- Email: [email protected]
Assessment of the effectiveness of spatiotemporal fusion of multi-source satellite images for cotton yield estimation
Computers and Electronics in Agriculture 162 (2019) 44–52
Contents lists available at ScienceDirect
Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag
Original papers
Assessment of the effectiveness of spatiotemporal fusion of multi-source satellite images for cotton yield estimation
T
⁎
Linghua Menga,b, Huanjun Liua,b, , Xinle Zhangb, Chunying Rena, Susan Ustinc, Zhengchao Qiub, Mengyuan Xub, Dong Guoa a
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China College of Resources and Environmental Sciences, Northeast Agricultural University, Harbin 150030, China c Center for Spatial Technologies and Remote Sensing (CSTARS), Department of Land, Air, and Water Resources, University of California, Davis, 95616, United States b
A R T I C LE I N FO
A B S T R A C T
Keywords: Data fusion Reconstructed MODIS NDVI Date information Yield estimation Field scale
Deficiencies in the spatiotemporal resolution of remote sensing (RS) images limit crop yield estimation at the farm and field scale. These deficiencies may be alleviated by fusion of high spatial and temporal resolution images such as MODIS and Landsat. In this study, a new daily MODIS NDVI product (reconstructed MODIS) was generated from 16-day composite images using the Extreme Model, which integrates the NDVI value with the corresponding specific date information at each pixel. The Flexible Spatiotemporal Data Fusion (FSDAF) model was then used to create two fused, high-resolution time-series products (fused MODIS and fused reconstructed MODIS) in order to enhance the spatial and temporal effectiveness of satellite images for field-scale applications. Three yield estimation models were then built using time-series data of Landsat NDVI, predicted NDVI from fused MODIS, and predicted NDVI from fused reconstructed MODIS. The methodology was tested on a farm field over the cotton growing season in the San Joaquin Valley of California. Results showed that: (1) the time trend of NDVI over the growing season for the fused reconstructed MODIS was more similar to that of Landsat than were either of MODIS or fused MODIS, indicating that the specific date of MODIS pixels is important for time-series analysis; (2) the NDVI from fused reconstructed MODIS provided the best correlation with Landsat NDVI, with R2 and RMSE values 15% higher than for fused MODIS; (3) correlation between cotton yield and all three datasets at the pixel level was statistically significant for all image dates, and (4) the accuracy of the cotton yield estimation model using predicted NDVI from fused reconstructed MODIS (R2 = 0.79; RMSE = 488.01) was higher than with fused MODIS (R2 = 0.77; RMSE = 513.96) and only slightly lower than with Landsat (R2 = 0.84, RMSE = 463.12). This study improved the accuracy of MODIS-based yield estimation using fusion images, and the results can be applied to improve vegetation monitoring and quantitative modeling using MODIS NDVI at the field scale.
1. Introduction Crop yield estimation is important as a basis for planning food imports and exports and developing national agricultural policies (Bolton and Friedl, 2013; Sakamoto et al., 2013; Meroni et al., 2013). Many models using remote sensing data can provide large-scale, objective, cost-effective, and timely estimation of crop yield (Moriondo et al., 2007; Nuarsa et al., 2012; Morel et al., 2014; Johnson, 2014; Huang et al., 2016; Xie et al., 2017). However, RS monitoring systems for crop growth and yield are generally based on the statistical relationship between a vegetation index (VI) and crop biophysiochemical properties, because radiation transfer and geometrical optics models are too
⁎
complex to be practical. Many studies have used VI and crop yield models (Becker-Reshef et al., 2010; Kouadio et al., 2014; Lopresti et al., 2015), resulting in the recognition that the single-date seasonal peak of Normalized Difference Vegetation Index (NDVI) derived from NOAAAVHRR or MODIS images are suitable as input variables for crop yield modeling over large areas (Doraiswamy and Cook, 1995; Son et al., 2014; Franch et al., 2015; Chipanshi et al., 2015). However, low spatial resolution images such as MODIS products can be negatively affected by environmental conditions, so maximum value composites (MVC), which select the ‘best’ value for each pixel over a certain time period, are commonly used to reduce interference from clouds, atmospheric conditions, and poor solar altitude angles (Beck et al., 2006; Chen et al.,
Corresponding author. E-mail address: [email protected] (H. Liu).
https://doi.org/10.1016/j.compag.2019.04.001 Received 5 November 2018; Received in revised form 28 March 2019; Accepted 1 April 2019 0168-1699/ © 2019 Elsevier B.V. All rights reserved.
Computers and Electronics in Agriculture 162 (2019) 44–52
L. Meng, et al.
Fig. 1. The study area in California; (a) Map showing the location of the San Joaquin Valley and the study plot; (b), (d) Landsat Thematic Mapper (TM5) 30 m resolution images of the farm and plot on DOY 174 (WRS2 path/row number-42/35); and (c), (e) MODIS NDVI 250 m resolution images of the farm and plot on DOY193.
up the impact of farm management decisions beyond the field scale without modifying the assessment methods and, similarly, national assessments of the effects of policy decisions are difficult to downscale in order to understand local impacts (Zurita-Milla et al., 2009; Zhu et al., 2018). The availability of a functional data fusion model to generate timeseries data provides an economical and consistent method of capitalizing on the spatial and temporal resolution advantages of different RS data (Gao et al., 2011, 2015). For example, the fusion of high temporalresolution MODIS data with high spatial-resolution Landsat data provides an accurate and timely methodology of using the same data in both regional and local studies (He et al., 2018). To this end, Gao et al. (2006) developed the spatial and temporal adaptive reflectance fusion model (STARFM), and Zhu et al. (2010) developed the enhanced STARFM (ESTARFM) to improve the accuracy of STARFM in areas of heterogeneous landscapes. Emelyanova et al. (2013) investigated the performance of STARFM and ESTARFM in two landscapes with contrasting spatial and temporal dynamics, and their results demonstrated that the performance of data fusion methods is strongly affected by spatial and temporal variations in land cover. Zhu et al. (2016) proposed the Flexible Spatiotemporal Data Fusion Model (FSDAF), which was shown to create more accurate fusion images and to retain more spatial detail than STARFM. In addition, the FSDAF model requires only one reference Landsat image and one MODIS image at time one (t1) and one MODIS image at t2 (the date of the fused image), thus effectively reducing the amount of data input and making it easier to implement. Our study addressed the problem of non-specific date information in common MODIS products by constructing time-series MODIS NDVI data using the mathematical model Extreme (Meng et al., 2017). We then used the FSDAF model to create Landsat/MODIS fusion images and with these constructed a model for yield estimation of California cotton. Our objectives were to: (1) analyze time-series Landsat and MODIS
2010; Sakamoto et al., 2014). Most studies ignore the specific date information of MODIS products, which may lead to discrepancies between modeled time-series trends and actual trends. For example, the 16-d composite MODIS NDVI product supplied for the 193rd day of the year (DOY 193) covers the time from DOY 193 to DOY 208 and the date of each pixel could be any day within that period. If date information is not considered, each pixel is assumed to have been acquired on the same day, which could lead to a 16-day deviation from the true date. The specific date information provides a key to determination of characteristic parameters of the time-series VI curve (such as peak greenness) and plays an important role in analyzing crop growth processes. It is, therefore, important to know whether it is possible and beneficial to incorporate specific date information of composite images in order to improve the accuracy of time-series VI curves. The most common approach to resolve problems associated with MVC technology is to smooth the time series curve (Shao et al., 2016), and the most popular smoothing routines include the Savitsky-Golay filter, non-symmetric Gauss function fitting, and logistic methods (Jönsson and Eklundh, 2002, 2004). These smoothing methods reduce the problems associated with variation in local NDVI values, but they also hide true variation of NDVI during crop growth. Here, we propose a mathematical model to reconstruct time-series VI data considering the date information (Meng et al., 2017). At present, the accuracy of crop yield estimation at the field scale needs improvement (Cheng et al., 2016; Donohue et al., 2018). Crop yield information at this scale is used by farmers, crop management advisors, and researchers to assist with and improve farm-level management actions. Unfortunately, the lower-resolution methods used to derive regional estimates of crop production are less useful at the farm and field scale (Raun et al., 2008; Hochman et al., 2009; Wu et al., 2015). It is therefore difficult to coordinate between the two spatial scales without changing data. This means that it is cumbersome to scale 45
Computers and Electronics in Agriculture 162 (2019) 44–52
L. Meng, et al.
Landsat TM 5 and ETM 7 images of the study area between April (planting) and September (defoliant application) were acquired (http:// earthexplorer.usgs.gov/) (see Table 1). There was no cloud cover in the study area during that time. The Landsat images have an 8 d temporal resolution and 30 m spatial resolution, and the study plot consisted of 519 pixels. Radiometric calibration and atmospheric correction of the Landsat data was performed as a preprocessing step using the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) in ENVI 5.1. The main objective of FLAASH is to eliminate the atmospheric effects caused by molecular and particulate scattering and absorption from the “radiance-at-detector” measurements to retrieve “reflectance-at-surface” values (Felde et al., 2003). The algorithm derives its first-principles physics-based calculations from the MODTRAN4 radiative transfer code (Anderson et al., 2002). Landsat NDVI values were calculated using Eq. (1).
NDVI data and verify the importance of date information of MODIS products; (2) reconstruct MODIS NDVI 16-d composite images to obtain a daily product for the growing season; (3) evaluate the results of the time-series predicted NDVI of the fusion datasets versus Landsat data over the cotton plot; and (4) build yield estimation models to compare the accuracy of field-scale yield estimates based on Landsat, fused MODIS and fused reconstructed MODIS data. 2. Material and methods 2.1. Study area The study was conducted in the southern portion of the San Joaquin Valley in central California, USA (Fig. 1a), an area that has been used extensively for time series RS research (Liu et al., 2015; Meng et al., 2017). This valley is known for its production of cotton (Gossypium sp.), garlic (Allium sativum L.), tomato (Lycopersicon esculentum Mill.), almonds (Prunus dulcis Mill. (D. A. Webb)), pistachio (Pistacia vera L.) and alfalfa hay (Medicago sativa L.), as well as grain and other crops. The San Joaquin Valley produces the majority of the value of agricultural production originating from California. The local Mediterranean climate of the San Joaquin Valley features hot, dry summers and cool, wet winters. The rainy season normally extends from November through April, with an average annual rainfall of 854 mm. Little rainfall was recorded during the growing season under study (2002) (Stull et al., 2008). The San Joaquin Valley has high levels of light, heat, and evaporation, with a maximum temperature of 40 °C and a day/night temperature difference of about 16 °C, making it very suitable for cotton cultivation. A square cotton field of 57.07 ha (Fig. 1b) was selected as the study area, as detailed cotton yield data from 2002 was available. Fig. 1b and 1c present a Landsat and a MODIS image (respectively) of the farm landscape in the study area, Fig. 1d shows a Landsat image of the study plot on DOY 174 and Fig. 1e shows a MODIS NDVI image of the plot on DOY 193.
