Crop Calendar Research Articles

Measuring the economic impact of climate change on major South African field crops: a Ricardian approach

This study employed a Ricardian model to measure the impact of climate change on South Africa's field crops and analysed potential future impacts of further changes in the climate. A regression of farm net revenue on climate, soil and other socio-economic variables was conducted to capture farmer-adapted responses to climate variations. The analysis was based on agricultural data for seven field crops (maize, wheat, sorghum, sugarcane, groundnut, sunflower and soybean), climate and edaphic data across 300 districts in South Africa. Results indicate that production of field crops was sensitive to marginal changes in temperature as compared to changes in precipitation. Temperature rise positively affects net revenue whereas the effect of reduction in rainfall is negative. The study also highlights the importance of season and location in dealing with climate change showing that the spatial distribution of climate change impact and consequently needed adaptations will not be uniform across the different agro-ecological regions of South Africa. Results of simulations of climate change scenarios indicate many impacts that would induce (or require) very distinct shifts in farming practices and patterns in different regions. Those include major shifts in crop calendars and growing seasons, switching between crops to the possibility of complete disappearance of some field crops from some region.

Agroclimatological Problems in the Taklimakan Desert and Its Surrounding Area in NW China

Agroclimatological problems in the Taklimakan Desert and its surrounding area in NW China were discussed with reference to the results of our previous studies carried on since the early 1990s, together with literature published in China. Farmers at the oases have been suffering from occasional heavy rainfall, causing floods and damaging construction works. It is worthy to note that human ecosystems in arid areas are more vulnerable to extremely wet conditions, which occur infrequently, than to dry conditions.Agroclimatologically, strong sand-dust storms, called “Kara Bran (Black Wind),” also play a seriously negative role in the Taklimakan Desert. Cotton, which is cultivated as a main cash crop for the farmers, has frequently been damaged severely. The amount of water used for irrigation per family is decreasing because of population growth. Limits on the ratio of irrigated land to total cultivated land differ between the northern fringe and the southern fringe of the Tarim Basin. The tributary size (population) is also important in determining the limits. The crop calendar is decided by the amount of water available. The income of farmers' families is very low, because of low production under the severe environmental conditions for cultivation, which are common at desert oases. Raising the income of the farmers is one way to decrease or alleviate desertification, because they wouldn't have to go to the desert to collect firewood. The farmers who are living under the severe economic conditions cannot buy coal because of the price. The farmer's recognition of an increasing trend in the frequency of very strong winds, Kara Bran, is quite accurate, according to the interview survey. The results coincide with the records at the local meteorological observatories.

Landsat‐5 Thematic Mapper data for pre‐planting crop area evaluation in tropical countries

This paper describes an approach in which multitemporal Landsat‐5 Thematic Mapper (TM) data from the pre‐planting season, when the cloud cover is minimal, was used in order to evaluate the area to be planted with annual crops in the rainy season. Both supervised and non‐supervised classification of multitemporal Normalized Difference Vegetation Index (NDVI) images were evaluated. The interpretation of the areas to be planted with summer crops was based on field work, NDVI temporal profiles, unitemporal classifications, and the crop calendar of the region. A correction based on the error matrix was applied to the classification and the results were compared to the estimation provided by the official agencies using kappa statistics analysis. The proposed method was shown to be a feasible alternative for improving the crop estimation programme, especially for the tropical countries where the cloud cover has restricted the use of optical remote sensing data.

Evaluation of ERS‐1 SAR Data for Agricultural Crop Monitoring in India

Multi‐temporal ERS‐1 SAR data acquired over a large agricultural region in West Bengal was used to classify kharif crops like rice, jute and sugarcane. Rice crop grown under lowland management practice showed a temporal characteristic. The dynamic range of backscatter was highest for this crop in temporal SAR data. This was used to classify rice using temporal SAR data. Such temporal character was not observed for the other study crops, which may be due to the difference in cultivation practice and crop calendar. Significant increase in backscatter from the ploughed fields was used to derive information on onset and duration of land preparations. Synergistic use of optical remote sensing data and SAR data increased the separability of rice crop from homesteads and permanent vegetation classes.

