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A Production Efficiency Model-Based Method for Satellite Estimates of Corn and Soybean Yields in the Midwestern US


Abstract and Figures

Remote sensing techniques that provide synoptic and repetitive observations over large geographic areas have become increasingly important in studying the role of agriculture in global carbon cycles. However, it is still challenging to model crop yields based on remotely sensed data due to the variation in radiation use efficiency (RUE) across crop types and the effects of spatial heterogeneity. In this paper, we propose a production efficiency model-based method to estimate corn and soybean yields with MODerate Resolution Imaging Spectroradiometer (MODIS) data by explicitly handling the following two issues: (1) field-measured RUE values for corn and soybean are applied to relatively pure pixels instead of the biome-wide RUE value prescribed in the MODIS vegetation productivity product (MOD17); and (2) contributions to productivity from vegetation other than crops in mixed pixels are deducted at the level of MODIS resolution. Our estimated yields statistically correlate with the national survey data for rainfed counties in the Midwestern US with low errors for both corn (R-2 = 0.77; RMSE = 0.89 MT/ha) and soybeans (R-2 = 0.66; RMSE = 0.38 MT/ha). Because the proposed algorithm does not require any retrospective analysis that constructs empirical relationships between the reported yields and remotely sensed data, it could monitor crop yields over large areas.
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Remote Sens. 2013, 5, 5926-5943; doi:10.3390/rs5115926
Remote Sensing
ISSN 2072-4292
A Production Efficiency Model-Based Method for Satellite
Estimates of Corn and Soybean Yields in the Midwestern US
Qinchuan Xin 1,*, Peng Gong 1,2,*, Chaoqing Yu 1, Le Yu 1, Mark Broich 3, Andrew E. Suyker 4
and Ranga B. Myneni 5
1 Ministry of Education Key Laboratory for Earth System Modeling, and Center for Earth System
Science, Tsinghua University, Beijing 100084, China; E-Mails: (C.Y.); (L.Y.)
2 Department of Environmental Science, Policy and Management, University of California, Berkeley,
Berkeley, CA 94720, USA
3 Plant Functional Biology and Climate Change Cluster Department, University of Technology
Sydney, Broadway, NSW 2007, Australia; E-Mail:
4 School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE 68583, USA;
5 Department of Earth and Environment, Boston University, Boston, MA 02215, USA;
* Authors to whom correspondence should be addressed; E-Mails: (Q.X.); (P.G.); Tel.: +86-188-1025-3088; Fax: +86-10-6279-7284.
Received: 26 September 2013; in revised form: 6 November 2013 / Accepted: 7 November 2013 /
Published: 14 November 2013
Abstract: Remote sensing techniques that provide synoptic and repetitive observations over
large geographic areas have become increasingly important in studying the role of
agriculture in global carbon cycles. However, it is still challenging to model crop yields
based on remotely sensed data due to the variation in radiation use efficiency (RUE) across
crop types and the effects of spatial heterogeneity. In this paper, we propose a production
efficiency model-based method to estimate corn and soybean yields with MODerate
Resolution Imaging Spectroradiometer (MODIS) data by explicitly handling the following
two issues: (1) field-measured RUE values for corn and soybean are applied to relatively
pure pixels instead of the biome-wide RUE value prescribed in the MODIS vegetation
productivity product (MOD17); and (2) contributions to productivity from vegetation other
than crops in mixed pixels are deducted at the level of MODIS resolution. Our estimated
yields statistically correlate with the national survey data for rainfed counties in the
Remote Sens. 2013, 5 5927
Midwestern US with low errors for both corn (R2 = 0.77; RMSE = 0.89 MT/ha) and
soybeans (R2 = 0.66; RMSE = 0.38 MT/ha). Because the proposed algorithm does not
require any retrospective analysis that constructs empirical relationships between the
reported yields and remotely sensed data, it could monitor crop yields over large areas.
Keywords: remote sensing; crop yield; MODIS GPP; radiation use efficiency;
spatial heterogeneity
1. Introduction
Because agricultural productivity directly influences food prices, trade and greenhouse-gas emission
policies, and human livelihood [1,2], accurate and timely yield estimation is of primary interest to the
scientific community and governments [3]. Compared with traditional ground-based methods, such as
visual examination and survey sampling, remote sensing that provides synoptic and repetitive
observations of the land surface is well suited for agricultural mapping [4–6] and monitoring [7,8] large
geographic areas. In particular, satellite-derived vegetation indices, as measures of plant chlorophyll
abundance and vegetation radiation absorption [9], have proven to be closely related to crop growth in
field studies and theoretical models [10–12].
Despite tremendous efforts, obtaining accurate yield estimations based on remotely sensed data
remains difficult [13,14]. One approach develops regression models that relate satellite-derived
vegetation indices directly to historical yield data [15–17]. These models are essentially retrospective
and are based empirically on indirect inferences, whereby changes in vegetation indices can determine
variations in plant productions [14,18]. Because the regression relationship varies largely on a
year-to-year basis due to inter-annual variations in climate, water availability, and management
practices, the application of these models is limited to the studied regions and periods and is difficult
under extreme conditions (e.g., flooding and drought) beyond historical records. Another approach
applies satellite data to calibrate physiology-based crop models [19–21] that simulate the physical
process of crop growth, where energy, water, carbon dioxide, and nutrients are converted into biomass [22,23].
These models have proven useful at the field scale but possess one limitation for large-scale application:
they often require numerous inputs related to soil characteristics, management practices, and local
weather conditions [24–26].
Given the difficulties of the two above-mentioned approaches, improving yield estimation with
satellite data based on the Production Efficiency Models (PEMs) is a highly desirable goal. The theory
proposed by Monteith [27,28] proposes a basis for estimating the primary productivity of vegetation
from remotely sensed data. It suggests that crop yields under non-stressed conditions linearly correlate
with the amount of absorbed photosynthetically active radiation. Satellite data at different resolutions
have been synthesized in production efficiency models to monitor vegetation productivities [29–32] and
estimate crop yields [33–35] on regional or global scales. Based on production efficiency models, a
routine product (MOD17) has been derived from high temporal and moderate spatial resolution MODIS
data, which has proven useful to studies on global carbon cycles [36,37].
Remote Sens. 2013, 5 5928
However, validations against flux tower data indicate that MOD17 estimates exhibit large
uncertainties in croplands [38]. Crop yield estimates based on the MOD17 products were found to have
weak correlation with national agricultural data at the county level for the states of Montana and North
Dakota in the US [18]. The MOD17 algorithm prescribes constant values of model parameters such as
radiation use efficiency (RUE) for the agricultural biome regardless of crop types [36,37]. However,
crops are known to possess distinctive carbon sequestration abilities and thereby different radiation use
efficiencies across species, notably the C3 and C4 pathways. Moreover, yield estimation based on
remotely sensed data requires acknowledging spatial heterogeneity effects because the pixels are
typically mixtures of diverse land-cover types at the MODIS spatial resolution [39,40]. Remote sensing
models that consider the conceptual partition of mixed pixels are likely to improve the yield estimates of
specific crop types.