NDVI =
NIR − RED NIR + RED
(1)
The MOD13Q1 (MODIS/Terra Vegetation Indices 16-Day L3 Global 250 m SIN Grid) product featuring NDVI data from 2002 was obtained from the NASA Reverb website (http://reverb.echo.nasa.gov/). The MOD13Q1 product includes 250 m resolution NDVI and quality assessment (QA) information (see Table 1). MODIS NDVI values are calculated by USGS from atmospherically-corrected surface reflectance values using Eq. (1) and are delivered as a 16-d composite image. The VI products from USGS (MOD13Q1) rely on a daily surface reflectance product (MOD09 series), and are corrected for molecular scattering, ozone absorption, and aerosols. The process at USGS generates products comparable to those generated using FLAASH. Hierarchical data format (HDF) images of MODIS products were converted into ENVI standard format and MODIS NDVI was resampled at 240 m resolution to fit the requirement of the FSDAF model. All original imagery was geometrically corrected, which aligns two images with different viewing geometry and/or different terrain distortions into the same coordinate system so that corresponding pixels represent the same objects (Min et al., 2012). The pre-processing steps related to geometric correction are as follows:
2.2. Cotton yield and management data collection Fig. 2 shows the 2002 yield map of the cotton plot, with an average yield of 2009.6 kg/ha. The yield data was collected using a yield monitor (Model AG700, AGRIplan, Stow, MA, USA; www.agriplaninc. com) onboard a mechanical harvester. The monitor uses infrared light to measure the flow of cotton in the chute, integrated with locations from a differential global positioning system (DGPS) with sub-meter accuracy. The accuracy of the yield estimates ranges from 95 to 98% as validated with field measurements conducted simultaneously, as reported by Zarco-Tejada et al. (2005). The yield per 4.5 m × 4.5 m field pixel was calculated from the number and width of cotton rows harvested, with the yield weight averaged over the pixel width and the 1–5 s time increments of DGPS collection. The yield and position data were prepared using manufacturer-supplied mapping software in the on-board computer and downloaded as ASCII text and database files. The yield map was produced in 3 steps:
Step 1: Locate and match a number of feature points (called tie points) in two images (a warp image-MODIS image and a base image-Landsat image) selected for registration. The overall RMSE values along the X and Y shifts during the registration process was 27.24 m for MODIS images at 250 m. Step 2: Use the corresponding tie points to compute the parameters of a geometric transformation between the two images. Step 3: Use ENVI to automatically and accurately generate tie points, then use those tie points to align and resample the warp image to match the base image.
2.4. Overview of data analysis and modeling
Step 1: ASCII text and database files were converted into vector shape files using ArcGIS; Step 2: The shape files were converted to raster, with the output raster cell size set to 0.5 m * 0.5 m to generate the yield map; Step 3: The outlying yield values caused by grain time lag and yield surges were removed using a statistical identifier based on a moving average mean and standard deviation. If the yield was less than or greater than three standard deviations from the average, it was identified as an outlier and removed.
Fig. 3 shows the technical flow chart for this study. Original MODIS NDVI data was first “reconstructed” to combine the NDVI value with accurate date information at each pixel using the Extreme model (Meng et al., 2017). Following the reconstruction, two fused outputs were generated by combining Landsat with each of MODIS and reconstructed MODIS using the FSDAF model. The fusion results were then evaluated at the pixel level for correlation with the original Landsat, MODIS and reconstructed MODIS data. Three cotton yield estimation models were developed, one each for the Landsat and two fusion images, and a yield accuracy evaluation was carried out.
2.3. Satellite images Based on the characteristics of the cotton growing period, all 46
Computers and Electronics in Agriculture 162 (2019) 44–52
L. Meng, et al.
Fig. 2. Cotton yield map (2002) of the study plot. Note: the white strips represent areas where the harvester head extends beyond the recorded GPS locations. The proprietary software averages yield figures across the head width. Table 1 Number and 2002 dates of the Landsat and MODIS images used in the study. Product
Sensors
Date
Number of images
Landsat
TM 5 ETM 7 MOD13Q1
May 6, 22; June 7, 23; July 1, 17; August 2, 26; September 11; May 14, 30; June 15, July 9, 25; August 18; September 3, 19; May 25; June 10, 26; July 12, 28; August 13, 29; September 14;
9 8 8
MODIS
2.5. NDVI reconstruction with specific pixel dates — Extreme model
previous study (Meng et al., 2017)
Reconstructed MODIS NDVI = MODIS NDVI + Ae (−e DOYi − DOYc = w
Data smoothing and fitting methods for time-series NDVI filtering, such as Savitzky-Golay (S-G) and Gauss, avoid very small and very large values of local NDVI that might not reflect the crop growth process (Tronstad et al., 2015; Jiang and Pickering, 2016). Four non-linear models were compared in a previous paper (Meng et al., 2017) and the Extreme model was found to have the highest accuracy and to fit a crop growth curve well. This study reconstructed the MODIS NDVI product into a specific-date time-series NDVI using the Extreme model (Eq. (2)). The specific experimental procedure is outlined as follows, and a flow chart is presented as Fig. 5:
(−z )
− z + 1) ;
z (2)
where: Reconstructed MODIS NDVI is the new MODIS NDVI value on the simulation date (DOYi), DOYi is the ith day of the year, DOYc (center) = the day of the year when the MODIS NDVI is at a maximum, w (width) = full width between DOYi andDOYc at half VImax, A = amplitude, and z is the conversion formula. Step 3: Using NDVI with accurate date information from MOD13Q1 as input to the Extreme model (Eq. (2)), reconstructed the daily time-series MODIS NDVI for each pixel.
Step 1: From the MOD13Q1 product, the start and end date of the simulated growth period was determined. This step identified the appropriate simulation period for cotton cultivation and ran from seeding to defoliation; Step 2: The Extreme model (Eq. (2) and Fig. 4) was selected for use in simulating time series MODIS NDVI with specific dates during the simulation period. This model was validated for such use in a
2.6. Fusion of imagery – FSDAF model The Flexible Spatiotemporal Data Fusion (FSDAF) method generates synthesized frequent high spatial resolution images by blending two types of data, such as frequent coarse spatial resolution MODIS images 47
Computers and Electronics in Agriculture 162 (2019) 44–52
L. Meng, et al.
Extreme Model
MODIS NDVI
Landsat NDVI
Reconstructed MODIS NDVI
FSDAF Model
FSDAF Model
Predicted NDVI from fused reconstructed MODIS
Predicted NDVI from fused MODIS
Landsat NDVI
Correlation Analysis
Cotton Yield Yield ModelC (MODIS)
Yield ModelR (Reconstructed MODIS)
Yield ModelL (Landsat)
Yield Accuracy Evaluation Fig. 3. Flow chart of the procedures employed in this study.
Time Series MODIS NDVI Data (MOD13Q1)
Composite Date Information
NDVI Value
Step 1
Step 2
Extreme Module Simulation Step 3 Non-Linear Curve Fitting Fig. 4. The Extreme model, showing the maximum Vegetation Index (VImax); DOYc, the day of the year when the normalized difference vegetation index (NDVI) is at the maximum, and the amplitude.
Reconstructed Time Series MODIS NDVI Fig. 5. Flow chart of reconstructing MODIS NDVI with accurate date information using the Extreme model.
and less frequent high spatial resolution Landsat images. The specific operational steps are detailed in Zhu et al. (2016). In this study, we generated two time-series NDVI fusion results; one fusing Landsat and MODIS and the other fusing Landsat and reconstructed MODIS. In the FSDAF model, input data includes a low spatial resolution image and a high spatial resolution image at t1, and a low spatial resolution image at the predicted date (t2). For t1 in the fusion process, we selected the Landsat TM5 image acquired on DOY 206, because studies have shown that yield estimation models using NDVI from close to maximum performed better than earlier or later images (Doraiswamy and Cook, 1995; Sakamoto et al., 2013; Son et al., 2014; Franch et al., 2015; Chipanshi et al., 2015). A MODIS image on DOY 193 was also available for use as a reference image, and each time-series MODIS
image (both MODIS and reconstructed MODIS) during the cotton growing season served as predicted images. Implementation of the FSDAF model employed six steps: (1) manual delineation of the study area on the Landsat image at t1; (2) calculation of the change in MODIS NDVI between t1 and t2; (3) prediction of the Landsat NDVI image at t2 using fine-resolution temporal change in MODIS NDVI and calculation of residuals at each MODIS pixel; (4) prediction of the Landsat NDVI image from the MODIS NDVI image 48
Computers and Electronics in Agriculture 162 (2019) 44–52
L. Meng, et al.
from fused reconstructed MODIS was consistent with all of the other datasets, and in addition provided a much smoother curve. The smooth curve is the result of the daily temporal resolution and could provide more accurate estimates of crop growth during the time interval between image acquisitions. The time-series predicted NDVI from fused reconstructed MODIS was more closely aligned with Landsat NDVI than with predicted NDVI from fused MODIS. Scatterplots of Landsat NDVI vs predicted NDVI from fused MODIS and Landsat NDVI vs predicted NDVI from fused reconstructed MODIS are shown in Fig. 7. The linear relationship between NDVI of Landsat and predicted NDVI from fused reconstructed MODIS was better than that between Landsat and fused MODIS. The results based on the fused reconstructed MODIS NDVI were more concentrated and provided R2 and RMSE values 15% higher than those for fused MODIS NDVI, probably due to the more accurate pixel date information. Scatterplots of original MODIS data could not be prepared due to spatial and temporal differences.
at t2 with a Thin Plate Spline (TPS) interpolator (Bookstein, 1997), which is a spatial interpolation technique for point data based on spatial dependence; (5) distribute the residuals based on TPS prediction; (6) obtain the final prediction of NDVI at each Landsat pixel using information in the neighborhood. After preparation of the reconstructed and fusion products, we prepared and reviewed graphical representations of time-series results for each dataset, and considering the original Landsat product to be the best representation of cotton growth, conducted correlation analysis between each one and Landsat NDVI. 2.7. Yield estimation models and accuracy assessment An earlier study on cotton yield estimation in the study area showed that a yield model for DOY 198 performed best (Liu et al., 2015), so we developed Yield ModelL, Yield ModelC, and Yield ModelR using Landsat NDVI on DOY 198, predicted NDVI from fused MODIS on DOY 193, and predicted NDVI from fused reconstructed MODIS on DOY 198, respectively. The general yield model equation is shown as Eq. (3). The yield models were evaluated using the decision coefficient (R2), Nash coefficient (Nash) and the root mean square error (RMSE) (Rojas, 2007; Dehghani et al., 2015).
