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Significant results obtained from ERTS-1 observations of agriculture, forestry, and range resources are summarized. Four major parts are covered: (1) crop classification and mensuration; (2) timber and range resources survey and classification; (3) soil survey and mapping; and (4) subdiscipline areas.
Spectral data of 4-8 GHz radar backscatter were gathered during the 1972 growing season at look angles between 0 and 70 deg and for all four possible polarization linear combinations. The data covers four crop types (corn, milo, alfalfa, and soybeans) and a wide range of soil moisture content. To insure statistical representation of the results, measurements were conducted over 128 fields corresponding to a total of about 40,000 data points. The use of spectral response signatures to separate different crop types and to separate healthy corn from blighted corn was investigated.
The Universal Soil Loss Equation (USLE) is a widely accepted tool for erosion prediction and conservation planning. Solving this equation yields the long-term average annual soil loss that can be expected from rill and inter-rill erosion. In this study, manual interpretation of color and color infrared 70 mm photography at the scale of 1:60,000 is used to determine the cropping management factor in the USLE. Accurate information was collected about plowing practices and crop residue cover (unharvested vegetation) for the winter season on agricultural land in Pheasant Branch Creek watershed in Dane County, Wisconsin.
A ground study conducted utilizing hand held radiometers to collect visible, near infrared and thermal infrared measurements. The data was analyzed and evaluated in terms of the ground measurements, which included percent crop canopy cover. The results used to recommend future action regarding use of satellite data in irrigation management.
The BOREAS TF-11 team collected several data sets in its efforts to fully describe the flux and site characteristics at the SSA-Fen site. This data set contains plant cover, standing crop of plant biomass, and estimated net primary productivity at each chamber site at the end of the 1994 field season. The measurements were conducted as part of a 2 x 2 factorial experiment in which we added carbon (300 g/sq m as wheat straw) and nitrogen (6 g/sq m as urea) to four replicate locations in the vicinity of the TF-11 tower. The data are stored in tabular ASCII files.
Studies have shown that irrigated agriculture has substantial effects on local to regional hydrologic and atmospheric processes. These effects include increased evapotranspiration and near surface humidity, decreased air and ground temperature, augmented downwind precipitation, enhanced recharge, depletion of source water stocks, and overall changes to the regional water budget. However, developers of global offline and coupled modeling systems have been slow to incorporate irrigation. This presentation will describe progress towards simulating irrigation worldwide in the Land Information System (LIS) and Global Land Data Assimilation System (GLDAS). We have developed and refined the ability to simulate sprinkler irrigation, furrow irrigation, paddy irrigation, and drip irrigation. We have reconciled multiple global and national irrigation, land cover, and crop type databases in an effort to (1) distinguish irrigated and rainfed agriculture, (2) simulate the proper distribution of crops (as sub-grid tiles) in each model grid pixel, and (3) apply the most likely irrigation method in each tile based on the crop type and country or state level information on the relative usage of sprinkler, flood, and drip irrigation. Using agricultural water withdrawal data, as available, for calibration and evaluation, we show that the implemented approach is promising but that much fine tuning remains to be done. In particular, irrigation model parameters that produce good results in one location often produce less satisfying results in others.
The results of the Domestic Crops and Land Cover Classification and Clustering study on large area crop estimation using LANDSAT and ground truth data are reported. The current crop area estimation approach of the Economics and Statistics Service of the U.S. Department of Agriculture was evaluated in terms of the factors that are likely to influence the bias and variance of the estimator. Also, alternative procedures involving replacements for the clustering algorithm, the classifier, or the regression model used in the original U.S. Department of Agriculture procedures were investigated.
Results of digital processing of airborne X-band synthetic aperture radar (SAR) data acquired over Dade County, Florida, and Acadia Parish, Louisiana are presented. The goal was to investigate the utility of SAR data for land cover mapping and area estimation under the AgRISTARS Domestic Crops and Land Cover Project. In the case of the Acadia Paris study area, LANDSAT multispectral scanner (MSS) data were also used to form a combined SAR and MSS data set. The results of accuracy evaluation for the SAR, MSS, and SAR/MSS data using supervised classification show that the combined SAR/MSS data set results in an improved classification accuracy of the five land cover classes as compared with SAR-only and MSS-only data sets. In the case of the Dade County study area, the results indicate that both HH and VV polarization data are highly responsive to the row orientation of the row crop but not to the specific vegetation which forms the row structure. On the other hand, the HV polarization data are relatively insensitive to the orientation of row crop. Therefore, the HV polarization data may be used to discriminate the specific vegetation that forms the row structure.
