A quantitative index is applied to monitor crop growth and predict agricultural yield in continental USA. The Climate-Variability Impact Index (CVII), defined as the monthly contribution to overall anomalies in growth during a given year, is derived from 1-km MODIS Leaf Area Index. The growing-season integrated CVII can provide an estimate of the fractional change in overall growth during a given year. In turn these estimates can provide fine-scale and aggregated information on yield for various crops. Trained from historical records of crop production, a statistical model is used to produce crop yield during the growing season based upon the strong positive relationship between crop yield and the CVII. By examining the model prediction as a function of time, it is possible to determine when the in-season predictive capability plateaus and which months provide the greatest predictive capacity

Anderson, Bruce

Barlow, Mathew

Myneni, Ranga

Tan, Bin

Zhang, Ping

NASA Technical Reports Server

MONITORING CROP YIELD IN USA USING A SATELLITE-BASED CLIMATE-
VARIABILITY IMPACT INDEX 
 
Ping Zhang1, 2, Bruce Anderson3, Bin Tan1, 2, Mathew Barlow4, Ranga Myneni3 
1. Hydrospheric and Biospheric Science Laboratory, NASA’s Goddard Space Flight Center, Greenbelt, MD, 20771, USA 
2. Earth Resource Technology Inc., Annapolis Junction, MD, 20701, USA  
3. Department of Geography, Boston University, Boston, MA, 02215, USA 
4. Environmental, Earth, and Atmospheric Sciences, University of Massachusetts Lowell, Lowell, MA, 01854, USA
ABSTRACT 
 
A quantitative index is applied to monitor crop growth and 
predict agricultural yield in continental USA. The Climate-
Variability Impact Index (CVII), defined as the monthly 
contribution to overall anomalies in growth during a given 
year, is derived from 1-km MODIS Leaf Area Index.  The 
growing-season integrated CVII can provide an estimate of 
the fractional change in overall growth during a given year. 
In turn these estimates can provide fine-scale and aggregated 
information on yield for various crops. Trained from 
historical records of crop production, a statistical model is 
used to produce crop yield during the growing season based 
upon the strong positive relationship between crop yield and 
the CVII. By examining the model prediction as a function 
of time, it is possible to determine when the in-season 
predictive capability plateaus and which months provide the 
greatest predictive capacity. 
 
Index Terms— Remote Sensing, agriculture, image 
region analysis, modeling, GIS 
 
1. INTRODUCTION 
 
The interannual variations of crop yields are strongly 
affected by the environment and its variability. To get the 
pre-harvest information on crop yields, numerous crop 
growth simulation models are generated using crop state 
variables and climate variables at the 
crop/soil/water/atmosphere interfaces [1]. Most of these 
models require complex and detailed inputs to address the 
plant physiology process [2], soil water balance [3], as well 
as the interactions between soil and root systems [4]. In 
addition, plot-scale field experiments with specific soil 
types, water stress, nitrogen contents, and management 
processes are required for validation of the models [5].  
 
A second type of yield forecast is based on data 
collected from farm operations and field observations, which 
require numerous time and labor in order to get a full sample 
size. In addition, these field studies have to be repeated 
frequently throughout the growing-season. The National 
Agricultural Statistics Service (NASS) monitors the crop 
conditions and yields via monthly-conducted Objective 
Yield Surveys in thousands of fields. 
 
Since the early 1980s, vegetation indices derived from 
satellite data have been applied for crop monitoring and 
forecasting purposes.  These indices include the ratio of the 
reflectance at near infrared to red, the Normalized 
Difference Vegetation Index (NDVI), Vegetation Condition 
Index (VCI), and the Climate-Variability Impact Index 
(CVII) derived from MODerate resolution Imaging 
Spectroradiometer (MODIS) Leaf Area Index (LAI) [6-10]. 
In general, these remotely-sensed metrics of vegetation 
activity have the following advantages: a unique vantage 
point, synoptic view, cost effectiveness, and a regular, 
repetitive view of nearly the entire earth’s surface [11], 
thereby making them potentially better suited for crop 
monitoring and yield estimation at large scales. 
 
We have previously demonstrated that the LAI-based 
CVII can quantify the percentage of the climatological 
annual production either gained or lost due to climatic 
variability and that it has a potential application in crop 
monitoring and yield estimation [9-10].  As a continuation of 
this effort, in this paper we will analyze the relationships 
between the CVII and crop yield using two case studies for a 
drought year in Illinois (2005) and a drought year in North 
and South Dakota (2006).   
 
