Index (CI green ) for crop GPP estimation, in addition to the NDVI and
EVI ( Gitelson et al., 2008, 2012; Peng and Gitelson, 2011, 2012; Peng et al., 2011 ).
However, since the empirical regressions between the VI*PAR products and GPP measured locally at ﬂux towers do not pass through the origin (i.e., the zero X–Y value for regressions) and produce offsets, they are somewhat difﬁcult to interpret and apply ( Gitelson et al., 2012; Sims et al., 2006; Zhang et al., 2014b ). This is considered to be a source of error affecting the accuracy and reliability of remote sensing GPP estimates based on VIs. In the literature, there is no paper that presents how to scale the VIs in space and time to solve the problem.
There is a growing interest in the estimation of grossprimary productivity (GPP) in crops due to its importance in regional and global studies of carbon balance. We have found that crop GPP was closely related to its total chlorophyll content, and thus chlorophyll can be used as a proxy of GPP in crops. In this study, we tested the performance of various vegetationindices for estimating GPP. The indices were derived from spectral data collected remotely but at close- range over a period of eight years, from 2001 through 2008. The results show that chlorophyll indices, based on near infrared and either the green or red-edge regions of the spectrum, are capable of accurately predicting widely variable GPP in maize under both rainfed and irrigated conditions.
vegetation BRDF impact varied with crop types, irrigation options, model options and VI options ( Table 7 ).
The modiﬁed gridding procedure ensures (1) the centers of the grid cells match the centers of the ﬁelds and (2) the grid cells are completely covered by the observations from each swath data, which makes the gridded MODIS observations more appropriate for the footprint impact study than the standard MODIS gridded observations. Diameters of the two circular ﬁelds (US-NE1 and US- NE2, ∼780 m) and length of the square ﬁeld (US-NE3, ∼790 m) are greater than the length of the 500 m grids. There is only one crop type at each ﬁeld in each year, and is relatively homoge- neous. Grass cover surrounding the study ﬁelds contributes to the worse performance of experiment 1 compared to experiment 2 since observations acquired at oblique angles are more likely to contain areas adjacent to the crop ﬁelds.
Maize/soybean rotations in the U.S. are either rainfed or irrigated agricultural ecosystems. Both conventional till and no-till manage- ment practices are common. It is known that carbon fluxes are sub- ject to change with different management practices (Angers et al., 1997; Winjum et al., 1992). Accurate estimation of spatial patterns and temporal dynamics of GPP of soybean fields at larger spatial scales under different management practices is essential to improve our understanding of carbon dynamics of this globally important ecosystem. Thus, it is necessary to upscale site-specific flux obser- vations beyond spatial limits of flux tower footprints. One upscaling approach is to use satellite remote sensing observations and climate data (Turner et al., 2003). Repetitive and systematic satellite remote sensing observations of vegetation dynamics and ecosystems allow us to characterize vegetation structure, and estimate GPP and NPP (Potter et al., 1993; Ruimy et al., 1994). A satellite-derived vegetation photosynthesis model (VPM) estimates GPP at daily to 8-day tempo- ral scales and has been evaluated over several flux tower sites (Xiao et al., 2004a). Previous work has examined the simulated dynamics of GPP for the maize growing seasons from two of three study sites selected in this study (Kalfas et al., 2011). The GPP simulation of soy- bean systems under a range of hydrometeorological conditions is a focus of this study. Eddy covariance flux data and MODIS-derived vegetationindices (VIs) from three soybean fields were used to: (a) test the capabilities of remotely sensed VIs and soybean phenology to estimate seasonal carbon dynamics, and (b) explore the underly- ing mechanisms of environmental controls of CO 2 fluxes in soybean systems. In addition, we also compared the modeled GPP (GPP VPM ) using VPM and the MODIS GPP (GPP MOD17A2 ) with GPP (GPP EC ) es- timated from eddy covariance measurements.