The phenology of crops such as corn, cotton, soybeans, sorghum, sunflowers, and rice and their observed signatures on LANDSAT imagery was studied. This was accomplished by photographing the various crops in segments 275 and 276 located in Wharton County, Texas and comparing those photographs with LANDSAT imagery of the same dates. These comparisons gave insight as to why a particular crop growth stage appeared as a definite signature on LANDSAT and how the percentage of ground cover of various crops affected the signatures on LANDSAT imagery. Numerous crop growth stages could not be directly compared due to cloud cover during several LANDSAT overpasses.
An airborne X-band SAR acquired multipolarization and multiflight pass SAR images over a truck garden vegetation area. Based on a variety of land cover and row crop direction variations, the vertical (VV) polarization data contain the highest contrast, while cross polarization contains the least. When the radar flight path is parallel to the row direction, both horizontal (HH) and VV polarization data contain very high return which masks out the specific land cover that forms the row structure. Cross polarization data are not that sensitive to row orientation. The inclusion of like and cross polarization data help delineate special surface features (e.g., row crop against non-row-oriented land cover, very-rough-surface against highly row-oriented surface).
Studies completed in fiscal year 1981 in support of the clustering/classification and preprocessing activities of the Domestic Crops and Land Cover project. The theme throughout the study was the improvement of subanalysis district (usually county level) crop hectarage estimates, as reflected in the following three objectives: (1) to evaluate the current U.S. Department of Agriculture Statistical Reporting Service regression approach to crop area estimation as applied to the problem of obtaining subanalysis district estimates; (2) to develop and test alternative approaches to subanalysis district estimation; and (3) to develop and test preprocessing techniques for use in improving subanalysis district estimates.
The temporal-spectral profile is a detailed indicator of the physical state of a field through time. Characteristic profiles have been observed for a variety of crops and other cover classes from Landsat data in the United States Corn Belt. These profiles contain information to support crop identification at various levels.
The Ourika watershed is characterized by higher than average flood flows, a rugged topography, a sparse vegetation cover and a friable substrate that makes it vulnerable to soil erosion. Soil is an important non-renewable asset capital, providing several ecosystem services that sustain life through physical and biological regulation processes. The loss, or otherwise, redistribution of biota by soil erosion in the Ourika watershed has received little attention and thus constitutes a key knowledge gap that needs to be addressed. This study quantifies the level of erosion and maps its spatial distribution in the watershed using the Sediment Delivery Ratio (SDR) of the Integrated Valuation of Ecosystem Services and Tradeoffs model (InVEST3.6.0). The SDR maps the overland sediment generation and delivery to the stream at the watershed level using rainfall, soil properties, topography, cover management and cropping practices as predefined set of input variables. Recommended internal model calibration was used and results were compared to similar studies carried out in different parts of Morocco for validation. The results reveal that 93% of the watershed is subject to a strong rainfall aggressiveness greater than 50 MJ mm ha–1 h–1 yr1 under an average rainfall intensity of 541 mm/year, rainfall capacity to initiate the detachment and transport of soil particles by runoff, and only 11.5% of the watershed area is protected with vegetative cover. We find that overall, the Ourika watershed is losing its soil to erosion at an alarming average rate of 258.48 ton.ha–1.yr–1, a rate well above the tolerable threshold of 7ton.ha–1.yr–1. The spatial distribution of this loss shows areas that need to be urgently remedied. The primary factors for soil erosion in the Ourika watershed appear to be related to the aggressive nature of local rainfall and the inherent susceptibility of soils, which can be costly to this cash-crop region. Because rainfall intensity, soil properties, and topography are intrinsic characteristics of the watershed, we suggest that seasonally adapted cropping rotations that maintains conservative practices with a high cover management may reduce soil erosion in the Ourika watershed.
