~\ .• " I"

Summary of the Presentation

ADVANCES IN ESTIMATING CROP YIELD THROUGH COMBINEDREMOTE SENSING AND GRO~TH MODELING

Stephan J. MaasPlant Physiologist

USDA-ARS Subtropical Agricultural Research Lab~eslaco, Texas

This presentation describes the advances made over the pasttwo years in a crop yield estimation technique that combinesaspects of satellite remote sensing and crop simulation modeling.The technique responds to the challenge to meet the goal estab-lished at the ARS Remote Sensing ~orkshop (20-22 October 1987,Beltsville, MD) of developing hybrid remote sensingjagroclimaticmodels that can estimate foreign yields more accurately anddomestic yields more economically than by current operationalmethods. From the start of the project, attention was paid todeveloping a technique that would require a minimum of inputdata, and the input data would be of a type routinely availablein an operational program. It was also considered beneficial todevelop a single technique that could be applied to both foreignand domestic yield estimation.

A number of yield estimation techniques have been proposedthat make use of either remote sensing or growth modeling. Thesetechniques rely on the inherent strengths of these technologies(Fig. 1). However, the inherent weaknesses of each technologyhave hindered the acceptance of these techniques in operationalyield estimation programs. The technique described irr~hispresentation combines aspects of remote sensing and growthmodeling such that the strengths of one technology make up forthe weaknesses of the other.

The Objective Yield Survey employed by the National Agricul-tural Statistics Service (NASS) was used as the starting pointfor developing the technique, since it sets the standard foryield estimation accuracy. It also demonstrates that observeddata and models can be combined in an operational program. Inthe Objective Yield Survey, observed data are obtained by ground-level field sampling. These data drive the empirical regressionmodels used to determine crop yield. NASS has investigated theuse of growth simulation models driven by weather data, ·buttypically these models were not consistently accurate or requiredweather or field data that were difficult to acquire operation-ally. Much of the detail in crop growth models is needed toaccurately simulate the development of the leaf canopy, since

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leaf growth is highly sensitive to genetic influences, environ-mental conditions (temperature, water stress), fertilization andplant population density. If the leaf canopy development of acrop planting is known, modeling the biomass production and yieldof the field is relatively uncomplicated. Vhile leaf canopydevelopment might be impractical to routinely measure by ground-based sampling for the large number of fields in an operationalyield estimation program, it could effectively be estimated formany fields in a region from satellite (like Landsat or SPOT)observations. Vith the availability of remotely-sensed values ofcanopy development, a much simpler growth simulation model(requiring much simpler weather inputs) can be used. Like theempirical regression models currently in use by NASS, theestimates produced by this model will be brought into agreement~ith what is really happening in the fields through in-seasonobservations.

Experience has shown that the main problem in using satel-lite estimates of canopy development in growth models is theinfrequency of observations resulting from the normal overpasscycle of the satellite and the occurrence of clouds. Thus, atechnique had to be found to incorporate infrequent satelliteobservations into the model simulation. A number of techniqueswere evaluated (Maas, 1988, Ecological Modelling 41:247-268), andone called re-parameterization was determined to satisfy thisrequirement. The manner in which it operates is shown in Fig. 2.The model contains relatively simple relationships that produce aleaf canopy growth curve with a shape that resembles what istypically observed in the field. Because the relationships aresimple, however, the magnitude of this growth curve a~ determinedfrom weather data alone might not match what actually occurs inthe field. By comparing the simulated growth curve to infrequentobservations of leaf canopy ~evelopment, parameters in therelationships can be manipulated until the magnitude of thesimulated canopy growth curve matches the observations. Inpractice, this is accomplished by an iterative numerical solutionthat produces a "best fit" of the simulation to the observations.This technique works with observations obtained at any time inthe growing season, and works with as few as one observation.