This study aims to improve yield estimation with MODIS data based on production efficiency
models. Our approach is to apply crop-type specific values of radiation use efficiency (RUE) to MODIS
pixels based on spatially disaggregated fine-resolution land use maps and to deduct the subpixel
productivity contribution from vegetation other than crops. We demonstrate our production efficiency
model-based method by quantifying the corn and soybean yields in the Midwestern US. We validate our
estimates against national survey data at both the county and state levels.
2. Study Area and Materials
The Midwestern US study area (Figure 1), one of the major agricultural regions in the world, includes
12 US states: North Dakota, South Dakota, Nebraska, Kansas, Minnesota, Iowa, Missouri, Michigan,
Wisconsin, Illinois, Indiana, and Ohio. In this region, diversified crops are cultivated but corn
(Zea mays) and soybeans (Glycine max) are the two dominant crops. In general, crop planting is
completed by mid-May, when the daily soil temperatures are warm enough (above 13 °C) to initiate
germination. Corn is often planted approximately one to two weeks earlier than soybeans [41]. The crops
usually mature in late September and are harvested from early October until late October.
The datasets used in this study (Table 1) were collected over a three-year span between 2009 and
2011. The satellite data include two standard Terra/MODIS products: the eight-day 1000 m Leaf Area
Index (LAI) & Fraction of Photosynthetically Active Radiation (FPAR) product (MOD15A2) [42] and
the eight-day 1,000 m vegetation productivity product (MOD17A2) [37]. The MODIS tiling scheme
separates the earth into 10° by 10° sections referenced with horizontal (H) and vertical (V) notations. Six
MODIS tiles (H09V05, H10V04, H10V05, H11V04, H11V05, and H12V04) that cover the Midwestern
US were downloaded from the U.S. Geological Survey (USGS) EROS data center [43]. All products
were composited every eight days, starting from Day-of-Year (DOY) 1 in each calendar year. The
product details are available on the MODIS data website [44].
The Quick Stats database from the National Agricultural Statistics Service (NASS) of the US
Department of Agriculture (USDA) provides reliable crop survey data at the county, state, and national
levels [45]. We obtained statistical data describing the yields and harvested areas of corn and soybean in
each county. Based on the Crop Production 2011 Summary released by the USDA, the Midwestern US
produced 277.1 Tg of corn and 70.2 Tg of soybeans in 2011, which were approximately 88.3% and
84.4%, respectively, of the total US corn and soybean productions. Planted areas for corn and soybean in
Remote Sens. 2013, 5 5929
Midwestern US were 5.6 × 107 ha in 2011, which were approximately 70.9% of the total planted areas
(7.9 × 107 ha) for all principle crops such as corn, sorghum, oats, barley, rye, wheat, rice, soybeans,
peanuts, sunflower, cotton, and potatoes.
Figure 1. The study area of the Midwestern US comprises 12 states.
Table 1. The datasets used in this study.
Data Type Data Source Description
satellite data USGS EROS center MODIS LAI/FPAR data (MOD15A2)
USGS EROS center MODIS vegetation productivity data (MOD17A2)
classification maps NASS Cropland Data Layer crop-type specific classification maps at 30 or 56 m
USGS early warning program a classification map of irrigated areas at 250 m
national survey NASS Quick Stats statistics of crop yields and harvested areas
The NASS Cropland Data Layer (CDL) program produces crop-type specific classification maps
based on multi-sensor satellite imagery using training data from extensive ground surveys [46]. The
classification map is available for each state at a resolution of 30 m. In the present study, the CDL maps
were reprojected to a sinusoidal projection, mosaicked, and scaled up to 500 m as percent maps (Figure 2)
to match the spatial resolution of MODIS. To avoid errors in regions with sparse agriculture, our analysis
was confined to counties that were planted with corn or soybean on at least 5% of their total areas.
Although irrigation helps boost crop yields, most PEMs to date have not compensated for the impacts
of irrigation. To investigate the yield estimation in irrigated areas, we sourced the irrigation map (Figure 3)
from the USGS early warning program [47]. The irrigated areas in the Midwestern US were mapped for
2007 and were primarily located in Nebraska and Kansas. Because irrigation is unlikely to vary on a
year-to-year basis, no change in irrigated area was assumed during the three years (from 2009 to 2011).
The spatial pattern of irrigated areas was consistent with that of the agriculture areas (Figure 2),
especially for corn (Figure 2A), which consumes large volumes of water before, during and for several
weeks after pollination. To study the effects of irrigation, counties were considered as irrigated if more
than 1% of the crop areas were under some form of irrigation.
Remote Sens. 2013, 5 5930
Figure 2. The percentage maps of corn and soybean in 2011 from the NASS CDL program.
(A) 2011 Corn map from NASS CDL; (B) 2011 Soybean map from NASS CDL.
Figure 3. The map of irrigated areas from the early warning program of USGS in 2007.
Remote Sens. 2013, 5 5931
3. Theoretical Background and Improvements
3.1. A Brief Description of the MOD17 Algorithm
Although the MOD17 algorithm has been described in detail in the MOD17 User’s Guide [36], we
briefly describe it here because the algorithm is fundamental to understanding our proposed method. The
MOD17 product [37,48] estimates the gross primary production (GPP) and net photosynthesis (PSN) of
each pixel every 8 days and estimates the GPP and NPP for each pixel annually. These estimates are
produced based on three upstream datasets: the MODIS land-cover classification product (MOD12Q1) [4],
MODIS LAI/FPAR product (MOD15) [42], and large-scale meteorological data from the NASA Data
Assimilation Office (DAO). The coefficients for each biome type (decided based on MOD12Q1) are
defined in a Biome Parameter Look-Up Table (BPLUT) from a general ecosystem model [49].
The daily MODIS GPP is estimated as:
where PAR (MJ·day1) is the daily incident photosynthetically active radiation, as estimated using the
DAO data; FPAR (dimensionless) is the fraction of absorbed photosynthetically active radiation, as
obtained from the upstream MOD15 product; ɛɡ (g·C·MJ1 PAR) is the radiation use efficiency (RUE)
for GPP calculations when the environment is not limiting plant carbon uptake, as defined in the BPLUT
for the agricultural biome; and f(ɛ) accounts for the influences of environmental stress, such as
temperature and vapor pressure deficit, as calculated based on the DAO data and BPLUT.
