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The main objective of this study is to analyze the potential use of L-band radar data for the estimation of soil moisture over tropical agricultural areas under dense vegetation cover conditions. Ten radar images were acquired using the Phased Array Synthetic Aperture Radar/Advanced Land Observing Satellite (PALSAR/ALOS)-2 sensor over the Berambadi watershed (south India), between June and October of 2018. Simultaneous ground measurements of soil moisture, soil roughness, and leaf area index (LAI) were also recorded. The sensitivity of PALSAR observations to variations in soil moisture has been reported by several authors, and is confirmed in the present study, even for the case of very dense crops. The radar signals are simulated using five different radar backscattering models (physical and semi-empirical), over bare soil, and over areas with various types of crop cover (turmeric, marigold, and sorghum). When the semi-empirical water cloud model (WCM) is parameterized as a function of the LAI, to account for the vegetation’s contribution to the backscattered signal, it can provide relatively accurate estimations of soil moisture in turmeric and marigold fields, but has certain limitations when applied to sorghum fields. Observed limitations highlight the need to expand the analysis beyond the LAI by including additional vegetation parameters in order to take into account volume scattering in the L-band backscattered radar signal for accurate soil moisture estimation.

The scale difference between point in situ soil moisture measurements and low resolution satellite products limits the quality of any validation efforts in heterogeneous regions. Cosmic Ray Neutron Probes (CRNP) could be an option to fill the scale gap between both systems, as they provide area-average soil moisture within a 150–250 m radius footprint. In this study, we evaluate differences and similarities between CRNP observations, and surface soil moisture products from the Advanced Microwave Scanning Radiometer 2 (AMSR2), the METOP-A/B Advanced Scatterometer (ASCAT), the Soil Moisture Active and Passive (SMAP), the Soil Moisture and Ocean Salinity (SMOS), as well as simulations from the Global Land Data Assimilation System Version 2 (GLDAS2). Six CRNPs located on five continents have been selected as test sites: the Rur catchment in Germany, the COSMOS sites in Arizona and California (USA), and Kenya, one CosmOz site in New South Wales (Australia), and a site in Karnataka (India). Standard validation scores as well as the Triple Collocation (TC) method identified SMAP to provide a high accuracy soil moisture product with low noise or uncertainties as compared to CRNPs. The potential of CRNPs for satellite soil moisture validation has been proven; however, biomass correction methods should be implemented to improve its application in regions with large vegetation dynamics.

The Sentinel-2 mission of the European Space Agency (ESA) Copernicus program provides multispectral remote sensing data at decametric spatial resolution and high temporal resolution. The objective of this work is to evaluate the ability of Sentinel-2 time-series data to enable classification of an inherent biophysical property, in terms of accuracy and uncertainty estimation. The tested inherent biophysical property was the soil texture. Soil texture classification was performed on each individual Sentinel-2 image with a linear support vector machine. Two sources of uncertainty were studied: uncertainties due to the Sentinel-2 acquisition date and uncertainties due to the soil sample selection in the training dataset. The first uncertainty analysis was achieved by analyzing the diversity of classification results obtained from the time series of soil texture classifications, considering that the temporal resolution is akin to a repetition of spectral measurements. The second uncertainty analysis was achieved from each individual Sentinel-2 image, based on a bootstrapping procedure corresponding to 100 independent classifications obtained with different training data. The Simpson index was used to compute this diversity in the classification results. This work was carried out in an Indian cultivated region (84 km2, part of Berambadi catchment, in the Karnataka state). It used a time-series of six Sentinel-2 images acquired from February to April 2017 and 130 soil surface samples, collected over the study area and characterized in terms of texture. The classification analysis showed the following: (i) each single-date image analysis resulted in moderate performances for soil texture classification, and (ii) high confusion was obtained between neighboring textural classes, and low confusion was obtained between remote textural classes. The uncertainty analysis showed that (i) the classification of remote textural classes (clay and sandy loam) was more certain than classifications of intermediate classes (sandy clay and sandy clay loam), (ii) a final soil textural map can be produced depending on the allowed uncertainty, and iii) a higher level of allowed uncertainty leads to increased bare soil coverage. These results illustrate the potential of Sentinel-2 for providing input for modeling environmental processes and crop management.