A prototype operational model called GRAMI has been devel-oped to test the accuracy of yield estimates to be expected fromthis technique. The model currently can simulate the growth of anumber of grain and cereal crops. There is indication that itcan also be adapted to simulate the growth of other crops, suchas soybean, cotton and sunflower. To simulate growth, GRAMIrequires observations of average daily air temperature, dailytotal solar irradiance, and an estimate of the planting date ofthe field. Rainfall, evapotranspiration or soil moisture dataare not required, since the effects of water stress are assumedto be present in the observations of leaf canopy development andthus are implicitly incorporated into the simulation through there-parameterization process. In an operational program, adequatevalues of average temperature could be interpolated to the fieldlocations from existing weather stations, or inferred fromweather satellite atmospheric soundings. Daily solar irradiance,

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which used to be difficult to acquire over large areas, can nowbe operationally estimated from satellite observations.

Examples of GRAMI simulations for four crops are presentedin Figs. 3-6. Frequent ground-based observations of GLAI (ameasure of leaf canopy development) were used to re-parameterizethe model to determine how well GRAMI could simulate the detailsof crop growth over the growing season. In the case of winterwheat (Fig. 6), the simulation was started at mid-winter toestimate the spring growth of the crop. The simulations of GLAIin Figs. 3-6 represent the best fits of modeled to observed dataobtained through the re-parameterization process. Thesimulations of biomass (AGDM) are not fits to the observed data.Rather, they were computed in the model based on temperature andthe absorption of solar irradiance by the simulated leaf canopy.+he fact that the biomass simulations reasonably match theirrespective observations emphasizes the earlier assertion thatmodeling crop biomass is relatively easy once the leaf canopydevelopment has been adequately described. Simulated andobserved values of yield displayed in Figs. 3-6 are also notmarkedly dissimilar.

An initial validation of GRAMI was performed to investigatethe accuracy of model estimates based on infrequent satelliteobservations of crop canopy development.· The yields of 37 grainsorghum fields grown during the period 1973-77 in Hidalgo County,Texas, were modeled using daily temperature and irradiance datameasured at one location in the county. Of the 37 fields, 24were under dryland cultivation and the remainder were irrigated.Values of GLAI used to re-parameterize the model were determinedfrom digitized Landsat MSS images using established conversionprocedures. No information from the study other than plantingdates, the weather data, and remotely-sensed GLAI was used insimulating the growth and yield of the 37 fields. Results of thestudy are presented in Fig. 7, which shows that simulated versusobserved yield values generally cluster along the 1:1 line. Sta-tistical analysis of the results using SAS indicated that thesets of simulated and observed yields exhibited equal variances(1)=1.65 with 36 and 36 df, P>I)=0.1377). The means of thesimulated and observed yields were not significantly different(t=0.0832 with 72 df, P>t=0.9339), while the mean differencebetween simulated and observed yield on an individual field basiswas not si~nificantly different from zero (t=-0.1556 with 36 df,P>t=0.8772). These results are encouraging, since only oneLandsat MSS observation was available for 25 of the 37 fields inthe study. The yield estimates involving only one Landsat ob-servation are indicated in Fig. 8. It is apparent that the yieldestimates exhibiting the greatest errors were determined usingonly one satellite observation.

More extensive data sets are in preparation for validatingGRAMI. The 1983 Upper Midwest Study contains yield and LandsatMSS data for approximately 150 fields each of spring wheat, cornand soybean, and approximately 50 fields each of winter wheat,grain sorghum and sunflower. The 1985-86 North American GreatPlains Study contains yield and ground-based remote sensingobservations for approximately 250 small plots of winter wheat.

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Preliminary results from these validation efforts should beavailable within a year.

In addition to estimating yields based on observed data, themodeling technique employed by GRAMI must be capable of providingin-season predictions of yield before harvest. A relativelysimple means of predicting yields within the growing season wouldinvolve running the model up to the current day using observedweather data and available satellite obsrvations. The simulatedvalues of biomass on the current day could then be used in anempirical regression model to predict yield at harvest. Thisprocedure would be similar to that used operationally by NASS. Amore sophisticated technique would involve running the model upto the current day using observed weather and satellite data, andcompleting the model simulations using "future" weather data.This future weather data could come from climatological records,long-range weather predictions, or computerized weather-synthesizing programs. Since future weather conditions areuncertain, the simulations could be completed using a number ofindividual sets of future daily weather, and the resultingdistribution of yield values used to estimate the probabilitiesof yields occurring within certain ranges.