The concept of daily PSN considers the maintenance respiration of leaves and fine roots:
where the daily maintenance respiration is estimated for the leaves (Leaf_MR) and fine roots
(Froot_MR) separately:
where LAI (m2·leaf·m2 ground area) is the leaf area index and SLA (projected leaf area m2·kg1·leaf C)
is the specific leaf area for a given pixel; leaf_mr_base and froot_mr_base (kg·C·kg·C1·day1·20 C) are
the maintenance respiration of leaves and fine roots per unit mass at 20 C, respectively; froot_leaf_ratio
(unitless) is the ratio of the fine root to leaf mass; Q10_mr (unitless) is an exponent shape parameter that
controls respiration as a function of temperature; and Tavg (°C) is the average daily temperature. LAI is
obtained from MOD15, Tavg is estimated from the DAO meteorological data, and other biome-specific
coefficients are obtained from BPLUT.
3.2. Improvements to Estimate Crop Productivity
Based on the MOD17 algorithm, we attempted to improve the crop yield estimation in the
Midwestern US using the following techniques: (1) crop-type specific masks were derived from
fine-resolution NASS CDL maps to identify relatively pure MODIS pixels; (2) a specific RUE was
PSN = GPP Leaf_MR Froot_MR−−
[(Tavg 20.0) / 10.0]
Leaf_MR = LAI / SLA leaf_mr_base Q10_mr
[(Tavg 20.0) / 10.0]
Froot_MR = LAI / SLA froot_leaf_ratio froot_mr_base Q10_mr
Remote Sens. 2013, 5 5932
applied for each crop type; and (3) contributions to primary production from other vegetation types
were deducted when possible.
First, the MOD17 algorithm relies on the MODIS land cover product (MOD12), a classification
scheme that does not differentiate crop types. Several studies have indicated that the use of the MODIS
land cover product is likely to overestimate the agriculture areas relative to those reported by the NASS [18].
For this reason, we analyzed relatively pure corn or soybean pixels based on spatially disaggregated
CDL maps (Figure 2) rather than applying a general cropland mask based on the MODIS land cover
product. By counting corn pixels in the CDL maps, we observed that the estimated corn areas agreed
well with the harvested corn areas reported by the NASS in 2011 (Figure 4A). The soybean areas were
slightly overestimated, especially for the top soybean-producing counties (Figure 4B). It is appropriate
to assume that pixels with more than 75% corn or soybean cover are relatively pure endmembers [41,50].
The satellite data (resampled to 500 m) were spatially averaged based on those representative pixels to
derive the mean values for each county every 8 days.
Figure 4. Comparisons between crop areas calculated from CDL maps and reported by
NASS in 2011 for counties in the Midwestern US. (A) Corn area estimates; (B) Soybean
area estimates.
(A) (B)
Second, we applied crop-type specific RUE values for corn and soybeans (Table 2) instead of a
biome-wide value in the MOD17 algorithm to estimate the yields. The MOD17 algorithm prescribes a
universal RUE value for all crops, regardless of their types [36,49]. However, the field measurements
that quantify RUE values based on the relationship between biomass accumulation and cumulative
radiation interception suggest that the carbon sequestration abilities vary widely across crop types [34,51].
Sinclair and Muchow [52] reviewed several studies and reported that the seasonal corn RUE ranges from
2.6 to 3.4 g·MJ1 and soybean RUE ranges from 1.32 to 2.52 g·MJ1. To account for the RUE
differences, we applied 3.35 g·MJ1 for corn and 1.44 g·MJ1 for soybeans as obtained from recent field
Remote Sens. 2013, 5 5933
measurements in Iowa [53]. Although some studies observed that the RUE values varied across
time [38,52], the RUE values applied here were derived as seasonal mean values in the experiments.
Because the respiration costs of annual crop plants were deducted from the field measurements of
accumulated biomass, the use of these RUE values would result in crop productivities that can be linked
to crop yields directly.
Table 2. The parameters used in our study for estimating crop yields.
Parameter Description Corn Soybean Units
the radiation use efficiency 3.35 1.44 g·MJ1 PAR
RS the root: shoot ratio 0.18 0.15 dimensionless
HI the harvest index 0.53 0.42 dimensionless
MC the moisture content of the grain 0.11 0.10 dimensionless
Finally, our analysis attempted to address the productivity contributed by vegetation other than crops
because pixels are commonly mixed at the subpixel level of MODIS. The conceptual partition of mixed
pixels could avoid the biased estimation of crop yields. In other words, the GPP for each pixel was
partitioned into two components in our algorithm:
where GPPtotal, DOY n is the GPP value for a specific Day Of Year (DOY) n; GPPcrop, DOY n is the
contribution from crops (i.e., corn or soybeans) for the same time period; and GPPother, DOY n is the
contribution from other vegetation types.
Separating each component in mixed pixels is challenging in remote sensing models because
dynamic information of subpixel land-cover composition is often unavailable. Here, we propose an
algorithm to approximate the GPPother, DOY n component based on plant LAI. Given that corn and
soybeans in the Midwestern US have not emerged or in very early growth stages by mid-May, when the
natural vegetation has already accumulated significant leaf area [41], it is appropriate to approximate the
LAI of vegetation other than crops during the growing season by the maximum LAI before mid-May:
where LAIother is the estimated LAI of vegetation other than crops; and max(LAI, :) is the
maximum LAI value between April (DOY 89) and before mid-May (DOY N0), as obtained from
MOD15. The DOY N0 value was assumed to be 129 for corn and 137 for soybeans because corn is
typically planted approximately one to two weeks earlier than soybeans.
Similar to Equations (3) and (4), the maintenance respiration of other vegetation is estimated to be:
where MRother is the maintenance respiration of other vegetation; Leaf_MRother and Froot_MRother are the
maintenance respiration of the leaves and fine roots of other vegetation, respectively; Tavg (°C) is the
total, DOY n crop, DOY n other, DOY n
other total, DOY n = 89:N
LAI max(LAI )
[(Tavg 20.0) / 10.0]
other other
Leaf_MR = LAI / SLA leaf_mr_base Q10_mr
[(Tavg 20.0) / 10.0]
other other
Froot_MR = LAI / SLA froot_leaf_ratio froot_mr_base Q10_mr
other other other
MR Leaf_MR Froot_MR=+
Remote Sens. 2013, 5 5934
average daily temperature, as estimated from the DAO datasets; and the values for the other parameters
are the same as the agricultural biome in the MOD17 algorithm.
Because the ratio of NPP to GPP, termed as the carbon use efficiency (CUE), is modeled as a
constant in many ecosystem models [29,31], the GPP contribution from other vegetation can then be
estimated to be:
where GPPother, DOY n is the GPP of other vegetation types for a specific DOY n, MRother, DOY n is the
maintenance respiration, and CUE is the carbon use efficiency. Published studies indicate a CUE value
from 0.3 to 0.5 for natural vegetation such as forests [54]. We approximated a carbon use efficiency of
0.4 in this study.
Our estimation of each GPP and MR component throughout the growing season is further illustrated
in Figure 5, which presents the example of Olmsted County, Minnesota. The model was applied at the
pixel level, and estimates were aggregated to the county level. The proposed algorithm captured the
subtle variation of the GPP and MR caused by climatic factors in the time series. The sudden drop of
MODIS GPP in the middle of the growing season (DOY 169) was most likely due to cloud
contamination rather than harvest or other cultural practices. Clouds and aerosols can largely influence
crop GPP in a short period of time by reducing the incident PAR [55].
