International Workshop on Remote Sensing and Eco-hydrology in Arid Regions

Regional Remote Sensing Products for

Eco-hydrological Application in Arid Regions

Qinhuo Liu(qhliu@irsa.ac.cn)

State Key Laboratory of Remote Sensing Sciences

Institute of Remote Sensing and Digital Earth, CAS

September 18, 2013

Beijing, China

Outlines

1. Introduction

2. Prototype system for Land Surface Parameters

Inversion Based on Multi-source Remote Sensing

Data

3. Remote Sensing Product Generation for Eco-

hydrological Application in Arid Regions

4. Expectations and Discussions

1. Introduction

Heihe is one of the largest

inland river basin in

Northwest China:

water-stressed ecosystems,

complicated eco-hydrological

processes, more fragile to

climate change and

anthropogenic disturbance

A major research plan

“Integrated research on the

eco-hydorlogical process

of the Heihe River Basin”

launched by NSFC in 2010

the objectives of the Heihe Plan include:

To reveal the processes and mechanisms of the eco-

hydrological system in an inland river basin at different

scales(e.g. Leaf, individual plant, community, landscape,

and watershed)

to improve the research capabilities and predictability of

the evolution of hydrological, ecological, and economic

systems;

to determine the responses of eco-hydrological

processes to climate change and human activities;

and to provide fundamental theory and technical support

for water security, ecological security, and sustainable

development in inland river basins.

• Distributed Hydrological Model ：DHSVM（Wigmosta et al., 1994）、SWAT（Arnold and Fohrer, 2005）and GEOtop（Rigon et al., 2006）；

• Underground Water model: MODFLOW（Harbaugh et al., 2000）；

• Dynamic vegetation model and Vegetation Growth model: LPJ-DGVM

（Sitch et al., 2003）、BIOME-BGC（Thornton and Running, 2002）and WOFOST（Vandiepen et al., 1989）；

• Land surface process model: SiB2（Sellers et al., 1996）、CoLM

（Dai et al., 2003）and CLM 3.0/4.0（Oleson et al., 2010）。

• Parameters

• Variables

• Driven Data

which types/what scales of the eco-hydrological models ?

Key Parameters/Variables for Regional for

regional Eco-hydrological process models

variables／parameters applications Chinese satellite International satellite

precipitation 流域水循环；驱动变量 FY-3 TRMM, GPM

Evaportranspiration 流域水循环；生态耗水；水分利用效

率（生态水文耦合）

FY-3成像光谱仪、HJ-1 ASTER, MODIS, MERIS, AATSR

Flow 流域水循环；分布式水文模型 OSTM/Jason-2 Radar Altimeter,

ENVISAT Radar Altimeter-2, TerraSAR-

X, SWOT

Undergroound water 流域水循环；地表－地下水相互作用 GOCE, GRACE, GRACE-II

Soil Moisture 流域水循环；冻土水文 FY-3微波辐射计 SMAP, SMOS, ALOS PALSAR, Envisat

ASAR, BIOMASS

Snow 流域水循环；积雪水文 FY-3微波辐射计 CoReH2O, SCLP

Vegetation Classification 分布式水文模型和生态模型所需参数 HJ-1, CBERS Proba CHRIS, HYSPIRI, 其他高光谱卫

星

Land use/Cover 分布式水文模型和生态模型所需参数；

人类活动影响评价

HJ-1, CBERS TM, SPOT, MODIS, 其他多光谱卫星

Plant structure 水文模型和生态模型所需参数；灌溉

管理

HJ-1, CBERS Proba CHRIS, HYSPIRI, 其他高光谱卫

星

Vegetation coverage 水文模型和生态模型所需参数 HJ-1, CBERS TM, SPOT, MODIS, 其他多光谱卫星

Leaf area index 水文模型和生态模型所需参数；生态

系统动态

HJ-1, CBERS TM, SPOT, Proba CHRIS, MODIS, 其他

多光谱卫星

Vegetation structure 生态模型所需参数

NPP、NEP 生态系统动态；水分利用效率（生态

水文耦合）

HJ-1 DESDynI, BIOMASS, 其他高光谱卫星，

其他激光雷达卫星

DEM 分布式水文模型和生态模型所需参数 ALOS PRISM, ASTER等立体像对数据

Parameters Spatial Resolution

Temporal Resolution Period Accuracy

LULC 30m/250m 1month/1 year 2012-2015 90%

Albedo 1KM 5 day 2012-2015 90%

VI/VC 250m/1KM 5 day 2012-2015 85%

PAR 1KM Hourly, Daily 2012-2015 90%

NPP 1KM 5 day 2012-2015 80%

the Most important Regional Remote Sensing

Products for Eco-hydrological Application

Here, we only list the Ecological system parameters

how can produce a series of

high-quality remote sensing

products that can support

basin-scale integrated eco-

hydrological studies, from

the multi-source satellite

observation, ?

