Estimation of cropland net primary production using remote sensing methods

Hua CHEN1,2 1. China Aero-Geophysical Survey and Remote Sensing

Center for Land and Resources Beijing, China

agrschen@gmail.com

Dafang ZHUANG2 2. Institute of Geographic Science and Natural Resources

Research, Chinese Academy of Sciences Beijing, China

zhuangdf@lreis.ac.cn

Abstract—Net primary production (NPP) is an important parameter for the global carbon cycle research, and the remote sensing methods to estimate of it are widely concerned. In this paper, we proposed a modified CASA model to estimate NPP. Firstly, we used a new method to estimate solar radiation based on the relationship between sunset angle and sunshine hour, and then the distribution of solar radiation needed for the regional NPP calculation was acquired. Secondly, we utilized the local parameters of maximum light use efficiency 0.51gC/MJ for winter wheat and 0.66gC/MJ for summer maize specifically. Thirdly, a new water limitation factor based on LSWI (The Land Surface Water Index), which can reflect the irrigation effects, was used. Lastly, a FPAR retrieval strategy based on searching table for MODIS replaced the former CASA method. Using the common meteorological data , 8-day MODIS reflection data (MOD 09) and the distribution map of winter wheat and summer maize at Huang-Huai-Hai plain, the NPP map was acquired using the above parameterized and optimized CASA model. The yearly NPP of winter wheat is 6.028 · 1013gC, and that of summer maize is 4.132·1013gC at this region. The trend at time and space is clear. Double apex distribution of NPP at temporal scale is shown, the spatial distribution of winter wheat NPP is obviously related with latitude (r=-0.640). The value of NPP is related with climate. Winter wheat NPP is related with temperature (r=0.637) and rainfall (r=0.446), but summer maize NPP is most affected by solar radiation (r=0.662) and sunshine (r=0.614). In the different subregions of the research area, the highest winter wheat NPP (1098.89 gC/m2a) and summer maize NPP(805.20gC/m2a) both appear in eastern Shandong district, but the lowest average NPP at subregion level is in the coastal area for winter wheat (216.62gC/m2a) and in northern Jiangsu plain for summer maize (482.10gC/m2a).

Keywords-remote sensing; NPP; MODIS; CASA model; Huang-Huai-Hai plain

I. INTRODUCTION Net Primary Production (NPP) is the outcome of the

interaction between vegetation and environment, which has been widely concerned in global climate change research[1]. The intensity of it is the reflection of plant photosynthesis activity and has great impact of combating carbon dioxide enrichment. Agricultural ecosystem is an important part of land ecosystems, which has been greatly influenced by human being with production activity. So, it should be an important part of

this research area. However, tradition research of forest net primary productivity has widely achieved and the study of agricultural NPP has been the lacking part.

At the same time, the development of remote sensing techniques provides an advanced and fast way to the research of NPP. As remote sensing technique has the advantage of repeating rapidly, covering broadly, and acquiring fast, it has became a unique and important data source of global climate change research. NOAA AVHRR has observed the earth surface for more than thirty years continually and stored vast material of land surface for analysis. The MODIS sensors developed in recent ten years have higher spatial, spectral and radioactive resolution, which will be more suitable for the global ecosystem research.

Because we can’t directly and widely measure the ecosystem NPP at regional and global scale, the estimation of it using models has been widely used. During several decades, many models have been developed and can be classified as three: statistical, parameterized, and process based model.

Statistical model was developed early when the material and data were very limited. It was supposed that vegetation productivity is controlled by climate and can be predicted by climatic factors, such as, solar radiation, temperature and rainfall with a regression equation. The meteorological data in model can be easily acquired, and the modeling results can reflect the zonal distribution plant NPP. So, these models have been widely used in earlier years. Especially, in recent decades, the statistical models in corporation with remotely sensed vegetation index has been concerned, which are more likely to reflect the actual NPP instead of potential NPP. And this kind of remote sensing model is based on the well relation of vegetation index with plant photosynthesis activities [2]. But these models only considered few factors and neglected the process, so it is necessary to build more comprehensive model.

