Estimating canopy photosynthetic parameters in maize field based on multi-spectral remote sensing

doi:10.3724/SP.J.1258.2014.00066

Abstract

Abstract:

Aims Determination of canopy photosynthetic parameters is key to accurate simulation of ecosystem function by using remote sensing methods. Currently, remote estimation of vegetation canopy structure characteristics has been widely adopted. However, directly estimating photosynthetic variables (photosynthetic capacity and efficiency) at canopy scale based on field spectrometry combined with CO₂ flux measurements is rare.
Methods In this study, we remotely estimated solar radiation use efficiency (ε_N, net ecosystem CO₂ exchange/absorbed photosynthetically active radiation (NEE_CO2/APAR); ε_G, gross primary productivity/absorbed photosynthetically active radiation (GPP/APAR); α, apparent quantum efficiency) and photosynthetic capacity (P_max) based on in situ measurements of spectral reflectance and ecosystem CO₂ fluxes, along with observational data on micrometeorological factors during the entire growing season for a maize canopy in Northeast China.
Important findings Results showed that the seasonal variations in P_max and α exhibited a single peak; whereas the values of ε_N and ε_G were higher at the start of vegetative stage and then rapidly decreased with the development of maize until displaying a single peak at the intermediate and late stages of the growing season, coinciding with the occurrence of peak values in P_max. A comparison was made on the predictive performance based on normalized difference vegetation index (NDVI), ratio vegetation index (RVI), wide dynamic range vegetation index (WDRVI), 2-band enhanced vegetation index (EVI2), and chlorophyll index (CI) in estimating four canopy photosynthetic parameters with any combination of two separate wavelengths at the range of 400-1300 nm, which showed that EVI2 was most closely and linearly related to photosynthetic capacity and efficiency. This study demonstrates that multi-spectral remote sensing information is sensitive to the variations in canopy photosynthetic parameters in maize field and can be used to quantitatively monitor seasonal dynamics of canopy photosynthesis, and to accurately assess crop productivity and ecosystem CO₂ exchange capacity.

Key words: eddy covariance, field spectrometry, photosynthetic capacity, photosynthetic efficiency, spectral vegetation indices

ZHANG Feng, ZHOU Guang-Sheng. Estimating canopy photosynthetic parameters in maize field based on multi-spectral remote sensing[J]. Chin J Plant Ecol, 2014, 38(7): 710-719.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.plant-ecology.com/EN/10.3724/SP.J.1258.2014.00066

https://www.plant-ecology.com/EN/Y2014/V38/I7/710

Figures/Tables 9

Table 1 Vegetation indices used in this study

遥感植被指数 Remote sensing vegetation index	计算式 Formulation of computation	参考文献 Reference
归一化差异植被指数 Normalized difference vegetation index	(ρ_NIR - ρ_red)/(ρ_NIR + ρ_red)	Tucker, 1979
2波段增强植被指数 2-band enhanced vegetation index	2.5((ρ_NIR - ρ_red)/(ρ_NIR + 2.4ρ_red + 1.0))	Jiang et al., 2008
比值植被指数 Ratio vegetation index	ρ_NIR/ρ_red	Rouse et al., 1973
宽范围动态植被指数 Wide dynamic range vegetation index	(α × ρ_NIR - ρ_red)/(α × ρ_NIR + ρ_red)	Gitelson, 2004
叶绿素指数 Chlorophyll index	ρ_NIR/ρ_green - 1 or ρ_NIR/ρ_{red edge} - 1	Gitelson et al., 2005

Fig. 1 Seasonal variations in net ecosystem CO2 exchange (NEECO2), leaf area index (LAI), and incident photosynthetically active radiation (PAR) in a maize field measured by eddy covariance method in 2011.

Fig. 2 Seasonal change in the GPP-PAR relationship. GPP, gross primary productivity; PAR, photosynthetically active radiation.

Fig. 3 Seasonal dynamics of (A) leaf area index (LAI) and daily mean air temperature (T); (B) maximum photosynthetic capacity (Pmax) and quantum efficiency (α); and (C) radiation use efficiency εN (NEECO2/APAR) and εG (GPP/APAR). APAR, absorbed photosynthetically active radiation; GPP, gross primary productivity; NEECO2, net ecosystem CO2 exchange. DOY, day of the year.

Fig. 4 A contour map of coefficient of determination (R2) in linear relationships of normalized difference vegetation index (NDVI) and 2-band enhanced vegetation index (EVI2) with εN with any combinations of two separate wavelengths at the range of 400-1300 nm. A, NDVI-linear. B, EVI2-linear.

Fig. 5 A contour map of coefficient of determination (R2) in linear and exponential relationships of normalized difference vegetation index (NDVI) and 2-band enhanced vegetation index (EVI2) with apparent quantum efficiency (α) with any combinations of two separate wavelengths at the range of 400-1300 nm. A, NDVI-liner. B, NDVI-exponential. C, EVI2-linear. D, EVI2-exponential.

Fig. 6 Relationship of apparent quantum efficiency (α) with remote sensing vegetation indices NDVI[1233, 1244], NDVI[1188, 1258], EVI2[766, 792], and EVI2[964, 1098]. EVI2, 2-band enhanced vegetation index; NDVI, normalized difference vegetation index.

Fig. 7 A contour map of coefficient of determination (R2) in linear and exponential relationships between the remote sensing vegetation index EVI2 and Pmax and EVI2 with any combinations of two separate wavelengths at the range of 400-1 300 nm. A, EVI2-linear. B, EVI2-exponential. EVI2, 2-band enhanced vegetation index; Pmax, maximum photosynthesis capacity.

Fig. 8 Relationships of maximum photosynthetic capacity (Pmax) with remote sensing vegetation indices EVI2[559, 721] and EVI2[401, 1148]. EVI2, 2-band enhanced vegetation index.