We have a lot of global remote

sensing product, such as

MODIS, does the accuracy

satisfy the requirements?

2. Prototype system for Land Surface Parameters

Inversion Based on Multi-source Remote Sensing Data

9

Land surface process model scale issues

Land surface process

model need different spatial

scale observation: 1KM,

5KM, 10KM, 100KM

crop

road

soil

urban

forest

Mixed pixel, terrain effect

Leaf：Component Spetrum

Vegetation canopy：3D structure

Hi-WATER

Objectives

Global Products Global scale--coarser

Developed global algorithm but not localized

Terrain effect is overlooked

Pre-knowledge is barely used

Usually based on one type of remotely sensed data

Pre-defined classification system

10

Reginoal Products River basin scale--finer

Specialized for local applications

Terrain effect is considered

Knowledge from field campaigns can be used

Based on multi-source remote sensing data

Classification system specialized for this area

Regional Remote Sensing Products for

Eco-hydrological Application the whole Heihe river basin

Hi-WATER

Design consideration

11

Parameter Retrieval from Multi-source remote sensing data for higher resolution and accuracy

Prior Knowledge from HiWATERexperiment

Topographic correction based on the DEM

New satellite datasuch as HJ-1 with720KM swath:WFI:0.43~0.90μm,

30m;

IRS: 0.75 ~12.5m,

150m/ 300m .

12

Quantitative Monitoring:

Inversion

Calibration corretionGeometric correction

Cloudy mask

Atmospheric correction

Prediction : assimilation

Qualitative application: Classification and

identification

Information

extraction

Preprocessing

Parameter Inversion algorithm development

13

Multi-source remote sensing data from different sensors

provide a chance to improve the temporal and spatial

resolution of existing hydrological products.

Pre-processing of multi-data

14

Preprocessing to normalize the multi-source remote

sensing datas: Reflectance dataset of USA(MODIS/

AVHRR), Chinese (FY3_VIRR),

European (VEGETATION) etc with

5km resolution

Dataset of USA(MODIS/AVHRR),

Chinese (FY3_VIRR), European

(VEGETATION) etc. with 1km resolution

• Geometric correction

• Resample, reprojection

• Band response function analysis

• Information augment analysis

net multisensors monosensorI I I

1 2 3ln ln ln ln( )I MSE

Preprocessing methods

15

Prototype system design

16

Institute of Remote Sensing Applications (IRSA),

CAS

Prototype system design

3. Remote Sensing Product Generation for Eco-

hydrological Application in Arid Regions

(1) Land use and Land Classification

(2) Vegetation Index/Vegetation

Coverage/ Leaf Area Index

(3) Photosynthesis Absorption

Radiation /Net Primary Production

Hi-WATER

Land use and Land Classification: What’s now?

Land

cover

dataset

Satellite

/ sensor

Time

period

Spatial

resolutio

n (m)

Number

of

classes

Classifica

tion

system

Classifica

tion

accuracy

*

GLC2000

v1.1

SPOT/

Vegetati

on

2000 1000 22 UN LCCS 39-64%

Mod12q1

C004

Terra/

MODIS2001 1000 17 IGBP 75-80%

Mod12q1

C004

Terra/

MODIS2005 500 17 IGBP 72-77%

GlobCove

r v2.2

Envisat/

Meris

2004,

2006300 22 UN LCCS 67.1%

FROM-

GLC

Landsat/

TM,

ETM+

Mainly

201030, 250 28

UN LCCS

modified64.9%

18

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What’s the problem?

Less land cover types

Unbalanced classification accuracy for different regions

Accuracy is not high enough for applications in river basin scale

Classification results from different datasets are not consistent

19

Hi-WATER

Main methods for LUCC mapping

Fine spatial

resolution data

High temporal

data

High spatio-

temporal data

Data

example

Landsat-TM/ETM+,

ASTER, etc.

MODIS, AVHRR, MERIS,

etc.