Parameterized model is referred light use efficient model [3], which calculate NPP with absorbed photosynthetically active radiation(APAR) and light use efficient. APAR can be calculated with total photosynthetically active radiation (PAR) and the fraction of incident photosynthetically active radiation that is absorbed by plants (FPAR), which can be estimated with NDVI because the plant can absorb more light when it grew well with high NDVI value. Therefore, the light use efficiency

model can be linked with remote sensing data, and can be used in large area and reflect the actual NPP value. Many remote sensing models have been developed such as CASA, GLOPEM, and VPM model [4-6].

NPP process model is built on the basis of plant physiology process and energy transfer mechanics. By the modeling of solar energy’s conversion to chemical energy process, evaportranspiration, photosynthesis, and allocation process, many models have been proposed. In the model, many input parameters are needed as inputs; part of them can be derived from remote sensing, so it can be used for regional spatial specific modeling effort.

Using the above models, numerous modeling results of global, continent, country and region have been reported. In it, land surface has been classified as several biomes, including forest, cropland, shrub, and so on. The modeling of forest is subclassed into several types, including broadleaf or needle, evergreen or deciduous. As for the crops, only C3 and C4 crops have been classified in many models. The models considered the detailed crop types is very few [7]. But, it is necessary to model the crops for the detailed types for the different parameters. In this paper, we will simulate winter wheat and summer maize NPP separately using different parameters for them in Huanghuaihai region with a modified CASA model.

II. DATA COLLECTION In this research, we collected the meteorological and

satellite data:

A. Meteorological data We collected the official meteorological data including

temperate, precipitation, wind speed, air pressure, relative humidity, and sunshine hours. The 75 stations in Beijing, Tianjin, Hebei, Shandong, Jiangsu, Anhui, and Henan province were used in this research. The solar radiation observation station is very limited. The data of solar radiation station in Beijing, Tianjin, Jinan, Zhengzhou, Gushi, Nanyang, and Leting were used.

B. MODIS data The standard MODIS data product of surface reflection of

8-day 250m ( MOD09Q1 ) and 500m ( MOD09A1 ) have been downloaded from NASA website. The data covered all of 2002 with 46 periods, and the spatial tiles included H26V04, H27V04, H26V05 and H27V05.

C. Crop distribution map We used the distribution map of winter wheat and summer

maize in Huanghuaihai region. The methods for it were described in other paper.

III. DATA PROCESSING For the result time step is set to be eight day, we process the

daily climate data to 8 day with the average daily temperature, rainfall, and sunshine hours. Then, we interpolated it to 250 meter at the research area using the Spline method at ARCGIS software.

The MODIS standard product is the SIN (Sinusoidal) projection with 1200km×1200km tile. We transformed it to the ALBERS projection (the first standard latitude 47 degree, the first standard latitude 25 degree, the standard longitude 105 degree, and the datum Krassovsky ellipsoid).

The NDVI and LSWI were calculated using the following equation:

12

12

ρρρρ

+−=NDVI (1)

62

62

ρρρρ

+−=LSWI (2)

where 1ρ , 2ρ , and 6ρ represent the reflectance rate of band 1, 2, and 6 respectively.

IV. RESEARCH AREA The research area is located in the northern China locating

in Beijing, Tianjin, Hebei, Shandong, and Jiangsu, Anhui, and Henan provinces. The accumulated temperature above 10℃ is about 2000-2400℃. The main crops in this region are winter wheat and summer maize. Main geomorphology in this area is Huabei plain. The northern western part is connected with Yashan and Taihang Mountain. In the east of area is Luzhong mountainous area and Jiaodong Hills.

V. NPP ESTIMATION MODEL In this paper, we modified the CASA (Carnegie-Ames-

Stanford Approach) model to estimate NPP using the better equations to calculate some parameters in the model.

NPP(x,t)= APAR(x,t)×E(x,t) (3)

Where APAR is absorbed photosynthetically active radiation, t is the date and x is the pixel number. E is the light energy conversion rate.