Monitoring the length of the growing season with NOAA

A vegetation index (NDVI) time series of 1984 to 1993 was used to calibrate a pixel-based assessment of the start, end and length of the growing season in the sahelian country of Burkina Faso. Annual curves of rainfall/ETP, NDVI crop calendars were prepared for meteorological stations representative for three climatic zones. Agrometereological criteria for assessment of the start and the end were extracted from literature. NDVI parameters were assessed at the dekad of the agrometeorological onset and end of the growing season, and used for the definition of decision rules. Decision rules were implemented in a GIS to the NDVI image time series. The start of the growing season assessed with the NDVI dynamics was slightly better correlated with given farmer's sowing dates than with original agro-meteorological criteria. The normal start of the growing season, calculated from the NDVI image time series, was in good agreement with the recommended sowing dates. A delay of the onset of the growing season with regard to normal was monitored under near real time conditions in Burkina Faso in 1995. It was found that the delay of up to five dekads could lead to a partial crop failure and to changes in decisions of farmers. The model for the start and the end of the growing season was validated with independent data from different years. From the maps of the end and the start, maps of the length of the growing season were calculated by subtraction. The length of the growing season was found to be in agreement with agrometeorological literature, although in northern sahelian areas, the real growing season measured by the vegetation index tended to be shorter by about one dekad. In addition, the climatic map was improved by a per pixel classification of a normal (1984-1995 median) image time profile. The 36 normal NDVI images were classified using a hyperspectral image-processing tool.

Remote Sensing and Geographical Information System for canopy cover mapping

Availability of remote sensing data from earth observation satellites has made it convenient to map and monitor land use/land cover at regional to local scales. A land cover map is very critical for a various planning activities including watershed planning. The spectral and spatial resolutions are major constraints for mapping the crop resources at microlevel. The cropping pattern zones have been mapped using the false color composite, physiography, irrigation and toposheets. The IRS LISS-III data is classified into various categories depending on spectral reflectance from crop canopy and are overlaid on cropping zones map. The re-classified resultant map provides land use/land cover information including dominant cropping systems. The canopy cover is estimated monthly considering the crop calendar for the area.

Land cover mapping in an agricultural setting using multiseasonal Thematic Mapper data

A multiseasonal Landsat Thematic Mapper (TM) data set consisting of five image dates from a single year was used to characterize agricultural and related land cover in the Willamette River Basin (WRB) of western Oregon. Image registration was accomplished using an automated ground control point selection program. Radiometric normalization was performed using a semiautomated approach based on the identification of no-change pixels in forest, urban, and water classes. Reference data were developed using existing data sets, including low-level 35-mm color slide photographs, 1:24,000 color airphotos, and ancillary geographic information system (GIS) coverages. Preliminary examination of the data structure included plotting of training set temporal trajectories in spectral space with reference to existing crop calendars. A subsequent stratified, unsupervised classification algorithm, in combination with a geoclimatic rule set and regression analysis, was used to label mapped cells. A map of 20 land cover classes was developed. Classes included agricultural crops and orchards, forest and natural cover types, and urban building densities. An accuracy assessment indicated a final map error of only 26%. The map is now being used to model present and future landscapes for the basin.

Crop discrimination with multitemporal SPOT/HRV data in the Saga Plains, Japan

Nine scenes of SPOT/HRV data obtained in eight different months in 1997 were evaluated for crop discrimination in the Saga Plains, Japan. All images were atmospherically corrected with the 6S code. Annual Normalized Difference Vegetation Index (NDVI) profiles were generated to characterize seasonal trends in six cropping systems (rice, rice-winter cereal, soybean, soybean-winter cereal, lotus, and rush). The dataset of this study showed the unique temporal change patterns of NDVI for each cropping system. Separability analyses determined optimal scene combinations for the highest accuracy in classifying the cropping systems. The scene combinations for the accurate classification of cropping systems were obtained from three separability measurements (Euclidean spectral distance, divergence, and Jeffries-Matsushita distance). Kappa statistics were applied to evaluate the classification accuracies. The four-scene combination that was derived from April, June, July and September classified the cropping systems almost as well as those combinations including more scenes. A colour composition technique applied to the three-scene combination that showed the highest separability also discriminated each cropping system. Based on these results, we can request observations during specific time intervals considering local crop calendars and environmental conditions.