HJ-1/CCD

Advantage Less mixed pixels

More precise boundary

Can capture the

phenology of vegetation

All advantages of

fine resolution and

high temporal data

Disadvanta

ge

Cannot capture the

phenology of vegetation

More mixed pixels

Classification uncertainty

is high in mixed area

N/A

20

Hi-WATER

The strategies

The advantage of high spatio-temporal resolution of HJ-1/CCD is fully taken

Getting support from multi-source remote sensing data, such as Landsat-TM/ETM+ and Google Earth images

Pre-knowledge from field campaigns are incorporated

Other information used, such as DEM

21

Hi-WATER

The advantages of high spatio-temporal resolution data

High temporal frequency can show the phenology of vegetation

22

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

小麦

大麦

油菜

玉米

Main crops NDVI variation at middle stream

5-15：wheat grows，maize

not

6-19:wheat grow s better

than maize 7-14：no difference

7-14：no difference8-3：Maize still

grows; others are

declining

8-20：Maize still

grows; wheat is

declining; rape is

harvested

9-2：only maize

left

Hi-WATER

The advantages of high spatio-temporal resolution data

Winter season is better for extracting evergreen forests

Multi-month images can partly get rid of shadow effects

24

Evergreen forests extracted from

images on Jan-Apr.

Combination from results of the

four month’s

Hi-WATER

The advantages of high spatio-temporal resolution data

Texture information from high spatial resolution data is used to differentiate grasslands and broadleaf forests

25

• Broadleaf forests have much

more texture than grass lands

and croplands

• Gray level co-occurrence matrix

can embody the difference

HJ image Texture

Hi-WATER

Multi-source remotely sensed data

Very high spatial resolution images are used to improve the samples accuracy

TM/ETM+ is used for NDBI calculation to help extract urban areas

26

Broadleaf

forests

Hi-WATER

Other information -- DEM

DEM and pre-knowledge are used to simplify the classification, such as urban areas and bare rocks below.

27

Urban areas’ altitude

usually lower than

3000m

Bare rocks’ altitude

usually higher than

3000mExtracted urban

area

Hi-WATER

Pre-knowledge from field campaigns

What can we get from field campaigns

Crops’ planting structure

Crops’ exact phenology

Main crops planted and its rotation information

Some first hand information

28

Field campaign in Zhangye area

Example of land cover filling

Land cover map of Jan. ~ Mar.

Land cover map of Apr. ~ Jun.

Land cover map of Jul. ~ Sep.

Land cover map of Oct. ~ Dec.

Accuracy evaluation• Lower stream has the highest accuracy；upper stream

is effected by shadows；Middle stream has the lowest

accuracy.

• Overall accuracy is higher than 95%，Kappa is higher

than 0.94 without considering finer crop classification.

• Overall accuracy is higher than 90%，Kappa is close to

0.9 considering finer crop classificationOA：92.19% Kappa：0.87

Types Commi

ssion

rate

（%）

Omissi

on

rate

（%）

Cropla

nds0/315 0.00 18/333 5.41

Water 0/54 0.00 13/67 19.40

Urban 1/76 1.32 15/90 16.67

Barren 45/144 31.25 0/99 0.00

OA：82.97% Kappa：0.80

typesCommis

sionrate（%）

Omissio

nrate（%）

Maize 21/93 22.58 18/90 20.00

Spring

wheat3/64 4.69 14/75 18.67

Barley 5/67 7.46 19/81 23.46

Rape 21/66 31.82 17/62 27.42

water 0/54 0.00 13/67 19.40

Urban 1/76 1.32 15/90 16.67

Barren 45/144 31.25 0/99 0.00

3. Remote Sensing Product Generation for Eco-

hydrological Application in Arid Regions

(1) Land use and Land Classification

(2) Vegetation Index/Vegetation

Coverage/ Leaf Area Index

(3) Photosynthesis Absorption

Radiation /Net Primary Production

• Normalized difference vegetation index(NDVI) is a key

parameter to describe physical and biological processes of

plants. (Asraret et al., 1984; Goward , 1992)

• Vegetation compositing technology offers a method to obtain

NDVI products with special consistency and continuity. (Huete, et

al., 1999; Lovell et al., 2003)

• BRDF compositing scheme reduces sun-target-sensor variations

with using of a BRDF model, represent major progress for

deriving biophysical variables from the radiative transfer model.(Huete, et al., 2002; Duchemin, et al., 2002; Vancutsem, et al., 2007)

• The quality of NDVI product from a single observation is always

unsatisfactory because of the ever-present atmosphere and

cloud contamination, even the data integrality.