The model based on total crop chlorophyll content and potential PAR was tested for estimating GPP in maize and soybean, crops with contrasting leaf structures and canopy architectures. Several vegetationindices were used as proxies of chlorophyll content. The model was capable of estimating GPP using atmospherically corrected Landsat data with coef ﬁcients of variation of 23% for maize and below 30% for soybean. The indices using green and NIR Landsat bands were found to be the most accurate in GPP estimation. Our results showed that the model based solely on satellite data is robust in estimating GPP and represents a signi ﬁcant improvement over MODIS GPP for croplands. One drawback is the poor temporal resolution of Landsat compared to MODIS. A data fusion of MODIS and Landsat may be a worthwhile next step in the effort to estimate daily GPP. The model was also capable of estimating GPP using raw imagery; i.e., TOA re ﬂec- tance. The algorithms established in the NE maize and soybean study sites were validated for the same crops in Minnesota, Iowa and Illinois. Future study should determine uncertainties of established algorithms for GPP estimation in other crops with no re-parameterization. It is also essential to test these algorithms in different geographic regions.
(4) The products of MODISvegetationindices (VIs) and daily PAR (VI × PAR) were computed and compared against the tower based daily GPP. For each VI, we tested two linear models with and without offset: y = ax and y = ax + b, where y = GPP, x = VI × PAR, the coefﬁcients “a” and “b” were computed with the least squares best ﬁt algorithm. To assess the impact of MODIS observation footprint and the impact of vegetation BRDF characteristics on crop daily GPP estimation, the data were ﬁltered into four categories and four experiments were conducted. The ﬁrst experiment included all observations. The second experiment included only observa- tions with view zenith angle (VZA) ≤ 35 ◦ to constrain the footprint size to achieve a better match with the agricultural ﬁelds, and their plant functional types. The third experiment included only the observations in the forward scatter direction (relative azimuth angle, RAA > 90 ◦ ) from the second experiment. The fourth exper- iment included only observations in the back scatter direction (RAA > 90 ◦ ) from experiment two. Comparison of experiment 2 vs. experiment 1 was used to examine the observation footprint impact whereas comparison of experiment 4 vs. experiment 3 was used to examine the BRDF impact. In summary, we tested thirty- two cases in total (four vegetationindices, two regression models, four experiments) for the product of VI and PAR versus daily GPP acquired at the towers for two crop types in three ﬁelds. Coefﬁ- cient of determination (R 2 ), root mean square error (RMSE), and
Correspondence to: T. Chen (firstname.lastname@example.org)
Received: 10 June 2013 – Published in Biogeosciences Discuss.: 28 February 2014 Revised: 3 June 2014 – Accepted: 10 June 2014 – Published: 24 July 2014
Abstract. Croplands cover about 12 % of the ice-free terres- trial land surface. Compared with natural ecosystems, crop- lands have distinct characteristics due to anthropogenic in- fluences. Their global grossprimaryproduction (GPP) is not well constrained and estimates vary between 8.2 and 14.2 Pg C yr − 1 . We quantified global cropland GPP using a light use efficiency (LUE) model, employing satellite ob- servations and survey data of crop types and distribution. A novel step in our analysis was to assign a maximum light use efficiency estimate (ε GPP ∗ ) to each of the 26 different crop types, instead of taking a uniform value as done in the past. These ε ∗ GPP values were calculated based on flux tower CO 2
a potential p(x) in the Gross–Pitaevskii equation (see ) or complex Ginzburg– Landau equations (see ). In the second case, IGP is a useful model in a BEC in which the vortices interact both with each other and with the trap potential. In the third case, IGP is useful in modeling optical vortices. See, for instance [3, 2, 34], which are related to motion of vortices with nonvanishing total charge in inhomoge- neous potentials and confinement.