The current version of GRAMI assumes that the effects ofwater stress on crop growth are implicitly contained in theremotely-sensed observations of leaf canopy development. Thus,the current version of the model does not need to consider rain-fall, evapotranspiration, or soil moisture. There is someevidence from physiological studies that water stress may producean effect on leaf photosynthetic rate in addition to its effecton leaf canopy development. This additional effect is related tostress-induced closing of leaf stomata. The reduction inphotosynthetic rate has been shown to be directly related to thevalue of the Idso-Jackson Crop ~ater Stress Index (C~SI). Thisindex can be evaluated from remote sensing measurements of leafcanopy temperature. A model called HYDRO has been developed thatcan use remotely-sensed observations of canopy development andtemperature to quantify these separate stress effects on thegrowth and yield of a crop. HYDRO consists of two submodels-- acrop growth submodel and a soil moisture balance submodel. Thecrop growth submodel is GRAMI modified to include the rela-tionship that reduces photosynthetic production as a function ofC~SI. The soil moisture balance submodel simulates C~SI over thegrowing season as a function of weather data, crop canopydevelopment, and soil moisture-related parameters. Like GRAMl,the soil moisture submodel uses an iterative numerical solutionto estimate the parameter values that result in a "best fit" ofsimulated to observed CVSI. This simulation is achieved withoutthe input of rainfall, evapotranspiration, or soil moisture data.

HYDRO was tested using spring wheat irrigation treatmentplot data from Phoenix, AZ (Maas et al., 1989, Proc. 19th Conf.Agric. Forest Meteorol., AMS, pp. 228-231). The fit of thesimulated to observed C~SI for six wheat varieties is shown inFig. 9, while the fit of the simulated to observed leaf canopycover (GLAI) is shown in Fig. 10. Also shown in Fig. 10 arebiomass (AGDM) simulations made with and without the stress-

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related effects on photosynthesis. It appears that the biomasssimulations that incorporate the stress-related effect match theobserved biomass values more closely than the simulations that donot incorporate the effect. This would indicate that, whensignificant water stress conditions occur, models that do notexplicitly contain this photosynthesis-related stress effect(including the current version of GRAMI) might tend to over-estimate crop growth and yield. The results of this study arenot conclusive. In the Phoenix experiments, water stress wasimposed on the crop abruptly after a period of irrigated growth.Under natural conditions, water stress develops more gradually,with an opportunity for the crop plants to acclimate to thechanging conditions. This may explain why some studies (Gibsonand Schertz, 1977, Crop Sci. 17:387-391) indicate that only the~ffect of water stress on leaf canopy development (and not photo-synthesis) is evident under natural field conditions. This wouldindicate that the current formulation of GRAMI may be adequatefor application to natural conditions. This will be known withmore certainty upon completion of the current GRAMI validationefforts and continued experimentation with HYDRO.

In conclusion, a yield estimation technique has been devel-oped that combines remote sensing and growth modeling in such away that the strengths of one technology make up for the weak-nesses of the other. The technique takes advantage of thedependence of model performance on growth-related parameters thatcan be evaluated using infrequent satellite observations andnumerical analysis procedures. Through re-parameterization,within-season satellite observations of crop canopy developmentconstrain the response of a relatively simple growth simulationmodel to bring it in line with what is happening in the field.The technique has a number of advantages with respect to its usein an operational yield estimation program, including,(1) The same model can be used for both foreign and domestic

applications(2

3) The same model can be used for many different crops

() The model is relatively simple, and requires weather datathat can be routinely obtained from existing source~

(4) The model requires infrequent (as few as one) observationsof crop canopy development, which can be easily obtained bysatellites for a large number of fields in a region

(5) The model can be used to produce probabilistic predictionsof yield during the growing season

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Figure 1. Summary of the strengths and weaknesses of theremote sensing and growth modeling technologies with respectto crop yield estimation.