Background for NDVI normalization

Sensor Produc

ts

Algorithm Time

resolution

Time

coverage

Spatial

resolution

AVHRR

GVI

MVC, Maximum value

compositing(Holben, 1986)

15 days 1982-present 4 km

8 kmGIMM

S

15 days 1981-2006

PAL 10 days 1981-1999

VEGETAT

ION

VGT-

S10

MVC 10 days 1998-present 1 km

VGT-

D10

BDC, BiDirectional

Compositing(Duchemin, et al., 2002)

2002-present

MODIS MOD1

3

BRDF compositing, back up

MVC,

CV-MVC, constrained- view

angle- MVC(Huete, et al., 2002)

16 days 1999-present 250m

1kmMYD1

3

2002-present

MERIS MGVI MC：mean compositing(Vancutsem, et al., 2007)

10 days 2002-present 1.2 km

Global and regional composited VI products

The commonly used global and regional composited vegetation products are listed. We can find that the MVC is still the most used algorithm, and the BRDF scheme is used more widely.

• The BRDF scheme interpolate all bidirectional reflectance

observations, of acceptable quality, to their nadir-

equivalent band reflectance values from which the VI is

computed and produced.

• To insure the interpolated quality, at least five good quality

observations is required. This limits the temporal

resolution of NDVI product and increases the change

uncertainty in the composite period, especially when the

vegetation grow fast.

• The multi-sensor composite strategy was developed to

improve the temporal resolution.

BRDF compositing scheme

Multi-sensor data compositing

• This paper was to conduct compositing and analyses on the NOAA 18

AVHRR, Terra and Aqua MODIS, and FY-3A VIRR multi-sensor

datasets.

http://fy3.satellite.cma.gov.cn/PortalSite/d

efault.aspx http://aqua.nasa.gov/;

http://terra.nasa.gov/;

http://www.oso.noaa.gov/poesstatus/;

and

http://www2.ncdc.noaa.gov/docs/podug/

Operational dates of NOAA

AVHRR, Terra and Aqua

MODIS, and FY-3A and FY-

3B VIRR.

1）multi-sensor datasets

In view of sensors homogeny

and time coverage, NOAA-15 to

NOAA-19 AVHRR, Terra and

Aqua MODIS, and FY-3A VIRR

can be used to build multi-

source dataset.

Multi-sensor data compositing

• The compositing period controls the number of

acquisitions considered. It is more pronounced as almost

4 tiles images per day for the multi-sensor datasets.

The distribution of

observation angles for

AVHRR, Terra and Aqua

MODIS, and VIRR

IN 4 or 5 days the BRDF

shape of the pixels has

been able to gain.

IN 7 or 8 days the BRDF

details information has

been able to gain

2）compositing period

Multi-sensor data compositing

• The technique employed depends on the number and quality of

observations n .

• The RossThick-LiSparseR BRDF model (Roujean et al., 1992) is used.

2）compositing period

Primary multi-sensor VI compositing methodology

),,(),,(),,( svvolvolsvgeogeoisosv kfkff

Multi-sensor data compositing

• Ri is a good indicator for the representation of the forepart i days

subset dataset to the whole N days dataset

• W e find the mean R2 can highlight the difference of Ri, and also

keep the trend

2）compositing period

The mean correlation coefficient of

accumulated data increased day by

day of each group

The 4th day mean R2 of each

group are

0.9269, 0.9314, 0.8552, so 4

days compositing period being

close to the whole group data with

the best temporal resolution

Ni

ii

kN

sizek

kkNki

sizek

kki

kN

sizek

kkNki

sizek

kki

i

RN

Rmean

NDVINDVINDVINDVI

NDVINDVINDVINDVI

R

1

22

2,

1,

2,

1,

,1

,,1

,

1

)()(

))((

Multi-sensor data compositing

• Many linear BRDF models was compared, the simple Walthall model

(Walthall et al, 1985) and Ross-thick/Li-sparse BRDF model(Roujean

et al., 1992 ) was found to perform well for most vegetation types.