References 24

[1]	Baldocchi DD (2003). Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future. Global Change Biology, 9, 479-492.
[2]	Cho MA, Skidmore AK (2009). Hyperspectral predictors for monitoring biomass production in Mediterranean mountain grasslands: Majella National Park, Italy. International Journal of Remote Sensing, 30, 499-515.
[3]	Gitelson AA (2004). Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. Journal of Plant Physiology, 161, 165-173.
[4]	Gitelson AA, Viña A, Arkebauer TJ, Rundquist DC, Keydan G, Leavitt B (2003). Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophysical Research Letters, 30, 1248, doi: 10.1029/2002GL016450.
[5]	Gitelson AA, Viña A, Ciganda V, Rundquist DC, Arkebauer TJ (2005). Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters, 32, L08403, doi: 10.1029/2005GL022688.
[6]	Gitelson AA, Viña A, Verma SB, Rundquist DC, Arkebauer TJ, Keydan G, Leavitt B, Ciganda V, Burba GG, Suyker AE (2006). Relationship between gross primary production and chlorophyll content in crops: implications for the synoptic monitoring of vegetation productivity. Journal of Geophysical Research, 111, D08S11, doi: 10.1029/2005- JD006017.
[7]	Goulden ML, Daube BC, Fan SM, Sutton DJ, Bazzaz A, Munger JW, Wofsy SC (1997). Physiological responses of a black spruce forest to weather. Journal of Geophysical Research, 102, 28987-28996.
[8]	Goward SM, Huemmerich KE (1992). Vegetation canopy PAR absorptance and the normalized difference vegetation index: an assessment using the SAIL model. Remote Sensing of Environment, 39, 119-140.
[9]	Han GX, Zhou GS, Xu ZZ, Yang Y, Liu JL, Shi KQ (2007). Biotic and abiotic factors controlling the spatial and temporal variation of soil respiration in an agricultural ecosystem. Soil Biology & Biochemistry, 39, 418-425.
[10]	Ide R, Nakaji T, Oguma H (2010). Assessment of canopy photosynthetic capacity and estimation of GPP by using spectral vegetation indices and the light-response function in a larch forest. Agricultural and Forest Meteorology, 150, 389-398.
[11]	Inoue Y, Peñuelas J, Miyata A, Mano M (2008). Normalized difference spectral indices for estimating photosynthetic efficiency and capacity at a canopy scale derived from hyperspectral and CO₂ flux measurements in rice. Remote Sensing of Environment, 112, 156-172.
[12]	Jiang Z, Huete AR, Didan K, Miura T (2008). Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment, 112, 3833-3845.
[13]	Li RP, Zhou GS, Wang Y (2010). Responses of soil respiration in non-growing seasons to environmental factors in a maize agroecosystem, Northeast China. Chinese Science Bulletin, 55, 2723-2730.
	[李荣平, 周广胜, 王宇 (2010). 中国东北玉米农田生态系统非生长季土壤呼吸作用及其对环境因子的响应. 科学通报, 55, 1247-1254.]
[14]	Lloyd J, Taylor JA (1994). On the temperature dependence of soil respiration. Functional Ecology, 8, 315-323.
[15]	Mutanga O, Skidmore AK (2004). Narrow band vegetation indices overcome the saturation problem in biomass estimation. International Journal of Remote Sensing, 25, 3999-4014.
[16]	Nakaji T, Ide R, Takagi K, Kosugi Y, Ohkubo S, Nasahara KN, Saigusa N, Oguma H (2008). Utility of spectral vegetation indices for estimation of light conversion efficiency in coniferous forests in Japan. Agricultural and Forest Meteorology, 148, 776-787.
[17]	Peng Y, Gitelson AA (2011). Application of chlorophyll-related vegetation indices for remote estimation of maize productivity. Agricultural and Forest Meteorology, 151, 1267-1276.
[18]	Reichstein M, Falge E, Baldocchi D, Papale D, Aubinet M, Berbigier P, Bernhofer C, Buchmann N, Gilmanov T, Granier A, Grünwald T, Havránková K, Ilvesniemi H, Janous D, Knohl A, Laurila T, Lohila A, Loustau D, Matteucci G, Meyers T, Miglietta F, Ourcival J-M, Pumpanen J, Rambal S, Rotenberg E, Sanz M, Tenhunen J, Seufert G, Vaccari F, Vesala T, Yakir D, Valentini R (2005). On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Global Change Biology, 11, 1424-1439.
[19]	Rouse JW, Haas Jr RH, Schell JA, Deering DW (1973). Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. Progress Report RSC 1978-1. Remote Sensing Center, Texas A & M University, College Station.
[20]	Ruimy A, Jarvis PG, Baldocchi DD, Saugier B (1995). CO₂ fluxes over plant canopies and solar radiation: a review. Advances in Ecological Research, 26, 1-68.
[21]	Running SW, Thornton PE, Nemani R, Glassy JM (2000). Global terrestrial gross and net primary productivity from the earth observing system. In: Sala OE, Jackson RB, Mooney HA, Howarth RW eds. Methods in Ecosystem Science. Springer-Verlag, New York. 44-57.
[22]	Tucker CJ (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8, 127-150.
[23]	Viña A, Gitelson AA (2005). New developments in the remote estimation of the fraction of absorbed photosynthetically active radiation in crops. Geophysical Research Letters, 32, L17403, doi: 10.1029/2005GL023647.
[24]	Wu CY, Niu Z, Tang Q, Huang WJ, Rivard B, Feng JL (2009). Remote estimation of gross primary production in wheat using chlorophyll-related vegetation indices. Agricultural and Forest Meteorology, 149, 1015-1021.