Results and Discussion Successful cover crop establishment and biomass accumulation is critical for implementing strip tillage. Cereal rye and hairy vetch established well and produced sufficient biomass (Figure 1). Cover crop biomass was 2.1, 3.2, and 3.9 tons/acre in
terrestrial grossprimary productivity (GPP) and play a critical role in global carbon and water cycles (Archer, 2010; Beer et al., 2010; Poulter
et al., 2014). Over the past century, woody plants have been observed worldwide to be increasing in abundance and density in grasslands and savannas, a process termed “woody plant encroachment” (WPE; Archer, Vavra, Laycock, & Pieper, 1994; Archer, 2010; Scott, Jenerette, Potts, & Huxman, 2009). Ecological succession of grasslands to woodlands may alter ecosystem structure and function and can threaten ecosystem services (Msanne et al., 2017; Petrie, Collins, Swann, Ford, & Litvak, 2015). For example, an increase of woody plants in grasslands has been reported to alter animal habitats (Coppedge, Engle, Masters, & Gregory, 2004) and reduce biological diversity (Ratajczak, Nippert, & Collins, 2012; Van Els, Will, Palmer, & Hickman, 2010), forage, and livestock production (Anadon, Sala, Turner, & Bennett, 2014). The shift of a herbaceous ecosystem to a woody ecosystem has affected carbon (Mcculley & Jackson, 2012; O'Donnell & Caylor, 2012), water (Liu et al., 2016; Scott, Huxman, Williams, &
Accurate measurements of regional to global scale vegetation dynamics (phenology) are required to improve models and understanding of inter-annual variability in terrestrial ecosystem carbon exchange and climate – biosphere interactions. Since the mid-1980s, satellite data have been used to study these processes. In this paper, a new methodology to monitor global vegetation phenology from time series of satellite data is presented. The method uses series of piecewise logistic functions, which are fit to remotely sensed vegetation index (VI) data, to represent intra-annual vegetation dynamics. Using this approach, transition dates for vegetation activity within annual time series of VI data can be determined from satellite data. The method allows vegetation dynamics to be monitored at large scales in a fashion that it is ecologically meaningful and does not require pre-smoothing of data or the use of user-defined thresholds. Preliminary results based on an annual time series of Moderate Resolution Imaging Spectroradiometer (MODIS) data for the northeastern United States demonstrate that the method is able to monitor vegetation phenology with good success.
In the current research, agricultural droughts were analyzed over the territory of Southern Ukraine for the period 2000-2012. Since agricultural drought is “insufficient soil moisture to meet the needs of a particular crop” according to a definition provided by the Food and Agricultural Organization of the United Nations (FAO), the conditions of cropvegetation along with surface soil moisture conditions were analyzed in this research. Analysis was performed using indices calculated from time series of remote sensing data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS). The Normalized Difference Vegetation Index (NDVI) was used as a proxy of agricultural vegetation conditions. Time series NDVI data used in the current research was acquired from the MODISvegetationindices collection. Temperature-Vegetation Dryness Index (TVDI) was used to reflect surface moisture conditions. TVDI was calculated from surface temperature and MODIS- NDVI datasets using software developed during the present research. In order to have pixels representing signal from agricultural areas only, agricultural fields were classified and masked before the analysis. Winter and summer crops were classified and analyzed separately in the research, due to their different growing seasons and cultivation schedules. Analysis of vegetation and moisture conditions was performed on an annual basis. For this purpose, maximum NDVI values and integral NDVI values were calculated from NDVI profiles of the growing season for each year of the analysis. Since winter and summer crops are not often planted in the same fields in different years, and in order to have continues time series of winter and summer crop parameters, analysis in the present research was conducted based on 10 kilometer grid, cells which were coded with the Morton key index. In order to reflect changes between various years’ standard deviation, inter-annual trends were calculated for each parameter in this research. Dependence of vegetation conditions on surface moisture was objectively assessed by Pearson’s correlation coefficient. Standardized values were calculated for vegetation and moisture parameters and compared in order to subjectively investigate impacts of droughts on crops in particular years of the analysis.