2. DATA AND METHODOLOGY 
 
In this research, we used the 1-km resolution MODIS LAI 
data, from 2000 to 2006, to generate the Climate-Variability 
Impact Index. The MODIS land cover map at 1-km 
resolution was used to select broadleaf and cereal crop 
pixels. Crop production estimates are given at county- and 
state- levels by the U.S. Department of Agriculture. 
Accordingly, we aggregated LAI over the same regions by 
https://ntrs.nasa.gov/search.jsp?R=20120003720 2019-08-30T19:34:38+00:00Z
 Unstandardized Coefficients 95% Confidence Interval Model 
B Std. Error 
t  Sig. Lower Upper 
1  Constant 1.00 0.007 137.85 <.0001 0.985 1.014 
CVII 0.024 0.001 17.94 <.0001 0.021 0.026 
2  Constant 1.009 0.012 86.85 <.0001 0.986 1.032 
CVII 0.021 0.001 21.57 <.0001 0.019 0.023 
3  Constant 1.003 0.006 161.81 <.0001 0.991 1.015 
CVII 0.022 0.001 28.94 <.0001 0.020 0.023 
 
Table 1: Linear Model between Crop Production (dependent) and Climate-Variability Impact Index (independent) at county Level. The 
first model is generated from corn counties. The second model is generated from wheat counties. The third model is generated both 
corn counties and wheat counties.  
overlapping the LAI map with the county map and then 
calculated the Climate-Variability Impact Index for each 
county.  
 
The Climate-Variability Impact Index (CVII), defined 
as the monthly contribution to anomalies in annual growth, 
quantifies the percentage of the climatological production 
either gained or lost due to climatic variability during a 
given month. For a given pixel p, let L(p,m,y) be the LAI in 
month m and year y, ),(' mpL  be the climatological LAI in 
month m and ∑ )(' pL  be the climatological annual LAI. The 
index CVII(p,m,y) in month m and year y is then calculated 
as : 
CVII ( p , m , y ) = 100 × L ( p , m , y ) − L ' ( p , m )
Σ L ' ( p )
  
 
A strong positive correlation is found between the crop 
production and the CVII for counties in both Illinois and 
North and South Dakota [Fig. 1]. While the CVII increases 
from negative 40% to positive 40%, the production anomaly 
increases from less than 10% to nearly 200% of the 
climatological mean. In general, fifty percent of the variance 
in crop production can be explained by the CVII. 
 
To test whether the regression coefficients are strongly 
dependent on crop types in the different study regions and 
for the two different crop types, we fitted three linear models 
for the 2000-2004 CVII and production anomalies. The first 
model uses all the corn sample counties from the study 
regions. The second model uses all the wheat sample 
counties. The third model uses both corn and wheat sample 
counties. The 95% confidence intervals of the coefficients of 
the three models overlap, which indicates that these three 
linear models are not significantly different from each other 
[as shown in Table 1]. Our results demonstrate that the 
CVII-production relationship appears to be crop-
independent for the study regions at county-level. 
 
3. RESULTS AND DISCUSSION 
 
3.1. 2005 corn yield forecast at Illinois 
 
In the 2005 growing season, Illinois suffered an extreme 
drought condition and corn yields were predicted to be 30% 
less than the record year of 2004 by NASS. However, after 
most of the corn had been harvested by the end of October, 
the Illinois Agricultural Statistics Service indicated the 
overall corn yield is 145 bushels per acre, or 7% below the 
previous 5-year average. 
 
In Figure 2, we compare the meteorological conditions 
represented by the 6-month SPI for March-August and the 
vegetative production represented by the integrated CVII 
map over the continental US in 2005 and 2002 (we show 
maps for 2002 because it had comparable crop losses to 
those expected in 2005 according to NASS). Focusing on 
Illinois, the 6-month SPI through the end of August indicates 
Illinois suffered a severe drought during the 2005 growing 
season, while conditions were slightly-above normal during 
2002. However, the April-August integrated CVII maps for 
Illinois suggest a decrease in vegetation growth of only 
-40 -20 0 20 40
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
 Wheat 
 Corn 
 
 
Pr
o
du
ct
io
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An
om
a
ly
Growing-season CVII
Growing-season CVII vs Production Anomaly
R=0.70
 
Fig. 1: Relationship between growing-season (Apr-Aug) CVII 
and crop production over the study regions of Illinois, South 
Dakota and North Dakota. MODIS landcover maps are used to 
select the cereal crops (wheat) and broadleaf crops (corn).  
  