Organic matter management is key to sustain ecosystem services provided by soils. However, it is rarely considered in a holistic view, considering local resources, agro-environmental effects and harmonization with farmers’ needs. Organic inputs, like compost and biochar, could represent a sustainable solution to massive current challenges associated to the intensification of agriculture, in particular for tropical regions. Here we assess the potential of agricultural residues as a resource for farmer communities in southwestern India to reduce their dependency on external inputs and sustain ecosystem services. We propose a novel joint evaluation of farmers’ aspirations together with agro-environmental effects of organic inputs on soils. Our soil quality evaluation showed that biochar alone or with compost did not improve unilaterally soils in the tropics (Anthroposol, Ferralsol and Vertisol). Many organic inputs led to an initial decrease in water-holding capacities of control soils (-27.3%: coconut shell biochar with compost on Anthroposol). Responses to organic matter inputs for carbon were strongest for Ferralsols (+33.4% with rice husk biochar), and mostly positive for Anthroposols and Vertisols (+12.5% to +13.8% respectively). Soil pH responses were surprisingly negative for Ferralsols and only positive if biochar was applied alone (between -5.6% to +1.9%). For Anthroposols and Vertisols, highest increases were achieved with rice husk biochar + vermicomposts (+7.2% and +5.2% respectively). Our socio-economic evaluation showed that farmers with a stronger economical position showed greater interest towards technology like biochar (factor 1.3 to 1.6 higher for farmers cultivating Anthroposols and/or Vertisols compared to Ferralsols), while poorer farmers more skepticism, which may lead to an increased economical gap within rural communities if technologies are not implemented with long-term guidance. These results advocate for an interdisciplinary evaluation of agricultural technology prior to its implementation as a development tool in the field.

A comprehensive assessment and bias corrections of two gridded daily precipitation products, based on gauge-only and multi-satellite observations, are undertaken in this study using independent rain gauge data in and around the Kabini River (KR) basin in South India. The KR basin, with complex terrain, highly variable precipitation and heterogeneous land use poses challenges in the development of accurate gridded precipitation products. The gauge-only gridded precipitation data from the India Meteorological Department (IMD) and multi-satellite gridded precipitation product from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA)-3B42 are evaluated at daily and monthly scales in this study. Both gridded precipitation products available at 0.25° spatial resolution are assessed using independent 57 rain gauge observations for the period of 2009–2016. Multiple visual and statistical metrics have been utilized to assess the error characteristics and capability of these gridded precipitation estimates in replicating extremes. Results indicate that gauge-only gridded product is generally better than the multi-satellite precipitation product. The multi-satellite product notably overestimates light precipitation and underestimates extreme precipitation over the study region. To mitigate the overestimation of dry days in the TMPA-3B42 estimates, a dry-day correction method is developed and is applied using nearby rain gauge observations. Furthermore, a quantile-based correction is also applied to both gridded precipitation products after confirming the stationarity of the data, which substantially improved both precipitation estimates for distributed hydrological modelling studies in the KR basin.

Groundwater has rapidly evolved as a primary source for irrigation in Indian agriculture. Over-exploitation of the groundwater substantially depletes the natural water table and has negative impacts on the water resource availability. The overarching goal of the proposed research is to identify the historical evolution of irrigated cropland for the post-monsoon (rabi) and summer cropping seasons in the Berambadi watershed (Area = 89 km2) of Kabini River basin, southern India. Approximately five-year interval irrigated area maps were generated using 30 m spatial resolution Landsat satellite images for the period from 1990 to 2016. The potential of Support Vector Machine (SVM) was assessed to discriminate irrigated and non-irrigated croplands. Three indices, Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI) and Enhanced Vegetation Index (EVI), were derived from multi-temporal Landsat satellite images. Spatially distributed intensive ground observations were collected for training and validation of the SVM models. The irrigated and non-irrigated croplands were estimated with high classification accuracy (kappa coefficient greater than 0.9). At the watershed scale, this approach allowed highlighting the contrasted evolution of multiple-cropping (two successive crops in rabi and summer seasons that often imply dual irrigation) with a steady increase in the upstream and a recent decrease in the downstream of the watershed. Moreover, the multiple-cropping was found to be much more frequent in the valleys. These intensive practices were found to have significant impacts on the water resources, with a drastic decline in the water table level (more than 50 m). It also impacted the ecosystem: Groundwater level decline was more pronounced in the valleys and the rivers are no more fed by the base flow.

The development of irrigation is generally considered an efficient way to reduce poverty in rural areas, although its impact on the inequality between farmers is more debated. In fact, assessing the impact of water management on different categories of farmers requires resituating it within the different dimensions of the local socio-technical context. We tested this hypothesis in a semi-arid area in Karnataka, South India, where groundwater irrigation was introduced five decades ago. Using the conceptual framework of comparative agriculture, based on farmers’ interviews, we built a farm typology, traced the trajectories of farm types over the last decades and assessed their current technical and economic performances. Our results show that the differentiation of farm trajectories since the 1950s has been linked with the development of groundwater irrigation, interplaying with their initial assets, and the evolution of the national and local contexts. We highlight the mechanisms by which irrigation indeed reduces poverty but engenders fragilities, particularly for poor households, whose situation was aggravated by the depletion of water resources over the last two decades. Finally, this extensive understanding of the agrarian context allowed us to formulate and assess the potential of different ways forward, including irrigation technology, change in cropping or livestock systems, land tenure, and value added distribution. As such, this analysis would be of major interest to policy makers involved in reforming the agricultural context for better agricultural water management.

