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For abstract, see volume 1 N77-30577. The enclosed maps indicate the areal coverage of the various models for spring (durum and other spring) and winter wheat. The given regions are the combination of several climatic divisions and many times comprise an entire state.
Leaf area index and percentage of vegetative cover, two indices of crop yield developed from Landsat multispectral scanning data, are discussed. Studies demonstrate that the Landsat indicators may be as highly correlated with winter wheat yield as estimates based on traditional field sampling methods; in addition, the Landsat indicators may account for variations in individual field yield which are not explainable by meteorological data. A simple technique employing early-season Landsat data to make wheat yield predictions is also considered.
The author has identified the following significant results. Improvement in the land use classification accuracy of ERTS-1 MSS multi-images over Kansas can be made using two distances between neighboring grey tone N-tuples instead of one distance. Much more information is contained texturally than spectrally on the Kansas image. Ground truth measurements indicate that reflectance ratios of the 545 and 655 nm wavebands provide an index of plant development and possibly physiological stress. Preliminary analysis of MSS 4 and 5 channels substantiate the ground truth interpretation. Results of the land use mapping experiment indicate that ERTS-1 imagery has major potential in regionalization. The ways in which land is utilized within these regions may then be studied more effectively than if no adequate regionalization is available. A model for estimating wheat yield per acre has been applied to acreage estimates derived from ERTS-1 imagery to project the 1973 wheat yields for a ten county area in southwest Kansas. The results are within 3% of the preharvest estimates for the same area prepared by the USDA. Visual identification of winter wheat is readily achieved by using a temporal sequence of images. Identification can be improve by stratifying the project area into subregions having more or less homogeneous agricultural practices and crop mixes.
The author has identified the following significant results. The soil moisture difference between the flight lines was partly due to water-holding capacity differences of the two soil types. Fields along the east flight line were in clay; while along the west flight line, the soil was sandy loam which holds less moisture. Due to differences in the amount of green material, the pastures were wetter than the wheat fields. Most of the pastures average from 40-80% green material, while wheat averages from 90-100% green material. A large amount of green material transpired more water and depleted the soil water content faster than dead vegetation. Visicorder data found temperature differences between the rangeland and winter wheat fields. Pasture had a larger percentage of dead material with different thermal properties than live vegetation, and surface temperature was primarily dependent on insolation. Dead material transpired less, but warms up faster than wheat fields.
The spectral reflectance of a cropped surface changes as the plant develops. An indicator of crop growth is leaf area index (ratio of green leaf area to soil area). The leaf area index, disease severity, and yield were determined for several winter wheat fields in Kansas during the 1973 growing season. Multispectral scanner (MSS) data from Earth Resources Technology Satellite-1 (ERTS-1) showed a high correlation (r greater than or equal to 0.90) between crop growth and MSS4/MSS5, and crop growth and MSS5/MSS6. Wheat disease severity and yields were significantly correlated at the 5% level with MSS4/MSS6 and with MSS4/MSS7. Further investigation is required before ERTS imagery can be routinely used detecting and estimating disease severity and yield reduction.
The author has identified the following significant results. Wheat area, yield, and production statistics as derived from satellite image analysis, combined with a weather model, are presented for a ten county area in southwest Kansas. The data (representing the 1972-73 crop year) are compared for accuracy against both the USDA August estimate and its final (official) tabulation. The area estimates from imagery for both dryland and irrigated winter wheat were within 5% of the official figures for the same area, and predated them by almost one year. Yield on dryland wheat was estimated by the Thompson weather model to within 0.1% of the observed yield. A combined irrigated and dryland wheat production estimate for the ten county area was completed in July, 1973 and was within 1% of the production reported by USDA in February, 1974.
The author has identified the following significant results. Study results indicate: (1) Vegetative growth was best predicted by a linear relationship between leaf area index and the radio of band 4 to band 5. All significant soil moisture effects were cancelled by the ratio. (2) Soil moisture at a depth of 0 to 15 cm, with specific soil factors, was predicted by band 4 and leaf area index with a high regression coefficient. (3) Vegetative growth, measured by leaf area index, was one of the necessary inputs in evaluating the winter wheat crop coefficient from March to maturity.