Yield Model(y) = a∗Expb ∗ x
3.2. Correlation between NDVI and cotton yield The results of the analysis of correlation between NDVI of each input dataset and cotton yield at the pixel level is presented in Table 2. It shows that correlation between NDVI and cotton yield was significant for all 3 sources and for all dates. The results derived from Landsat provided the highest correlation coefficients except at one point early in the season (DOY 166) and later in the season (DOY 246–254). Otherwise, the correlation results of predicted NDVI from fused reconstructed MODIS were slightly lower than from Landsat and those from fused MODIS somewhat lower than that. Correlation coefficients calculated for the middle stage of the cotton season were higher than for the rest of the growing season for all three data sets. The correlation coefficients between Landsat NDVI and cotton yield on DOY 182 and DOY 198 (both 0.90) were the highest across all inputs and dates, while the correlation coefficient between predicted NDVI from fused reconstructed MODIS and cotton yield on those same dates was also high (0.88). Correlation between predicted NDVI from fused MODIS and yield was somewhat lower, with the highest value (0.87) occurring on DOY 198.
(3)
where y is the predicted yield, x is Landsat NDVI on DOY 198, predicted NDVI from fused MODIS on DOY 193, and predicted NDVI from fused reconstructed on DOY 198, respectively; a and b are coefficients. 3. Results and discussion 3.1. Time-series NDVI trends In this study we compared the fused time-series curves with both high spatial resolution imagery (Landsat) as well as the original MODIS imagery through graphic presentation and statistical analysis. Fig. 6 shows the time-series NDVI of Landsat and MODIS and predicted NDVI from fused MODIS and fused reconstructed MODIS. By comparing these time-series results, we were able to evaluate the effect of image fusion. The predicted NDVI from fused MODIS had a trend similar to and a little higher than Landsat NDVI, but more closely aligned with Landsat NDVI than with MODIS NDVI. In addition, MODIS NDVI fluctuates a bit from DOY 190 to DOY 254, perhaps due to not considering the specific date information of the pixel. The time-series trend of predicted NDVI
3.3. Yield accuracy assessment In this study, predicted NDVI from fused MODIS and fused reconstructed MODIS were used to compare the effect of fusion, before and after reconstruction, on the accuracy of the yield estimation model. Similarly to previous studies (Liu et al., 2015; Meng et al., 2017), we obtained the best yield estimation (highest accuracy and highest correlation coefficient based on a single-date) near the mid-point of the growing season, on DOY 198. The accuracy statistics of our single-date yield models for different inputs (Yield ModelR, Yield ModelC and Yield ModelL) are presented in Table 3. These data show that Yield ModelL, based on Landsat NDVI, had the highest accuracy among the three models, Yield ModelR (predicted NDVI from fused reconstructed MODIS) was second in accuracy, and both models were more accurate than Yield ModelC (predicted NDVI from fused MODIS). In addition, the Nash coefficient had a similar trend to that observed with R2 values, with Yield ModelL and Yield ModelR showing higher accuracy than Yield ModelC. 4. Conclusions In this paper, we analyzed time-series Landsat and MODIS NDVI data and assessed the importance of spatial and temporal resolution on yield estimation. The temporal problem of ignoring specific date information when using of MODIS 16-d composite imagery was addressed by reconstructing the imagery to include a specific date at each pixel, while the problem of using low spatial resolution (MODIS) imagery for
Fig. 6. Interpolated time-series curves of Landsat NDVI, MODIS NDVI, predicted NDVI from fused MODIS, and predicted NDVI from fused reconstructed MODIS. 49
Computers and Electronics in Agriculture 162 (2019) 44–52
L. Meng, et al.
0.60
a
0.60
0.55 y = 0.9887x - 0.054 R² = 0.61 RMSE=0.052
0.50
0.50
0.45
Landsat NDVI
Landsat NDVI
0.55
0.40 0.35
b y = 1.3977x - 0.2522 R² = 0.72 RMSE=0.044
0.45 0.40 0.35
0.30
0.30
0.25 0.20 0.30
0.35
0.40 0.45 0.50 0.55 Predicted NDVI from fused MODIS
0.60
0.25 0.25
0.65
0.30
0.35
0.40
0.45
0.50
0.55
0.60
Predicted NDVI from fused reconstructed MODIS
Fig. 7. Scatter plot of Landsat NDVI and (a) predicted NDVI from fused MODIS and (b) predicted NDVI from fused reconstructed MODIS.
field-level applications was addressed by fusing it with high spatial resolution Landsat imagery. Finally, yield estimation models for application at the field scale were developed, applied and assessed for accuracy. Three aspects of the study can be highlighted. First, we enhanced the application of fusion modeling for high spatial resolution applications. We showed that in the absence of sufficient time-series of high spatial resolution images, data fusion can accurately predict results at a high spatial resolution by using one or a few high spatial resolution images and several low-resolution images. Fusion models are typically used in land use and land cover analysis for dynamic monitoring of the Earth’s surface, but NDVI values of different land use types vary more than they do within a single crop field, and thus R2 and correlation coefficient values tend to be higher than those of this paper. However, this study shows that application of the FSDAF fusion model can be successful in the development of yield estimates at the field scale. Of course, to determine whether the results of other fusion models such as STDFA and MSTDFA would provide higher accuracy remains the domain of future studies. Our study proposed and applied a different accuracy evaluation method for the results of image fusion. Studies proposing the use of fusion modeling for crop classification, wetland area extraction, and yield estimation have primarily used the spatial variation of the fusion and original images as a criterion for accuracy evaluation (Singh, 2011; Wu et al., 2015; Liao et al., 2019; Senf et al., 2015). We compared not only the accuracy of fusion images with respect to the original images, but also compared the fused time-series curves with high spatial resolution (Landsat) results through graphic presentation and correlation analysis. By comparing the time-series results, we were able to evaluate the fusion effect of different MODIS NDVI products. We also considered the importance of specific date information of MODIS composite products for image fusion and yield estimation. A standard MODIS composite (usually 8-day or 16-day) image uses the
Table 3 Accuracy of yield estimation models. Model
Exponential Model Model
Yield ModelL on DOY 198 Yield ModelC on DOY 193 Yield ModelR on DOY 198
1.8109x
y = 1473.2e y = 794.55e2.4079x y = 716.72e2.5895x
R2
Nash
RMSE/g
0.84 0.80 0.82
0.82 0.77 0.79
463.12 513.96 488.01
‘best’ image within that time window for each pixel, meaning that the timing of acquisition within an image can vary by more than a week or two. Our reconstructed MODIS NDVI specified the day information for each pixel and improved the cotton yield estimation model accuracy at the field scale. The linear relationship between predicted NDVI from fused reconstructed MODIS and Landsat NDVI was better than that between fused MODIS and Landsat NDVI and the accuracy of the yield model based on the fused reconstructed MODIS data (Yield ModelR) was higher than that based on the fused MODIS data (Yield ModelC). Although the yield estimation results from the reconstructed MODIS NDVI were not as accurate as the actual Landsat NDVI results, the approach could be helpful in applications where suitable Landsat imagery is not available. Despite the success of this study on cotton yield estimation at the field level, there are areas that need further study; (1) Landsat imagery is unlikely to meet the requirements of a fusion model being developed for a large area. In future research, increasing the number of RS images with high spatial resolution (higher than 30 m) would be useful. For example, Sentinel-2 and Landsat images could be combined to improve the spatial and temporal resolution and enable a more precise estimation of crop yield. (2) Challenges in identifying tie points in the MODIS images during the registration process exist due to the large, mixed
Table 2 Correlation coefficients between Landsat NDVI, predicted NDVI from MODIS and predicted NDVI from reconstructed MODIS and cotton yield. DOY
158
166
174
182
190
198
206
214
230
246
254
Landsat NDVI DOY Predicted NDVI from fused MODIS DOY Predicted NDVI from fused reconstructed MODIS
0.86** 145 0.63** 145 0.81**
0.63** 161 0.78** 161 0.85**
0.88**
0.90** 177 0.81** 177 0.88**
0.86**
0.90** 193 0.87** 193 0.88**
0.88**
0.86** 209 0.83** 209 0.83**
0.86** 225 0.81** 225 0.85**
0.78**
0.77** 241 0.76** 241 0.86**
0.82**
Note: “**” significantly related at the 0.01 level (bilateral). 50
0.84**
0.87**
0.85**
Computers and Electronics in Agriculture 162 (2019) 44–52
L. Meng, et al.
pixels. It may be necessary to extend the area of image cover beyond a small study area in order to identify appropriate tie points. (3) Ease of application of fusion models could be improved by downloading all reflectance products directly from the USGS website rather than downloading digital numbers or top of the atmosphere (ToA) values and using FLAASH to generate surface reflectance products. (4) In addition to NDVI, the Enhanced Vegetation Index (EVI) could be an alternative for densely vegetated areas. EVI has been shown to provide a high degree of separation of vegetative reflection and could enhance yield estimation results. In our next study, we will test MODIS EVI in the FSDAF model and evaluate the accuracy.