Fig. 2: 6-month Standardized Precipitation Index (SPI) vs. the growing-season Climate-Variability Impact Index (CVII) in 
2002 and 2005.  Six-month SPI maps are produced by National Drought Mitigation Center 
(http://www.drought.unl.edu/monitor/spi.htm).  CVII values represent fractional loss (red) or gain (blue) of vegetation 
growth during the growing season (April-August), compared with the 2000-2004 mean. Only Broadleaf crops are shown.  
 
about 10% in 2005 compared with a 10-20% decrease in 
2002 [Fig. 2]. 
 
Although on a continental scale the CVII maps 
integrated over the growing season agree with the growing 
season water deficit conditions represented by 6-month SPI 
through August, our results highlight the need for explicit 
monitoring of vegetation growth when estimating yield. The 
case study in Illinois is particular demonstrates that drought-
monitoring indices based upon meteorological data alone, 
such as SPI, may miss important variability in vegetative 
production because they can both overestimate (2005) and 
underestimate (2002) impacts upon vegetation in drought-
stricken regions [Fig. 2]. However, the CVII maps appear to 
have better success capturing the crop yield. The CVII 
model predicts a 7% decrease in 2005 corn yield in Illinois 
(compared to the previous 5 year average or 145 bushel/acre 
overall), which is almost identical to the actual state-wide 
corn yield from NASS released after the harvesting (8% 
decrease, or 143 bushel/acre). 
 
3.2. 2006 Yield Forecast at North and South Dakota 
 
In 2006, the persistence of anomalous warmth made the 
summer the second warmest June-August period in the 
continental US in the past 110 years. An area stretching 
from south central North Dakota to central South Dakota is 
identified as drought-stricken, with the potential for 
significant crop loss according to the US drought monitor 
map released in August 2006. Using the CVII model, the 
2006 corn and wheat production are predicted at county- and 
state-level in North and South Dakota. The model predicts a 
23% decrease compared to the climatological mean in wheat 
and 4% decrease in corn, compared with the latest state-wide 
NASS estimates of 15% decrease in wheat and 1% increase 
in corn (released in November).  While not as accurate as 
the 2005 predictions for Illinois, they actually represent a 
better prediction than the NASS September forecasts 
released concurrently with the CVII-based estimates 
 
3.3. Possibility of early yield forecast using CVII 
 
Because the satellite data used to derive the CVII are 
available in near real-time, satellite-based data can provide 
yield estimates before the end of the growing season. For 
instance, the forecast of 145 bushel/acre (7% decrease 
compared to the climatological mean) at Illinois was 
produced in mid-September, compared with the USDA’s 
forecast of 136 bushel/acre at the same time, and the 
USDA’s forecast of 145 bushel/acre released in October and 
November. As such, acceptable model forecasts of corn 
yield could be obtained at least one month prior to the end of 
the growing season due to the advantages of the satellite 
data. In the following, we examine the possibility of early 
forecast using our model.  
 
Figure 3 shows the relationship between actual corn 
yield, NASS’s forecasts released in August, September and 
October, and our model predictions made during July, 
August, and September using CVII for South Dakota from 
2000 to 2006. The model predictions based on integrated 
CVII have a typical lag of approximately 2 week. As such, 
the April-July/August/September integrated CVII 
predictions are concurrent with the NASS estimates released 
in August/September/October respectively. The actual corn 
yield of South Dakota is estimated by NASS after harvesting 
in each year and is used as the actual yield for the given 
year. The in-season CVII model shows predictability 
comparable to the concurrent NASS estimates [Fig. 3]. For 
instance, in mid-August 2006 the CVII model predicted a 
corn yield of 108 bushel/acre for South Dakota, which is 
almost identical to the actual yield of 107 bushel/acre.  In 
comparison, the NASS estimate released in August predicted 
a yield of 100 bushel/acre and in October predicted a 105 
bushel/acre. 
 