Accurate vegetation detection is important for many applications, such as crop yield estimation, land cover land use monitoring, urban growth monitoring, drought monitoring, etc. Popular conventional approaches to vegetation detection incorporate the normalized difference vegetation index (NDVI), which uses the red and near infrared (NIR) bands, and enhanced vegetation index (EVI), which uses red, NIR, and the blue bands. Although NDVI and EVI are efficient, their accuracies still have room for further improvement. In this paper, we propose a new approach to vegetation detection based on land cover classification. That is, we first perform an accurate classification of 15 or more land cover types. The land covers such as grass, shrub, and trees are then grouped into vegetation and other land cover types such as roads, buildings, etc. are grouped into non-vegetation. Similar to NDVI and EVI, only RGB and NIR bands are needed in our proposed approach. If Laser imaging, Detection, and Ranging (LiDAR) data are available, our approach can also incorporate LiDAR in the detection process. Results using a well-known dataset demonstrated that the proposed approach is feasible and achieves more accurate vegetation detection than both NDVI and EVI. In particular, a Support Vector Machine (SVM) approach performed 6% better than NDVI and 50% better than EVI in terms of overall accuracy (OA).
The CropManage(CM) decision-support web application was originally developed by U.C. Cooperative Extension to support evapotranspiration (ET) based irrigation scheduling and nutrient management of cool-season vegetables. The model uses prescribed crop phenology curves to develop daily estimates of fractional green canopy cover (Fc) within the field. Periodic Fc observations acquired by ground-based methods or imported from NASA’s Satellite Irrigation Management Support (SIMS) can be used to adjust the prescribed timeseries for such factors as weather anomalies or non-standard agronomic practice, as needed. Fc is then converted to daily crop coefficient (fraction of reference ET) values. The crop coefficient is combined with reference evapotranspiration, collected by the California Irrigation Management Information System, to derive daily ET estimates for the given field. Irrigation runtime recommendations are issued for a given date based on total ET since the last irrigation event (less any rainfall), and corrected for distribution uniformity of the water delivery system. In this project, CM was adapted to vineyards by adding sub-models to account for early-season depletion of stored soil moisture, cover crop presence, and intentional water stress. An initial trial was performed on a Central Coast winegrape vineyard, where an eddy covariance tower measured daily ET during from May-Dec 2020. Total ET for the period showed strong agreement between the tower-based measurements (443 mm) and the CM model (431 mm). The model tended to overestimate cumulative ET during mid-June by up to 30 mm (about 15%) and later underestimated cumulative ET by as much as 65 mm (about 23%) in late September, suggesting that additional model calibration is needed to improve simulation of within-season variability. Results will be reported for trials on additional vineyard sites conducted during the 2021 season.
Using an FM-CW radar mounted atop a truck-mounted boom, 4-8 GHz backscatter spectral data was gathered during the 1972 growing season at incidence angles of 0-70 deg in 10 deg steps for each of the four linear polarization combinations. The data covers four mature crop types (corn, milo, soybeans and alfalfa) and bare ground taken under a wide range of soil and plant moisture contents. To insure statistical representation of the results, measurements were conducted over 147 fields corresponding to a total of about 50,000 data points. During 1973, a higher frequency version of the above system was used to collect additional data over the 8-18 GHz frequency region. This paper presents a summary of the results and suggests design criteria for future radar remote sensing missions.
Multitemporally registered LANDSAT MSS data from four acquisitions during the 1978 growing season were used in classification of eight sample segments in Iowa and Indiana. The results illustrate that use of LANDSAT acquisition when corn has tasseled is critical, as this is the optimum time for separation of corn and soybeans. An early season acquisition when the summer crops appear as bare soil can be beneficial in reducing the confusion between these two crops and other cover types. A subset of one visible and one infrared band from each date was found to produce results not significantly different from the use of all bands. Selection of a subset of these bands may also be feasible for multitemporal analysis.
Papers are presented on remote sensing applications in resource monitoring and management, data classification and modeling procedures, and the use of remote sensing techniques in developing nations. The subjects of land use/land cover, soil mapping, crop identification, mapping of geological resources, renewable resource analysis, and oceanographic applications are discussed. Papers from Argentina, Bolivia, Brazil, Costa Rica, the Syrian Arab Republic, the People's Republic of China, the Phillipines, Italy, Upper Volta and the United States are included.