Damm, A; Erler, A; Gioli, B; Hamdi, K; Hutjes, R; Kosvancova, M; Meroni, M; Miglietta, F; Moersch, A; Moreno, J; Schickling, A; Sonnenschein, R; Udelhoven, T; van der Linden, S; van der Tol, C; Hostert, P; Rascher, U (2010). Remote sensing of sun-induced fluorescence to improve modeling of diurnal courses of grossprimaryproduction (GPP). Global Change Biology, 16(1):171-186.
crop evapotranspiration through remote sensing and GIS techniques for Tarafeni South Main Canal (TSMC) Command area. The study area is located between 86°45’E to 87°15’E longitude and 22°15’N to 22°45’N latitude is a part of right bank main canal of Kangsabati project and supplemented with a barrage on Tarafeni river in the Pashchim Midnapur districts of West Bengal state of India. The gross irrigation com- mand area of TSMC is about 70,450 ha and major crops grown are paddy in monsoon season, wheat, po- tato, mustard and vegetables in winter season (Gontia and Tiwari, 2004). At present overall irrigation effi- ciency (the ratio of water available for crop to the wa- ter supplied from reservoir) for paddy crop is 37% and about 45% for other crops as per the annual report- 2001 of Kangsabati Command Area Development Authority (Gontia and Tiwari, 2010). Low irrigation efficiency can be improved by proper irrigation scheduling as per crop water demand. About 50% of irrigation command area is under cultivation, more than 40% area is covered by forest, and remaining area is orchards, fallow land, wasteland and establishments. Soil of the location is acid lateritic with sandy loam in texture. The climate of TSMC Command area is classified as humid and subtropical. It is characterized as hot and humid in the summer (April and May), rainy
The classical approach to investigate the effects of climate extremes on the carbon cycle is based on a “forward" anal- ysis: The analyst identifies extreme events in climate vari- ables or other environmental drivers and subsequently ana- lyzes the impacts on ecosystems and the carbon cycle (ex- amples can be found in Page et al., 2002; Ciais et al., 2005; Kurz et al., 2008; Zeng et al., 2009; Zhao and Running, 2010). This forward approach is appealing because one can emphasize a certain region or specific time span or concen- trate on a single extreme event to study the consequences for ecosystem functioning. However, one has to be prepared to acknowledge that climate extremes do not necessarily trans- late into extreme responses of the biosphere. Inversely, not all extreme responses of the terrestrial biosphere are unam- biguously explicable by some climate extreme or disturbance event. For instance, a very unlikely constellation of drivers, none of which are extreme in their own domain, might still cause extreme changes in ecosystems (so-called compound extremes, IPCC, 2012; Leonard et al., 2013). To tackle this aspect, Smith (2011) suggested the definition of an extreme climatic event (ECE) as “an episode or occurrence in which a statistically rare or unusual climatic period alters ecosystem structure and/or function”. A pure forward analysis, instead, is at risk of overlooking extreme changes in the state of the biosphere and hence is not always desirable. An event-based analysis of the sort described above can potentially also lead to a biased perception of extreme events. Extreme events that affect regions of social or economical interest gain more at- tention than extreme events in regions with less public inter- est. For instance, very few experimental studies are done in Africa. In contrast, in a global analysis of extreme events the attention is distributed more equally.