Emissivities were calculated from the Nimbus 5 electrically scanning microwave radiometer (ESMR) over 25 km grid cells for the southern Great Plains includin the western two-thirds of Kansas and Oklahoma and northwest Texas. These emissivities, normalized for seasonal temperature changes, were in excellent agreement with theory and measurements made from aircraft and truck sensors at the 1.55 cm wavelength of ESMR. These emissivities were related to crop moisture conditions of the winter wheat in the major wheat producing counties of the three states. High correlations were noted between emissitivity and an antecedent precipitation index (API) used to infer soil moisture for periods when the soils were essentially bare. The emissivities from ESMR were related through API and actual crop condition reports to progress of fall planting, adequacy of crop moisture for stand establishment, and periods of excessive moisture that necessitated replanting. Periods of prolonged frozen soil in the winter were observable at several grid points. The average emissivities of the canopy/soil surface during the maximum canopy development times in the spring showed a good agreement with moisture stress inferred from rainfall and yield data.
Wheat is the most important commodity traded in the international food market. Thus, accurate and timely information on wheat production can help mitigate food price fluctuations. Within the existing operational regional and global scale agricultural monitoring systems that provide information on global crop yield and area forecasts, there are still fundamental gaps: #1. Lack of quantitative Earth Observation (EO) derived crop information, #2. Lack of global but detailed (national or subnational level) and timely crop production forecasts and #3. Lack of information on forecast uncertainties. In this study we present the Agriculture Remotely-sensed Yield Algorithm (ARYA) an EO-based method, advancing the state of EO data application and usage (addressing gap #1) to forecast wheat yield. The algorithm is based on the evolution of the Difference Vegetation Index (DVI) using MODIS data at 1km resolution and the Growing Degree Days (GDD) from reanalysis data. Additionally, we explore how Land Surface Temperature (LST) can be included into the model and whether this parameter adds any value to the model performance when combined with the optical information. ARYA is implemented at the national and subnational level to forecast winter wheat yield in the main wheat exporting countries of US, Russia, Ukraine, France, Germany, Australia and Argentina from 2001 to 2019 (covering over 70% of wheat exports globally) in a timely manner by providing daily forecasts (addressing gap #2). The results show that ARYA provides yield estimations with RMSE’s within 0.3 ± 0.1 t/ha at national level and 0.6 ± 0,1 t/ha at subnational level after Day Of the Year (DOY) 140 (mid May) in the Northern Hemisphere and DOY 280 (beginning of October) in the Southern Hemisphere. This means that ARYA can provide crop yield estimates of wheat yield with 5-15 % error at national and 7-20 % error at subnational level starting from 2 to 2.5 months prior to harvest.
In this study we present a model to forecast wheat yield based on the evolution of the Difference Vegetation Index (DVI)and the Growing Degree Days (GDD), presented in Franch et al. (2015), but adapted to Franch et al. (2019) model. Additionally, we explore how the Land Surface Temperature (LST) can be included into the model and if this parameter adds any value to the model when combined with the optical information. This study is applied toMODIS data at 1km resolution to monitor the national and state level yield of winter wheat in the United States and Ukraine from 2001 to 2019.
From an operational standpoint, the most significant item the classification and mensuration subsystem (CAMS) had to overcome in providing the acreage component of the wheat production estimates for LACIE was the scope (segment volume processing required). Peak processing requirements per day increased from 16 to 20 for phase 1 with 700 total segments, to 35 to 40 per day for phase 2 with 1700 total segments, to 75 to 80 per day for phase 3 with 3000 total segments. Key issues regarding interrelationships between man and machines were identified during phase 1 using first generation technology. Procedure 1, tested and evaluated during phase 2 and continued through the initial phase 3 processing period for winter wheat, showed the need for software modification, procedures development, and analyst training. CAMS operations are described with emphasis on the training backgrounds of the analysts, the available data, and the labeling logic.