Rice crop duration and leaf appearance rate in a variable thermal environment. III. Heritability of photothermal traits

In arid, irrigated, rice environments, crop duration is highly variable, resulting in uncertain crop calendars for double cropping. The main causes of this variability are varying temperature and daylength. Breeding for stable crop duration in such environments might make a major contribution to rice production. A previous study established genetic differences in phenological responses to temperature and photoperiod, based on a field study with sequential planting dates in Senegal. The present study applied this experimental approach to a five-parent complete dialled scheme. The five parents and 20 F1 progeny populations were sown on 14 dates at 2-week intervals at Ndiaye, Senegal (16°14′ N) during the cold-dry, hot-dry and wet seasons in 1996. The model LAP (Leaf Appearance) described previously was used to disaggregate photoperiodic and thermal traits, which gave reproducible results for the thermal component, but not for the photoperiodic responses. Responses to photoperiod were therefore measured using a subset of environments in the wet season (stable temperatures and 12.6–13.1 h daylength). Cross involving IR29725-40-3-2-3 (IR29725; short duration) and IR4630-22-2 (IR4630; medium duration), in combination with any of the other lines in the diallel, gave consistent patterns of heritability for earliness of flowering in the different seasons, the sensitivity of panicle induction to low temperatures, and photoperiod-sensitivity in the range of 12.6–13.1 h daylength. The results permitted three conclusions: (1) short duration in the wet season was a dominant trait, and in the cold-dry season, a super-dominant trait. IR29725 is a promising donor for this trait. (2) Despite its moderate to high phenological sensitivity to low temperatures, IR29725 acted as donor for low sensitivity of panicle initiation to temperature. This trait was dominant. (3) At least under the thermally stable conditions of the wet season, IR4630 acted as donor of a dominant trait for photoperiod-sensitivity, whereas IR29725 acted as donor for photoperiod-insensitivity. In summary, IR29725 is a promising donor of traits, such as short duration, low photoperiod-sensitivity and low sensitivity of phenological processes to temperature.

Satellite derived crop calendar for canal operation schedule in Bhadra project command area, India

Spatial information on crop calendars in the command areas of irrigation systems is useful to irrigation engineers in order to achieve chronological synchronization between water delivery pattern and crop life cycle. Such synchronization is vital for efficient use of water. The capability of satellite remote sensing technology to generate spatial crop calendar information in an irrigated command area and its usefulness in the evaluation of water delivery patterns are demonstrated in this paper. The study pertains to the major crop paddy during the rabi season (December to June) of 1992-93, in the Bhadra project command area of Karnataka state, India. Analyses of multidate Normalized Difference Vegetation Index (NDVI) profiles of paddy crop generated from Indian Remote Sensing (IRS) satellite data for each distributary command reveal three distinct growth patterns in the study area with each pattern characterized by a particular crop calendar. The spatial variability in crop calendar over the total command area has thus been derived. The ground truth data obtained in crop cutting experiments (CCEs) validate the satellite derived crop calendar. Distributary wise, water delivery data have been studied in conjunction with the satellite derived crop calendar to determine whether the existing pattern of water delivery covers the required length of crop life cycle in the command area. It was found that the water supply was stopped about 30 days before harvesting in some distributaries and in some about 20-30 days before harvesting. A list of distributaries with greater lags between cessation of water supply and crop harvest was provided to irrigation system engineers to aid their plans for providing a reliable and predictable irrigation service. This is possible either through reorganization of canal operation schedule or through educating farmers about the need for adjusting their agricultural activities to match water supply patterns.