estimation (Case Study: Sufi Chay Basin). Donohue, R.J., Lawes, R.A., Mata, G., Gobbett, D., Ouzman, J., 2018. Towards a national, remote-sensing-based model for predicting field-scale crop yield. Field Crops Res. 227, 79–90. Emelyanova, I.V., McVicar, T.R., Van Niel, T.G., Li, L.T., van Dijk, A.I., 2013. Assessing the accuracy of blending Landsat–MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics: a framework for algorithm selection. Remote Sens. Environ. 133, 193–209. Felde, G.W., Anderson, G.P., Cooley, T.W., Matthew, M.W., Berk, A., Lee, J., 2003. Analysis of Hyperion data with the FLAASH atmospheric correction algorithm. In: IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No. 03CH37477), vol. 1. IEEE, pp. 90–92. Franch, B., Vermote, E.F., Becker-Reshef, I., Claverie, M., Huang, J., Zhang, J., Sobrino, J.A., 2015. Improving the timeliness of winter wheat production forecast in the United States of America, Ukraine and China using MODIS data and NCAR growing degree day information. Remote Sens. Environ. 161, 131–148. Gao, F., Masek, J., Schwaller, M., Hall, F., 2006. On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance. IEEE Trans. Geosci. Remote Sens. 44 (8), 2207–2218. Gao, F., Anderson, M.C., Yang, Z., Kustas, W.P., Mueller, R., 2011. Integrating multiple satellite data for crop condition monitoring through data fusion approach. In: AGU Fall Meeting Abstracts. Gao, F., Hilker, T., Zhu, X., Anderson, M., Masek, J., Wang, P., Yang, Y., 2015. Fusing Landsat and MODIS data for vegetation monitoring. IEEE Geosci. Remote Sens. Mag. 3 (3), 47–60. Hochman, Z., Holzworth, D., Hunt, J.R., 2009. Potential to improve on-farm wheat yield and WUE in Australia. Crop Pasture Sci. 60 (60), 708–716. Huang, J., Sedano, F., Huang, Y., Ma, H., Li, X., Liang, S., Wu, W., 2016. Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation. Agric. For. Meteorol. 216, 188–202. He, M., Kimball, J., Maneta, M., Maxwell, B., Moreno, A., Beguería, S., Wu, X., 2018. Regional crop gross primary productivity and yield estimation using fused LandsatMODIS data. Remote Sens. 10 (3), 372. Jönsson, P., Eklundh, L., 2002. Seasonality extraction by function fitting to time-series of satellite sensor data. Geosci. Remote Sens. IEEE Trans. 40 (8), 1824–1832. Jönsson, P., Eklundh, L., 2004. TIMESAT—a program for analyzing time-series of satellite sensor data. Comput. Geosci. 30 (8), 833–845. Johnson, D.M., 2014. An assessment of pre-and within-season remotely sensed variables for forecasting corn and soybean yields in the United States. Remote Sens. Environ. 141, 116–128. Jiang, G., Pickering, S.J., 2016. Structure–property relationship of recycled carbon fibres revealed by pyrolysis recycling process. J. Mater. Sci. 51 (4), 1949–1958. Kouadio, L., Newlands, N.K., Davidson, A., Zhang, Y., Chipanshi, A., 2014. Assessing the performance of MODIS NDVI and EVI for seasonal crop yield forecasting at the ecodistrict scale. Remote Sens. 6 (10), 10193–10214. Liu, H., Meng, L., Zhang, X., Susan, U., Ning, D., Sun, S., 2015. Estimation model of cotton yield with time series Landsat images. Trans. Chinese Soc. Agric. Eng. 31 (17), 215–220. Lopresti, M.F., Di Bella, C.M., Degioanni, A.J., 2015. Relationship between MODIS-NDVI data and wheat yield: a case study in Northern Buenos Aires province, Argentina. Inform. Process. Agric. 2 (2), 73–84. Liao, C., Wang, J., Dong, T., Shang, J., Liu, J., Song, Y., 2019. Using spatio-temporal fusion of Landsat-8 and MODIS data to derive phenology, biomass and yield estimates for corn and soybean. Sci. Total Environ. 650, 1707–1721. Moriondo, M., Maselli, F., Bindi, M., 2007. A simple model of regional wheat yield based on NDVI data. Eur. J. Agron. 26 (3), 266–274. Min, J.E., Ryu, J.H., Lee, S., Son, S., 2012. Monitoring of suspended sediment variation using Landsat and MODIS in the Saemangeum coastal area of Korea. Mar. Pollut. Bull. 64 (2), 382–390. Meroni, M., Marinho, E., Sghaier, N., Verstrate, M.M., Leo, O., 2013. Remote sensing based yield estimation in a stochastic framework—Case study of durum wheat in Tunisia. Remote Sens. 5 (2), 539–557. Morel, J., Bégué, A., Todoroff, P., Martiné, J.F., Lebourgeois, V., Petit, M., 2014. Coupling a sugarcane crop model with the remotely sensed time series of fIPAR to optimise the yield estimation. Eur. J. Agron. 61, 60–68. Meng, L., Zhang, X.L., Liu, H., Guo, D., Yan, Y., Qin, L., Pan, Y., 2017. Estimation of cotton yield using the reconstructed time-series vegetation index of landsat data. Canadian J. Remote Sens. 43 (3), 244–255. Nuarsa, I.W., Nishio, F., Hongo, C., Mahardika, I.G., 2012. Using variance analysis of multitemporal MODIS images for rice field mapping in Bali Province, Indonesia. Int. J. Remote Sens. 33 (17), 5402–5417. Rojas, O., 2007. Operational maize yield model development and validation based on remote sensing and agro-meteorological data in Kenya. Int. J. Remote Sens. 28 (17), 3775–3793. Raun, W.R., Solie, J.B., Taylor, R.K., Arnall, D.B., Mack, C.J., Edmonds, D.E., 2008. Ramp calibration strip technology for determining midseason nitrogen rates in corn and wheat. Agron. J. 100 (4), 1088–1093. Stull, C.L., Messam, L.M., Collar, C.A., Peterson, N.G., Castillo, A.R., Reed, B.A., VerBoort, W.R., 2008. Precipitation and temperature effects on mortality and lactation parameters of dairy cattle in California. J. Dairy Sci. 91 (12), 4579–4591. Singh, D., 2011. Generation and evaluation of gross primary productivity using Landsat data through blending with MODIS data. Int. J. Appl. Earth Obs. Geoinf. 13 (1), 59–69. Sakamoto, T., Gitelson, A.A., Arkebauer, T.J., 2013. Modis-based corn grain yield estimation model incorporating crop phenology information. Remote Sens. Environ. 131, 215–231.
Acknowledgements This research was supported by the Natural Science Foundation of Heilongjiang Province of China (Grant No. D2017001); Talent Recruitment Project of the Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences. We thank Dr. Xiaolin Zhu for the FSDAF code and Dr. Ted Huffman (Agriculture and Agri-Food Canada (retired) for providing presentation and linguistic assistance during manuscript revision. Author contributions Linghua Meng tested the fusion model (FSDAF), analyzed the fusion results and wrote the paper. Xinle Zhang and Huanjun Liu provided suggestions and Chunying Ren revised the paper. Susan Ustin provided the RS images and yield data. Zhengchao Qiu processed RS images. During manuscript revision, Mengyuan Xu and Dong Guo provided much help. Conflicts of interest The authors declare no conflicts of interest. Appendix A. Supplementary material Supplementary data to this article can be found online at https:// doi.org/10.1016/j.compag.2019.04.001. References Anderson, G.P., Felde, G.W., Hoke, M.L., Ratkowski, A.J., Cooley, T.W., Chetwynd, J.H., Bernstein, L.S., 2002. MODTRAN4-based atmospheric correction algorithm: FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes). In: Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII, vol. 4725. International Society for Optics and Photonics, pp. 65–72. Bookstein, F.L., 1997.Shape and the information in medical images: a decade of the morphometric synthesis. In: Workshop on Mathematical Methods in Biomedical Image Analysis. Beck, P.S., Atzberger, C., Høgda, K.A., Johansen, B., Skidmore, A.K., 2006. Improved monitoring of vegetation dynamics at very high latitudes: a new method using MODIS NDVI. Remote Sens. Environ. 100 (3), 321–334. Becker-Reshef, I., Vermote, E., Lindeman, M., Justice, C., 2010. A generalized regressionbased model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data. Remote Sens. Environ. 114 (6), 1312–1323. Bolton, D.K., Friedl, M.A., 2013. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics. Agric. For. Meteorol. 173 (Complete), 74–84. Chipanshi, A., Zhang, Y., Kouadio, L., Newlands, N., Davidson, A., Hill, H., Reichert, G., 2015. Evaluation of the Integrated Canadian Crop Yield Forecaster (ICCYF) model for in-season prediction of crop yield across the Canadian agricultural landscape. Agric. For. Meteorol. 206, 137–150. Chen, Yanli, Long, Buju, Pan, Xuebiao, Mo, Weihua, 2010. Grassland vegetation change based on MODIS NDVI data and climate information. J. Appl. Meteorol. Sci. 21 (2), 229–236. Cheng, Z., Meng, J., Wang, Y., 2016. Improving spring maize yield estimation at field scale by assimilating time-series HJ-1 CCD data into the WOFOST model using a new method with fast algorithms. Remote Sens. 8 (4), 303. Doraiswamy, P.C., Cook, P.W., 1995. Spring wheat yield assessment using NOAA AVHRR data. Canadian J. Remote Sens. 21 (1), 43–51. Dehghani, R., Ghorbani, M.A., Teshnehlab, M., Rikhtehgar, G.A., Asadi, E., 2015. Comparison and evalution of bayesian neural network, gene gramming, support vector machine and multiple expression proLinear regression in river discharge
51
Computers and Electronics in Agriculture 162 (2019) 44–52
L. Meng, et al.
3907–3933. Xie, Y., Wang, P., Sun, H., Zhang, S., Li, L., 2017. Assimilation of leaf area index and surface soil moisture with the CERES-wheat model for winter wheat yield estimation using a particle filter algorithm. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10 (4), 1303–1316. ZarcoTejada, P.J., Ustin, S.L., Whiting, M.L., 2005. Temporal and spatial relationships between within-field yield variability in cotton and high-spatial hyperspectral remote sensing imagery. Agron. J. 97 (3), 641–653. Zurita-Milla, R., Kaiser, G., Clevers, J.G.P.W., Schneider, W., Schaepman, M.E., 2009. Downscaling time series of MERIS full resolution data to monitor vegetation seasonal dynamics. Remote Sens. Environ. 113 (9), 1874–1885. Zhu, X., Chen, J., Gao, F., Chen, X., Masek, J.G., 2010. An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions. Remote Sens. Environ. 114 (11), 2610–2623. Zhu, X., Helmer, E.H., Gao, F., Liu, D., Chen, J., Lefsky, M.A., 2016. A flexible spatiotemporal method for fusing satellite images with different resolutions. Remote Sens. Environ. 172, 165–177. Zhu, H., Tang, X., Xie, J., Song, W., Mo, F., Gao, X., 2018. Spatio-temporal super-resolution reconstruction of remote-sensing images based on adaptive multi-scale detail enhancement. Sensors 18 (2), 498.
