, Y.; Zhao, S.; Liu, S.; Sui, X.; Dong, H. Evaluation of EDI derived from the
exponential evapotranspiration model for monitoring China’s surface drought. Environ. Earth Sci.
2011
,63,
425–436. [CrossRef]
49.
Qin, Z.; Karnieli, A.; Berliner, P. A mono-window algorithm for retrieving land surface temperature from
Landsat TM data and its application to the Israel-Egypt border region. Int. J. Remote Sens.
2001
,22, 3719–3746.
[CrossRef]
50.
Meng, X.; Li, H.; Du, Y.; Cao, B.; Liu, Q.; Li, B. Retrieval and validation of the land surface temperature
derived from Landsat 8 data: A case study of the Heihe River Basin. J. Remote Sens.
2018
,22, 857–871.
[CrossRef]
51.
Zhu, Z.; Cheng, P.; Gui, X.; Teng, Y.; Tong, C. Overview of Surface Temperature Inversion Algorithm. Geomat.
Spat. Inf. Technol. 2016,39, 70–75.
52.
Sun, J.; Zhao, P.; Ye, Q. A Split-window Algorithm for Retrieving Land Surface Temperature from ASTER
Data. Remote Sens. Technol. Appl. 2012,27, 728–734.
53.
Ye, X.; Ren, H.; Liu, R.; Qin, Q.; Liu, Y.; Dong, J. Land Surface Temperature Estimate from Chinese Gaofen-5
Satellite Data Using Split-Window Algorithm. IEEE Trans. Geosci. Remote Sens.
2017
,55, 5877–5888.
[CrossRef]
54.
Kan, A.; Wang, X.; Gao, Z.; LI, G. Vegetation spatio-temporal changes and driving factors in the Mt.
Qomolangma Nature Reserve in 2000-2007. Ecol. Environ. Sci. 2010,19, 1261–1271. [CrossRef]
55.
Liu, Q.; Liu, G.; Huang, C.; Zong, X. Using Remote Sensing Data to Monitor Dynamic Changes of Nature
Reserve of the Yellow River Delta. Chin. Agric. Sci. Bull. 2010,26, 376–381.
Remote Sens. 2020,12, 1130 23 of 25
56.
Zhang, H.; Zang, S.; Zhang, Y.; Su, D.; Xie, R. Research on the impact of land use activities on nature reserves:
Heilongjiang Province as a case. Environ. Sci. Technol. 2015,38, 271–276. [CrossRef]
57.
Liu, X.; Li, J.; Liu, Y.; Liu, H.; Wen, R. Method for supervising and assessing of human activities in nature
reserve based on sky and earth. China Environ. Sci. 2016,36, 3135–3142. [CrossRef]
58.
Xu, W.; Qin, W.; Liu, X.; Xia, X.; Zhou, D.; Fan, L.; Jiang, M. Status Quo of Distribution of Human Activities
in the National Nature Reserves. J. Ecol. Rural Environ. 2015,31, 802–807. [CrossRef]
59.
Wang, K.; Tang, Y.; Wang, L.; Xinghua, W.U.; Yang, S. The monitoring and evaluation of protective eectiveness
in Shibalichangxia Nature Reserve of Hubei based on Remote Sensing technology. J. Cent. China Norm. Univ.
2015,49, 929–935. [CrossRef]
60.
Liu, X.; Fu, J.; Jiang, D.; Luo, J.; Sun, C.; Liu, H.; Wen, R.; Wang, X. Improvement of Ecological Footprint
Model in National Nature Reserve Based on Net Primary Production (NPP). Sustainability
2018
,11, 16.
[CrossRef]
61.
Liu, H.; Gao, J.; Zhang, H.; Ma, X.; Xu, X. Human Disturbance Monitoring and Assessment in the Biodiversity
Conservation Priority Area China. J. Geo-Inf. Sci. 2017,19, 1456–1465. [CrossRef]
62.