Remote Sensing Data Acquisition, Platforms and Sensor Requirements

Although data available from various earth observation systems have been routinely used in many resource applications, however there have been gaps, and data needs of applications at different levels of details have not been met. There is a growing demand for availability of data at higher repetivity, at higher spatial resolution, in more and narrower spectral bands etc. Some of the thrust areas of applications particularly in the Indian context are; Each of these thrust area of application has many components, related to basic resource areas such as agriculture, forestry, water resources, minerals, marine resources etc. and the field of cartography. Observational requirements for major applications have been summarized as under. Monitoring vegetation health from space remains the most important observational parameter with applications, in agriculture, forestry, environment, hydrology etc. Vegetation extent, quantity and temporal changes are the three main requirements which are not fully realized with RS data available. Vegetation productivity, forest biomass, canopy moisture status, canopy biogeochemistry are some examples. Crop production forecasting is an important application area. Remotely sensed data has been used for identification of crops and their acreage estimation. Fragmented holdings, large spread in crop calendars and different management practices continue to pose a challenge lo remote sensing. Remotely sensed data at much higher spatial resolution than hitherto available as well as at greater repetivity are required to meet this need. Non-availability of cloud-free data in the kharif season is one of the serious problems in operational use of remote sensing for crop inventory. Synthetic aperture radar data al X & Ku bands is necessary to meet this demand. Nutrient stress/disease detection requires observations in narrow spectral bands. In case of forestry applications, multispectral data at high spatial resolution of the order of 5 to 10 metres is required to make working plans at forest compartment level. Observations from space for deriving tree height are required for volume estimation. Observations in the middle infrared region would greatly enhance capability of satellite remote sensing in forest fire detection. Temporal, spatial and spectral observational requirements in various applications on vegetation viewing are diverse, as they address processes at different spatial and time scales. Hence, it would be worthwhile to address this issue in three broad categories. a) Full coverage, moderate spatial resolution with high repetivity (drought, large scale deforestation, forest phenology....). b) Full coverage, moderate to high spatial resolution and high repetivity (crop forecasting, vegetation productivity). c) Selected viewing at high spatial resolution, moderate to high repetivity and with new dimensions to imaging (narrow spectral bands, different viewing angles). A host of agrometeorological parameters are needed to be measured from space for their effective use in development of yield models. Estimation of root-zone soil moisture is an important area requiring radar measurements from space. Surface meteorological observations from space at the desired spatial and temporal distributions has not developed because of heavy demands placed on the sensor as well as analytical operational models. Agrometeorology not only provides quantitative inputs to other applications such as crop forecasting, hydrological models but also could be used for farmer advisory services by local bodies. Mineral exploration requires information on geological structures, geomorphology and lithology. Surface manifestation over localized regions requires large scale mapping while the lithology can be deciphered from specific narrow bands in visible. NIR, MIR and TIR regions. Sensors identified for mapping/cartography in conjunction with imaging spectrometer would seem to cover requirements of this application. Narrow spectral bands in the short regions which provide diagnostics of relevant geological phenomenon are necessary for mineral exploration. Thermal inertia measurements help in better discrimination of different rock units. Measurements from synthetic aperture data which would provide information on geological structures and geomorphology are necessary for mineral exploration. The applications related to marine environment fall in three major areas: (i) Ocean colour and productivity, biological resources; (ii) Land-ocean interface, this includes coastal landforms, bathymetry, littoral transport processes, etc. and; (iii) Physical oceanography, sea surface temperature, winds, wave spectra, energy and mass exchange between atmosphere and ocean. Measurement of chlorophyll concentration accurately on daily basis, sea surface temperature with an accuracy of 0.5 °K. and information on current patterns arc required for developing better fishery forecast models. Improved spatial resolution data are desirable for studying sediment and other coastal processes. Cartography is another important application area. The major problems encountered in relation to topographic map updation are location and geometric accuracy and information content. Two most important requirements for such an application are high spatial resolution data of 1 to 2 metre and stereo capability to provide vertical resolution of 1 metre. This requirement places stringent demands on the sensor specifications, geometric processing, platform stability and automated digital cartography. The requirements for the future earth observation systems based on different application needs can be summarized as follows:

An approach for using AVHRR data to monitor U.S. Great Plains Grasslands

Abstract Environmental monitoring requires regular observations regarding the status of the landscape‐ The concept behind most monitoring efforts using satellite data involve deriving normalized difference vegetation index (NDVI) values or accumulating the NDVI over a specified time period. These efforts attempt to estimate the continuous growth of green biomass by using continuous additions of NDVI as a surrogate measure. To build upon this concept, this study proposes three refinements; 1) use an objective definition of the current growing season to adjust the time window during which the NDVI is accumulated, 2) accumulate only the NDVI values which are affected by green vegetation, and 3) base monitoring units upon land cover type. These refinements improve the sensitivity of detecting interannual vegetation variability, reduce the need for extensive and detailed knowledge of ground conditions and crop calendars, provide a framework in which several types of monitoring can take place over diverse land cover types, and provide an objective time frame during which monitoring takes place.

