The United States Department of Agriculture (USDA) Cropland Data Layer (CDL) provides spatially explicit information about crop production area and has served as a prevalent data source for characterizing cropland change in the U.S. in the last decade. Understanding the accuracy of the CDL is paramount because of the reliance on it for management and policy making. This study examined the characteristics of the CDL from 2007 to 2017 using comparisons to other USDA datasets. The results showed when examining the cropland area for the same year, the CDL produced comparable trends with other datasets (R2 > 0.95), but absolute area differed. The estimated area of cropland changes from 2007 to 2012, 2008 to 2012 and 2012 to 2017 varied from weak to moderate correlation between the CDL and the tabular data (R2 = 0.005~0.63). Differences in area of cropland change varied widely between data sources with the CDL estimating much larger change area. A series of image processing techniques designed to improve the confidence in cropland change estimated using the CDL reduced the area of estimated cropland change. The techniques also, unexpectedly, lowered the correlation in change estimated between the CDL and the tabular datasets. Estimated land cover change area varied widely based on analyses applied and could reverse from increasing to declining area in cropland. Further analyses showed unlikely change scenarios when comparing different year combinations. The authors recommend the CDL only be used for land cover change analysis if the error can be estimated and is within change estimates.

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In recent years, regulatory agencies in the USA and Europe have begun to require documentation that land used to produce crops and biofuels has not been converted from carbon-capturing grasslands or forests. Precise measurement of these land cover changes, however, has proven difficult. Analysis to date has focused primarily on moderate resolution (30 m) satellite imagery, which has not provided the land cover granularity or accuracy needed. These studies have estimated large-scale land conversion to crops in the USA. This study analyzed the satellite datasets but included tabular datasets and aerial imagery of the USA to determine whether the combination of datasets, focusing on more detailed analysis in these locations, could more accurately identify potential locations of land use change. Analyses of satellite imagery data from 1985 to 2020 found that much of the land that 2008 to 2020 satellite datasets classified as natural-to-crop land change was idle cropland. The results indicate a dynamic landscape of marginal land moving in and out of cropland. Approximately as much land was allowed to go fallow (6145 hectares) as land going into crop (7901 hectares) from 1985 to 2020. The results from this study indicate regulatory agencies could more accurately measure the impacts of conversion of natural lands to crop if long-term historical land cover/land use was also analyzed.

Human exploration missions to the Moon or Mars might be helped by the development of a bioregenerative advanced life-support (ALS) system that utilizes higher plants to regenerate water, oxygen and food. In order to make bioregenerative ALS systems competitive to physiochemical life-support systems, the ‘equivalent system mass’ (ESM) must be reduced by as much as possible. One method to reduce the ESM of a bioregenerative ALS system would be to deploy an automated remote sensing system within plant production modules to monitor crop productivity and disease outbreaks. The current study investigated the effects of canopy structure and imaging geometries on the efficiency of measuring the spectral reflectance of individual plants and crop canopies in a simulated ALS system. Results indicate that canopy structure, shading artefacts and imaging geometries are likely to create unique challenges in developing an automated remote sensing system for ALS modules. The cramped quarters within ALS plant growth units will create problems in collecting spectral reflectance measurements from the nadir position (i.e. directly above plant canopies) and, thus, crop canopies likely will be imaged from a diversity of orientations relative to the primary illumination source. In general, highly reflective white or polished surfaces will be used within an ALS plant growth module to maximize the stray light that is reflected onto plant canopies. Initial work suggested that these highly reflective surfaces might interfere with the collection of spectral reflectance measurements of plants, but the use of simple remote sensing algorithms such as 760/685 band ratios or normalized difference vegetation index (NDVI) images greatly reduced the effects of the reflective backgrounds. A direct comparison of 760/685 and NDVI images from canopies of lettuce, pepper and tomato plants indicated that unique models of individual plants are going to be required to properly assess the health conditions of canopies. A mixed model of all three plant species was not effective in predicting plant stress using either the 760/685 or NDVI remote sensing algorithms.

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