Sakamoto, T., Gitelson, A.A., Arkebauer, T.J., 2014. Near real-time prediction of US corn yields based on time-series MODIS data. Remote Sens. Environ. 147, 219–231. Son, N.T., Chen, C.F., Chen, C.R., Minh, V.Q., Trung, N.H., 2014. A comparative analysis of multitemporal MODIS EVI and NDVI data for large-scale rice yield estimation. Agric. For. Meteorol. 197, 52–64. Senf, C., Leitão, P.J., Pflugmacher, D., van der Linden, S., Hostert, P., 2015. Mapping land cover in complex Mediterranean landscapes using Landsat: improved classification accuracies from integrating multi-seasonal and synthetic imagery. Remote Sens. Environ. 156, 527–536. Shao, Y., Lunetta, R.S., Wheeler, B., Iiames, J.S., Campbell, J.B., 2016. An evaluation of time-series smoothing algorithms for land-cover classifications using MODIS-NDVI multi-temporal data. Remote Sens. Environ. 174, 258–265. Tronstad, C., Staal, O.M., Sælid, S., Martinsen, Ø.G., 2015. Model-based filtering for artifact and noise suppression with state estimation for electrodermal activity measurements in real time. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, pp. 2750–2753. Wu, M., Huang, W., Niu, Z., Wang, C., 2015a. Generating daily synthetic Landsat imagery by combining Landsat and MODIS data. Sensors 15 (9), 24002–24025. Wu, B., Gommes, R., Zhang, M., Zeng, H., Yan, N., Zou, W., van Heijden, A., 2015b. Global crop monitoring: a satellite-based hierarchical approach. Remote Sens. 7 (4),
52
Contents lists available at ScienceDirect
Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag
Original papers
Assessment of the effectiveness of spatiotemporal fusion of multi-source satellite images for cotton yield estimation
T
⁎
Linghua Menga,b, Huanjun Liua,b, , Xinle Zhangb, Chunying Rena, Susan Ustinc, Zhengchao Qiub, Mengyuan Xub, Dong Guoa a
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China College of Resources and Environmental Sciences, Northeast Agricultural University, Harbin 150030, China c Center for Spatial Technologies and Remote Sensing (CSTARS), Department of Land, Air, and Water Resources, University of California, Davis, 95616, United States b
A R T I C LE I N FO
A B S T R A C T
Keywords: Data fusion Reconstructed MODIS NDVI Date information Yield estimation Field scale
Deficiencies in the spatiotemporal resolution of remote sensing (RS) images limit crop yield estimation at the farm and field scale. These deficiencies may be alleviated by fusion of high spatial and temporal resolution images such as MODIS and Landsat. In this study, a new daily MODIS NDVI product (reconstructed MODIS) was generated from 16-day composite images using the Extreme Model, which integrates the NDVI value with the corresponding specific date information at each pixel. The Flexible Spatiotemporal Data Fusion (FSDAF) model was then used to create two fused, high-resolution time-series products (fused MODIS and fused reconstructed MODIS) in order to enhance the spatial and temporal effectiveness of satellite images for field-scale applications. Three yield estimation models were then built using time-series data of Landsat NDVI, predicted NDVI from fused MODIS, and predicted NDVI from fused reconstructed MODIS. The methodology was tested on a farm field over the cotton growing season in the San Joaquin Valley of California. Results showed that: (1) the time trend of NDVI over the growing season for the fused reconstructed MODIS was more similar to that of Landsat than were either of MODIS or fused MODIS, indicating that the specific date of MODIS pixels is important for time-series analysis; (2) the NDVI from fused reconstructed MODIS provided the best correlation with Landsat NDVI, with R2 and RMSE values 15% higher than for fused MODIS; (3) correlation between cotton yield and all three datasets at the pixel level was statistically significant for all image dates, and (4) the accuracy of the cotton yield estimation model using predicted NDVI from fused reconstructed MODIS (R2 = 0.79; RMSE = 488.01) was higher than with fused MODIS (R2 = 0.77; RMSE = 513.96) and only slightly lower than with Landsat (R2 = 0.84, RMSE = 463.12). This study improved the accuracy of MODIS-based yield estimation using fusion images, and the results can be applied to improve vegetation monitoring and quantitative modeling using MODIS NDVI at the field scale.
1. Introduction Crop yield estimation is important as a basis for planning food imports and exports and developing national agricultural policies (Bolton and Friedl, 2013; Sakamoto et al., 2013; Meroni et al., 2013). Many models using remote sensing data can provide large-scale, objective, cost-effective, and timely estimation of crop yield (Moriondo et al., 2007; Nuarsa et al., 2012; Morel et al., 2014; Johnson, 2014; Huang et al., 2016; Xie et al., 2017). However, RS monitoring systems for crop growth and yield are generally based on the statistical relationship between a vegetation index (VI) and crop biophysiochemical properties, because radiation transfer and geometrical optics models are too
⁎
complex to be practical. Many studies have used VI and crop yield models (Becker-Reshef et al., 2010; Kouadio et al., 2014; Lopresti et al., 2015), resulting in the recognition that the single-date seasonal peak of Normalized Difference Vegetation Index (NDVI) derived from NOAAAVHRR or MODIS images are suitable as input variables for crop yield modeling over large areas (Doraiswamy and Cook, 1995; Son et al., 2014; Franch et al., 2015; Chipanshi et al., 2015). However, low spatial resolution images such as MODIS products can be negatively affected by environmental conditions, so maximum value composites (MVC), which select the ‘best’ value for each pixel over a certain time period, are commonly used to reduce interference from clouds, atmospheric conditions, and poor solar altitude angles (Beck et al., 2006; Chen et al.,
Corresponding author. E-mail address: [email protected] (H. Liu).
https://doi.org/10.1016/j.compag.2019.04.001 Received 5 November 2018; Received in revised form 28 March 2019; Accepted 1 April 2019 0168-1699/ © 2019 Elsevier B.V. All rights reserved.
Computers and Electronics in Agriculture 162 (2019) 44–52
L. Meng, et al.
Fig. 1. The study area in California; (a) Map showing the location of the San Joaquin Valley and the study plot; (b), (d) Landsat Thematic Mapper (TM5) 30 m resolution images of the farm and plot on DOY 174 (WRS2 path/row number-42/35); and (c), (e) MODIS NDVI 250 m resolution images of the farm and plot on DOY193.
up the impact of farm management decisions beyond the field scale without modifying the assessment methods and, similarly, national assessments of the effects of policy decisions are difficult to downscale in order to understand local impacts (Zurita-Milla et al., 2009; Zhu et al., 2018). The availability of a functional data fusion model to generate timeseries data provides an economical and consistent method of capitalizing on the spatial and temporal resolution advantages of different RS data (Gao et al., 2011, 2015). For example, the fusion of high temporalresolution MODIS data with high spatial-resolution Landsat data provides an accurate and timely methodology of using the same data in both regional and local studies (He et al., 2018). To this end, Gao et al. (2006) developed the spatial and temporal adaptive reflectance fusion model (STARFM), and Zhu et al. (2010) developed the enhanced STARFM (ESTARFM) to improve the accuracy of STARFM in areas of heterogeneous landscapes. Emelyanova et al. (2013) investigated the performance of STARFM and ESTARFM in two landscapes with contrasting spatial and temporal dynamics, and their results demonstrated that the performance of data fusion methods is strongly affected by spatial and temporal variations in land cover. Zhu et al. (2016) proposed the Flexible Spatiotemporal Data Fusion Model (FSDAF), which was shown to create more accurate fusion images and to retain more spatial detail than STARFM. In addition, the FSDAF model requires only one reference Landsat image and one MODIS image at time one (t1) and one MODIS image at t2 (the date of the fused image), thus effectively reducing the amount of data input and making it easier to implement. Our study addressed the problem of non-specific date information in common MODIS products by constructing time-series MODIS NDVI data using the mathematical model Extreme (Meng et al., 2017). We then used the FSDAF model to create Landsat/MODIS fusion images and with these constructed a model for yield estimation of California cotton. Our objectives were to: (1) analyze time-series Landsat and MODIS
2010; Sakamoto et al., 2014). Most studies ignore the specific date information of MODIS products, which may lead to discrepancies between modeled time-series trends and actual trends. For example, the 16-d composite MODIS NDVI product supplied for the 193rd day of the year (DOY 193) covers the time from DOY 193 to DOY 208 and the date of each pixel could be any day within that period. If date information is not considered, each pixel is assumed to have been acquired on the same day, which could lead to a 16-day deviation from the true date. The specific date information provides a key to determination of characteristic parameters of the time-series VI curve (such as peak greenness) and plays an important role in analyzing crop growth processes. It is, therefore, important to know whether it is possible and beneficial to incorporate specific date information of composite images in order to improve the accuracy of time-series VI curves. The most common approach to resolve problems associated with MVC technology is to smooth the time series curve (Shao et al., 2016), and the most popular smoothing routines include the Savitsky-Golay filter, non-symmetric Gauss function fitting, and logistic methods (Jönsson and Eklundh, 2002, 2004). These smoothing methods reduce the problems associated with variation in local NDVI values, but they also hide true variation of NDVI during crop growth. Here, we propose a mathematical model to reconstruct time-series VI data considering the date information (Meng et al., 2017). At present, the accuracy of crop yield estimation at the field scale needs improvement (Cheng et al., 2016; Donohue et al., 2018). Crop yield information at this scale is used by farmers, crop management advisors, and researchers to assist with and improve farm-level management actions. Unfortunately, the lower-resolution methods used to derive regional estimates of crop production are less useful at the farm and field scale (Raun et al., 2008; Hochman et al., 2009; Wu et al., 2015). It is therefore difficult to coordinate between the two spatial scales without changing data. This means that it is cumbersome to scale 45
Computers and Electronics in Agriculture 162 (2019) 44–52
L. Meng, et al.
Landsat TM 5 and ETM 7 images of the study area between April (planting) and September (defoliant application) were acquired (http:// earthexplorer.usgs.gov/) (see Table 1). There was no cloud cover in the study area during that time. The Landsat images have an 8 d temporal resolution and 30 m spatial resolution, and the study plot consisted of 519 pixels. Radiometric calibration and atmospheric correction of the Landsat data was performed as a preprocessing step using the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) in ENVI 5.1. The main objective of FLAASH is to eliminate the atmospheric effects caused by molecular and particulate scattering and absorption from the “radiance-at-detector” measurements to retrieve “reflectance-at-surface” values (Felde et al., 2003). The algorithm derives its first-principles physics-based calculations from the MODTRAN4 radiative transfer code (Anderson et al., 2002). Landsat NDVI values were calculated using Eq. (1).
NDVI data and verify the importance of date information of MODIS products; (2) reconstruct MODIS NDVI 16-d composite images to obtain a daily product for the growing season; (3) evaluate the results of the time-series predicted NDVI of the fusion datasets versus Landsat data over the cotton plot; and (4) build yield estimation models to compare the accuracy of field-scale yield estimates based on Landsat, fused MODIS and fused reconstructed MODIS data. 2. Material and methods 2.1. Study area The study was conducted in the southern portion of the San Joaquin Valley in central California, USA (Fig. 1a), an area that has been used extensively for time series RS research (Liu et al., 2015; Meng et al., 2017). This valley is known for its production of cotton (Gossypium sp.), garlic (Allium sativum L.), tomato (Lycopersicon esculentum Mill.), almonds (Prunus dulcis Mill. (D. A. Webb)), pistachio (Pistacia vera L.) and alfalfa hay (Medicago sativa L.), as well as grain and other crops. The San Joaquin Valley produces the majority of the value of agricultural production originating from California. The local Mediterranean climate of the San Joaquin Valley features hot, dry summers and cool, wet winters. The rainy season normally extends from November through April, with an average annual rainfall of 854 mm. Little rainfall was recorded during the growing season under study (2002) (Stull et al., 2008). The San Joaquin Valley has high levels of light, heat, and evaporation, with a maximum temperature of 40 °C and a day/night temperature difference of about 16 °C, making it very suitable for cotton cultivation. A square cotton field of 57.07 ha (Fig. 1b) was selected as the study area, as detailed cotton yield data from 2002 was available. Fig. 1b and 1c present a Landsat and a MODIS image (respectively) of the farm landscape in the study area, Fig. 1d shows a Landsat image of the study plot on DOY 174 and Fig. 1e shows a MODIS NDVI image of the plot on DOY 193.