Wan, H.; Wang, C.; Li, Y.; Qiao, W.; Jing, L.; Liu, X. Monitoring an invasive plant using hyperspectral remote
sensing data. Trans. Chin. Soc. Agric. Eng. 2010,26, 59–63. [CrossRef]
63.
Yang, H.; Li, Y.; Hong, Y.; Zhu, H. Biodiversity Monitoring and Assessment Using Remote Sensing Technology
at County’s Scale. Remote Sens. Technol. Appl. 2015,30, 1138–1145. [CrossRef]
64.
Zhang, Z.; Xiong, K.; Huang, D. Analysis on the Spatiotemporal Change and Influence Factors of Vegetation
Cover in Fanjingshan Mountain over the Last 30 Years. Res. Soil Water Conserv.
2018
,25, 183–189. [CrossRef]
65.
Huang, Y.; Zhao, C.; Yang, H.; Ding, F.; LI, Z. Spatial distribution and aggregation analysis of human activity
in national key ecological function regions in China. Resour. Sci. 2016,38, 1423–1433. [CrossRef]
66.
Zhai, J.; Yuping, L.; Peng, H.; Tong, X.; Cao, G. Water Conservation Service Assessment and Its Spatiotemporal
Features in National Key Ecological Function Zones. Adv. Meteorol. 2016,2016, 1–11. [CrossRef]
67.
Li, X. Ecological Function Evaluation of Changbai Mountain Ecological Function Area. Master ’s Thesis,
Yanbian University, Yanbian, China, 2016.
68.
Wang, Y.; Chen, H.; Wang, P. The Ecological Environment Change Monitoring in Earthquake Disaster Area
Based on Remote Sensing. Surv. Mapp. 2018,41, 76–78.
69.
Liu, Y.; Ren, Y.; Wei, C.; Wang, A.; Zhou, H.; Chi, Y. Study on monitoring of informal open-air solid waste
dumps based on Beijing-1 images. J. Remote Sens. 2009,13, 320–326. [CrossRef]
70.
Wang, C.; Yin, S.; Meng, B.; Ma, W.; Zhu, L.; Wu, C. Analysis of the non-regular garbage sites in the region
of Beijing, Tianjin and Hebei using remote sensing monitoring images. Chin. High Technol. Lett.
2016
,26,
799–807. [CrossRef]
71.
Jiang, M.; Li, W.; Wen, J. Application of Satellite Image and Digital Elevation Model in Rural Environmental
Quality Monitoring. Environ. Monit. China 2014,30, 184–187. [CrossRef]
72.
Yang, D.; Chen, J.; Zhou, Y.; Chen, X.; Chen, X.; Cao, X. Mapping plastic greenhouse with medium spatial
resolution satellite data: Development of a new spectral index. ISPRS-J. Photogramm. Remote Sens.
2017
,128,
47–60. [CrossRef]
73.
Qu, R.; Nie, Y.; Zhang, Y.; Xiong, W.; Lou, Q.; Teng, J. Study on Extraction of Plasticulture Basing on GF-1 of
High Resolution Image of China. Environ. Sustain. Dev. 2018,43, 66–69.
74.
Liu, H.; Zhang, L. A predictive model for the hyperspectral character of saltmarsh soil to its heavy metal
content at Chongming Dongtan. Acta Ecol. Sin. 2007,08, 3427–3434. [CrossRef]
75.
Cai, D.; Wu, Q.; Cao, X.; Meng, Y.; Zhou, L.; Liu, S.; Zhang, L. Quantitative Retrieval of Soil Heavy Metal
Content in Longkou Wastewater Irrigation Area Based on HJ1A-HSI Images. Saf. Environ. Eng.
2015
,22,
33–39. [CrossRef]
76.
Gong, S.; Wang, X.; Shen, R.; Liu, Z.; Li, Y. Study on Heavy Metal Element Content in the Coastal Saline Soil
by Hyperspectral Remote Sensing. Remote Sens. Technol. Appl. 2010,25, 169–177.