Figure 5. A simple representation of MODIS GPP (Gross Primary Production) and MR
(Maintenance Respiration) components of corn pixels in Olmsted County, Minnesota.
3.3. Converting MODIS GPP Estimates to Crop Yields
To compare with the NASS Quick Stats dataset, the GPP estimated by our method and the GPP
derived from the MOD17 product (hereafter referred as to the MODIS-GPP algorithm) were converted
to crop yields based on a common method used in agricultural studies [18,56]:
other, DOY n other, DOY n
GPP = MR / (1 CUE)
n = N
crop, DOY n
n > N
HI 1
Yield = GPP (1+RS) 1 MC
Remote Sens. 2013, 5 5935
where GPPcrop, DOYn is the estimated GPP of crops (i.e., corn or soybeans) for a specific DOY n. The
yield was integrated for an apparent growing season, which began in mid-May (DOY N0) and ended in
late-October (DOY N1). The value for N1, the growing season endpoint, was set as 297 in our study,
and N0 was defined as 129 for corn and 137 for soybeans, as in Equation (6). RS is the root to shoot ratio,
HI is the harvest index, and MC is the grain moisture content. We chose MODIS GPP instead of NPP in
this formulation because our preliminary tests as similar to other studies [18] revealed that yield
estimates derived from MODIS NPP were relatively low.
The crop-type specific parameters involved in our calculations are defined in Table 2. The root: shoot
ratio, which depends on the growing stages and fertilization of crops, is considered to be constant for a
specific crop type when harvested. Prince [56] reviewed past studies and chose the root: shoot ratio of
0.18 for corn and 0.15 for soybeans. The harvest index (HI) varies little for each crop species, except
under extreme stress conditions [56]. The HI of corn is 0.53, as measured by experiments in nine US
locations [57]. The soybean HI is 0.42 based on reported regression equations [56,58]. The moisture
content is estimated as 11% for corn grain and 10% for soybeans [35]. The values of these parameters
vary in different studies. Lobell et al. [34] observed that regional yield predictions were insensitive to the
variability in the radiation use efficiency and harvest index. Prince et al. [56] attributed most of the
uncertainty to the variability in the harvest index and root:shoot ratio, which resulted in an overall
variability of ~10%. The uncertainty associated with variation in these defined parameters was
considered to be of secondary importance in the context of other modeling uncertainties such as the RUE
variations and the MODIS data.
4. Results
4.1. Analysis at the County Level
Our estimated corn and soybean yields for 2011 agree well with the NASS reported survey data for
counties dominated by rainfed cultivation (Figure 6). The coefficients of determination (R2), as
measures of the correlations between estimated values and reference data, are 0.77 for corn and 0.66 for
soybeans. The corn yields were slightly underestimated, as indicated by the mean error (ME) of
0.18 MT·ha1, which was possibly due to the variation of parameters defined in Table 2. The variation
in the parameters, as the linear coefficients in the GPP-Yield relationship (Equation (11)), has little
influence on the R2 values. The high R2 values obtained for both the corn and soybeans demonstrate
that the crop yields estimated from MODIS data strongly correlate with the NASS survey data at the
county level.
Table 3 compares our algorithm with the MODIS-GPP algorithm (in parentheses) in the period 2009
to 2011. Our algorithm demonstrates improved crop yield estimation over the MODIS-GPP algorithm,
as indicated by higher R2 values and lower root mean square errors (RMSE). Similar to the findings in
other studies [18], the R2 values based on the MODIS-GPP algorithm between estimated crop yields and
NASS survey data were only 0.15~0.46 for corn and 0.35~0.53 for soybeans in rainfed counties. In
addition, the yields were considerably underestimated for corn (negative mean error) but overestimated
for soybeans (positive mean error). Our estimated biases were considerably smaller, as indicated by the
mean errors. Because the soybean RUE (1.44 g·MJ1 PAR) is close to the biome-wide value in MOD17
Remote Sens. 2013, 5 5936
(1.36 g·MJ1 PAR), the GPP contributed by other vegetation was the major factor that resulted in
overestimation in MOD17. In contrast, corn as a C4 plant has a much higher RUE value
(3.35 g·MJ1 PAR) based on a photosynthetic pathway different from soybean, a C3 plant. The low RUE
value prescribed in MOD17 is the main contributor to the large underestimation of corn yields.
Therefore, we need to consider the effects of mixed pixels and the RUE variation across crop types when
estimating yields based on remotely sensed data.
Figure 6. Comparisons between crop yields estimated from MODIS data and reported by the
NASS for rainfed counties in the Midwestern US. The black line is the 1:1 line. (A) Rainfed
corn; (B) Rainfed soybean.
Table 3. Statistics between crop yields estimated from our approach using MODIS data and
reported by the NASS for rainfed counties for each year from 2009 to 2011. For comparison,
values in parentheses are statistics using the standard MOD17 products.
Ye a r Corn Soybean
R2 RMSE (MT/ha) ME (MT/ha) R2 RMSE (MT/ha) ME (MT/ha)
2009 0.55 (0.15) 1.21 (5.52) 0.60 (5.39) 0.50 (0.35) 0.38 (0.86) 0.07 (0.77)
2010 0.54 (0.22) 1.17 (4.65) 0.14 (4.38) 0.73 (0.53) 0.30 (0.97) 0.09 (0.89)
2011 0.77 (0.46) 0.89 (4.56) 0.18 (4.28) 0.66 (0.53) 0.38 (1.06) 0.02 (0.95)
4.2. Analysis at the State Level
The analysis at the state level was performed in the same manner as at the county level, except that we
did not try to separate irrigated counties. The lower R2 values of the yields in 2011 (Figure 7) for corn (0.51)
and for soybeans (0.58) were partly caused by a large underestimation in Kansas and Nebraska due to the
irrigation effects. Because plants in irrigated areas often have higher transpiration rates that help cool
their leaves, the MODIS-GPP algorithm, which is sensitive to the environmental factors such as daily
temperature or vapor pressure deficit, would tend to underestimate GPP in irrigated areas. Interestingly,
the yields were overestimated for the northern states, such as North Dakota and Wisconsin, which may
Remote Sens. 2013, 5 5937
have been because crop planting typically starts later in those states than in other regions. It would be
more appropriate to use the LAI values from late May rather than mid-May to approximate the LAI of
other vegetation during the growing season. Future analysis should incorporate the timing of crop
phenology derived from MODIS to improve the yield estimation [59].
Figure 7. Comparisons between crop yields estimated from MODIS data and reported by the
NASS for states in the Midwestern US. The black line is the 1:1 line. (A) Corn;
(B) Soybean.