Methane emissions from wetland rice areas of Asia

Khalil and Rasmussen (1990) reviewed eleven global methane budgets published between 1978 through 1988. They found that methane emissions from rice paddies ranged from 18 to 280 Tg year −1 which correspond to between 10 and 70% of the total anthropogenic methane emissions. For this paper, we have reviewed and replicated three published techniques to estimate methane emissions from rice paddies. We present the results obtained and we propose to include soil characteristics to revise these estimates. Since 90% of rice production occurs in Asia, we have only focused our study on rice in Asia. The first technique we replicated, uses the Food and Agriculture Organization (FAO)'s country statistics and crop calendars to determine the land area under rice cultivation each month. Assuming a constant emission rate, Asian rice fields emit about 82 Tg methane year −1. The second technique we replicated, assumes that methane emissions represent a constant fraction of the net primary production and uses empirical relationships between net primary production and temperature and precipitation records. Asian rice fields then only produce 57 Tg methane year −1. The third technique we replicated, relates methane emissions to rice grain production. It involves the calculation of total organic matter added to rice paddy soils and assumes that a constant fraction is emitted as methane. This leads to an estimate of methane emissions from Asian rice fields of about 63 Tg year −1. We propose to use a classification of rice soils to categorize rice growing locations from potentially methane producing to non-methane producing areas. Using this distinction with any of the three methods we discussed, Asian rice fields emissions are reduced by about 25%.

An evaluation of multi-date SPOT data for agriculture and land use mapping in the United Kingdom

Abstract Four SPOT HRV images of the same area of East Anglia, acquired between February and September 1986, have been evaluated at the National Remote Sensing Centre for their potential use in agricultural land cover mapping. Spectral coincidence plots were used in feature selection. Information from single images contained a high level of spectral confusion between cover types. Vegetation index images and original data were used in supervised maximum likelihood classification. Higher classification accuracies were achieved using the original data than the vegetation indices. An overall classification accuracy of 71 per cent for 10 land cover types was improved to 88 per cent by reducing the number of classes. Although the imagery acquired for the study did not correspond well to key dates in the crop calendar, the broad land cover categories, cereal crops, field crops (sugar beet and vegetables), grass land and broadleaved woodlands could be mapped from SPOT. Using vegetation indices from the whole scen...

A Leaf Area Index Model for Corn with Moisture Stress Reductions1

AbstractThe leaf area index (LAI) of a crop is an input in many agronomic models, but is time consuming to measure. An accurate method of estimating LAI is needed. Using measurements from 72 plots over 17 yr, a model was developed to predict corn (Zea mays L.) LAI based on a normalized thermal crop calendar (NC). The seasonal change in LAI was divided into three periods: 1) planting to silking; 2) silking to the start of rapid senescence; and 3) rapid senescence. The LAI data during the first period were fitted with a logistic function of NC and the maximum leaf area per plant (LAmax) using plot‐years with no moisture stress. The LAmax was predicted using the population density and a hybrid coefficient. The decline in LAI during the second period was predicted with NC, the maximum LAI, and a hybrid coefficient. During the third period, LAI prediction was based on the number of days past the beginning of rapid senescence. Leaf area measurements in plot‐years with moisture stress were used to develop a stress growth factor (SGF) to reduce the growth of LAI in the first period. The SGF was determined from the daily ratios of actual to potential evapotranspiration (ET/PET). Daily ET/PET values < 1.00 reduced LAI growth. Moisture stress did not affect LAI during Periods 2 and 3. The error of predicted LAI for independent plot‐years averaged 19% for the first period, 11% for the second, and 58% for the period of rapid senescence. The model is recommended for broader testing and use.

Soyphen: Soybean Growth Stages Modeled from Temperature, Daylength, and Water Availability1

AbstractThe soybean plant (Glycine max L.) responds strongly to daylength and temperature in its phasic development. Daylength responses vary greatly between varieties. Under very dry conditions, water stress may also affect development. Attempts to develop physiologically based large area yield models for soybeans have been hampered by the lack of a growth stage model incorporating these responses and applicable to a wide geographic and climatic range. The objective of this work was to develop a physiologically based model to predict growth stage dates for soybeans for the widest possible range of maturity groups and geographic areas. A phenology model was developed and tested for soybean Maturity Groups I to V based on temperature, daylength, and water availability. Coefficients were also developed, but not tested independently for Maturity Groups 00, 0, and VI to IX. Coefficients for Maturity Groups I to V were developed from 3 years (1971 to 1973) of data with five planting dates per year collected at Columbia, MO. The coefficients were tested on data sets taken at the same time at Spickard and Mt. Vernon, MO and on data for the 1981 season from 22 international sites. The test results indicate that the model will generate reliable estimates of soybean growth stages over a wide range of climatic conditions. The coeffients for Maturity Groups 00, 0, and VI to IX were developed from data from international sites, but no data were available for independent testing. The model is available in the FORTRAN and PL/I programming languages. It is suitable for incorporation into a soybean growth simulation model or for running as an independent crop calendar model. The model may be run wherever daily maximum and minimum temperatures and rainfall are available.