APAR(x,t)= SOL(x,t)×FPAR(x,t)×0.5 (4)

Where SOL(x,t)is the global solar radiation of the month t at pixel x (MJm- 2);FPAR(x,t) is the fraction of incident photosynthetically active radiation that is absorbed by plants. The constant 0.5 is the fraction of solar radiation that can be used for photosynthesis within bandwidth 0.14-0.17μm .

In the CASA model, FPAR is controlled by plant type and cover rate, and it has a strong relation with NDVI.

FPAR(x,t)=min((SR(x,t)-SRmin)/(SRmax-SRmin),0.95) (5)

Where SRmin is set to be 1.08, SRmax is the maximum one among growing season.

SR(x,t)=[1 + NDVI(x,t)]/[1 - NDVI(x,t)] (6)

In this research, we used a searching table method for FPAR estimation, which is used for MODIS FPAR standard production.

The light energy conversion rate (E) is the ability of plant to covert solar energy to the organism. It can be calculated as:

E(x,t)= TE1(x,t)× TE2(x,t)×WE(x,t)×Emax (7)

Where TE1(x,t) and TE2(x,t) are the temperature influence of light use efficiency, WE(x,t) is the water stress index. Emax is the maximum light use efficiency. Emax is set to be 0.389gCMJ- 1 in the previous CASA model. In this paper it is set to be 0.66 gCMJ- 1 for summer maize and 0.51 gCMJ- 1 for winter wheat.

TE1 reflects the low and high temperature’s effects on plant photosynthesis.

TE1(x)= 0.8 + 0.02Topt(x)- 0.0005[Topt(x)]2 (8)

Where Topt(x) is the temperature of the month when NDVI reaches top.

TE2 showed the environmental temperature limitation of light conversion efficiency offsetting the optimal temperature.

TE2(x,t)=1.1814/{1+e[0.2(Topt(x)-10-T(x,t))]}/{1+ e[0.3(-Topt(x)-10+T(x,t))]} (9)

Where T(x,t) is monthly average temperature and Topt(x) is the optimal temperature.

WE(x,t) is calculated by a water balance model in previous CASA model. We use a water limitation model in VPM which can reflect water limitation more directly.

WE = (1 + LSWI) / (1 + LSWImax) (10)

Where LSWImax is the maximum value of LSWI in growing season.

LSWI = (Rnir - Rswir)/(Rnir + Rswir) (11)

Where, Rnir is shortwave infrared reflection, and Rswir is the shortwave infrared reflection.

VI. DISTRIBUTION OF CROPLAND NPP Using the modified CASA model, we produced the NPP of

winter wheat and summer maize. The distribution maps are shown in Fig.1 and Fig.2.

The yearly NPP of winter wheat is 6.028·1013gC, and that of summer maize is 4.132·1013gC at the study area. The spatial distribution of winter wheat NPP is related with latitude. The value of southern is larger than that of the northern. The low value appears at the north and west edge of the study area, the mid-Shandong mountain and Jiaodong hilly. The coastal area around Bohai gulf is also very low because of the effect of salt. In the different subregions of the research area, the highest winter wheat NPP (1098.89 gC/m2a) and summer maize NPP(805.20gC/m2 a) both appear in eastern Shandong district, but the lowest average NPP at subregion level is in the coastal area for winter wheat (216.62gC/m2 a) and in northern Jiangsu plain for summer maize (482.10gC/m2 a)

So the production condition is main controlling factor of NPP. As for summer maize, the distribution coverage is similar with that of winter wheat. But the high value isn’t the southern part, it appear at the middle region. The reason is that radiation is limited in summer in the south.

The analysis between NPP with the related factors showed that NPP of wheat is closer related with latitude (r=-0.640) temperature (r=0.637) and rainfall (r=0.446) because the growing season is mainly within spring and early summer when the temperature is a limited factor for the growth and the rainfall determines the productivity in most Huabei plain with study area. As for summer maize, its growing season lies in the summer when the limitation factor become to be solar radiation (r=0.662)and sunshine hours(r=0.614), at the southern part, the rainy day is more than that of the northern, crop need more solar radiation for the growing. At the northern, the rainfall is less; it will limit the crop growth. As the result, the top value appears in the middle part of the study area.

