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Climate change affects the functioning of all of the components of food systems, often in ways that exacerbate existing predicaments and inequalities among regions of the world and groups in society. At the same time, food systems are a major cause of climate change, accounting for a third of all greenhouse gas (GHG) emissions. Therefore, food systems can and should play a much bigger role in climate policies. This chapter highlights nine action points for climate change adaptation and mitigation in food systems. The chapter shows that numerous practices, technologies, knowledge and social capital already exist for climate action in food systems, with multiple synergies with other important goals, such as the conservation of biodiversity, the safeguarding of ecosystem services, sustainable land management and reducing social and gender inequalities. Many of these solutions are presently being applied at local scales around the world, even if not at sufficient levels. Hence, the major effort to unleash their potential would involve overcoming various technical, political-economic and structural barriers for their much wider application. Some other solutions require research and development investments now, but will focus on helping us meet the longer-term challenges of climate change in regard to food systems in the second half of this century, when most existing food production practices will face unprecedented challenges. In the short term, these pro-poor policy changes and support systems can have a range of positive effects well beyond food systems without delay. In the long term, investments in research will help ensure food security and ecosystem integrity for coming generations.

Soils are essential for life and civilization. They have a long history of political attention, documented at least since Ancient Greece and the Roman Empire. Soil played a key political role in the founding of the USA but gradually lost its clout in the last hundred years. The recent IPCC Special Report on Climate Change and Land may symbolize a return of soils to the political arena. The renewed political interest should be harnessed to leverage sustainable land management, which can create synergies between climate-change mitigation and adaptation while attending to a nexus of additional environmental and socio-economic predicaments. This chapter provides a brief history of the political importance of soils, its links to climate change, and how vested economic and political interests perpetuate unsustainable agricultural practices. It ends on a positive note by outlining a pathway towards a sustainable agricultural future.

Given the diversity of the biophysical and socioeconomic processes involved, the types, extent, and severity of land degradation cannot be encapsulated by a few simple measures (Stocking and Murnaghan 2000). In the assessment of land degradation or changes in land productivity, two complementary approaches may be distinguished

A substantial body of research has established the correlation between NDVI and aboveground biomass, and knowledge of the theoretical basis for using satellite-derived NDVI as a general proxy for vegetation conditions has advanced (Mbow et al. 2014; Pettorelli et al. 2005; Sellers et al. 1994). Reduction of primary productivity is a reliable indicator of the decrease or destruction of the biological productivity, particularly in drylands (Wessels et al. 2004; Li et al. 2004). NPP expressed in g of C m−2 years−1 and quantifies net carbon fixed by vegetation. According to Cao et al. (2003), NPP is “the beginning of the carbon biogeochemical cycle,” defined mathematically as in Eq. (5.1): $$ \mathrm{N}\mathrm{P}\mathrm{P}=f\left(\mathrm{NDVI,,,,PAR,,,,fPAR,,,,aPAR,,,,LAI}\right) $$ where fPAR is the fraction of absorbed photosynthetic active radiation, aPAR is the absorbed photosynthetic active radiation, and LAI is the leaf area index. Changes in NPP or, rather, its proxy NDVI induced by land degradation can be measured using a range of remote sensing techniques so remote sensing has become an essential tool for global, regional, and national studies of land degradation (Anyamba and Tucker 2012; Bai et al. 2008; Bajocco et al. 2012; de Jong et al. 2011b; Field et al. 1995; Horion et al. 2014; Le et al. 2014; Prince and Goward 1995). Many approaches have been developed to estimate NPP, notably the Global Production Efficiency Model (GLO‐PEM) (Prince and Goward 1995), the Light-Use Efficiency (LUE) Model (Monteith and Moss 1977), the Production Efficiency Approach (Goetz et al. 1999; Goward and Huemmrich 1992), and the Sim‐CYCLE (Ito and Oikawa 2002). And models have been developed to estimate NPP directly from remotely sensed NDVI at a global scale. Running et al. (2004) offered Eq. (5.2): $$ \mathrm{N}\mathrm{P}\mathrm{P}=\varSigma \left(\varepsilon \times \mathrm{N}\mathrm{DVI}\times \mathrm{P}\mathrm{A}\mathrm{R}-{\mathrm{R}}_{lr}\right)-{\mathrm{R}}_g-{\mathrm{R}}_m $$ where e is the conversion efficiency; PAR is photosynthetically active radiation; R lr is 24-h maintenance respiration of leaves and fine roots; R g is annual growth respiration required to construct leaves, fine roots, and new woody tissues; and R m is the maintenance respiration of live cells in woody tissues. Drawing on this relationship, Bai et al. (2008) adopted an empirical relationship to translate NDVI trends to NPP trends for their proxy global assessment of land degradation (Eq. 5.3): $$ {\mathrm{NPP}}_{\mathrm{MOD}17}\left( kg\kern0.24em C\kern0.24em h{a}^{-1}{\mathrm{year}}^{-1}\right)=1106.37\times \varSigma \mathrm{NDVI}-564.55 $$ where NPPMOD17 is the annual mean NPP derived from MODIS MOD17 Collection four data and sum NDVI is the 4-year (2000–2003) mean annual sum NDVI derived from GIMMS.

As discussed in the Introduction, both the UNCCD and the GEF use land cover to monitor land degradation. The UNCCD progress indicators (formerly known as impact indicators) should show progress made in achieving long-term benefits for people living in areas affected by desertification, land degradation, and drought, for affected ecosystems, and for the global environment. At its eleventh session, the COP adopted a refined set of six progress indicators (Decision 22/COP.11; see Annex 6) which will be used for the first time during the second leg of the fifth reporting process in 2016. Recommendations were made to the latest Conference of the Parties of the UNCCD (ICCD/COP(11)/CST/2) for refinements to the provisionally adopted set of impact indicators (Annex 6).