77.
Xiao, J.; Wang, Y.; Zhao, W. Study on Soil Cd Monitoring in Sewage Irrigation Area by Hyperspectral Remote
Sensing. South North Water Transf. Water Sci. Technol. 2013,11, 62–66.
78.
Xu, Y.; Zhang, Z.; Wang, X.; Wen, Q.; Liu, F.; Li, N. Remote Sensing Monitoring and Temporal Variation
Analysis of Coastal Aquaculture in Shandong Province in the Recent Three Decades. J. Geo-Inf. Sci.
2014
,16,
482–489. [CrossRef]
Remote Sens. 2020,12, 1130 24 of 25
79.
Xia, L.; Liu, Z.; Xu, S. Estimation on pollution load and analysis on spatial characteristics of mariculture in
Zhelin Bay. Guangdong Agric. Sci. 2017,44, 151–157. [CrossRef]
80.
Luo, X.; Liu, M. Application analysis on the urban heat environment monitoring of Chongqing using HJ-1B
satellite remotely sensed data. Sci. Sin. 2011,41, 108–116. [CrossRef]
81.
Zhang, H.; Xu, H.; Li, L.; Fan, Y. Analysis of the Relationship between Urban Heat Island Eect and Urban
Expansion in Chengdu, China. J. Geo-Inf. Sci. 2014,16, 70–78. [CrossRef]
82.
Hou, H.; Ding, F.; Li, Q. Remote Sensing Analysis of Changes of Urban Thermal Environment of Fuzhou
City in China in the Past 20 Years. J. Geo-Inf. Sci. 2018,20, 385–395. [CrossRef]
83.
Chen, J.; Yang, X. A Study of Remote Sensing Monitoring of Urban Thermal Environment Based on ASTER
Data. Remote Sens. Land Resour. 2011,19, 100–105. [CrossRef]
84.
Liu, L.; Li, C.; Yin, J.; Zhao, J. Monitoring of Urban Thermal Environment in Shanghai Area from 1995 to
2012 Using Thermal Satellite Remote Sensing. Infrared Technol. 2016,38, 53–58.
85.
Xu, S.; Li, F.; Zhang, L.; Zhou, L. Spatiotemporal changes of thermal environment landscape pattern in
Changsha. Acta Ecol. Sin. 2015,35, 3743–3754. [CrossRef]
86.
Huang, L. Study on Dynamic Monitoring Method of Urban Greening Land. Master’s Thesis, Kunming
University of Science and Technology, Kunming, China, 2009.
87.
Meng, J.; Wang, J. Monitoring of urban ecological land use based on Gaofen-1 data. Sci. Surv. Mapp.
2016
,41,
33–37. [CrossRef]
88.
Zhang, C.; Yu, H.; Gong, P.; Ju, W. Relationship Between Land Brightness Temperature and Vegetation
Abundance in Wuhan City. Sci. Geogr. Sin. 2009,29, 740–744. [CrossRef]
89.
Tang, Z.; Zheng, H.; Ren, Z.; Cui, M.; He, X. Spatial and temporal changes to urban surface thermal landscape
patterns: A case study of Changchun City. Acta Ecol. Sin. 2017,37, 3264–3273. [CrossRef]
90.
Xiong, C. Vegetation Coverage Based on Landsat8 Images in Guangzhou. Ecol. Environ. Sci.
2015
,38,
383–386. [CrossRef]
91.
Xu, Y.; Liu, Y. Study on the thermal environment and its relationship with impervious surface in Beijing city
using TM image. Ecol. Environ. Sci. 2013,22, 639–643. [CrossRef]
92.
Tang, F.; Xu, H. Quantitative Relationship Between Impervious Surface and Land Surface Temperature Based
on Remote Sensing Technology. J. Jilin Univ. 2013,43, 1987–1996. [CrossRef]
93.