(Privette et al. 1997; Leeuwen et al. 1996)

3）BRDF model of compositing

Ross-thick/Li-sparse require 7 good

observation and Walthall require 5 to

insure the fitting quality

Walthall BRDF

Ross-Li BRDF Model

The two BRDF

models was

compared from

the multi-sensor

datasets

compositing

R2 of 4 days subset and the

whole dataset composited NDVI

of each group

The Walthall BRDF

model perform better

and required less good

quality observations

Results Analysis

Composited NDVI from Jul.21

to Aug.4

• Consistency problems of BRDF back up pixels (MVC)

The MVC composited

value are always

come from VIRR and

AVHRR，nearly

92%；Need to improve the

sensor differences

BRDF back up

Algorithm

source tracker

Results Analysis• Stripe noises of CV-MVC pixels

Composited NDVI

from Aug.1 to

Aug.4

CV-MVC

source tracker

The sign map of

the CV-MVC

composited pixels

It shows the

problem is also

arise from the

sensor differences

Results Analysis• Liner band switch of AVHRR, VIRR, MODIS sensors

Improve the sensor differences

d

d

0

0

)(

)()(

The Liner band switch Data

The Bold line present the sensitivity of

Sensors

The thin lines mean the measured

spectrum curve

The Liner band switch

result of AVHRR,

VIRR, MODIS

sensors of Red and

NIR

AVHRR_MRed = 1.0465 * AVHRRRed +

0.0039

AVHRR_MNIR = 1.0975 * AVHRRNIR +

0.0093

VIRR_MRed = 1.6558* VIRR Red + 0.0132

VIRR _MNIR = 1.5999 * VIRR NIR +

0.0000

Results Analysis• Multi-sensor dataset compositing algorithm

Conclusions• Cross validation of MOD12A2

Cross validation of MOD12A2 and

multi-sensor datasets composited NDVI

by developed algorithm a)7.28-7.31

b)8.1-8.4 c)8.5-8.8 d)8.9-8.12

The R2 of Multi-sensor

subset products and

MOD12A2 are 0.8850,

0.8629, 0.9146, 0.9205,

presenting a good

results.

• Cross validation with MOD12A2 shows the R2 of multi-

sensor subset products are all higher than 0.86,

presenting a good precision.

• Multi-sensor strategy improves the temporal resolution

from 16 days to 4 days.

• The success in compositing the primary product

illustrating the multi-sensor strategy is feasible and

promising.

FVC product and validation

30m FVC 250m FVC

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 200

FV

C

Julian Day

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0 100 200 300

FV

C

Julian DayFVC accuracy : 85%

a MODIS pixel

(250m)a HJ

pixel

(30m)

3. Remote Sensing Product Generation for Eco-

hydrological Application in Arid Regions

(1) Land use and Land Classification

(2) Vegetation Index/Vegetation

Coverage/ Leaf Area Index

(3) Photosynthesis Absorption

Radiation /Net Primary Production

Hi-WATER

The problem in NPP estimation model---light use efficiency model

53

The expression of NPP LUE model:

is the incidence solar radiation between 400-700nm that reached the canopy.PARQ

FPAR is the fraction of solar radiation absorbed by canopy.

n is the light-use efficiency.

nPARQFPARNPP

FPAR is inversed by the relationship between FPAR and VI /LAI.

1）VI has saturation problem.

2）The method based on physical model does not distinguish direct radiation and diffuse

radiation.

PAR is estimated by the relationship between PAR and solar

radiation.

1）The relationship is changed with location and the weather.

2）Most atmospheric radiative transfer model does not distinguish direct radiation and

diffuse radiation

Hi-WATER

FPAR/PAR variation under different weather condition

54

0

0.2

0.4

0.6

0.8

1

1.2

9.5 11.5 13.5 15.5 17.5

有云天晴天

Time

FP

AR

Cloudy sky

Clear sky

The observed daily change of FPAR under

cloudy sky and clear sky

The percentage changing of direct and

diffuse PAR with AOT

The daily change of FPAR under

cloudy sky has obvious difference with

that under clear sky.

The direct FPAR and the diffuse FPAR

has different characteristic.

The percentage of direct PAR and

diffuse PAR changes with different

weather condition, different seasons

and areas.

Hi-WATER

PAR estimation

55

Key process: Look-up table

LUT=f(AOD , COT , WV, OZ, α, θs )

Key parameters：Cloud

optical thickness

Instantaneous PAR estimation

on horizontal surface.

Terrain Corrections ：Altitude,

Slope and Aspect

Topographic obstruction on direct PAR; sky

view factor affection on diffuse PAR .

Temporal integration ：

Integrate instantaneous PAR to

hourly and daily PAR.