Early assessments of land degradation like the Global Assessment of Soil Degradation (GLASOD) (Oldeman et al. 1990) were compilations of expert opinion. They are unrepeatable and systematic data show them to be unreliable (Sonneveld and Dent 2009). Under the FAO/UNEP program Land Degradation in Drylands (LADA), Bai et al. (2008) undertook a global assessment of land degradation and improvement (GLADA) by analysis of linear trends of climate-adjusted GIMMS NDVI data. GLADA, the first quantitative assessment of global land degradation, aimed to identify and delineate hot spots of land degradation, and their counterpoint—bright spots of land improvement (Bai et al. 2008). The study revealed that about 24 % of the global land area was affected by land degradation between 1981 and 2003. Humid areas accounted for 78 % of the global degraded land area, while arid and semiarid areas accounted for only 13 %. Cropland and rangelands accounted for 18 % and 43 %, respectively, of the 16 % of global land area where the NDVI increased. The authors observed a positive correlation between population density and NDVI but, also, a correlation between poverty and land degradation. They emphasized that NDVI cannot be other than a proxy for land degradation and that it reveals nothing about the kind of degradation or the drivers (Bai et al. 2008).

Methodological issues were raised by Wessels (2009) regarding the GLADA assessment, chiefly the interpretation of RUE outside arid and semiarid regions, growing season differences between the northern and southern hemisphere and their implications for calendar year summations of NPP, and issues of scale in the interpretation of AVHRR NDVI vs. MODIS NPP relationships. He also maintained that the RESTREND technique provided a more dependable alternative. In response, Dent et al. (2009) clarified that RUE was not being used as an indicator of land condition but simply to separate NDVI trends caused by drought in those areas where biomass potential is directly related to rainfall, essentially drylands. Regarding seasonal differences in growing season between the northern and southern hemispheres, there was no difference in the long-term trends when the hydrological year was used for the southern hemisphere. And, finally, the RESTREND approach was also applied to the GLADA data and showed no significant difference with the RUE-adjusted NDVI approach; the choice of the RUE-adjusted NDVI was made on account of its simplicity and amenability to economic evaluation (Dent et al. 2009).

During the past half century, NDVI has been widely used for vegetation mapping and monitoring as well as in the assessment of land-cover and associated changes. This is because remotely sensed satellite-derived datasets provide spatially continuous data (data that are not sampled at individual points) and yield time-series signatures from which temporal patterns, trends, variations, and relationships may be derived (Jacquin et al. 2010). This has not prevented the misuse of NDVI—care is needed in the use of any scientific methodology.

Coarse spatial resolution datasets are invaluable at the global scale, but they lack the thematic and spatial detail required for habitat assessments at the country level and for finer-resolution assessments such as vegetation species distribution or high-quality forest-change monitoring. Mapping, monitoring, and assessments at the national and subnational level are performed using moderate-resolution sensors such as Landsat, ASTER, SPOT HRV, and IRS with spatial resolutions from 15 to 60 m. Newer, high-resolution optical sensors (5 m or better) provide enough spatial and spectral detail to discriminate between individual trees and, in some cases, species, but high-resolution imagery is prohibitively costly (see Annex 7) for many national governments and research institutions (Strittholt and Steininger 2007).

For ecological studies and environmental change research, Pettorelli et al. (2005) distinguish two main groups of satellite products

In the late 1960s, several researchers began using red and near-infrared reflected light to study vegetation (Pearson and Miller 1972). In the late 1960s, ratios of red and near-infrared light were used to assess turf grass condition and tropical rain forest leaf area index (Birth and McVey 1968; Jordan 1969). Compton Tucker was the first to use it for determining total dry matter accumulation, first from hand-held instruments (Tucker 1979), and then from NOAA AVHRR satellite data (Tucker et al. 1981, 1985), demonstrating that the growing season integral of frequent NDVI measurements represented the summation of photosynthetic potential as total dry matter accumulation. Starting in July 1981, a continuous time series of global NDVI data at a spatial resolution of 8 km has been available from the AVHRR instrument mounted on NOAA weather satellites. Soon, researchers realized the value of NDVI time-series remote sensing (Goward et al. 1985; Justice et al. 1985; Townshend et al. 1985; Tucker et al. 1985). This early work was the spur for development of the higher-resolution Moderate-Resolution Imaging Spectroradiometer (MODIS) instrument. The application of satellite NDVI data has blossomed into many fields of natural resources investigation (see Annex 1). One particular appeal of remote sensing in the study of large geographic areas, or at multiple times over the year(s), is the potential for cost savings (Pettorelli 2013). We examine the use of NDVI in research on land-use and land-cover change, drought, desertification, soil erosion, vegetation fires, biodiversity monitoring and conservation, and soil organic carbon (SOC).

Technological barriers: Currently, most global datasets useful for environmental applications are archived in databases that can be accessed using the Internet. These include the GIMMS, NOAA-PAL, LTD, and FASIR datasets. There are also free online data service platforms for executing preprocessing operations (such as data smoothing, spatial and temporal subsetting, mosaiking, and re-projection) of MODIS time-series vegetation indices (such as NDVI and EVI). Currently, the Internet speed for many parts of the world remains too slow to enable effective access to these datasets or online processing.