NDVI =
NIR − RED NIR + RED
(1)
The MOD13Q1 (MODIS/Terra Vegetation Indices 16-Day L3 Global 250 m SIN Grid) product featuring NDVI data from 2002 was obtained from the NASA Reverb website (http://reverb.echo.nasa.gov/). The MOD13Q1 product includes 250 m resolution NDVI and quality assessment (QA) information (see Table 1). MODIS NDVI values are calculated by USGS from atmospherically-corrected surface reflectance values using Eq. (1) and are delivered as a 16-d composite image. The VI products from USGS (MOD13Q1) rely on a daily surface reflectance product (MOD09 series), and are corrected for molecular scattering, ozone absorption, and aerosols. The process at USGS generates products comparable to those generated using FLAASH. Hierarchical data format (HDF) images of MODIS products were converted into ENVI standard format and MODIS NDVI was resampled at 240 m resolution to fit the requirement of the FSDAF model. All original imagery was geometrically corrected, which aligns two images with different viewing geometry and/or different terrain distortions into the same coordinate system so that corresponding pixels represent the same objects (Min et al., 2012). The pre-processing steps related to geometric correction are as follows:
2.2. Cotton yield and management data collection Fig. 2 shows the 2002 yield map of the cotton plot, with an average yield of 2009.6 kg/ha. The yield data was collected using a yield monitor (Model AG700, AGRIplan, Stow, MA, USA; www.agriplaninc. com) onboard a mechanical harvester. The monitor uses infrared light to measure the flow of cotton in the chute, integrated with locations from a differential global positioning system (DGPS) with sub-meter accuracy. The accuracy of the yield estimates ranges from 95 to 98% as validated with field measurements conducted simultaneously, as reported by Zarco-Tejada et al. (2005). The yield per 4.5 m × 4.5 m field pixel was calculated from the number and width of cotton rows harvested, with the yield weight averaged over the pixel width and the 1–5 s time increments of DGPS collection. The yield and position data were prepared using manufacturer-supplied mapping software in the on-board computer and downloaded as ASCII text and database files. The yield map was produced in 3 steps:
Step 1: Locate and match a number of feature points (called tie points) in two images (a warp image-MODIS image and a base image-Landsat image) selected for registration. The overall RMSE values along the X and Y shifts during the registration process was 27.24 m for MODIS images at 250 m. Step 2: Use the corresponding tie points to compute the parameters of a geometric transformation between the two images. Step 3: Use ENVI to automatically and accurately generate tie points, then use those tie points to align and resample the warp image to match the base image.
2.4. Overview of data analysis and modeling
Step 1: ASCII text and database files were converted into vector shape files using ArcGIS; Step 2: The shape files were converted to raster, with the output raster cell size set to 0.5 m * 0.5 m to generate the yield map; Step 3: The outlying yield values caused by grain time lag and yield surges were removed using a statistical identifier based on a moving average mean and standard deviation. If the yield was less than or greater than three standard deviations from the average, it was identified as an outlier and removed.
Fig. 3 shows the technical flow chart for this study. Original MODIS NDVI data was first “reconstructed” to combine the NDVI value with accurate date information at each pixel using the Extreme model (Meng et al., 2017). Following the reconstruction, two fused outputs were generated by combining Landsat with each of MODIS and reconstructed MODIS using the FSDAF model. The fusion results were then evaluated at the pixel level for correlation with the original Landsat, MODIS and reconstructed MODIS data. Three cotton yield estimation models were developed, one each for the Landsat and two fusion images, and a yield accuracy evaluation was carried out.
2.3. Satellite images Based on the characteristics of the cotton growing period, all 46
Computers and Electronics in Agriculture 162 (2019) 44–52
L. Meng, et al.
Fig. 2. Cotton yield map (2002) of the study plot. Note: the white strips represent areas where the harvester head extends beyond the recorded GPS locations. The proprietary software averages yield figures across the head width. Table 1 Number and 2002 dates of the Landsat and MODIS images used in the study. Product
Sensors
Date
Number of images
Landsat
TM 5 ETM 7 MOD13Q1
May 6, 22; June 7, 23; July 1, 17; August 2, 26; September 11; May 14, 30; June 15, July 9, 25; August 18; September 3, 19; May 25; June 10, 26; July 12, 28; August 13, 29; September 14;
9 8 8
MODIS
2.5. NDVI reconstruction with specific pixel dates — Extreme model
previous study (Meng et al., 2017)
Reconstructed MODIS NDVI = MODIS NDVI + Ae (−e DOYi − DOYc = w
Data smoothing and fitting methods for time-series NDVI filtering, such as Savitzky-Golay (S-G) and Gauss, avoid very small and very large values of local NDVI that might not reflect the crop growth process (Tronstad et al., 2015; Jiang and Pickering, 2016). Four non-linear models were compared in a previous paper (Meng et al., 2017) and the Extreme model was found to have the highest accuracy and to fit a crop growth curve well. This study reconstructed the MODIS NDVI product into a specific-date time-series NDVI using the Extreme model (Eq. (2)). The specific experimental procedure is outlined as follows, and a flow chart is presented as Fig. 5:
(−z )
− z + 1) ;
z (2)
where: Reconstructed MODIS NDVI is the new MODIS NDVI value on the simulation date (DOYi), DOYi is the ith day of the year, DOYc (center) = the day of the year when the MODIS NDVI is at a maximum, w (width) = full width between DOYi andDOYc at half VImax, A = amplitude, and z is the conversion formula. Step 3: Using NDVI with accurate date information from MOD13Q1 as input to the Extreme model (Eq. (2)), reconstructed the daily time-series MODIS NDVI for each pixel.
Step 1: From the MOD13Q1 product, the start and end date of the simulated growth period was determined. This step identified the appropriate simulation period for cotton cultivation and ran from seeding to defoliation; Step 2: The Extreme model (Eq. (2) and Fig. 4) was selected for use in simulating time series MODIS NDVI with specific dates during the simulation period. This model was validated for such use in a
2.6. Fusion of imagery – FSDAF model The Flexible Spatiotemporal Data Fusion (FSDAF) method generates synthesized frequent high spatial resolution images by blending two types of data, such as frequent coarse spatial resolution MODIS images 47
Computers and Electronics in Agriculture 162 (2019) 44–52
L. Meng, et al.
Extreme Model
MODIS NDVI
Landsat NDVI
Reconstructed MODIS NDVI
FSDAF Model
FSDAF Model
Predicted NDVI from fused reconstructed MODIS
Predicted NDVI from fused MODIS
Landsat NDVI
Correlation Analysis
Cotton Yield Yield ModelC (MODIS)
Yield ModelR (Reconstructed MODIS)
Yield ModelL (Landsat)
Yield Accuracy Evaluation Fig. 3. Flow chart of the procedures employed in this study.
Time Series MODIS NDVI Data (MOD13Q1)
Composite Date Information
NDVI Value
Step 1
Step 2
Extreme Module Simulation Step 3 Non-Linear Curve Fitting Fig. 4. The Extreme model, showing the maximum Vegetation Index (VImax); DOYc, the day of the year when the normalized difference vegetation index (NDVI) is at the maximum, and the amplitude.
Reconstructed Time Series MODIS NDVI Fig. 5. Flow chart of reconstructing MODIS NDVI with accurate date information using the Extreme model.
and less frequent high spatial resolution Landsat images. The specific operational steps are detailed in Zhu et al. (2016). In this study, we generated two time-series NDVI fusion results; one fusing Landsat and MODIS and the other fusing Landsat and reconstructed MODIS. In the FSDAF model, input data includes a low spatial resolution image and a high spatial resolution image at t1, and a low spatial resolution image at the predicted date (t2). For t1 in the fusion process, we selected the Landsat TM5 image acquired on DOY 206, because studies have shown that yield estimation models using NDVI from close to maximum performed better than earlier or later images (Doraiswamy and Cook, 1995; Sakamoto et al., 2013; Son et al., 2014; Franch et al., 2015; Chipanshi et al., 2015). A MODIS image on DOY 193 was also available for use as a reference image, and each time-series MODIS
image (both MODIS and reconstructed MODIS) during the cotton growing season served as predicted images. Implementation of the FSDAF model employed six steps: (1) manual delineation of the study area on the Landsat image at t1; (2) calculation of the change in MODIS NDVI between t1 and t2; (3) prediction of the Landsat NDVI image at t2 using fine-resolution temporal change in MODIS NDVI and calculation of residuals at each MODIS pixel; (4) prediction of the Landsat NDVI image from the MODIS NDVI image 48
Computers and Electronics in Agriculture 162 (2019) 44–52
L. Meng, et al.
from fused reconstructed MODIS was consistent with all of the other datasets, and in addition provided a much smoother curve. The smooth curve is the result of the daily temporal resolution and could provide more accurate estimates of crop growth during the time interval between image acquisitions. The time-series predicted NDVI from fused reconstructed MODIS was more closely aligned with Landsat NDVI than with predicted NDVI from fused MODIS. Scatterplots of Landsat NDVI vs predicted NDVI from fused MODIS and Landsat NDVI vs predicted NDVI from fused reconstructed MODIS are shown in Fig. 7. The linear relationship between NDVI of Landsat and predicted NDVI from fused reconstructed MODIS was better than that between Landsat and fused MODIS. The results based on the fused reconstructed MODIS NDVI were more concentrated and provided R2 and RMSE values 15% higher than those for fused MODIS NDVI, probably due to the more accurate pixel date information. Scatterplots of original MODIS data could not be prepared due to spatial and temporal differences.
at t2 with a Thin Plate Spline (TPS) interpolator (Bookstein, 1997), which is a spatial interpolation technique for point data based on spatial dependence; (5) distribute the residuals based on TPS prediction; (6) obtain the final prediction of NDVI at each Landsat pixel using information in the neighborhood. After preparation of the reconstructed and fusion products, we prepared and reviewed graphical representations of time-series results for each dataset, and considering the original Landsat product to be the best representation of cotton growth, conducted correlation analysis between each one and Landsat NDVI. 2.7. Yield estimation models and accuracy assessment An earlier study on cotton yield estimation in the study area showed that a yield model for DOY 198 performed best (Liu et al., 2015), so we developed Yield ModelL, Yield ModelC, and Yield ModelR using Landsat NDVI on DOY 198, predicted NDVI from fused MODIS on DOY 193, and predicted NDVI from fused reconstructed MODIS on DOY 198, respectively. The general yield model equation is shown as Eq. (3). The yield models were evaluated using the decision coefficient (R2), Nash coefficient (Nash) and the root mean square error (RMSE) (Rojas, 2007; Dehghani et al., 2015).