We also estimated the final crop production based on satellite data using the following techniques:
(i) the crop yields were estimated from MODIS data; (ii) the harvested areas were derived from the CDL
maps by counting pixels within each state, and (iii) the crop production was calculated as the product of
crop yields and harvested areas. Compared with the NASS reported data in the Midwestern US, the total
crop productions were underestimated (Table 4) due to the underestimated yields in irrigated counties.
The errors of estimated total productions were 1.5% for corn and 0.6% for soybeans in 2011 and fall
within the acceptable ranges in other years. Note that the crop areas derived from the NASS CDL maps
may also contribute errors to our estimation of crop production here.
Table 4. Estimated and reported corn and soybean production for 12 states in the
Midwestern US.
Ye a r Corn Production Soybean Production
Estimated (Tg) Reported (Tg) Error Estimated (Tg) Reported (Tg) Error
2009 253.6 291.7 13.04% 68.2 75.6 9.74%
2010 268.1 276.3 2.95% 73.9 77.7 4.79%
2011 273.0 277.1 1.49% 69.8 70.2 0.56%
Remote Sens. 2013, 5 5938
5. Discussions
5.1. Major Findings
We employed three key methods to estimate crop yields based on satellite data. First, there are clear
benefits to retrieving subpixel areas of different crop types and other vegetation based on land-cover
maps at a fine resolution. Although some studies concluded that coarse spatial resolution satellite data (0.05
degree or approximately 5.5 km) were advantageous over higher spatial resolution data for estimating the
yield because individual pixels will likely shift between crop types on a year-to-year basis [15], we
observed that a combination of fine and moderate resolution data was preferable for accurately
estimating yield because retrieving information related to crop types is essential to yield modeling.
Second, our analysis of corn and soybeans reveals that applying crop-type specific RUE values in the
PEMs is necessary to estimate crop yields properly. Global-scale PEMs mainly focus on quantifying
vegetation productivity for a general biome type [38]. For example, the default RUE value in MOD17 is
the same for all crops and grasses [36,48]. However, it would be inappropriate to apply flat RUE values
to estimate the yields for different crops, especially for productive C4 plants such as corn.
Finally, it is necessary to deduct the GPP contributed by vegetation other than crops when estimating
the yield at the spatial resolution of MODIS. As illustrated, crop yields are likely to be overestimated
without such considerations when applying RUE values measured in field studies. Our findings are
consistent with other studies, which observed that the direct use of field-measured RUE values in
global-scale PEMs would result in an overestimated GPP [35].
5.2. Limitations and Future Improvements
Although we were able to adequately estimate crop yields over large geographical regions, some
limitations necessitate further improvements. Our algorithm is based on the assumption that the LAI of
other vegetation is relatively constant after mid-May. For much of the study area where corn and
soybeans are planted, this assumption is reasonable, and our model improved the estimated crop yields
as demonstrated. However, such an assumption is not entirely correct and does not hold for crops with
different phenological cycles such as winter wheat. It would be of interest to identify appropriate
solutions to modeling GPP by integrating MODIS data and fine-resolution land use maps.
There are also uncertainties related to irrigation practice and cloud cover. Although the MODIS
GPP/NPP algorithm considers the daily atmospheric water deficit [48], our study implies that it still
requires better parameterization for irrigated areas. Given the availability of time-series measurements
from the flux towers located in irrigated areas [60], we should be able to calibrate the production
efficiency models to improve yield estimation. Cloud cover is another factor that may influence the yield
estimation from remotely sensed data. Even though the fitting process can reduce the influence of
intermittent cloud-cover, the proposed method may not perform well in areas with persistent cloud
covers, which could result in large data gaps in the MODIS time series.
Remote Sens. 2013, 5 5939
6. Conclusions
This study proposes a production efficiency model-based algorithm for estimating crop yield in
the Midwestern US. The yield estimates have shown to agree well with the NASS survey data in
rainfed counties for both corn (R2 = 0.77; RMSE = 0.89 MT·ha1) and soybeans (R2 = 0.66;
RMSE = 0.38 MT·ha1). Different from previous studies, we found that the MODIS GPP algorithm was
capable of making reasonable yield estimates with certain considerations. Our analysis suggests using
field-measured RUE values instead of biome-wide RUE values in the production efficiency models
when estimating the yield for a specific crop type. Moreover, it is important to consider the productivity
contribution from other vegetation in mixed pixels. The proposed algorithm does not require a
retrospective analysis that constructs the empirical relationships between reported yields and remotely
sensed data, and it has the potential for monitoring crop yields over large areas. Future work on yield
estimation based on production efficiency models should focus on investigations that consider the
subpixel spatial heterogeneity and irrigation effects.
We thank Jie Wang, Xuecao Li, and Han Chen at Tsinghua University for data preparation. We also
thank the USGS and NASA for free data distribution. This study was partially funded by the China
Postdoctoral Science Foundation (No. 2013M540087) and National Science Foundation of China
(No. 41301445), and an international cooperation grant from Tsinghua University. RBM participation
was made possible by funding from NASA Earth Science Division.
Conflict of Interest
The authors declare no conflict of interest.
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... Grain yield (metric ton/ha) and grain production (metric ton) of maize and soybean crops are a function of aboveground biomass (AGB), gross and net primary production (GPP, NPP), which can be estimated by satellite images and models (Guan et al. 2016;He et al. 2018;Marshall et al. 2018;Sakamoto et al. 2014;Xin et al. 2013). Harvest Index (HI) is calculated as the ratio between crop grain yield and crop aboveground biomass (HI AGB ), or NPP (HI NPP ) or GPP (HI GPP ). ...
... GPP can be estimated by using a light use efficiency (LUE) model driven by remote sensing images and climate data, and the regional and global GPP data products are available to the public (Running et al. 2004;Wu et al. 2018;Zhang et al. 2017). Some studies used the model-based GPP to estimate NPP, AGB and grain yield, and then compared the resultant yield estimates with the yield data from the NASS crop statistics at the county scale, for example, croplands in the Midwest during 2009-2012 (Xin et al. 2013), and croplands in the CONUS during 2010-2015 (Marshall et al. 2018). These studies reported moderate relationships between the model-based yield estimates and the yield data from NASS crop statistics, with a range of R 2 values from 0.5 to 0.7. ...
... The training and validation ground reference data were sampled from USDA Farm Service Agency (FSA) Common Land Unit (CLU) database and its associated attributes reported by famers. Note that several global GPP data products, e.g., MOD17A2 (Running et al. 2004), have not considered the different photosynthetic capability of C 3 and C 4 crops and not incorporated the CDL dataset that contains information on individual crop types, which can partly explain that they underestimate GPP of maize and other C 4 crops (Guanter et al. 2014;Xin et al. 2013). Our previous study in the CONUS clearly show that the use the CDL dataset is essential for simulations of VPM and other data-driven models . ...