STRENGTHS VEAKNESSES

REMOTESENSING

GROllTllMODELING

Provide a quantification of Observations are discretethe actual state of the crop time events that tell littleduring the growing season about how the crop got to

the observed stateInformation on crop status Growth and yield must becan be obtained for many inferred through empiricalfields more economically methods with questionablethan by field sampling general application

Provide a continuous Must contain a considerabledescription of crop growth amount of detailover the growing seasonGrowth and yield are de- Must have detailed on-sitetermined from environmental observations of environ-conditions based on mental inputsphysiological principles

Figure 2. Schematic diagram of how remotely-sensedobservations are used to constrain the growth modelsimulation.

MODEL

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Parametersthat Control

Grollth

1CHANGE IFESTIllATED AND

onSERVED GROnnARr. DIFFERENT

I

ESTIMATEDGROVTH

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1SATELLITEOnSERVATIONSOF GROVTJI

ESTIMATEDYIELD

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Figure 3. GRAMl simulation of leaf canopy development(GLAl) and biomass growth (AGDM) for corn (maize). Circlesrepresent observations, while the solid lines are therespective model simulations.

CORN (MAIZE)2000

400

800

Yield (kg/he)OBSERVED 4215SIMULATED 4301

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Figure 4. GRAMI simulation of leaf canopy development(GLAI) and biomass growth (AGDH) for grain sorghum. Circlesrepresent observations, while the solid lines are therespective model simulations.

GRAIN SORGHUM2000

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Yield (kg/ha)OBSERVED 7316SIMULATED 7005

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DAYS AFTER PLANTING

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Figure 5. GRAMI simulation of leaf canopy development(GLAI) and biomass growth (AGDM) for spring wheat. Circlesrepresent observations, while the solid lines are therespective model simulations.

SPRING WHEAT2000

Yield (kg/he) 0 00

1600 OBSERVED 7120SfMUU\TED 6747 0

~ 12000

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« 800

400

011 20 40 60 80 100 120 140 16010 0

98-

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43210

0 120 140 160DAYS AFTER PLANTING

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Figure 6. GRAMI simulation of leaf canopy development(GLAI) and biomass growth (AGDM) for winter wheat. Circlesrepresent observations, while the solid lines are therespective model simulations. Solid circles indicate growththat occurred during autumn. "Days after planting" areactually days after the start of the simulation in mid-winter.

WINTER WHEAT2000

Yield (kg/ho)1600 OBSERVED 3807

SIMULATED3602:2 12000

G

« 800,:',

400

•00 20 40 60 80 100 120 140 160

765

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2 •0-20 0 20 40 60 80 100 120 140 160

DAYS AFTER PLANTING

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Figure 7. Results of the initial validation of GRAMI using37 grain sorghum fields in Hidalgo County, Texas.

0 0

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OBSERVED YIELD (kg/ha)

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Figure 8. Same as Fig. 7, except that yield estimates basedon only one Landsat observation are indicated by crosses •

... ...0 2-4 Observations

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Figure 9. HYDRO simulations of CYSI for the six springwheat varieties grown at Phoenix, Arizona.

2 CIANO 79

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2 GENARO 81

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-1o 20 40 60 80 100 120 140

DAY OF YEAR

Figure 10. HYDRO simulations of leaf canopy development(GLAI) and biomass (dry mass) for the six spring wheatvarieties grown at Phoenix, Arizona. Dotted curvesrepresent biomass simulations that incorporate the stresseffect on photosynthesis, while dashed curves representbiomass simulations that do not incorporate the effect.

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12 JOOO10 CIANO 798 SI~ AGOt.t. CWSI 20006 Sl~ AGo•••••NO CWSI 0

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