Yield Model(y) = a∗Expb ∗ x
3.2. Correlation between NDVI and cotton yield The results of the analysis of correlation between NDVI of each input dataset and cotton yield at the pixel level is presented in Table 2. It shows that correlation between NDVI and cotton yield was significant for all 3 sources and for all dates. The results derived from Landsat provided the highest correlation coefficients except at one point early in the season (DOY 166) and later in the season (DOY 246–254). Otherwise, the correlation results of predicted NDVI from fused reconstructed MODIS were slightly lower than from Landsat and those from fused MODIS somewhat lower than that. Correlation coefficients calculated for the middle stage of the cotton season were higher than for the rest of the growing season for all three data sets. The correlation coefficients between Landsat NDVI and cotton yield on DOY 182 and DOY 198 (both 0.90) were the highest across all inputs and dates, while the correlation coefficient between predicted NDVI from fused reconstructed MODIS and cotton yield on those same dates was also high (0.88). Correlation between predicted NDVI from fused MODIS and yield was somewhat lower, with the highest value (0.87) occurring on DOY 198.
(3)
where y is the predicted yield, x is Landsat NDVI on DOY 198, predicted NDVI from fused MODIS on DOY 193, and predicted NDVI from fused reconstructed on DOY 198, respectively; a and b are coefficients. 3. Results and discussion 3.1. Time-series NDVI trends In this study we compared the fused time-series curves with both high spatial resolution imagery (Landsat) as well as the original MODIS imagery through graphic presentation and statistical analysis. Fig. 6 shows the time-series NDVI of Landsat and MODIS and predicted NDVI from fused MODIS and fused reconstructed MODIS. By comparing these time-series results, we were able to evaluate the effect of image fusion. The predicted NDVI from fused MODIS had a trend similar to and a little higher than Landsat NDVI, but more closely aligned with Landsat NDVI than with MODIS NDVI. In addition, MODIS NDVI fluctuates a bit from DOY 190 to DOY 254, perhaps due to not considering the specific date information of the pixel. The time-series trend of predicted NDVI
3.3. Yield accuracy assessment In this study, predicted NDVI from fused MODIS and fused reconstructed MODIS were used to compare the effect of fusion, before and after reconstruction, on the accuracy of the yield estimation model. Similarly to previous studies (Liu et al., 2015; Meng et al., 2017), we obtained the best yield estimation (highest accuracy and highest correlation coefficient based on a single-date) near the mid-point of the growing season, on DOY 198. The accuracy statistics of our single-date yield models for different inputs (Yield ModelR, Yield ModelC and Yield ModelL) are presented in Table 3. These data show that Yield ModelL, based on Landsat NDVI, had the highest accuracy among the three models, Yield ModelR (predicted NDVI from fused reconstructed MODIS) was second in accuracy, and both models were more accurate than Yield ModelC (predicted NDVI from fused MODIS). In addition, the Nash coefficient had a similar trend to that observed with R2 values, with Yield ModelL and Yield ModelR showing higher accuracy than Yield ModelC. 4. Conclusions In this paper, we analyzed time-series Landsat and MODIS NDVI data and assessed the importance of spatial and temporal resolution on yield estimation. The temporal problem of ignoring specific date information when using of MODIS 16-d composite imagery was addressed by reconstructing the imagery to include a specific date at each pixel, while the problem of using low spatial resolution (MODIS) imagery for
Fig. 6. Interpolated time-series curves of Landsat NDVI, MODIS NDVI, predicted NDVI from fused MODIS, and predicted NDVI from fused reconstructed MODIS. 49
Computers and Electronics in Agriculture 162 (2019) 44–52
L. Meng, et al.
0.60
a
0.60
0.55 y = 0.9887x - 0.054 R² = 0.61 RMSE=0.052
0.50
0.50
0.45
Landsat NDVI
Landsat NDVI
0.55
0.40 0.35
b y = 1.3977x - 0.2522 R² = 0.72 RMSE=0.044
0.45 0.40 0.35
0.30
0.30
0.25 0.20 0.30
0.35
0.40 0.45 0.50 0.55 Predicted NDVI from fused MODIS
0.60
0.25 0.25
0.65
0.30
0.35
0.40
0.45
0.50
0.55
0.60
Predicted NDVI from fused reconstructed MODIS
Fig. 7. Scatter plot of Landsat NDVI and (a) predicted NDVI from fused MODIS and (b) predicted NDVI from fused reconstructed MODIS.
field-level applications was addressed by fusing it with high spatial resolution Landsat imagery. Finally, yield estimation models for application at the field scale were developed, applied and assessed for accuracy. Three aspects of the study can be highlighted. First, we enhanced the application of fusion modeling for high spatial resolution applications. We showed that in the absence of sufficient time-series of high spatial resolution images, data fusion can accurately predict results at a high spatial resolution by using one or a few high spatial resolution images and several low-resolution images. Fusion models are typically used in land use and land cover analysis for dynamic monitoring of the Earth’s surface, but NDVI values of different land use types vary more than they do within a single crop field, and thus R2 and correlation coefficient values tend to be higher than those of this paper. However, this study shows that application of the FSDAF fusion model can be successful in the development of yield estimates at the field scale. Of course, to determine whether the results of other fusion models such as STDFA and MSTDFA would provide higher accuracy remains the domain of future studies. Our study proposed and applied a different accuracy evaluation method for the results of image fusion. Studies proposing the use of fusion modeling for crop classification, wetland area extraction, and yield estimation have primarily used the spatial variation of the fusion and original images as a criterion for accuracy evaluation (Singh, 2011; Wu et al., 2015; Liao et al., 2019; Senf et al., 2015). We compared not only the accuracy of fusion images with respect to the original images, but also compared the fused time-series curves with high spatial resolution (Landsat) results through graphic presentation and correlation analysis. By comparing the time-series results, we were able to evaluate the fusion effect of different MODIS NDVI products. We also considered the importance of specific date information of MODIS composite products for image fusion and yield estimation. A standard MODIS composite (usually 8-day or 16-day) image uses the
Table 3 Accuracy of yield estimation models. Model
Exponential Model Model
Yield ModelL on DOY 198 Yield ModelC on DOY 193 Yield ModelR on DOY 198
1.8109x
y = 1473.2e y = 794.55e2.4079x y = 716.72e2.5895x
R2
Nash
RMSE/g
0.84 0.80 0.82
0.82 0.77 0.79
463.12 513.96 488.01
‘best’ image within that time window for each pixel, meaning that the timing of acquisition within an image can vary by more than a week or two. Our reconstructed MODIS NDVI specified the day information for each pixel and improved the cotton yield estimation model accuracy at the field scale. The linear relationship between predicted NDVI from fused reconstructed MODIS and Landsat NDVI was better than that between fused MODIS and Landsat NDVI and the accuracy of the yield model based on the fused reconstructed MODIS data (Yield ModelR) was higher than that based on the fused MODIS data (Yield ModelC). Although the yield estimation results from the reconstructed MODIS NDVI were not as accurate as the actual Landsat NDVI results, the approach could be helpful in applications where suitable Landsat imagery is not available. Despite the success of this study on cotton yield estimation at the field level, there are areas that need further study; (1) Landsat imagery is unlikely to meet the requirements of a fusion model being developed for a large area. In future research, increasing the number of RS images with high spatial resolution (higher than 30 m) would be useful. For example, Sentinel-2 and Landsat images could be combined to improve the spatial and temporal resolution and enable a more precise estimation of crop yield. (2) Challenges in identifying tie points in the MODIS images during the registration process exist due to the large, mixed
Table 2 Correlation coefficients between Landsat NDVI, predicted NDVI from MODIS and predicted NDVI from reconstructed MODIS and cotton yield. DOY
158
166
174
182
190
198
206
214
230
246
254
Landsat NDVI DOY Predicted NDVI from fused MODIS DOY Predicted NDVI from fused reconstructed MODIS
0.86** 145 0.63** 145 0.81**
0.63** 161 0.78** 161 0.85**
0.88**
0.90** 177 0.81** 177 0.88**
0.86**
0.90** 193 0.87** 193 0.88**
0.88**
0.86** 209 0.83** 209 0.83**
0.86** 225 0.81** 225 0.85**
0.78**
0.77** 241 0.76** 241 0.86**
0.82**
Note: “**” significantly related at the 0.01 level (bilateral). 50
0.84**
0.87**
0.85**
Computers and Electronics in Agriculture 162 (2019) 44–52
L. Meng, et al.
pixels. It may be necessary to extend the area of image cover beyond a small study area in order to identify appropriate tie points. (3) Ease of application of fusion models could be improved by downloading all reflectance products directly from the USGS website rather than downloading digital numbers or top of the atmosphere (ToA) values and using FLAASH to generate surface reflectance products. (4) In addition to NDVI, the Enhanced Vegetation Index (EVI) could be an alternative for densely vegetated areas. EVI has been shown to provide a high degree of separation of vegetative reflection and could enhance yield estimation results. In our next study, we will test MODIS EVI in the FSDAF model and evaluate the accuracy.