The United States of America ranked first in maize export and second in soybean export in the world. Accurate and timely data and information on maize and soybean production in the Contiguous United States (CONUS) are important for food security at the regional and global scales. In this study, we firstly compare the maize and soybean planted area from cropland data layer (CDL) with NASS area statistics over the CONUS during 2008-2018, and evaluate the interannual changes of planted and harvested area based on the two datasets. Secondly, we investigate the relationship between grain production and gross primary production (GPP) simulated by Vegetation Photosynthesis Model (VPM) at national and county scales. Finally, we evaluate the linear regression models between grain production and cumulated GPPVPM over time at 8-day resolution. We found strong spatial-temporal consistency between CDL and NASS datasets in maize and soybean planted areas. Maize and soybean planted areas increased by mid-2010s, largely driven by markets and international trade. Severe summer drought in 2012 had little impact on soybean planted and harvested area and maize planted area, but substantially reduced maize harvested area. and grain production. Annual county-level GPPVPM had strong linear relationship with NASS grain production for maize and soybean. The Harvest Index, defined as the ratio between grain production and GPPVPM (HIGPP_VPM), ranged from 0.25 (2012) to 0.36 for maize and from 0.13 to 0.15 for soybean. The linear regression models between grain production and cumulated GPPVPM (GPPVPM_CUM) over time at 8-day resolution showed that by the end of July, GPPVPM_CUM accounted for ~90% of variance in maize and soybean grain production, which was approximately two months before farmers started to harvest. This study clearly shows that VPM and GPPVPM data are useful for monitoring and in-season forecasting of maize and soybean grain production in the CONUS.
... NPP is GPP minus the energy lost to the environment through respiration costs. Other research has used NPP to calculate yield but early work in this study and others showed that using NPP often resulted in low yield estimation (Reeves et al., 2005;Xin et al., 2013). These measurements came from the Landsat Gross Primary ...
... Initial testing at the field scale using NPP resulted in yields that averaged ~20% of actual, which has been noted as an issue with final yield calculations and NPP (Reeves et al., 2005;Xin et al., 2013). When the source of biomass was switched to GPP, calculated yields were still low at the field level for corn but were higher than actual for soybean (blue line in Figure 3.1). ...
Marginal cropland is suboptimal due to historically low and variable productivity and limiting biophysical characteristics. To support future agricultural management and policy decisions in Nebraska, U.S.A, it is important to understand where cropland is marginal for its two most economically important crops: corn (Zea mays) and soybean (Glycine max). As corn and soybean are frequently planted in a crop rotation, it is important to consider if there is a relationship with cropland marginality. Based on the current literature, there exists a need for a flexible yet robust methodology for identifying marginal land at different scales, which takes advantage of high spatial and temporal resolution data and can be applied by researchers and outreach professionals alike. This research seeks to individually identify where cropland is marginal for corn and soybean as well as classify the extent of marginality that exists. This research also seeks to classify cropland as being part of a long-term corn-soybean crop and see if marginality differs between this cropland and the remainder of cropland. Two crop-specific multi-criteria evaluations (MCE), consisting of crop production, climate, and soil criteria, was performed using Google Earth Engine to identify and classify marginal cropland. Criteria were individually thresholded before addition to the MCEs. Cropland that was classified as part of a long-term corn-soybean crop rotation was identified by factoring in the balance of corn and soybean occurrence on long established cropland. Most cropland in Nebraska has at least some marginality for corn while most has no marginality for soybean. Marginality classification is spatially distributed with increasing marginality from the northeast to the southwest. Cropland under a long-term crop rotation shows much less marginality compared to non-rotation cropland. This study improves upon previous attempts to identify marginal cropland in Nebraska by increasing spatial and temporal resolution, providing a programmatic and replicable methodology, and confining the classification to existing cropland. The implications of these findings are useful for policy makers and agricultural extension efforts in Nebraska to identify opportunities for conservation, solar energy capture, and biofuel production on cultivated land. Advisor: Yi Qi
... Maize and soybean have different photosynthesis pathways. Generally, the four-carbon compound (C4) plant (maize) has a much higher RUE value than the three-carbon compound (C3) plant (soybean), so it is considered that maize yield has a higher correlation with GPP than soybean yield [37]. The correlations of state-level crop yields and the summer meteorological cond are compared to investigate whether the regional sensitivity of the crop yield to TC PCI exists as it does in GPP (Figure 7). ...
Full-text available
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... Th e local water balance also depends on evaporation, SM storage, and runoff relative to precipitation. Th e SM index has the advantage of quantitatively describing both dry and wet episodes (Chen, 2014) and tracking photosynthesis and productivity of plants (Xin et al., 2013;Tao et al., 2005). Many studies of drought suggest that there are relationships at a regional level between meteorological variability and sensitivity or adaptation of vegetation to water stress (Chen, 2014). ...
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Remotely sensed soil moisture products showed sensitivity to vegetation cover density and soil typology at regional dryland level. In these regions, drought monitoring is significantly performed using soil moisture index and rainfall data. Recently, rainfall and soil moisture observations have increasingly become available. This has hampered scientific progress as regards characterization of land surface processes not just in meteorology. The purpose of this study was to investigate the relationship between a newly developed precipitation dataset, SM2RAIN (Advanced SCATterometer (SM2RAIN-ASCAT), and NDVI (eMODIS-TERRA) in monitoring drought events over diverse rangeland regions of Morocco. Results indicated that the highest polynomial correlation coefficient and the lowest root mean square error (RMSE) between SM2RAIN-ASCAT and NDVI were found in a 10-year period from 2007 to 2017 in all rangelands (R = 0.81; RMSE = 0.05). This relationship was strong for degraded rangeland, where there were strong positive correlation coefficients for NDVI and SM2RAIN (R = 0.99). High correlations were found for sparse and moderate correlations for shrub rangeland (R = 0.82 and 0.61, respectively). The anomalies maps showed a very good similarity between SM2RAIN and Normalized Difference Vegetation Index (NDVI) data. The results revealed that the SM2RAIN-ASCAT and NDVI product could accurately predict drought events in arid and semi-arid rangelands.
... Satellite-based methods have been widely used to simulate crop production over large areas, benefitting from temporally and spatially continuous crop growth information derived from satellite data [27,28]. LUE models, which are based on satellite data, are a powerful tool for quantifying crop yield on a large scale [29,30]. LUE models are designed to simulate vegetation gross primary production (GPP) based on the assumption that GPP is directly dependent on the absorbed photosynthetically active radiation (APAR) through LUE [31,32]. ...