While the satellite-based estimates of yield are not 
necessarily a substitute for those provided by ground-based 
methods (as done by agricultural services for instance), 
satellites can provide a secondary, independent estimate that 
can pinpoint regions where agricultural failure is greatest. 
Overall, the high temporal and spatial resolution as well as 
the availability of the timely access to the needed MODIS 
products makes CVII a useful tool for near real-time crop 
monitoring and yield forecasts before harvesting. More 
importantly, the cost effectiveness and repetitive, near-
global view of earth’s surface suggest this LAI-based CVII 
may significantly improve crop monitoring and yield 
estimation at regional scales. Furthermore, with inclusion of 
fine temporal resolution MODIS data, future applications of 
the 8-day and 16-day CVII maps may provide detailed crop 
monitoring at different growth stages and provide earlier 
warning signals. 
 
4. REFERENCES 
 
[1] J.L. Monteith, “Climate and the efficiency of crop production 
in Britain”, Philosphical transactions of the Royal Society of 
London. Ser. B,  277-294, 1977. 
 
[2] R.G. Allen, L.S. Pereira, D. Raes, and M. Smith, “Crop 
Evapotranspiration – Guidelines for Computing Crop Water 
Requirements” - FAO Irrigation and Drainage Paper 56, Food and 
Agriculture Organization, Rome, Italy.  pp 145–167, 1998. 
 
[3] A.R. Sepaskhah, A.R. Bazrafshan-Jahromi, and Z.  
Shirmohammadi-Aliakbarkhani, “Development and Evaluation of 
a Model for Yield Production of Wheat, Maize and Sugarbeet 
under Water and Salt Stresses,” Biosystems Engineering 93 (2), pp 
139–152, 2006.  
 
[4] S. Zand-Parsa, and A. Ronaghi,” Development and evaluation 
of integrated water and nitrogen model for maize.” Agricultural 
Water Management, 81(3), pp 227-256, 2006. 
 
[5] A.A. Boling, B.A.M. Bouman, T.P. Tuong, M.V.R, Murty, 
S.Y. Jatmiko, “Modelling the effect of groundwater depth on yield-
increasing interventions in rainfed lowland rice in Central Java, 
Indonesia,” Agricultural Systems, 92, pp. 115-139, 2007.  
 
[6] B.F.T. Rudorff, and G.T. Batista, “Yield estimation of 
sugarcane based on agrometeorological-spectral models,” Remote 
Sensing of Environment, 33, pp. 183-192, 1990. 
 
[7] K.P. Hochheim, and D.G. Barber, “Spring wheat yield 
estimation for western Canada using NOAA NDVI data,” 
Canadian Journal of Remote Sensing, 24(1), pp. 17–27, 1998. 
 
[8] F.N. Kogan, “Droughts of the late 1980s in the United States as 
derived from NOAA polar-orbiting satellite data,” Bull. Amer. 
Meteor. Soc., 76, pp. 655–668, 1995.  
 
[9] P. Zhang, B. Anderson, B. Tan, D. Huang, R. Myneni, 
“Potential Monitoring of Crop Production Using a Satellite-Based 
Climate-Variability Impact Index,” Agricultural and Forest 
Meteor, 132, pp. 344-358, 2005.  
 
[10] P. Zhang, B. Anderson, R. Myneni, “Monitoring 2005 Corn 
Belt Yields from Space”, EOS, Transactions, American 
Geophysical Union,VOL. 87, No. 15, pp.150, 11th April, 2006.  
 
[11] G.E. Johnson, V.R. Achutuni, S. Thiruvengadachari, F. 
Kogan, “The role of NOAA satellite data in drought early warning 
and monitoring: selected case studies,” in Drought Assessment, 
Management, and Planning: Theory and Case Studies, edited by D. 
A. Wilhite, pp. 31–49. Kluwer Academic, 1993. 
2000 2001 2002 2003 2004 2005 2006
0
90
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130
 
 
Co
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Yi
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(bu
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)
 CVII predict (Apr-Jul)
 CVII predict (Apr-Aug)
 CVII predict (Apr-Sep)
 Actual yield
 NASS estimate (Aug)
 NASS estimate (Sep)
 NASS estimate (Oct)
 
Fig. 3: The actual corn yield and the estimated yield in South 
Dakota made by NASS and CVII model over the course of the 
growing season. The NASS estimates (blue bars) are released 
in August, September and October for each year. The CVII 
model predictions (red bars) are based upon the CVII values at 
the end of July, August, and September. The actual yield is 
observed by NASS after harvesting. 


Monitoring Crop Yield in USA Using a Satellite-Based Climate-Variability Impact Index

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