estimation (Case Study: Sufi Chay Basin). Donohue, R.J., Lawes, R.A., Mata, G., Gobbett, D., Ouzman, J., 2018. Towards a national, remote-sensing-based model for predicting field-scale crop yield. Field Crops Res. 227, 79–90. Emelyanova, I.V., McVicar, T.R., Van Niel, T.G., Li, L.T., van Dijk, A.I., 2013. Assessing the accuracy of blending Landsat–MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics: a framework for algorithm selection. Remote Sens. Environ. 133, 193–209. Felde, G.W., Anderson, G.P., Cooley, T.W., Matthew, M.W., Berk, A., Lee, J., 2003. Analysis of Hyperion data with the FLAASH atmospheric correction algorithm. In: IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No. 03CH37477), vol. 1. IEEE, pp. 90–92. Franch, B., Vermote, E.F., Becker-Reshef, I., Claverie, M., Huang, J., Zhang, J., Sobrino, J.A., 2015. Improving the timeliness of winter wheat production forecast in the United States of America, Ukraine and China using MODIS data and NCAR growing degree day information. Remote Sens. Environ. 161, 131–148. Gao, F., Masek, J., Schwaller, M., Hall, F., 2006. On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance. IEEE Trans. Geosci. Remote Sens. 44 (8), 2207–2218. Gao, F., Anderson, M.C., Yang, Z., Kustas, W.P., Mueller, R., 2011. Integrating multiple satellite data for crop condition monitoring through data fusion approach. In: AGU Fall Meeting Abstracts. Gao, F., Hilker, T., Zhu, X., Anderson, M., Masek, J., Wang, P., Yang, Y., 2015. Fusing Landsat and MODIS data for vegetation monitoring. IEEE Geosci. Remote Sens. Mag. 3 (3), 47–60. Hochman, Z., Holzworth, D., Hunt, J.R., 2009. Potential to improve on-farm wheat yield and WUE in Australia. Crop Pasture Sci. 60 (60), 708–716. Huang, J., Sedano, F., Huang, Y., Ma, H., Li, X., Liang, S., Wu, W., 2016. Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation. Agric. For. Meteorol. 216, 188–202. He, M., Kimball, J., Maneta, M., Maxwell, B., Moreno, A., Beguería, S., Wu, X., 2018. Regional crop gross primary productivity and yield estimation using fused LandsatMODIS data. Remote Sens. 10 (3), 372. Jönsson, P., Eklundh, L., 2002. Seasonality extraction by function fitting to time-series of satellite sensor data. Geosci. Remote Sens. IEEE Trans. 40 (8), 1824–1832. Jönsson, P., Eklundh, L., 2004. TIMESAT—a program for analyzing time-series of satellite sensor data. Comput. Geosci. 30 (8), 833–845. Johnson, D.M., 2014. An assessment of pre-and within-season remotely sensed variables for forecasting corn and soybean yields in the United States. Remote Sens. Environ. 141, 116–128. Jiang, G., Pickering, S.J., 2016. Structure–property relationship of recycled carbon fibres revealed by pyrolysis recycling process. J. Mater. Sci. 51 (4), 1949–1958. Kouadio, L., Newlands, N.K., Davidson, A., Zhang, Y., Chipanshi, A., 2014. Assessing the performance of MODIS NDVI and EVI for seasonal crop yield forecasting at the ecodistrict scale. Remote Sens. 6 (10), 10193–10214. Liu, H., Meng, L., Zhang, X., Susan, U., Ning, D., Sun, S., 2015. Estimation model of cotton yield with time series Landsat images. Trans. Chinese Soc. Agric. Eng. 31 (17), 215–220. Lopresti, M.F., Di Bella, C.M., Degioanni, A.J., 2015. Relationship between MODIS-NDVI data and wheat yield: a case study in Northern Buenos Aires province, Argentina. Inform. Process. Agric. 2 (2), 73–84. Liao, C., Wang, J., Dong, T., Shang, J., Liu, J., Song, Y., 2019. Using spatio-temporal fusion of Landsat-8 and MODIS data to derive phenology, biomass and yield estimates for corn and soybean. Sci. Total Environ. 650, 1707–1721. Moriondo, M., Maselli, F., Bindi, M., 2007. A simple model of regional wheat yield based on NDVI data. Eur. J. Agron. 26 (3), 266–274. Min, J.E., Ryu, J.H., Lee, S., Son, S., 2012. Monitoring of suspended sediment variation using Landsat and MODIS in the Saemangeum coastal area of Korea. Mar. Pollut. Bull. 64 (2), 382–390. Meroni, M., Marinho, E., Sghaier, N., Verstrate, M.M., Leo, O., 2013. Remote sensing based yield estimation in a stochastic framework—Case study of durum wheat in Tunisia. Remote Sens. 5 (2), 539–557. Morel, J., Bégué, A., Todoroff, P., Martiné, J.F., Lebourgeois, V., Petit, M., 2014. Coupling a sugarcane crop model with the remotely sensed time series of fIPAR to optimise the yield estimation. Eur. J. Agron. 61, 60–68. Meng, L., Zhang, X.L., Liu, H., Guo, D., Yan, Y., Qin, L., Pan, Y., 2017. Estimation of cotton yield using the reconstructed time-series vegetation index of landsat data. Canadian J. Remote Sens. 43 (3), 244–255. Nuarsa, I.W., Nishio, F., Hongo, C., Mahardika, I.G., 2012. Using variance analysis of multitemporal MODIS images for rice field mapping in Bali Province, Indonesia. Int. J. Remote Sens. 33 (17), 5402–5417. Rojas, O., 2007. Operational maize yield model development and validation based on remote sensing and agro-meteorological data in Kenya. Int. J. Remote Sens. 28 (17), 3775–3793. Raun, W.R., Solie, J.B., Taylor, R.K., Arnall, D.B., Mack, C.J., Edmonds, D.E., 2008. Ramp calibration strip technology for determining midseason nitrogen rates in corn and wheat. Agron. J. 100 (4), 1088–1093. Stull, C.L., Messam, L.M., Collar, C.A., Peterson, N.G., Castillo, A.R., Reed, B.A., VerBoort, W.R., 2008. Precipitation and temperature effects on mortality and lactation parameters of dairy cattle in California. J. Dairy Sci. 91 (12), 4579–4591. Singh, D., 2011. Generation and evaluation of gross primary productivity using Landsat data through blending with MODIS data. Int. J. Appl. Earth Obs. Geoinf. 13 (1), 59–69. Sakamoto, T., Gitelson, A.A., Arkebauer, T.J., 2013. Modis-based corn grain yield estimation model incorporating crop phenology information. Remote Sens. Environ. 131, 215–231.
Acknowledgements This research was supported by the Natural Science Foundation of Heilongjiang Province of China (Grant No. D2017001); Talent Recruitment Project of the Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences. We thank Dr. Xiaolin Zhu for the FSDAF code and Dr. Ted Huffman (Agriculture and Agri-Food Canada (retired) for providing presentation and linguistic assistance during manuscript revision. Author contributions Linghua Meng tested the fusion model (FSDAF), analyzed the fusion results and wrote the paper. Xinle Zhang and Huanjun Liu provided suggestions and Chunying Ren revised the paper. Susan Ustin provided the RS images and yield data. Zhengchao Qiu processed RS images. During manuscript revision, Mengyuan Xu and Dong Guo provided much help. Conflicts of interest The authors declare no conflicts of interest. Appendix A. Supplementary material Supplementary data to this article can be found online at https:// doi.org/10.1016/j.compag.2019.04.001. References Anderson, G.P., Felde, G.W., Hoke, M.L., Ratkowski, A.J., Cooley, T.W., Chetwynd, J.H., Bernstein, L.S., 2002. MODTRAN4-based atmospheric correction algorithm: FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes). In: Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII, vol. 4725. International Society for Optics and Photonics, pp. 65–72. Bookstein, F.L., 1997.Shape and the information in medical images: a decade of the morphometric synthesis. In: Workshop on Mathematical Methods in Biomedical Image Analysis. Beck, P.S., Atzberger, C., Høgda, K.A., Johansen, B., Skidmore, A.K., 2006. Improved monitoring of vegetation dynamics at very high latitudes: a new method using MODIS NDVI. Remote Sens. Environ. 100 (3), 321–334. Becker-Reshef, I., Vermote, E., Lindeman, M., Justice, C., 2010. A generalized regressionbased model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data. Remote Sens. Environ. 114 (6), 1312–1323. Bolton, D.K., Friedl, M.A., 2013. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics. Agric. For. Meteorol. 173 (Complete), 74–84. Chipanshi, A., Zhang, Y., Kouadio, L., Newlands, N., Davidson, A., Hill, H., Reichert, G., 2015. Evaluation of the Integrated Canadian Crop Yield Forecaster (ICCYF) model for in-season prediction of crop yield across the Canadian agricultural landscape. Agric. For. Meteorol. 206, 137–150. Chen, Yanli, Long, Buju, Pan, Xuebiao, Mo, Weihua, 2010. Grassland vegetation change based on MODIS NDVI data and climate information. J. Appl. Meteorol. Sci. 21 (2), 229–236. Cheng, Z., Meng, J., Wang, Y., 2016. Improving spring maize yield estimation at field scale by assimilating time-series HJ-1 CCD data into the WOFOST model using a new method with fast algorithms. Remote Sens. 8 (4), 303. Doraiswamy, P.C., Cook, P.W., 1995. Spring wheat yield assessment using NOAA AVHRR data. Canadian J. Remote Sens. 21 (1), 43–51. Dehghani, R., Ghorbani, M.A., Teshnehlab, M., Rikhtehgar, G.A., Asadi, E., 2015. Comparison and evalution of bayesian neural network, gene gramming, support vector machine and multiple expression proLinear regression in river discharge
51
Computers and Electronics in Agriculture 162 (2019) 44–52
L. Meng, et al.
3907–3933. Xie, Y., Wang, P., Sun, H., Zhang, S., Li, L., 2017. Assimilation of leaf area index and surface soil moisture with the CERES-wheat model for winter wheat yield estimation using a particle filter algorithm. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10 (4), 1303–1316. ZarcoTejada, P.J., Ustin, S.L., Whiting, M.L., 2005. Temporal and spatial relationships between within-field yield variability in cotton and high-spatial hyperspectral remote sensing imagery. Agron. J. 97 (3), 641–653. Zurita-Milla, R., Kaiser, G., Clevers, J.G.P.W., Schneider, W., Schaepman, M.E., 2009. Downscaling time series of MERIS full resolution data to monitor vegetation seasonal dynamics. Remote Sens. Environ. 113 (9), 1874–1885. Zhu, X., Chen, J., Gao, F., Chen, X., Masek, J.G., 2010. An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions. Remote Sens. Environ. 114 (11), 2610–2623. Zhu, X., Helmer, E.H., Gao, F., Liu, D., Chen, J., Lefsky, M.A., 2016. A flexible spatiotemporal method for fusing satellite images with different resolutions. Remote Sens. Environ. 172, 165–177. Zhu, H., Tang, X., Xie, J., Song, W., Mo, F., Gao, X., 2018. Spatio-temporal super-resolution reconstruction of remote-sensing images based on adaptive multi-scale detail enhancement. Sensors 18 (2), 498.
Sakamoto, T., Gitelson, A.A., Arkebauer, T.J., 2014. Near real-time prediction of US corn yields based on time-series MODIS data. Remote Sens. Environ. 147, 219–231. Son, N.T., Chen, C.F., Chen, C.R., Minh, V.Q., Trung, N.H., 2014. A comparative analysis of multitemporal MODIS EVI and NDVI data for large-scale rice yield estimation. Agric. For. Meteorol. 197, 52–64. Senf, C., Leitão, P.J., Pflugmacher, D., van der Linden, S., Hostert, P., 2015. Mapping land cover in complex Mediterranean landscapes using Landsat: improved classification accuracies from integrating multi-seasonal and synthetic imagery. Remote Sens. Environ. 156, 527–536. Shao, Y., Lunetta, R.S., Wheeler, B., Iiames, J.S., Campbell, J.B., 2016. An evaluation of time-series smoothing algorithms for land-cover classifications using MODIS-NDVI multi-temporal data. Remote Sens. Environ. 174, 258–265. Tronstad, C., Staal, O.M., Sælid, S., Martinsen, Ø.G., 2015. Model-based filtering for artifact and noise suppression with state estimation for electrodermal activity measurements in real time. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, pp. 2750–2753. Wu, M., Huang, W., Niu, Z., Wang, C., 2015a. Generating daily synthetic Landsat imagery by combining Landsat and MODIS data. Sensors 15 (9), 24002–24025. Wu, B., Gommes, R., Zhang, M., Zeng, H., Yan, N., Zou, W., van Heijden, A., 2015b. Global crop monitoring: a satellite-based hierarchical approach. Remote Sens. 7 (4),
52