Mapping cropping intensity of smallholder farms: A comparison of methods using multiple sensors

Abstract

The food security of smallholder farmers is vulnerable to climate change and climate variability. Cropping intensity, the number of crops planted annually, can be used as a measure of food security for smallholder farmers given that it can greatly affect net production. Current techniques for quantifying cropping intensity may not accurately map smallholder farms where the size of one field is typically smaller than the spatial resolution of readily available satellite data. We evaluated four methods that use multi-scalar datasets and are commonly used in the literature to assess cropping intensity of smallholder farms: 1) the Landsat threshold method, which identifies if a Landsat pixel is cropped or uncropped during each growing season, 2) the MODIS peak method, which determines if there is a phenological peak in the MODIS Enhanced Vegetation Index time series during each growing season, 3) the MODIS temporal mixture analysis, which quantifies the sub-pixel heterogeneity of cropping intensity using phenological MODIS data, and 4) the MODIS hierarchical training method, which quantifies the sub-pixel heterogeneity of cropping intensity using hierarchical training techniques. Each method was assessed using four criteria: 1) data availability, 2) accuracy across different spatial scales (at aggregate scales 250 × 250 m, 1 × 1 km, 5 × 5 km, and 10 × 10 km), 3) ease of implementation, and 4) ability to use the method over large spatial and temporal scales. We applied our methods to two regions in India (Gujarat and southeastern Madhya Pradesh) that represented diversity in crop type, soils, climatology, irrigation access, cropping intensity, and field size. We found that the Landsat threshold method is the most accurate (R 2 ≥ 0.71 and RMSE ≤ 0.14), particularly at smaller scales of analysis. Yet given the limited availability of Landsat data, we find that the MODIS hierarchical training method meets multiple criteria for mapping cropping intensity over large spatial and temporal scales. Furthermore, the adjusted R 2 between predicted and validation data generally increased and the RMSE decreased with spatial aggregation ≥5 × 5 km (R 2 up to 0.97 and RMSE as low as 0.00). Our model accuracy varied based on the region and season of analysis and was lowest during the summer season in Gujarat when there was high sub-pixel heterogeneity due to sparsely cropped agricultural land-cover. While our results specifically apply to our study regions in India, they most likely also apply to smallholder agriculture in other locations across the globe where the same types of satellite data are readily available.

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

“…Studies revealing pressures from unsustainable agriculture practices have mainly focused on effects from irrigation strategies (Abbas et al 2013;Martínez-López et al 2014;Shahriar Pervez et al 2014), nitrogen treatment (Tilling et al 2007;Chen et al 2010;Perry et al 2012), and crop characterization (Zhong et al 2014;Alcantara et al 2012;Jain et al 2013). Structural properties of the studied areas are less revealing than spectral ones for these tasks, therefore passive multispectral or hyperspectral data have mainly been used.…”

“…Different methodologies employing Landsat TM/ETM+ and MODIS data have been evaluated in cropping intensity mapping in smallholder farms, in different spatial scales (Jain et al 2013). Thresholding Landsat-derived NDVI values outperformed three MODIS based methodologies in almost all scales for both winter and summer periods, with hierarchical training method being the best among the MODIS ones.…”

Multiscale Remote Sensing to Map the Spatial Distribution and Extent of Cropland in the Sudanian Savanna of West Africa

“…This suggest that the predictive power of the respective models were sufficient to accurately predict fractional cropland cover within a distance of zero to about ninety kilometers (half width of Landsat tile) east or west of the training data. Proximity of the training and reference tiles, and the possibility of the adjoining areas having similar biophysical properties or belonging to the same "local" agro-ecological zone is a possible reason for this observation [56].…”

The Relative Indicators to Agriculture Policy Development Paradigms: Local Restraints, Global Trends

“…Satellite imagery is useful to identify the land use and land cover change dynamics with vegetation index (Setiawan & Yoshino, 2012;Masek, Honzak, Goward, Liu, & Pak, 2001) and agriculture suitability and cropping pattern (Jain et al, 2013;Verburg & Veldkamp, 2001) in a delineated land area. Moreover, agriculture suitability THE RELATIVE INDICATORS TO AGRICULTURE POLICY DEVELOPMENT PARADIGMS 105 assessment in the wetlands or fairly an abrupt land area is affected by inundations because of excessive precipitations or late water recessions.…”