Argentina is one of the major producers and exporter of soybeans, corn, and wheat to the world market; therefore, the accurate and timely forecasting of those crops yield is crucial to national crop management and global food security. Previous studies have mainly focused on developing forecasting models for a specific crop type and location using a single source of data (e.g., vegetation indices), thus providing little insight into the forecasting models' performance on different crop types and regions. Besides, these models are based on traditional statistical regression algorithms, while more advanced machine learning approaches have not been explored. This study investigated the estimation of crop yields of three major crops (corn, soybean, and winter wheat) using Multiple Linear Regression (MLR) and Support Vector Machine (SVM), over major growing provinces in Argentina. Our models were trained and evaluated on data from 2015 to 2020, where three remote sensing products (Normalized difference vegetation index (NDVI), SMAP soil moisture, and MODIS evapotranspiration) were used as predictors. Our results indicated that accurate crop yield forecasts using the developed regression models could be made one to two months before harvest. The MLR and SVM model performance varied among different crop types, where soybean and corn exhibited better predictability compare to the wheat. In most cases, the SVM outperformed the multiple linear regression model due to its ability to capture the nonlinear and complex features of the crop-production process. The forecasted model that combines data from multiple sources outperformed single-source satellite data. The highest accuracy was obtained when the three data sources were all considered in the model development. Results also indicated that the inclusion of SMAP soil moisture improved crop yield forecasting in most provinces, and the most significant improvements occurred in the drier region.
The Advanced Very High Resolution Radiometer (AVHRR) sensor provides a unique global remote sensing dataset that ranges from the 1980's to the present. Over the years, several efforts have been made on the calibration of the different instruments to establish a consistent land surface reflectance time-series and to augment the AVHRR data record with data from other sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS). In this paper, we present a summary of all the corrections applied to the AVHRR Surface Reflectance and NDVI Version 4 Product, developed in the framework of the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) program. These corrections result from assessment of the geo-location, improvement of the cloud masking and calibration monitoring. Additionally, we evaluate the performance of the surface reflectance over the AERONET sites by a cross-comparison with MODIS, which is an already validated product, and evaluation of a downstream Leaf Area Index (LAI) product. We demonstrate the utility of this long time-series by estimating the winter wheat yield over the USA. The methods developed by [1] and [2] are applied to both the MODIS and AVHRR data. Comparison of the results from both sensors during the MODIS-era shows the consistency of the dataset with similar errors of 10%. When applying the methods to AVHRR historical data from the 1980's, the results have errors equivalent to those derived from MODIS.
A study was made of some of the changing land use practices in a small area of the American Winter Wheat Belt as they might relate to remote sensing. In addition, interviews were conducted with farmers and local country agents in order to determine some of the needs regarding land use and farming practices as perceived by these people. A list of elements of land use is given which provides potential variables and parameters to be considered in interpreting radar imagery. The results of interviews provide a better concept of what potential remote sensing users at the primary level need and want.
The author has identified the following significant results. A user of an ERTS data collection system must supply the sensors and signal conditioning interface. The electronic interface must be compatible with the NASA-furnished data collection platform (DCP). A universal signal conditioning system for use with a wide range of environmental sensors is described. The interface is environmentally and electronically compatible with the DCP and has operated satisfactorily for a complete winter wheat growing season in Kansas.
Three conclusions are drawn on the usability, inherent variations, and noise aspects of the spectral signatures processed from data collected by the Field Signature Acquisition System (FSAS). Conclusions are based on the spectral data collected from winter wheat of the 1972/73 season, grown at Texas A and M University, College Station, Texas.
The author has identified the following significant results. A user of an ERTS data collection system (DCS) must supply the sensors and signal-conditioning interface. The electronic interface must be compatible with the NASA-furnished data collection platform. A universal signal-conditioning system for use with a wide range of environmental sensors is described. The interface is environmentally and electronically compatible with the DCP and has operated satisfactorily for a complete winter wheat growing season in Kansas.
The remote estimation of the leaf area index of winter wheat at Finney County, Kansas was studied. The procedure developed consists of three activities: (1) field measurements; (2) model simulations; and (3) response classifications. The first activity is designed to identify model input parameters and develop a model evaluation data set. A stochastic plant canopy reflectance model is employed to simulate reflectance in the LANDSAT bands as a function of leaf area index for two phenological stages. An atmospheric model is used to translate these surface reflectances into simulated satellite radiance. A divergence classifier determines the relative similarity between model derived spectral responses and those of areas with unknown leaf area index. The unknown areas are assigned the index associated with the closest model response. This research demonstrated that the SRVC canopy reflectance model is appropriate for wheat scenes and that broad categories of leaf area index can be inferred from the procedure developed.