Quantification of root-zone soil moisture (RZSM) is crucial for agricultural applications and the soil sciences. RZSM impacts processes such as vegetation transpiration and water percolation. Surface soil moisture (SSM) can be assessed through active and passive microwave remote-sensing methods, but no current sensor enables direct RZSM retrieval. Spatial maps of RZSM can be retrieved via proxy observations (vegetation stress, water storage change and surface soil moisture) or via land surface model predictions. In this study, we investigated the combination of surface soil moisture information with process-related inferred features involving artificial neural networks (ANNs). We considered the infiltration process through the soil water index (SWI) computed with a recursive exponential filter and the evaporation process through the evaporation efficiency computed based on a Moderate Resolution Imaging Spectroradiometer (MODIS) remote-sensing dataset and a simplified analytical model, while vegetation growth was not modeled and was only inferred through normalized difference vegetation index (NDVI) time series. Several ANN models with different sets of features were developed. Training was conducted considering in situ stations distributed in several areas worldwide characterized by different soil and climate patterns of the International Soil Moisture Network (ISMN), and testing was applied to stations of the same data-hosting facility. The results indicate that the integration of process-related features into ANN models increased the overall performance over the reference model level in which only SSM features were considered. In arid and semiarid areas, for instance, performance enhancement was observed when the evaporation efficiency was integrated into the ANN models. To assess the robustness of the approach, the trained models were applied to observation sites in Tunisia, Italy and southern India that are not part of the ISMN. The results reveal that joint use of surface soil moisture, evaporation efficiency, NDVI and recursive exponential filter represented the best alternative for more accurate predictions in the case of Tunisia, where the mean correlation of the predicted RZSM based on SSM only sharply increased from 0.443 to 0.801 when process-related features were integrated into the ANN models in addition to SSM. However, process-related features have no to little added value in temperate to tropical conditions.

In semi-arid areas, intermittent streams are often equipped with small reservoirs to store water for irrigation and/or groundwater recharge, and to capture sediments lost through erosion. These reservoirs must be periodically desilted to maintain their storage capacity. While bottom sediments are generally considered waste, their reuse in agricultural fields is a centuries-old practice in India. Our study aimed to test the hypothesis that local farmers’ knowledge and current practices can help in understanding and rationalizing this practice. The study relied on both interviews of farmers and physico-chemical analysis of soil and sediment samples collected in a cultivated watershed in South India. First, our results disprove our hypothesis as we found a wide diversity of (i) application rates ranging from light soil amendment to creation of anthropogenic soils, and costs, which were not explained by the distance between reservoirs and fields neither by the field size, suggesting that there is no consensus among farmers on the optimal dose, and (ii) opinions on the impact of sediments on soil functions with the majority citing an improvement in the physical and/or chemical properties of the soil, suggesting that there is no consensus on the sediment impact on soil. Secondly, our results highlight that (i) only farmers with access to irrigation implemented this practice and they sourced sediment from the nearest reservoir, (ii) a slight majority of farmers used less irrigation water and less fertilizer after sediment application, and (iii) differences in sediments and soils composition suggest that sediment application is more likely to improve soil physical structure than nutrient status. The reuse of sediments on cropland could therefore be a promising agroecological practice, likely to increase the resource circularity and the sustainability of cropping systems. However, expressing its potential would require defining optimal application rates, assessing potential risks, sharing knowledge and promoting collective management of the resource.

Better representations of groundwater processes need to be incorporated into large-scale hydrological models to improve simulations of regional- to global-scale hydrology and climate, as well as understanding of feedbacks between the human and natural systems. We incorporated a 2D groundwater flow model into the variable infiltration capacity (VIC) hydrological model code to address its lack of a lateral groundwater flow component. The water table was coupled with the variably saturated VIC soil column allowing bi-directional exchange of water between the aquifer and the soil. We then investigated how variations in aquifer properties and grid resolution affect modelled evapotranspiration (ET), runoff and groundwater recharge. We simulated nine idealised, homogenous aquifers with different combinations of transmissivity, storage coefficient, and three grid resolutions. The magnitude of cell ET, runoff, and recharge significantly depends on water table depth. In turn, the distribution of water table depths varied significantly as grid resolution increased from 1° to 0.05° for the medium and high transmissivity systems, resulting in changes of model-average fluxes of up to 12.3% of mean rainfall. For the low transmissivity aquifer, increasing the grid resolution has a minimal effect as lateral groundwater flow is low, and the VIC grid cells behave as vertical columns. The inclusion of the 2D groundwater model in VIC will enable the future representation of irrigation from groundwater pumping, and the feedbacks between groundwater use and the hydrological cycle.