TABLE 1 THE CORELATIONSHIP BETWEEN NPP WITH RELATED FACTORS.

winter wheat NPP

summer maize NPP

longitude latitude solar radiation temperature rainfall sunshine

winter wheat NPP 1 -0.129* -0.460** -0.640** -0.298** 0.637** 0.446** -0.515** summer maize NPP -0.129* 1 0.403** 0.172** 0.662** -0.273** -0.211** 0.614** longitude -0.460** 0.403** 1 0.231** 0.516** 0-.406** -0.01 0.575** latitude -0.640** 0.172** 0.231** 1 0.187** -0.644** -0.692** 0.546** solar radiation -0.298** 0.662** 0.516** 0.187** 1 -0.379** -0.292** 0.910**

temperature 0.637** -0.273** -0.406** -0.644** -0.379** 1 0.413** -0.617** rainfall 0.446** -0.211** -0.01 -0.692** -0.292** 0.413** 1 -0.477** sunshine -0.515** 0.614** 0.575** 0.546** 0.910** -0.617** -0.477** 1

* siginificant at 0.05 level, ** siginificant at 0.01 level

Figure 1. Distribution map of NPP for winter wheat in Huanghuaihai region.

Figure 2. Distribution map of NPP for summer maize in Huanghuaihai region.

The temporal distribution of NPP statistics shows a double apex distribution trajectory similar with that of their growing season. It’s a reflection of growing process. When the winter wheat is harvested, the NPP reached to a low value during the growing season.

The Standard Deviation showed that the winter wheat is larger than summer maize. The value range is much broader in spring and summer for the winter wheat growing season.

Figure 3. The seasonal change of NPP in Huanghuaihai district.

VII. CONCLUSION In this paper, we proposed a modified CASA model to

estimate NPP. This model used different maximum light use efficiency values of 0.51gC/MJ for winter wheat and 0.66gC/MJ for summer maize specifically. And a new water limitation factor based on LSWI was used. Using the common meteorological data, 8-day MODIS reflection data (MOD 09) and the distribution map of winter wheat and summer maize at Huang-Huai-Hai plain, the NPP map was produced. The yearly NPP of winter wheat is 6.028·1013gC, and that of summer maize is 4.132·1013gC at this region. The temporal trend is related with the growing stages; the spatial trend is controlled by climate and land condition.

ACKNOWLEDGMENT

This research was supported and funded by Ministry of Science and Technology of China (Grants 2012FY111800）and Chinese Academy of Sciences(Grants KZZD-EW-08).

REFERENCES

[1] J. Gonzalo, N. Irisarri, M.Oesterheld, J. M. Paruelo , and M. A. Texeira, “Patterns and controls of above-ground net primary production in meadows of Patagonia. A remote sensing approach,” Journal of Vegetation Science, vol. 23,pp. 114–126,2012.

[2] A. Moreno, F. Maselli, M.A. Gilabert, M. Chiesi, B. Martínez, and G. Seufert, “Assessment of MODIS imagery to track light-use efficiency in a water-limited Mediterranean pine forest,” Remote Sensing of Environment,Vol. 123, pp.359–367, 2012.

[3] G. Asrar, M.Fuchs, E.T. Kanemasu, and J.H.Hatfield, “Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat,” Agronomy Journal, vol. 76, pp.300-306,1984.

[4] C.B. Field, J.T. Randerson, and C.M. Malmstrom, “Global net primary production: combining ecology and remote sensing，” Remote Sensing of Environment, vol. 51, pp.74-80,1995.

[5] S. D. Prince, and S. N.Goward, “Global primary production ： a remote sensing approach,”Journal of Biogeography，vol.22, pp.815-835,1995.

[6] X.M. Xiao, Q.Y. Zhang, B. Braswell, S. Urbanski, S. Boles, S. Wofsy, M. Berrien, and D. Ojima, “Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data,”Remote Sensing of the Environment,vol. 91,pp. 256-270, 2004.

[7] D.B. Lobell，G.P. Asner，J. I. Ortiz-Monasterio，and et al. , “Remote sensing of regional crop production in the Yaqui Valley, Mexico: estimates and uncertainties,” Agriculture Ecosystems and Environment,vol.94,pp.205-220,2003.