Hi-WATER

FPAR inversion

56

= (1 )*dir diffFPAR k FPAR k FPAR

(1 )*(1 )

[1 ( 1)* ]

b gap

dir

gap

PFPAR

a P

(1 )*(1 )

[1 ( 1)* ]

w open

diff

open

KFPAR

a K

Direct FPAR is calculated from Black

sky Albedo and canopy gap

probability.

Diffuse FPAR is calculated from

White sky Albedo and the openness

of the crown at the top of canopy.

K is the ratio of direct-to-total which

can be inversed from PAR estimation.

is the absorptivity ratio of soil and

crown canopy .

a

Hi-WATER

Product verification data

57

Downward PAR

Observation

Upward PAR

Observation

plant soilPAR PAR =APAR +APAR

*plant plantAPAR FPAR PAR

Downward/upward PAR field sites

covering three different surface types

(Forest, Grassland and farmland) on

Heihe Basin.

These observation data could be used to

verify indirectly.plantAPAR

Hi-WATER

PAR products

58

Daily direct PAR Daily total PAR

Product Period:2012.6.1-2012.8.31

Temporal resolution: Instantaneous, hourly, daily PAR

Product classification: Direct PAR, Diffuse PAR, Total PAR, Ratio of

direct-to-total.

Hi-WATER

FPAR products

59

Product Period:2012.6.1-2012.8.31

Temporal resolution: 8 day

Product classification: Direct FPAR, Diffuse FPAR, Total FPAR.

Hi-WATER

Further research and product specification

APAR validation on Heihe Basin.

The light use efficiency estimation which distinguish direct radiation

and diffuse radiation.

The research on NPP estimation methodology and its validation.

Product production.

60

Products Period Temporal

Resolution

Spatial Resolution Area

FPAR 2012-2015 8 day 1KM Heihe Basin

PAR 2012-2015 Hourly, daily 1KM Heihe Basin

K 2012-2015 Hourly, daily 1KM Heihe Basin

NPP 2012-2015 8day 1KM Heihe Basin

61

Expectations:

The first version products for 2012 will be released in the end of this year;

The second version products for 2012-2013 will be released in March 2014.

The third version products for 2012-2014 will be released in March 2015.

Discussions:

(1) How to combine accumulated earth observation data to generate the long-

term, consistent and accurate land surface products?

Synergic inversion based on information content and error propagation

using Multi-scale, multi-spectral (VIS-NIR-TIR-MIR), multi-satellites

(Geostationary and polar orbiting, USA, China, European and others)?

(2) How to validate the remote sensing inversed land surface parameters at

different scale?

Multi-scale and long term field observation and Scaling transformation?

(3) How to combine the Regional Remote Sensing Products for Eco-

hydrological process model in Arid Regions?

Assimilation?

4. Expectations and Discussions

62

Xin Li, Guodong Cheng, Shaomin Liu, Qing Xiao, Mingguo Ma, Rui Jin, Tao Che, Qinhuo Liu, Weizhen

Wang, Yuan Qi, Jianguang Wen, Hongyi Li, Gaofeng Zhu, Jianwen Guo, Youhua Ran, Shuoguo Wang,

Zhongli Zhu, Jian Zhou, Xiaoli Hu, and Ziwei Xu , HEIHE WATERSHED ALLIED TELEMETRY

EXPERIMENTAL RESEARCH (HiWATER), BAMS 2013

DongqinYou, Jianguang Wen, Qiang Liu, Qinhuo Liu, Yong Tang，The Angular and Spectral Kernel-

driven Model:assessment and application. IEEE journal of selected topics in applied earth observations

and remote sensing, JSTARS. 2013.2271502,2013.

Yu ShanShan; Xin XiaoZhou; Liu QinHuo, Estimation of clear-sky longwave downward radiation from HJ-

1B thermal data, SCIENCE CHINA-EARTH SCIENCES V: 56 N: 5 P: 829-842 , MAY 2013

Zhang, Hailong; Xin, Xiaozhou; Li, Li; An Improved Parametric Model for Simulating Cloudy Sky Daily

Direct Solar Radiation on Tilted Surfaces, IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH

OBSERVATIONS AND REMOTE SENSING V: 6 N: 1 P: 180-187, FEB 2013

Hua Li, Qinhuo Liu,Yongming Du, Jinxiong Jiang, Heshun Wang. Evaluation of the NCEP and MODIS

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