Since the 1970s, remote sensing has been used to monitor the environment, address national security concerns, and manage Earth resources within a market framework. However, environmental organizations can also utilize remote sensing data infrastructure to support oppositional narratives, legal processes, and direct action. We present a framework for the socio-technical practice of remote sensing in alliance with those communities and organizations that are struggling for environmental justice on the global commodity frontiers. Positioned at the intersection of critical geography and political ecology, we examine the ways that critical remote sensing has been adopted in five major types of environmental conflict: struggles opposing fossil fuel exploitation, timber extraction, intensive food production, water management practices, and the effects of mining. We present a baseline inventory of remote sensing resources that are useful to the five conflict types and are freely accessible online. A global perspective on the planetary environmental crisis is essential, and we suggest that remote sensing practitioners can, through workshops or online tutorials, help environmental justice organizations towards independent use of remote sensing data. The local communities should then determine whether remote sensing products can contribute to their struggles.

The Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions provide estimates of soil moisture (SM) at similar spatial resolutions using L-band brightness temperatures ( $T_{\mathrm{B}}$). These missions meet the requirement of SM retrievals with an unbiased root-mean-square difference (ubRMSD) $<$ 0.04 (m ${ }^3$ m⁻³) compared to in-situ measurements over most of the ecosystems; however, their SM estimates over forests present an ubRMSD $>$ 0.10 (m ${ }^3$ m⁻³). In this paper, we compared the SM retrievals from the SMOS and SMAP SM products with in-situ SM over a tropical forest in Southern Mexico. The L-band passive SM retrievals were evaluated in terms of four statistical metrics: root-mean-square difference (RMSD), bias, ubRMSD, and correlation coefficient ( $r$). In-situ measurements of SM, soil and vegetation temperatures, precipitation, soil surface roughness, tree heights, diameters at the breast height of trunks, and forest cover fraction were collected during a field campaign from 6 January to 14 June 2015 in the Biosphere Reserve of Calakmul, Mexico, covering two areas of about 40 km $\times$ 40 km each. The comparison between SM retrievals from SMOS and SMAP and in-situ SM showed an RMSD ranging from 0.107 to 0.322 (m ${ }^3$ m⁻³) and an ubRMSD from 0.049 to 0.128 (m ${ }^3$ m⁻³). Overall, the SMAP SM estimates showed higher values of $r$ and were closer to in-situ SM. Because the SMAP and SMOS radiometers performed very similar, these differences are due to the values assigned to the vegetation optical depth ( $\tau$), the scattering albedo ( $\omega$), and the representation of the dynamics in vegetation and soil temperatures in the SMOS and SMAP retrieval algorithm. Based on an optimization process, we estimated simultaneously optimal $\omega$ and $\tau$ values for the tropical forest by using $T_{\mathrm{B}}$ observations from SMAP and SMOS radiometers. The optimal value of $\omega$ was 0.0655 for the tropical forest, and constant over the study period. In contrast, the optimal values of $\tau$ showed to be variant on time and ranging between 1.0 and 1.7, with an averaged value of 1.4 and a standard deviation of 0.24. When applying the optimal values of $\omega$ and $\tau$ and in-situ soil and vegetation temperatures, the SM retrievals showed an ubRMSD of 0.035–0.070 (m ${ }^3$ m ${ }^{-3}$), improving the SM retrievals about 45%. A sensitivity analysis was conducted to evaluate the effect of the uncertainties in $\tau$, $\omega$, and soil and vegetation temperatures on the estimates of $T_{\mathrm{B}}$. It was found that vegetation temperature ( $T_{\mathrm{v}\mathrm{e}\mathrm{g}}$) was the most sensitive parameter, with $r$ higher than 0.70 for both polarizations in $T_{\mathrm{B}}$. When comparing in-situ $T_{\mathrm{v}\mathrm{e}\mathrm{g}}$ and surface temperature values used in the SMAP and SMOS SM retrieval algorithms, differences up to 10 K were observed, affecting the SM estimates. The results presented in this paper could be useful in the preparation of the SMAP Calibration/Validation Experiment 2019, aiming at improving SM retrievals over forests.

Gebhardt et al. (2014) presented the Monitoring Activity Data for the Mexican REDD+ program (MAD-MEX), an automatic nation-wide land cover monitoring system for the Mexican REDD+ MRV. Though MAD-MEX represents a valuable first effort toward establishing a national reference emissions level for the implementation of REDD+ in Mexico, in this paper, we argue that this land cover system has important limitations that may prevent it from becoming operational for REDD+ MRV. Specifically, we show that (1) the accuracy assessment of MAD-MEX land cover maps is optimistically biased; (2) the ability of MAD-MEX to monitor land cover change, including deforestation and forest degradation; is poor and (3) the use of an entirely automatic classification approach, such as that followed by MAD-MEX, is highly problematic in the case of a large and heterogeneous country like Mexico. We discuss these limitations and call into question the ability of a land cover monitoring system, such as MAD-MEX, both to elaborate a national reference emissions level and to monitor future forest cover change, as part of a REDD+ MRV system. We provide some insights with the aim of improving the development of nation-wide land cover monitoring systems in Mexico and elsewhere.

Sustainable water management is a core sustainable development goal (SDG) that also contributes to other SDGs, including food and water security, ecosystem health, and climate adaptation. To achieve these synergies, policies must target efforts to regions that best correspond with development objectives. This study designs a targeting strategy for irrigation expansion in southern Mexico—a region long considered to have strong potential for sustainable irrigation development. We use an integrated farm typology and decision tree approach to identify priority municipalities for irrigation expansion. We use multivariate statistics to examine the relationships among farm characteristics in 933 municipalities, classifying each according to four farm types: lowland, midland, midland-irrigated, and highland. We then partition municipalities into 11 farm-type subgroups, each ranked by priority level for receiving irrigation interventions following Mexico’s National Water Program guidelines. Results identify a ‘highest-priority’ subgroup of 73 municipalities comprised mostly of midland and highland farm types. These types are characterized by low irrigation use, small farmland areas, high vulnerability to climate, high marginalization (poverty), strong representation from indigenous communities, low maize yield, and high rates of subsistence production. Findings provide a crucial first approximation of where irrigation expansion would best address water policy priorities and sustainable development objectives in southern Mexico. This study also provides a useful framework for scaling organizations tasked with targeting development efforts across large spatial scales.