Full-text available
Satellite-based models have tremendous potential for monitoring crop production because satellite data can provide temporally and spatially continuous crop growth information at large scale. This study used a satellite-based vegetation production model (i.e., eddy covariance light use efficiency, EC-LUE) to estimate national winter wheat gross primary production, and then combined this model with the harvest index (ratio of aboveground biomass to yield) to convert the estimated winter wheat production to yield. Specifically, considering the spatial differences of the harvest index, we used a cross-validation method to invert the harvest index of winter wheat among counties, municipalities and provinces. Using the field-surveyed and statistical yield data, we evaluated the model performance, and found the model could explain more than 50% of the spatial variations of the yield both in field-surveyed regions and most administrative units. Overall, the mean absolute percentage errors of the yield are less than 20% in most counties, municipalities and provinces, and the mean absolute percentage errors for the production of winter wheat at the national scale is 4.06%. This study demonstrates that a satellite-based model is an alternative method for crop yield estimation on a larger scale.
... Some of the applications include modeling/mapping irrigated area, agriculture and cropland, hydrological/water resource modeling, and meteorological modeling. The MIrAD-US was coupled with other variables including biophysical variables (NASS Quick Stat, NASS CDL, NLCD, and Soil Survey); climatic variables (Tmax, Tmin, and percp); remote sensing imageries (MODIS); and others (land use land cover, elevation, ecoregion type) to model and map irrigated land at regional, ecoregion, or global scale [3,[46][47][48][49][50][51][52][53][54][55][56][57][58][59]. Data are available for download from the USGS ScienceBase website (doi:10.5066/P9NA3EO8). ...
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The United States has a geographically mature and stable land use and land cover system including land used as irrigated cropland; however, changes in irrigation land use frequently occur related to various drivers. We applied a consistent methodology at a 250 m spatial resolution across the lower 48 states to map and estimate irrigation dynamics for four map eras (2002, 2007, 2012, and 2017) and over four 5-year mapping intervals. The resulting geospatial maps (called the Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset or MIrAD-US) involved inputs from county-level irrigated statistics from the U.S. Department of Agriculture, National Agricultural Statistics Service, agricultural land cover from the U.S. Geological Survey National Land Cover Database, and an annual peak vegetation index derived from expedited MODIS satellite imagery. This study investigated regional and periodic patterns in the amount of change in irrigated agriculture and linked gains and losses to proximal causes and consequences. While there was a 7% overall increase in irrigated area from 2002 to 2017, we found surprising variability by region and by 5-year map interval. Irrigation land use dynamics affect the environment, water use, and crop yields. Regionally, we found that the watersheds with the largest irrigation gains (based on percent of area) included the Missouri, Upper Mississippi, and Lower Mississippi watersheds. Conversely, the California and the Texas–Gulf watersheds experienced fairly consistent irrigation losses during these mapping intervals. Various drivers for irrigation dynamics included regional climate fluctuations and drought events, demand for certain crops, government land or water policies, and economic incentives like crop pricing and land values. The MIrAD-US (Version 4) was assessed for accuracy using a variety of existing regionally based reference data. Accuracy ranged between 70% and 95%, depending on the region.
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Quantitative assessment of crop water-use efficiency (WUE) is an important basis for high-efficiency use of agricultural water. Here we assess the WUE of maize in the Hetao Irrigation District, which is a representative irrigation district in the arid region of Northwest China. Specifically, we firstly mapped the location of the maize field by using a remote sensing/phenological–based vegetation classifier and then quantified the maize water use and yield by using a dual-source remote-sensing evapotranspiration (ET) model and a crop water production function, respectively. Validation results show that the adopted phenological-based vegetation classifier performed well in mapping the spatial distributions and inter-annual variations of maize planting, with a kappa coefficient of 0.86. In addition, the ET model based on the hybrid dual-source scheme and trapezoid framework also obtained high accuracy in spatiotemporal ET mapping, with an RMSE of 0.52 mm/day at the site scale and 26.21 mm/year during the maize growing season (April–October) at the regional scale. Further, the adopted crop water production function showed high accuracy in estimating the maize yield, with a mean relative error of only 4.3%. Using the estimated ET, transpiration, and yield of maize, the mean maize WUE based on ET and transpiration in the study region were1.94 kg/m3 and 3.06 kg/m3, respectively. Our results demonstrate the usefulness and validity of remote sensing information in mapping regional crop WUE.
Convenient and reliable large-scale crop yield prediction is needed when formulating administrative plans and ensuring food security, especially under changing climate and international conditions. In this study, we explored Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices- and phenology-based yield prediction generalization model taking the US Corn Belt as an example. We calculated the normalized difference vegetation index (NDVI) and 2-band enhanced vegetation index (EVI2) time series, which were adjusted using greenup dates derived from the Land Cover Dynamics product MCD12Q2. Based on the adjusted VI (NDVI, EVI2) time series, the VI change rate (dVI) time series was calculated, which represents crop growth rate. The first step was to cluster the adjusted VI and dVI time series, called ‘greenup groups’, according to corresponding greenup dates with a five-day interval. Then in different greenup groups, we constructed empirical univariate models with VI having the maximum correlation with crop yield, and multivariate models with VI and dVI, which were also used to construct the generalized model. After clustering, the days with maximum VI correlation gradually decreased as greenup days increasing, and the univariate VI model and multivariate VI and dVI model performances in different groups improved. The generalized models with specific VI and dVI variables in each group predicted yields of corn and soybean with R² values mainly ranging from 0.55 to 0.75 and 0.55 to 0.70, while RMSE mainly ranging from 1000 to 1500 kg/ha and 300 to 400 kg/ha for both NDVI and EVI2 from 2008 to 2018 with leave-one-year-out cross-validation for all groups. The model using MODIS data was convenient and scalable with limited data requirements and date-determined variables after greenup, and offered a generalized method to predict crop yields at a large scale before harvest with good performance.
The present work describes about estimation of crop yield of sugarcane crop from medium resolution LANDSAT 8 OLI (30 m) imageries by the development of a nonlinear empirical model using classical artificial neural networks. Sugarcane crop attributes were retrieved from high-resolution (30 m) satellite imageries to develop yield prediction models. The feed-forward back-propagation neural network algorithm developed and calibrated using the remote sensing retrieved crop parameters and ground truth data in MATLAB environment. The perceptron was trained with 75 out of the 100 possible inputs upto10,000 epochs with 1–10 hidden neurons. Four performance indices: coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE) and the average ratio of estimated yield to target crop yield (Rratio) were calculated, to achieve optimum neural network. Several runs were performed in determining the optimum number of hidden neurons. The best performance of the models was observed at i + 1 and i + 2 hidden nodes (i = No of input parameters). The range of R2 values of best performed models were between 0.867 and 0.916 for training and same for testing it ranged from 0.829 to 0.991 and Rratio values from 0.997 to 1.006, the normalized RMSE values ranged from 0.066 to 0.150; MAE ranged from 0.034 to 0.119 for training and 0.017–0.184 for testing. The statistical analysis recommends the reliability of the ANN model in sugarcane yield estimation. Multivariate linear regression was also performed for training and testing data separately to test the superiority of the ANN model. The estimated yield was in the range of 60,000 kg/ha–1,30,000 with an average of 73,000 kg/ha.