With world commodity markets becoming more competitive and the deregulation of the wheat industry in Australia during the nineties, advanced knowledge of likely production and its geographical distribution has become highly sought-after information. During the past 5 years, the Queensland Department of Primary Industries & Fisheries (DPI&F) has generated shire/state and national yield (t/ha) forecasts for wheat and sorghum crops on a monthly basis throughout the crop-growing season with appreciable success. However, to achieve an accurate near real-time production forecast, a real-time estimate of the crop area planted is required. Generating objective estimates of planted area will allow near real-time cropproduction estimates, which can then be used in updating supply chain information at the regional, state and national levels. While there are alternative methods (e.g. subjective opinions, surveys, censuses, etc.) to derive the required information, the use of remote sensing (RS) offers more objectivity, timeliness, repeatability and accuracy. Furthermore, the use of multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) imagery (spanning an entire cropping season) is novel, and has been rarely used in determining crop area planted in targeted agricultural systems. In this paper, we provided a brief background of regional commodity forecasting in Queensland, and have reported some preliminary results on the use of digital image processing techniques to determine crop area planted. More specifically, different multivariate approaches to analysing remote sensing data [i.e. Harmonic Analysis of Time Series (HANTS) and Principal Component Analysis (PCA)] were compared in determining winter crop area planted from MODIS imagery for a specific case study in the Darling Downs region, Queensland. The methodology was validated for the 2003 and 2004 seasons at a shire level by contrasting aggregated shire total area planted with surveyed ABARE estimates. Finally, the ability of these methods to discriminate area planted for wheat, barley and chickpea at the shire level was determined. Preliminary results showed a significant potential to capture total crop area planted at a regional level and a good overall capability (>95% correct classification) in discriminating between these winter crops.
The leaf area index (LA!), pod area index (PAl), above ground biomass and grain yield were highly affected by dates of sowing, delayed sowing resulting in lower peak values in all the cultivars in both seasons (Table I). The reduction in crop growth parameters and yield in the late sown crop in both the seasons may be attributable to the fact that the late sown crop (th iI'd SOW! ng) was exposed to relatively higher mean temperatures (3 to 4°C) and higher evaporative demand during the vegetative and reproductive phases of the crop growth as compared to the tirst and second sown crop. The higher temperatures might have enhanced the respiration rate during the seed tilling stage and restricted the pod area development. The profound effect of the ambient environmental conditions
Although a number of methods for the modification of camera hardware have been proposed, each with its own benefits, this paper proposes a model-based approach to estimating VI from RGB images. The main idea is to learn the spatio-spectral relationships between information in RGB images of vegetation and their corresponding VI values (sourced from MSI). This is achieved by leveraging a deep neural network (DNN) to model the non-linear relationship between an RGB image and its vegetation index. Deep learning is classified as a machine learning method for learning multi-level representations of data . It has performed well on a wide range of plant phe- notyping tasks like organ counting [25–27], age estima- tion , feature detection [29, 30], species and disease detection [31, 32]. Our motivation to use DNN was to formulate a regression problem such that the multilay- ered convolutional features learned by the model relate RGB image data to NDVI. The rationale of our proposed approach is simple but effective, i.e. the spatial density and spectral signature (color) of vegetation reflects its VI.
Resumo – O objetivo deste trabalho foi estimar e mapear as áreas com as culturas de soja e milho, no Paraná, com uso de imagens multitemporais EVI/Modis. Foram avaliados os anos-safra de 2004/2005 a 2007/2008. Em razão da alta dinâmica temporal e da heterogeneidade de datas de semeadura das culturas no estado, foram utilizadas cenas que contemplavam as fases de pré-plantio e de desenvolvimento inicial das culturas, para gerar a imagem de mínimo EVI (IMIE), e cenas que consideravam o pico vegetativo das culturas, para gerar a imagem de máximo EVI (IMAE). Estas imagens foram utilizadas para gerar a composição colorida RGB (R, IMAE; GB, IMIE), o que permitiu a confecção de máscara das áreas com soja e milho. As estimativas das áreas de máscara por município foram comparadas com dados oficiais de produção agrícola municipal, tendo-se observado bons ajustes (R²>0,84, d>0,95, c>0,85) entre os dados. Para a avaliação da exatidão espacial das máscaras, imagens Landsat-5/TM e AWiFS/IRS foram usadas como referência para construção da matriz de erros. Os resultados obtidos são indicativos de que a metodologia proposta é altamente eficiente e pode ser utilizada para mapeamento dessas culturas.