MaiMaiTiJiang, M.; Kasimu, A. Study on Land Surface Characteristics and Its Relationship with Land Surface
Thermal Environment of Typical City in Arid Region. Ecol. Environ. Sci. 2015,24, 1865–1871. [CrossRef]
94. Chen, W.; Gao, X.; Liu, R.; He, Z. An Analysis on Urban Impervious Surface and Its Relation with Thermal
Environments Based on Landsat 8. Geomat. Spat. Inf. Technol. 2015,38, 92–95.
95.
Wang, Z.; Dong, B.; Chen, D. Surface Parameters Analysis of Shenyang Urban Heat Island Based on Landsat.
Geomat. Spat. Inf. Technol. 2018,41, 4–7.
96.
Shao, Z.; Fu, H.; Li, D.; Altan, O.; Cheng, T. Remote sensing monitoring of multi-scale watersheds
impermeability for urban hydrological evaluation. Remote Sens. Environ. 2019,232, 111338. [CrossRef]
97.
Gong, J.; Liu, C.; Huang, X. Advances in urban information extraction from high-resolution remote sensing
imagery. Sci. China Earth Sci. 2020,63, 463–475. [CrossRef]
98.
Ma, X.; Li, C.; Tong, X.; Liu, S. A New Fusion Approach for Extracting Urban Built-up Areas from Multisource
Remotely Sensed Data. Remote Sens. 2019,11, 2516. [CrossRef]
99.
Song, H.; Xue, L. Dynamic monitoring and analysis of ecological environment in Weinan City, Northwest
China based on RSEI model. Chin. J. Appl. Ecol. 2016,27, 3913–3919. [CrossRef]
100.
Wang, Z.; Li, J.; Yang, Y.; Li, H.; Wu, M.; Wang, K.; Shi, X.; Shi, W.; Xie, B. Quantitative Assess the Dynamic
Change of Urban Ecological Environment Based on Remote Sensing-A Case Study in Yixing City, Jiangsu
Province. J. Ningxia Univ. 2017,38, 294–301.
101.
Wu, L.; Ma, B.; Liu, S. Analysis to vegetation coverage change in Shendong mining area with SPOT NDVI
data. J. China Coal Soc. 2009,34, 1217–1222. [CrossRef]
102.
Yao, F.; Guli, J.; Bao, A.; Zhang, J.; Li, C.; Liu, J. Damage assessment of the vegetable types based on remote
sensing in the open coalmine of arid desert area. China Environ. Sci. 2013,33, 707–713.
103.
Zha, D.; Shen, Z.; Liu, Z.; Liao, B.; Wang, W. Changes of ecological environment in the Dexing copper mine
based on TM images. Remote Sens. Land Resour. 2015,27, 109–114. [CrossRef]
Remote Sens. 2020,12, 1130 25 of 25
104.
Yao, W.; Yu, J.; Lu, Y. Investigation and Assessment of Artificial Influencing Factors of Land Degradation in
Shendong Coal Mining Area Based on ZY-3 Satellite Data. J. Ecol. Rural Environ.
2016
,32, 355–360. [CrossRef]
105.
Song, T.; Fu, X.; Chen, Y.; Wei, Y.; Wang, Q.; Cheng, X. Remote Sensing Inversion of Soil Zinc Pollution in
Gejiu Mining Area of Yunnan. Remote Sens. Technol. Appl. 2018,33, 88–95. [CrossRef]
106.
He, D.; Le, B.; Xiao, D.; Mao, Y.; Shan, F.; Ha, T. Coal mine area monitoring method by machine learning and
multispectral remote sensing images. Infrared Phys. Technol. 2019, 103. [CrossRef]
107.
Xu, J.; Zhao, H.; Yin, P.; Wu, L.; Li, G. Landscape ecological quality assessment and its dynamic change in
coal mining area: A case study of Peixian. Environ. Earth Sci. 2019,78, 708. [CrossRef]
108.