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In the current context of climate change and its impact on human and natural resources, remote sensing has some advantages for combating extreme events, especially in pasture arid Morocco. Assessing quality of remote sensing data is an essential step in pastoral areas when droughts that have a significant impact on productivity. In order to provide a method that gives a description of future drought yield situation we have studied two types of regression established between rainfall data measured by station, soil moisture index (SWI), Normalized difference vegetation index (NDVI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) and dry matter productivity (DMP) from MetOp-A / ASCAT, eMODIS-TERRA, SPOT VEGETATION and PROBA-V satellites 30 km from 2007 to 2017. The main objective of this study is to test accuracy of these data used for claim not only areas affected by drought, but also areas likely to be affected. The results obtained show that models based on polynomial regression of NDVI, FAPAR, DMP are most consistent and accurate for estimation of herbaceous biomass from rainfall. Using of SWI index must be justified according to averages values. However, drought can be predicted based on results of strong correlations between soil moisture and vegetation index and rainfall anomalies.
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This review article gives an overview of how satellite observations are used to feed or tune crop models and improve their capability to predict crop yields in a region. Relations between crop characteristics which correspond to models state variables and satellite observations are briefly analysed, together with the various types of crop models commonly used. Various strategies for introducing short wavelength radiometric information into specific crop models are described, from direct update of model state variables to optimization of model parameter values, and some of them are exemplified. Methods to unmix crop-specific information from mixed pixels in coarse resolution-high frequency imagery are analysed. The conditions of use of the various methods and types of information are discussed.
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Until recently, advanced very high-resolution radiometer (AVHRR) observations were the only viable source of data for global land cover mapping. While many useful insights have been gained from analyses based on AVHRR data, the availability of moderate resolution imaging spectroradiometer (MODIS) data with greatly improved spectral, spatial, geometric, and radiometric attributes provides significant new opportunities and challenges for remote sensing-based land cover mapping research. In this paper, we describe the algorithms and databases being used to produce the MODIS global land cover product. This product provides maps of global land cover at 1-km spatial resolution using several classification systems, principally that of the IGBP. To generate these maps, a supervised classification methodology is used that exploits a global database of training sites interpreted from high-resolution imagery in association with ancillary data. In addition to the IGBP class at each pixel, the MODIS land cover product provides several other parameters including estimates for the classification confidence associated with the IGBP label, a prediction for the most likely alternative class, and class labels for several other classification schemes that are used by the global modeling community. Initial results based on 5 months of MODIS data are encouraging. At global scales, the distribution of vegetation and land cover types is qualitatively realistic. At regional scales, comparisons among heritage AVHRR products, Landsat TM data, and results from MODIS show that the algorithm is performing well. As a longer time series of data is added to the processing stream and the representation of global land cover in the site database is refined, the quality of the MODIS land cover product will improve accordingly.
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Biophysical crop simulation models are normally forced with precipitation data recorded with either gauges or ground-based radar. However, ground-based recording networks are not available at spatial and temporal scales needed to drive the models at many critical places on earth. An alternative would be to employ satellite-based observations of either precipitation or soil moisture. Satellite observations of precipitation are currently not considered capable of forcing the models with sufficient accuracy for crop yield predictions. However, deduction of soil moisture from space-based platforms is in a more advanced state than are precipitation estimates so that these data may be capable of forcing the models with better accuracy. In this study, a mature two-source energy balance model, the Atmosphere Land Exchange Inverse (ALEXI) model, was used to deduce root zone soil moisture for an area of North Alabama, USA. The soil moisture estimates were used in turn to force the state-of-the-art Decision Support System for Agrotechnology Transfer (DSSAT) crop simulation model. The study area consisted of a mixture of rainfed and irrigated cornfields. The results indicate that the model forced with the ALEXI moisture estimates produced yield simulations that compared favorably with observed yields and with the rainfed model. The data appear to indicate that the ALEXI model did detect the soil moisture signal from the mixed rainfed/irrigation corn fields and this signal was of sufficient strength to produce adequate simulations of recorded yields over a 10 year period.
Empirical studies report several plausible correlations between transforms of spectral reflectance, called vegetation indexes, and parameters descriptive of vegetation leaf area, biomass and physiological functioning. However, most indexes can be generalized to show a derivative of surface reflectance with respect to wavelength. This derivative is a function of the optical properties of leaves and soil particles. In the case of optically dense vegetation, the spectral derivative, and thus the indexes, can be rigorously shown to be indicative of the abundance and activity of the absorbers in the leaves. Therefore, the widely used broad-band &near-infrared vegetation indexes are a measure of chlorophyll abundance and energy absorption.
Monitoring crop condition and production estimates at the state and county level is of great interest to the U.S. Department of Agriculture. The National Agricultural Statistical Service (NASS) of the U.S. Department of Agriculture conducts field interviews with sampled farm operators and obtains crop cuttings to make crop yield estimates at regional and state levels. NASS needs supplemental spatial data that provides timely information on crop condition and potential yields. In this research, the crop model EPIC (Erosion Productivity Impact Calculator) was adapted for simulations at regional scales. Satellite remotely sensed data provide a real-time assessment of the magnitude and variation of crop condition parameters, and this study investigates the use of these parameters as an input to a crop growth model. This investigation was conducted in the semi-arid region of North Dakota in the southeastern part of the state. The primary objective was to evaluate a method of integrating parameters retrieved from satellite imagery in a crop growth model to simulate spring wheat yields at the sub-county and county levels. The input parameters derived from remotely sensed data provided spatial integrity, as well as a real-time calibration of model simulated parameters during the season, to ensure that the modeled and observed conditions agree. A radiative transfer model, SAIL (Scattered by Arbitrary Inclined Leaves), provided the link between the satellite data and crop model. The model parameters were simulated in a geographic information system grid, which was the platform for aggregating yields at local and regional scales. A model calibration was performed to initialize the model parameters. This calibration was performed using Landsat data over three southeast counties in North Dakota. The model was then used to simulate crop yields for the state of North Dakota with inputs derived from NOAA AVHRR data. The calibration and the state level simulations are compared with spring wheat yields reported by NASS objective yield surveys.
Accurate, objective, reliable, and timely predictions of crop yield over large areas are critical to helping ensure the adequacy of a nation’s food supply and aiding policy makers on import/export plans and prices. Development of objective mathematical models of crop yield prediction using remote sensing is highly desirable. In this study, we develop a new methodology using an artificial neural network (ANN) to estimate and predict corn and soybean yields on a county-by-county basis, in the “corn belt” area in the Midwestern and Great Plains regions of the United States. The historical yield data and long time-series NDVI derived from AVHRR and MODIS are used to develop the models. A new procedure is developed to train the ANN model using the SCE-UA optimization algorithm. The performance of ANN models is compared with multivariate linear regression (MLR) models and validation is made on the model’s stability and forecasting ability. The new algorithms can effectively train ANN models, and the prediction accuracy can be as high as 85 percent.