Four types of relationships between rainfall and paddy production in Sri Lanka

Due to the absence of marked thermal differences in Sri Lanka, the paddy-climate relationship has been confined to rainfall alone in this study. Increase in total paddy production is highly attributed by the increase in paddy lands rather than yields, particularly in the drier parts, called Dry Zone. The effective rainfall period for the two principal cropping seasons in Sri Lanka, Maha and Yala, were determined according to the crop calendar of the Districts. The deviation values from the trend lines of 20 years (1961–1980) observations in sown, harvest and yield were well correlated with the deviations from the mean rainfall. The effects of rainfall on paddy production are well pronounced in the Dry Zone during the Maha season and in the Wet Zone during the Yala season. From the statictical analyses, four types of relationship, were observed: (1) Type I is the positive relationship, (2) Type II is the negative relationship, (3) Type III is the no realtionship and (4) Type IV a complex one which has a positive relationship below certain critical rainfall value and no or negative relationship above the critical rainfall value. The combination of Types I and IV shows the areas which need irrigation facilities with proper water management while the combination of the Types II and III indicates the areas which are prone to frequent floods and waterloggings.

Crop identification using Landsat temporal-spectral profiles

A temporal-spectral profile is the multitemporal representation of Landsat data, optimally in the form of a green vegetation indicator, from a single labeling target. The profile is a detailed indicator of the physical state of a field or other landscape class through time. Through the examination of numerous sample segments in the United States Corn Belt, it was determined that individual corps have characteristic profiles which are recognizably different from those of other crops. Differences and similarities in profile-related features can be used to support crop identification at various levels. At the first level, annual crops are separable from other classes based on a general trajectory that starts below a green vegetation detection threshold, rises to some maximum amplitude, and then falls below the threshold once more; this trajectory is related to the sequence of development stages through which most crops pass. At the second level, annual crop profiles can be separated into crop groups based on the period during which the profile is above the detection threshold, which varies in timing and duration as a result of crop calendar differences from group to group. At the third level, individual crop types (e.g., corn and soybeans) can be identified based on the amplitude and distinctive shape of the profile.

Rainfall and Paddy Production in Sri Lanka

Sri Lanka experienced severe droughts and floods during the recent years which resulted much crop losses in the paddy production. Effective rainfall periods for the major paddy producing seasons, Maha and Yala seasons are determined according to the crop calendar of the Districts. Here, the impact of rainfall on the paddy production is analysed for each Districts of Sri Lanka during the period from 1961 to 1980. Significant correlation coefficients reveal the clear effect of rainfall on the paddy production, especially in harvest acreage in the Wet and Dry zones. The regions in the Wet Zone have significant positive relationship in the Southwest monsoon season and the Dry Zone in the second intermonsoon and Northeast monsoon seasons as their principal cropping seasons. The Maha rain effects on the sown extent and harvest acreage not only in the same Maha season, but also in the subsequent Yala season sometimes. The excess rainfall amount does not increase the paddy acreage and causes floods even in the drier parts of the country, indicating a negative relationship between rainfall and sown or harvest acreage. Therefore, we can recognize four types of relationships between rainfall and paddy production: (1) significant positive relationship, (2) significant negative relationship, (3) no significant relationship and (4) others, for instance, there is a significant positive relationship below certain critical values and no relationship above the values.

Automatic corn-soybean classification using landsat MSS data. I. Near-harvest crop proportion estimation

This paper presents a methodology to extract characteristics of a given crop type from multitemporal-multispectral Landsat MSS data. Two of these characteristics may be calculated using weather data and crop calendar model(s). A fully automated procedure based on these features has been tested on a large geographic area of the United States over 3 years and yields essentially unbiased crop proportion estimates. Moreover, because of the established link of derived features to crop growth, these features are expected to hold in foreign countries.