Li, X.; Li, S.; Gao, J. Aanlysis on Dynamic Change of Eco-Environmental Quality in Kailuan Mining Subsidence
Area. Adv. Mater. Res. 2012,433–440, 1433–1436. [CrossRef]
109.
Fu, X. Monitoring of Vegetation Coverage Variation Based on Multi-Temporal Remote Sensing Data in
Shendong Centre. Environ. Monit. China 2014,30, 186–190. [CrossRef]
110.
Liu, Y.; Hou, E.; Yue, H. Dynamic monitoring and trend analysis of vegetation change in Shendong mining
area based on MODIS. Remote Sens. Land Resour. 2017,29, 132–137. [CrossRef]
111.
Wang, G.; Qiu, K. Dynamic Monitoring of Vegetation Coverage in Huainan Mining Area Based on MODIS
NDVI. Bull. Surv. Mapp. 2018, 33–40. [CrossRef]
112. Gao, J. How China will protect one-quarter of its land. Nature 2019,569, 457. [CrossRef]
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For the sake of retrieving soil moisture by the polarized information, a polarized reflection model of soil surface was established, and an experimental study of soil moisture and reflected light's polarized information was carried out in the visible wavebands. Firstly, the polarized reflection model of soil surface was established based on the geometrical optics theory and the measured data. Secondly, the soil samples of different moisture contents were made, the experimental platform was set up, the soil samples polarized information were obtained by a polarized imaging detection system with high precision in a variety of incident and observation conditions, the model was verified by analyzing the experimental data. Finally, a quantitative relationship among the micro-surface slope variance, the diffuse reflection coefficient, the equivalent refractive index and the soil moisture was established in the model and the soil moisture was retrieved according to the quantitative relationship. The research results show that: on 476 nm, the polarized information of a soil sample with a moisture content of 26% is measured by a polarized imaging detection system in the indoor environment, the soil moisture is calculated as 24.73% according to the proposed retrieval method in an error of 4.88%. Therefore, it is proved that the proposed method of retrieving soil moisture by the polarized information is correct and feasible. © 2018, Editorial Board of Journal of Infrared and Laser Engineering. All right reserved.
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Based on relevant MODIS NDVI data, spatio-temporal variation of the vegetation cover in the Shule River Valley during 2000-2014 was analyzed using the linear trend analysis, M-K test and Hurst index methods, with the aid of ArcGIS and Matlab software. Results show that in the past 15 years, vegetation cover has been increasing rapidly and steadily in the middle and lower reaches of the Shule River Valley, while fluctuating with a mild rising trend in the upper reaches. However, as a whole, it has been improving remarkably in the river valley. The areas with vegetation cover significantly or extra-significantly improved now account for 30.86% of the valley, and are mainly distributed in the irrigated agricultural regions in the middle and lower reaches and mountainous areas with slope less than 20 degree in the upper reaches; while the areas with vegetation cover significantly or extra-significantly degraded do less than 1%, and are concentrated in the regions with natural vegetation cover in the middle and lower reaches of the valley. Since 2004, the valley has been under a relatively warm and humid climate. The total precipitation and mean temperature of the period from June to August affects growth of the vegetation the most significantly in the upper reaches of the valley, showing a significant positive relationship, and that the effect of precipitation is much higher than that of temperature. The vegetation in the middle and lower reaches is not so sensitive to meteorological factors as that in the upper reaches. The areas with vegetation to be improved in future, mostly to be continuously improved, account for 18.63%, and are mainly distributed around the new settlements and in nature reserves in the middle and lower reaches of the Shule River Valley and the west section of the northern piedmont of the Qilian-Altun Mountains, whereas the areas with vegetation degraded amount to 14.25%, and are mostly transformed from areas with vegetation improved and mainly distributed on slopes less than 20 degree in the mountainous areas of Danghenan Mountain, Yema Mountains and Zhaobi Mountain, in the upper reaches of the valley. © 2016, China Environmental Science Press. All rights reserved.